From 6b02eb9e2a56449820f62a91f09cf72e33cba689 Mon Sep 17 00:00:00 2001
From: Even Solbraa <41290109+EvenSol@users.noreply.github.com>
Date: Wed, 7 Oct 2026 13:12:30 +0200
Subject: [PATCH] Add executed subsea-to-shore design and response-surface
 notebook for NeqSim #4253

---
 README.md                                     |    3 +
 notebooks/examples_of_NeqSim_in_Colab.ipynb   |    2 +
 ...ubsea_to_shore_response_surface_4253.ipynb | 3778 +++++++++++++++++
 ..._shore_response_surface_4253_20261007.json |  153 +
 notebooks/notebook_maintenance_ledger.json    |    4 +-
 5 files changed, 3938 insertions(+), 2 deletions(-)
 create mode 100644 notebooks/fluidflow/subsea_to_shore_response_surface_4253.ipynb
 create mode 100644 notebooks/maintenance_ledger/subsea_to_shore_response_surface_4253_20261007.json

diff --git a/README.md b/README.md
index 25f2454b..90432b00 100644
--- a/README.md
+++ b/README.md
@@ -50,6 +50,8 @@ Advanced notebooks use JPype directly or through the Python distribution. They c
 * [Seismic acquisition to RMS-ready subsurface inputs](notebooks/reservoir/seismic_to_rms_input_workflow.ipynb) – Calculate CMP moveout and stacking, interpret public Reek seismic and wells, validate horizons and faults, screen seismic attributes, and export a checked RMS import package.
 * [RMS-origin reservoir to OPM Flow, ERT, and NeqSim](notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb) – Audit public Reek ROFF exports, demonstrate blocking and property spreading, run OPM Flow and ERT, and define a governed RMS-agent contract.
 
+* [Subsea-to-shore hydraulic design and response surfaces](notebooks/fluidflow/subsea_to_shore_response_surface_4253.ipynb) — Version 1.0.0, companion to equinor/neqsim#4253: 86 executed synthetic rate cases, Beggs–Brill/TwoFluidPipe comparison, held-out RSM qualification, mesh checks, a public-data adapter, and the verified flow-solve defect #4258. Source-pinned NeqSim master; results are retained inline.
+
 ## Getting Started
 See the [NeqSim Colab page](https://colab.research.google.com/github/EvenSol/NeqSim-Colab/blob/master/notebooks/examples_of_NeqSim_in_Colab.ipynb) for how to start using NeqSim in Colab/Python.
 
@@ -60,3 +62,4 @@ Repository-wide notebook integrity is checked with `python scripts/check_noteboo
 
 
 * [Hydrocarbon phase evolution: NeqSim PVT to OPM Flow](notebooks/reservoir/hydrocarbon_phase_evolution_neqsim_opm.ipynb) — Companion to equinor/neqsim#4198: saturation-pressure temperature sensitivity, CME/CVD laboratory paths, an executed 300-cell black-oil reservoir, and a NeqSim surface process. Synthetic inputs; explicit limits on migration and compositional interpretation.
+
diff --git a/notebooks/examples_of_NeqSim_in_Colab.ipynb b/notebooks/examples_of_NeqSim_in_Colab.ipynb
index 4f0fb703..a6d3734c 100644
--- a/notebooks/examples_of_NeqSim_in_Colab.ipynb
+++ b/notebooks/examples_of_NeqSim_in_Colab.ipynb
@@ -165,6 +165,7 @@
     "* [Water-ammonia thermodynamics and generator screening](thermodynamics/water_ammonia_properties.ipynb): calculate pure-fluid references, binary equilibrium, composition and pressure sensitivities, EOS uncertainty, and a connected heater-separator generator workflow with recovery, carryover, and closure checks.\n",
     "\n",
     "## Fluid mechanics\n",
+    "* **Advanced — current master** - [Subsea-to-shore hydraulic design and response surfaces](fluidflow/subsea_to_shore_response_surface_4253.ipynb): compare Beggs–Brill, TwoFluidPipe and a homogeneous diagnostic; solve 86 pressure-constrained synthetic rate cases, fit and test quadratic/physics-informed surrogates, inspect mesh sensitivity, and reproduce the flow-solve termination defect in equinor/neqsim#4258. Companion to #4253; no experimental accuracy claim.\n",
     "* **Advanced — current master** - [Wellstream two- and three-phase flow: steady state, dynamics, hydrate and wax](fluidflow/wellstream_steady_dynamic_multiphase_master.ipynb): build NeqSim from master; inspect steady pressure, temperature, holdup and phase slip; run 120 s rate cycles with phase mass ledgers, mesh/time-step comparisons, time–distance maps and a saved animation; evaluate hydrate margins and a separately characterized wax-equilibrium screen; demonstrate bounded unsplit execution and transactional rollback.\n",
     "* [Fluid mechanics](fluidflow/FluidMechanics.ipynb)\n",
     "* [Natural-gas pipeline linepack and operational flexibility](fluidflow/natural_gas_pipeline_linepack.ipynb): combine NeqSim real-gas properties and native PipeBeggsAndBrills pressure profiles with inventory integration, pack/unpack transients, deliverability limits, and operating-margin checks.\n",
@@ -623,3 +624,4 @@
  "nbformat": 4,
  "nbformat_minor": 0
 }
+
diff --git a/notebooks/fluidflow/subsea_to_shore_response_surface_4253.ipynb b/notebooks/fluidflow/subsea_to_shore_response_surface_4253.ipynb
new file mode 100644
index 00000000..ac4ae1be
--- /dev/null
+++ b/notebooks/fluidflow/subsea_to_shore_response_surface_4253.ipynb
@@ -0,0 +1,3778 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "88934457",
+   "metadata": {},
+   "source": [
+    "# Subsea-to-shore design with NeqSim and response surfaces\n",
+    "\n",
+    "**Version 1.0.0 — companion to [NeqSim issue #4253](https://github.com/equinor/neqsim/issues/4253).**\n",
+    "\n",
+    "Build a reproducible hydraulic screening study, compare Beggs–Brill and\n",
+    "TwoFluidPipe, learn a quadratic response surface, and replay its predictions\n",
+    "in the original simulator. A homogeneous AdiabaticPipe calculation provides\n",
+    "a third, deliberately simpler hydraulic comparison.\n",
+    "\n",
+    "By the end you can define a composable pipe model, estimate the rate at a\n",
+    "receiving-pressure constraint, separate surrogate error from model disagreement,\n",
+    "check mesh sensitivity, and attach evidence to a NeqSim improvement issue.\n",
+    "\n",
+    "**Evidence boundary:** all fluids and design cases below are synthetic.\n",
+    "There is no measured Ormen Lange or Brazilian field dataset in this notebook.\n",
+    "No comparison here establishes which multiphase model is more accurate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7e50621",
+   "metadata": {},
+   "source": [
+    "## 1. What the paper supports\n",
+    "\n",
+    "The [public ASME submission abstract](https://omae.secure-platform.com/a/solicitations/267/sessiongallery/23063/application/180942)\n",
+    "describes flow simulations varying fluid properties, pipe diameter and distance\n",
+    "to shore, followed by response-surface functions for production rate. Its author\n",
+    "list also includes Marcelo Igor Lourenço de Souza, absent from the automated issue.\n",
+    "The retrieved abstract supplies no numerical cases, coefficients, uncertainties\n",
+    "or downloadable measurement dataset. The notebook follows that **method**;\n",
+    "it does not reproduce the paper's numerical results.\n",
+    "\n",
+    "We freeze current NeqSim master at\n",
+    "[1768fc1fec78ff974fe9ff4232582f09ed504686](https://github.com/equinor/neqsim/tree/1768fc1fec78ff974fe9ff4232582f09ed504686),\n",
+    "inspected on 7 October 2026. The Python package supplies the bridge, while all\n",
+    "Java calculations use the source-built JAR. Change the source revision only\n",
+    "after repeating execution, mesh checks and surrogate qualification.\n",
+    "\n",
+    "[PipeBeggsAndBrills guide](https://github.com/equinor/neqsim/blob/1768fc1fec78ff974fe9ff4232582f09ed504686/docs/process/PipeBeggsAndBrills.md)\n",
+    "· [TwoFluidPipe model and limits](https://github.com/equinor/neqsim/blob/1768fc1fec78ff974fe9ff4232582f09ed504686/docs/process/TWOFLUIDPIPE_MODEL.md)\n",
+    "· [Existing full wellstream tutorial](wellstream_steady_dynamic_multiphase_master.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1e2f0b4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dependencies available. Java source provenance is checked next.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import importlib.util\n",
+    "import os\n",
+    "import subprocess\n",
+    "import sys\n",
+    "\n",
+    "required_modules = [\"jpype\", \"numpy\", \"pandas\", \"matplotlib\", \"scipy\", \"sklearn\"]\n",
+    "missing_modules = [\n",
+    "    name for name in required_modules if importlib.util.find_spec(name) is None\n",
+    "]\n",
+    "if missing_modules:\n",
+    "    packages = [\n",
+    "        \"neqsim==3.23.0\",\n",
+    "        \"jpype1==1.7.1\",\n",
+    "        \"numpy==2.5.3\",\n",
+    "        \"pandas==3.0.6\",\n",
+    "        \"matplotlib==3.11.2\",\n",
+    "        \"scipy==1.18.1\",\n",
+    "        \"scikit-learn==1.9.1\",\n",
+    "    ]\n",
+    "    subprocess.run(\n",
+    "        [sys.executable, \"-m\", \"pip\", \"install\", \"--quiet\", *packages],\n",
+    "        check=True,\n",
+    "    )\n",
+    "os.environ[\"NEQSIM_JVM_AUTOSTART\"] = \"0\"\n",
+    "print(\"Dependencies available. Java source provenance is checked next.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "159ba10a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"source_repository\": \"equinor/neqsim\",\n",
+      "  \"source_ref\": \"1768fc1fec78ff974fe9ff4232582f09ed504686\",\n",
+      "  \"jar_sha256\": \"c9ca3a016a9b1e524e53e22e2ff9bb0b8fc98ef629df566e261ec5a3c084f9c8\",\n",
+      "  \"python\": \"3.12.14\",\n",
+      "  \"java\": \"17.0.20\",\n",
+      "  \"packages\": {\n",
+      "    \"neqsim\": \"3.23.0\",\n",
+      "    \"jpype1\": \"1.7.1\",\n",
+      "    \"numpy\": \"2.5.3\",\n",
+      "    \"pandas\": \"3.0.6\",\n",
+      "    \"matplotlib\": \"3.11.2\",\n",
+      "    \"scipy\": \"1.18.1\",\n",
+      "    \"scikit-learn\": \"1.9.1\"\n",
+      "  }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import hashlib\n",
+    "import json\n",
+    "import platform\n",
+    "import shutil\n",
+    "import time\n",
+    "from importlib.metadata import version\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import jpype\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from IPython.display import display\n",
+    "from scipy.optimize import brentq\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
+    "\n",
+    "NEQSIM_SOURCE_REF = \"1768fc1fec78ff974fe9ff4232582f09ed504686\"\n",
+    "source_root_override = os.environ.get(\"NEQSIM_SOURCE_ROOT\")\n",
+    "source_root = Path(source_root_override or \"/content/neqsim-4253-source\").resolve()\n",
+    "if not source_root_override:\n",
+    "    source_root.mkdir(parents=True, exist_ok=True)\n",
+    "    if not (source_root / \".git\").exists():\n",
+    "        subprocess.run([\"git\", \"init\", str(source_root)], check=True, capture_output=True)\n",
+    "        subprocess.run(\n",
+    "            [\n",
+    "                \"git\", \"-C\", str(source_root), \"remote\", \"add\", \"origin\",\n",
+    "                \"https://github.com/equinor/neqsim.git\",\n",
+    "            ],\n",
+    "            check=True,\n",
+    "            capture_output=True,\n",
+    "        )\n",
+    "    subprocess.run(\n",
+    "        [\"git\", \"-C\", str(source_root), \"fetch\", \"--depth=1\", \"origin\", NEQSIM_SOURCE_REF],\n",
+    "        check=True,\n",
+    "        capture_output=True,\n",
+    "    )\n",
+    "    subprocess.run(\n",
+    "        [\"git\", \"-C\", str(source_root), \"checkout\", \"--detach\", \"FETCH_HEAD\"],\n",
+    "        check=True,\n",
+    "        capture_output=True,\n",
+    "    )\n",
+    "    java_executable = Path(shutil.which(\"java\")).resolve()\n",
+    "    build_environment = dict(os.environ)\n",
+    "    build_environment[\"JAVA_HOME\"] = str(java_executable.parent.parent)\n",
+    "    subprocess.run(\n",
+    "        [\"bash\", \"mvnw\", \"-q\", \"-Dmaven.test.skip=true\", \"-Dmaven.javadoc.skip=true\", \"package\"],\n",
+    "        cwd=source_root,\n",
+    "        env=build_environment,\n",
+    "        check=True,\n",
+    "        capture_output=True,\n",
+    "    )\n",
+    "\n",
+    "resolved_source_ref = subprocess.check_output(\n",
+    "    [\"git\", \"-C\", str(source_root), \"rev-parse\", \"HEAD\"],\n",
+    "    text=True,\n",
+    ").strip()\n",
+    "assert resolved_source_ref == NEQSIM_SOURCE_REF\n",
+    "jar_override = os.environ.get(\"NEQSIM_SOURCE_JAR\")\n",
+    "source_jar = Path(jar_override or source_root / \"target/neqsim-3.23.0.jar\").resolve()\n",
+    "assert source_jar.is_file()\n",
+    "assert not jpype.isJVMStarted(), \"Restart the runtime before changing the Java class path.\"\n",
+    "jpype.addClassPath(str(source_jar))\n",
+    "jpype.startJVM(\"-Xmx2g\", convertStrings=True)\n",
+    "JClass = jpype.JClass\n",
+    "required_classes = [\n",
+    "    \"neqsim.process.equipment.pipeline.PipeBeggsAndBrills\",\n",
+    "    \"neqsim.process.equipment.pipeline.TwoFluidPipe\",\n",
+    "    \"neqsim.process.equipment.pipeline.SteadyStateConvergenceReport\",\n",
+    "]\n",
+    "for class_name in required_classes:\n",
+    "    location = JClass(class_name).class_.getProtectionDomain().getCodeSource().getLocation()\n",
+    "    assert Path(str(location.toURI().getPath())).resolve() == source_jar\n",
+    "\n",
+    "provenance = {\n",
+    "    \"source_repository\": \"equinor/neqsim\",\n",
+    "    \"source_ref\": resolved_source_ref,\n",
+    "    \"jar_sha256\": hashlib.sha256(source_jar.read_bytes()).hexdigest(),\n",
+    "    \"python\": platform.python_version(),\n",
+    "    \"java\": str(JClass(\"java.lang.System\").getProperty(\"java.version\")),\n",
+    "    \"packages\": {\n",
+    "        name: version(name)\n",
+    "        for name in [\"neqsim\", \"jpype1\", \"numpy\", \"pandas\", \"matplotlib\", \"scipy\", \"scikit-learn\"]\n",
+    "    },\n",
+    "}\n",
+    "print(json.dumps(provenance, indent=2))\n",
+    "plt.rcParams.update({\"figure.dpi\": 130, \"font.size\": 10})\n",
+    "pd.set_option(\"display.max_columns\", 12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dec9c853",
+   "metadata": {},
+   "source": [
+    "## 2. Engineering question and assumptions\n",
+    "\n",
+    "What **hydraulic screening rate** reaches an onshore receiver at 80 bara from\n",
+    "a 100 bara inlet? The inlet temperature is 40 °C; roughness is 45 µm.\n",
+    "We vary inner diameter (0.25–0.45 m), horizontal length (20–60 km), and\n",
+    "decane mole fraction (0.02–0.10) in methane. Composition sums to one.\n",
+    "SRK with the classic mixing rule and multiphase checking provides the fluid.\n",
+    "\n",
+    "The main comparison is **isothermal**, with no elevation change or terrain\n",
+    "tracking. This isolates hydraulic/model and surrogate effects. It excludes\n",
+    "seabed heat loss, shore rise, reservoir deliverability, equipment capacity,\n",
+    "hydrate/wax margins, erosion and transient slugging. It is not the maximum\n",
+    "safe production rate of a complete field. AdiabaticPipe is given the same\n",
+    "outlet temperature as a fixed-temperature homogeneous diagnostic; its name\n",
+    "does not imply an adiabatic energy balance in this experiment.\n",
+    "\n",
+    "The pressure condition is $P_{\\mathrm{out}}(\\dot m,D,L,z_d)=80\\ \\mathrm{bara}$.\n",
+    "Here $\\dot m$ is mass rate in kg/s, $D$ is inner diameter in m, $L$ is length\n",
+    "in m and $z_d$ is decane mole fraction. A fresh stream and pipe are built\n",
+    "for each evaluation, so candidates never share mutable state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "66b1f6ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>rate_kg_s</th>\n",
+       "      <th>outlet_bara</th>\n",
+       "      <th>holdup</th>\n",
+       "      <th>mass_error</th>\n",
+       "      <th>termination</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>89.602212</td>\n",
+       "      <td>0.207207</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>91.559082</td>\n",
+       "      <td>0.106533</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AdiabaticPipe</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>95.931953</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                model  rate_kg_s  outlet_bara    holdup  mass_error  \\\n",
+       "0  PipeBeggsAndBrills       30.0    89.602212  0.207207         0.0   \n",
+       "1        TwoFluidPipe       30.0    91.559082  0.106533         0.0   \n",
+       "2       AdiabaticPipe       30.0    95.931953       NaN         0.0   \n",
+       "\n",
+       "           termination  \n",
+       "0  FORWARD_CALCULATION  \n",
+       "1            CONVERGED  \n",
+       "2  FORWARD_CALCULATION  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "inlet_pressure_bara = 100.0\n",
+    "inlet_temperature_c = 40.0\n",
+    "receiver_pressure_bara = 80.0\n",
+    "roughness_m = 4.5e-5\n",
+    "default_sections = 30\n",
+    "pressure_tolerance_bar = 0.02\n",
+    "model_names = [\"PipeBeggsAndBrills\", \"TwoFluidPipe\", \"AdiabaticPipe\"]\n",
+    "base_design = {\"diameter_m\": 0.35, \"length_km\": 40.0, \"decane_mol_fraction\": 0.06}\n",
+    "design_bounds = np.array([[0.25, 0.45], [20.0, 60.0], [0.02, 0.10]])\n",
+    "feature_names = [\"diameter_m\", \"length_km\", \"decane_mol_fraction\"]\n",
+    "\n",
+    "def make_inlet(decane_fraction, mass_rate_kg_s, component=None):\n",
+    "    fluid = JClass(\"neqsim.thermo.system.SystemSrkEos\")(\n",
+    "        inlet_temperature_c + 273.15,\n",
+    "        inlet_pressure_bara,\n",
+    "    )\n",
+    "    if component is None:\n",
+    "        fluid.addComponent(\"methane\", 1.0 - decane_fraction)\n",
+    "        fluid.addComponent(\"nC10\", decane_fraction)\n",
+    "    else:\n",
+    "        fluid.addComponent(component, 1.0)\n",
+    "    fluid.setMixingRule(\"classic\")\n",
+    "    fluid.setMultiPhaseCheck(True)\n",
+    "    inlet = JClass(\"neqsim.process.equipment.stream.Stream\")(\"subsea feed\", fluid)\n",
+    "    inlet.setFlowRate(float(mass_rate_kg_s), \"kg/sec\")\n",
+    "    inlet.run()\n",
+    "    inlet.getFluid().initProperties()\n",
+    "    return inlet\n",
+    "\n",
+    "def build_pipe(model_name, inlet, design, sections=default_sections):\n",
+    "    pipe = JClass(\"neqsim.process.equipment.pipeline.\" + model_name)(\n",
+    "        model_name + \" export line\",\n",
+    "        inlet,\n",
+    "    )\n",
+    "    pipe.setLength(float(design[\"length_km\"] * 1000.0))\n",
+    "    pipe.setDiameter(float(design[\"diameter_m\"]))\n",
+    "    if model_name == \"TwoFluidPipe\":\n",
+    "        pipe.setRoughness(roughness_m)\n",
+    "        pipe.setNumberOfSections(int(sections))\n",
+    "        elevations = jpype.JArray(jpype.JDouble)([0.0] * (sections + 1))\n",
+    "        pipe.setElevationProfile(elevations)\n",
+    "        pipe.setEnableTerrainTracking(False)\n",
+    "        pipe.setHeatTransferCoefficient(0.0)\n",
+    "        pipe.setEnableJouleThomson(False)\n",
+    "    else:\n",
+    "        pipe.setPipeWallRoughness(roughness_m)\n",
+    "    if model_name == \"PipeBeggsAndBrills\":\n",
+    "        pipe.setNumberOfIncrements(int(sections))\n",
+    "        thermal_mode = JClass(\n",
+    "            \"neqsim.process.equipment.pipeline.PipeBeggsAndBrills$HeatTransferMode\"\n",
+    "        )\n",
+    "        pipe.setHeatTransferMode(thermal_mode.ISOTHERMAL)\n",
+    "    if model_name == \"AdiabaticPipe\":\n",
+    "        pipe.setOutletTemperature(inlet_temperature_c + 273.15)\n",
+    "    return pipe\n",
+    "\n",
+    "def evaluate_pipe(model_name, design, mass_rate_kg_s, sections=default_sections):\n",
+    "    started = time.perf_counter()\n",
+    "    inlet = make_inlet(design[\"decane_mol_fraction\"], mass_rate_kg_s)\n",
+    "    pipe = build_pipe(model_name, inlet, design, sections)\n",
+    "    process = JClass(\"neqsim.process.processmodel.ProcessSystem\")()\n",
+    "    process.add(inlet)\n",
+    "    process.add(pipe)\n",
+    "    process.run()\n",
+    "    outlet = pipe.getOutletStream()\n",
+    "    outlet.getFluid().initProperties()\n",
+    "    row = {\n",
+    "        \"model\": model_name,\n",
+    "        **design,\n",
+    "        \"rate_kg_s\": float(mass_rate_kg_s),\n",
+    "        \"outlet_bara\": float(outlet.getPressure(\"bara\")),\n",
+    "        \"outlet_temperature_c\": float(outlet.getTemperature(\"C\")),\n",
+    "        \"mass_balance_relative\": abs(\n",
+    "            outlet.getFlowRate(\"kg/sec\") - inlet.getFlowRate(\"kg/sec\")\n",
+    "        ) / mass_rate_kg_s,\n",
+    "        \"inlet_phases\": int(inlet.getFluid().getNumberOfPhases()),\n",
+    "        \"seconds\": time.perf_counter() - started,\n",
+    "        \"converged\": True,\n",
+    "        \"termination\": \"FORWARD_CALCULATION\",\n",
+    "    }\n",
+    "    if model_name == \"TwoFluidPipe\":\n",
+    "        report = pipe.getSteadyStateConvergenceReport()\n",
+    "        row.update({\n",
+    "            \"converged\": bool(report.isConverged()),\n",
+    "            \"termination\": str(report.getTerminationReason()),\n",
+    "            \"iterations\": int(report.getIterations()),\n",
+    "            \"mass_flux_residual\": float(report.getMassFluxResidual()),\n",
+    "            \"pressure_momentum_residual\": float(report.getPressureMomentumResidual()),\n",
+    "            \"liquid_holdup\": float(pipe.getAverageLiquidHoldup()),\n",
+    "        })\n",
+    "    elif model_name == \"PipeBeggsAndBrills\":\n",
+    "        holdup = np.array(pipe.getLiquidHoldupProfile(), dtype=float)\n",
+    "        row[\"liquid_holdup\"] = float(holdup.mean())\n",
+    "    if not row[\"converged\"]:\n",
+    "        raise RuntimeError(f\"Rejected {model_name} candidate: {row['termination']}\")\n",
+    "    assert np.isfinite(row[\"outlet_bara\"]) and row[\"outlet_bara\"] > 1.0\n",
+    "    assert row[\"mass_balance_relative\"] < 1e-9\n",
+    "    assert abs(row[\"outlet_temperature_c\"] - inlet_temperature_c) < 1e-6\n",
+    "    return row, {\"inlet\": inlet, \"pipe\": pipe, \"outlet\": outlet, \"process\": process}\n",
+    "\n",
+    "sample_rows = []\n",
+    "sample_models = {}\n",
+    "for model_name in model_names:\n",
+    "    result, objects = evaluate_pipe(model_name, base_design, 30.0)\n",
+    "    sample_rows.append(result)\n",
+    "    sample_models[model_name] = objects\n",
+    "display(pd.DataFrame(sample_rows)[\n",
+    "    [\n",
+    "        \"model\", \"rate_kg_s\", \"outlet_bara\", \"liquid_holdup\",\n",
+    "        \"mass_balance_relative\", \"termination\",\n",
+    "    ]\n",
+    "].rename(columns={\n",
+    "    \"mass_balance_relative\": \"mass_error\",\n",
+    "    \"liquid_holdup\": \"holdup\",\n",
+    "}).round(6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b819ae6b",
+   "metadata": {},
+   "source": [
+    "## 3. Solve the receiving-pressure constraint\n",
+    "\n",
+    "The outer root solver stops only when a **replayed, converged** pipe satisfies\n",
+    "the receiving pressure to 0.02 bar. Its bracket is estimated from a low-rate\n",
+    "pressure drop, and checked explicitly. Unconverged TwoFluidPipe states and\n",
+    "unclassified Java exceptions stop the calculation; they never enter training.\n",
+    "The root solver deliberately does not use Beggs–Brill's built-in flow mode\n",
+    "because the iteration-limit defect is reproduced in section 8.\n",
+    "\n",
+    "A root is a local hydraulic rate within these assumptions. Monotonicity and\n",
+    "nearby-point checks below qualify its use in this bounded design box."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6a45608b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>rate_kg_s</th>\n",
+       "      <th>outlet_bara</th>\n",
+       "      <th>liquid_holdup</th>\n",
+       "      <th>outer_evaluations</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>40.38349</td>\n",
+       "      <td>79.99999</td>\n",
+       "      <td>0.17776</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>44.04026</td>\n",
+       "      <td>80.00000</td>\n",
+       "      <td>0.09986</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AdiabaticPipe</td>\n",
+       "      <td>63.70336</td>\n",
+       "      <td>80.00000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                model  rate_kg_s  outlet_bara  liquid_holdup  \\\n",
+       "0  PipeBeggsAndBrills   40.38349     79.99999        0.17776   \n",
+       "1        TwoFluidPipe   44.04026     80.00000        0.09986   \n",
+       "2       AdiabaticPipe   63.70336     80.00000            NaN   \n",
+       "\n",
+       "   outer_evaluations  \n",
+       "0                 11  \n",
+       "1                 12  \n",
+       "2                 11  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def solve_hydraulic_rate(model_name, design, sections=default_sections):\n",
+    "    low_rate = 2.0\n",
+    "    low_result, _ = evaluate_pipe(model_name, design, low_rate, sections)\n",
+    "    low_drop = inlet_pressure_bara - low_result[\"outlet_bara\"]\n",
+    "    target_drop = inlet_pressure_bara - receiver_pressure_bara\n",
+    "    assert 0.0 < low_drop < target_drop\n",
+    "    high_rate = low_rate * np.sqrt(target_drop / low_drop) * 1.08\n",
+    "    evaluations = []\n",
+    "\n",
+    "    def pressure_residual(rate):\n",
+    "        result, _ = evaluate_pipe(model_name, design, rate, sections)\n",
+    "        evaluations.append(result)\n",
+    "        return result[\"outlet_bara\"] - receiver_pressure_bara\n",
+    "\n",
+    "    low_residual = pressure_residual(low_rate)\n",
+    "    high_residual = pressure_residual(high_rate)\n",
+    "    expansion_count = 0\n",
+    "    while high_residual > 0.0 and expansion_count < 8:\n",
+    "        high_rate *= 1.15\n",
+    "        high_residual = pressure_residual(high_rate)\n",
+    "        expansion_count += 1\n",
+    "    assert low_residual > 0.0 and high_residual < 0.0, \"Unqualified root bracket\"\n",
+    "    rate = brentq(\n",
+    "        pressure_residual,\n",
+    "        low_rate,\n",
+    "        high_rate,\n",
+    "        xtol=1e-4,\n",
+    "        rtol=1e-6,\n",
+    "        maxiter=40,\n",
+    "    )\n",
+    "    final, objects = evaluate_pipe(model_name, design, rate, sections)\n",
+    "    final[\"pressure_residual_bar\"] = final[\"outlet_bara\"] - receiver_pressure_bara\n",
+    "    final[\"outer_evaluations\"] = len(evaluations) + 2\n",
+    "    assert abs(final[\"pressure_residual_bar\"]) <= pressure_tolerance_bar\n",
+    "    assert final[\"inlet_phases\"] == 2\n",
+    "    return final, objects\n",
+    "\n",
+    "baseline_rows = []\n",
+    "baseline_objects = {}\n",
+    "for model_name in model_names:\n",
+    "    row, objects = solve_hydraulic_rate(model_name, base_design)\n",
+    "    baseline_rows.append(row)\n",
+    "    baseline_objects[model_name] = objects\n",
+    "baseline = pd.DataFrame(baseline_rows)\n",
+    "display(baseline[\n",
+    "    [\"model\", \"rate_kg_s\", \"outlet_bara\", \"liquid_holdup\", \"outer_evaluations\"]\n",
+    "].round(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cd316acc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1430x520 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n",
+    "for model_name in model_names[:2]:\n",
+    "    pipe = sample_models[model_name][\"pipe\"]\n",
+    "    pressures = np.array(pipe.getPressureProfile(), dtype=float)\n",
+    "    holdups = np.array(pipe.getLiquidHoldupProfile(), dtype=float)\n",
+    "    if model_name == \"TwoFluidPipe\":\n",
+    "        pressures /= 1e5\n",
+    "    distance_km = np.linspace(0.0, base_design[\"length_km\"], len(pressures))\n",
+    "    holdup_distance_km = np.linspace(0.0, base_design[\"length_km\"], len(holdups))\n",
+    "    axes[0].plot(distance_km, pressures, label=model_name)\n",
+    "    axes[1].plot(holdup_distance_km, holdups, label=model_name)\n",
+    "axes[0].set(xlabel=\"Distance (km)\", ylabel=\"Pressure (bara)\", title=\"Same rate: 30 kg/s\")\n",
+    "axes[1].set(xlabel=\"Distance (km)\", ylabel=\"Liquid holdup (fraction)\")\n",
+    "for axis in axes:\n",
+    "    axis.grid(alpha=0.25)\n",
+    "    axis.legend(fontsize=8)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b69f7eb",
+   "metadata": {},
+   "source": [
+    "## 4. Validation before machine learning\n",
+    "\n",
+    "Three separate checks are used: a single-liquid Darcy–Weisbach reference,\n",
+    "numerical mesh refinement, and nearby operating/design trends.\n",
+    "The analytical reference uses NeqSim liquid density and viscosity and an\n",
+    "independently evaluated Colebrook friction factor. It checks hydraulics\n",
+    "and units; it is not independent validation of SRK liquid properties.\n",
+    "\n",
+    "$$\\Delta P=f_D\\frac{L}{D}\\frac{\\rho v^2}{2}$$\n",
+    "\n",
+    "$\\Delta P$ is pressure loss in Pa, $f_D$ is the Darcy friction factor,\n",
+    "$\\rho$ is liquid density in kg/m³ and $v$ is bulk velocity in m/s.\n",
+    "This single-phase check cannot validate multiphase holdup or slip."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8df0bf5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Single-liquid hydraulic check passed; Reynolds number = 40,336.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>drop_bar</th>\n",
+       "      <th>Darcy_reference_bar</th>\n",
+       "      <th>relative_difference_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.024578</td>\n",
+       "      <td>0.024895</td>\n",
+       "      <td>-1.274652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.024179</td>\n",
+       "      <td>0.024895</td>\n",
+       "      <td>-2.874175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AdiabaticPipe</td>\n",
+       "      <td>0.025792</td>\n",
+       "      <td>0.024895</td>\n",
+       "      <td>3.605134</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                model  drop_bar  Darcy_reference_bar  relative_difference_pct\n",
+       "0  PipeBeggsAndBrills  0.024578             0.024895                -1.274652\n",
+       "1        TwoFluidPipe  0.024179             0.024895                -2.874175\n",
+       "2       AdiabaticPipe  0.025792             0.024895                 3.605134"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "liquid_design = {\"diameter_m\": 0.25, \"length_km\": 1.0, \"decane_mol_fraction\": 1.0}\n",
+    "liquid_rate_kg_s = 10.0\n",
+    "liquid_inlet = make_inlet(1.0, liquid_rate_kg_s, component=\"nC10\")\n",
+    "liquid_fluid = liquid_inlet.getFluid()\n",
+    "assert liquid_fluid.getNumberOfPhases() == 1\n",
+    "density_kg_m3 = float(liquid_fluid.getDensity(\"kg/m3\"))\n",
+    "viscosity_pa_s = float(liquid_fluid.getViscosity(\"kg/msec\"))\n",
+    "area_m2 = np.pi * liquid_design[\"diameter_m\"] ** 2 / 4.0\n",
+    "velocity_m_s = liquid_rate_kg_s / density_kg_m3 / area_m2\n",
+    "reynolds = density_kg_m3 * velocity_m_s * liquid_design[\"diameter_m\"] / viscosity_pa_s\n",
+    "\n",
+    "def colebrook_residual(darcy_factor):\n",
+    "    return 1.0 / np.sqrt(darcy_factor) + 2.0 * np.log10(\n",
+    "        roughness_m / (3.7 * liquid_design[\"diameter_m\"])\n",
+    "        + 2.51 / (reynolds * np.sqrt(darcy_factor))\n",
+    "    )\n",
+    "\n",
+    "darcy_factor = brentq(colebrook_residual, 0.005, 0.1)\n",
+    "reference_drop_bar = (\n",
+    "    darcy_factor\n",
+    "    * liquid_design[\"length_km\"] * 1000.0 / liquid_design[\"diameter_m\"]\n",
+    "    * density_kg_m3 * velocity_m_s ** 2 / 2.0 / 1e5\n",
+    ")\n",
+    "analytical_rows = []\n",
+    "for model_name in model_names:\n",
+    "    pipe = build_pipe(model_name, liquid_inlet, liquid_design, sections=60)\n",
+    "    pipe.run()\n",
+    "    calculated_drop_bar = inlet_pressure_bara - pipe.getOutletStream().getPressure(\"bara\")\n",
+    "    analytical_rows.append({\n",
+    "        \"model\": model_name,\n",
+    "        \"drop_bar\": calculated_drop_bar,\n",
+    "        \"Darcy_reference_bar\": reference_drop_bar,\n",
+    "        \"relative_difference_pct\": 100.0 * (\n",
+    "            calculated_drop_bar / reference_drop_bar - 1.0\n",
+    "        ),\n",
+    "    })\n",
+    "analytical = pd.DataFrame(analytical_rows)\n",
+    "display(analytical.round(6))\n",
+    "assert (analytical[\"relative_difference_pct\"].abs() < 15.0).all()\n",
+    "print(f\"Single-liquid hydraulic check passed; Reynolds number = {reynolds:,.0f}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "750a19cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>sections</th>\n",
+       "      <th>rate_kg_s</th>\n",
+       "      <th>rate_difference_from_60_pct</th>\n",
+       "      <th>termination</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>15</td>\n",
+       "      <td>40.46091</td>\n",
+       "      <td>0.28802</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>30</td>\n",
+       "      <td>40.38349</td>\n",
+       "      <td>0.09612</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>60</td>\n",
+       "      <td>40.34471</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>15</td>\n",
+       "      <td>44.93642</td>\n",
+       "      <td>3.04002</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>30</td>\n",
+       "      <td>44.04026</td>\n",
+       "      <td>0.98509</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>60</td>\n",
+       "      <td>43.61065</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                model  sections  rate_kg_s  rate_difference_from_60_pct  \\\n",
+       "0  PipeBeggsAndBrills        15   40.46091                      0.28802   \n",
+       "1  PipeBeggsAndBrills        30   40.38349                      0.09612   \n",
+       "2  PipeBeggsAndBrills        60   40.34471                      0.00000   \n",
+       "3        TwoFluidPipe        15   44.93642                      3.04002   \n",
+       "4        TwoFluidPipe        30   44.04026                      0.98509   \n",
+       "5        TwoFluidPipe        60   43.61065                      0.00000   \n",
+       "\n",
+       "           termination  \n",
+       "0  FORWARD_CALCULATION  \n",
+       "1  FORWARD_CALCULATION  \n",
+       "2  FORWARD_CALCULATION  \n",
+       "3            CONVERGED  \n",
+       "4            CONVERGED  \n",
+       "5            CONVERGED  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>rate_kg_s</th>\n",
+       "      <th>outlet_bara</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>38.36432</td>\n",
+       "      <td>82.22346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>40.38349</td>\n",
+       "      <td>79.99999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>42.40267</td>\n",
+       "      <td>77.57247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>41.83824</td>\n",
+       "      <td>82.17983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>44.04026</td>\n",
+       "      <td>80.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>46.24227</td>\n",
+       "      <td>77.63716</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                model  rate_kg_s  outlet_bara\n",
+       "0  PipeBeggsAndBrills   38.36432     82.22346\n",
+       "1  PipeBeggsAndBrills   40.38349     79.99999\n",
+       "2  PipeBeggsAndBrills   42.40267     77.57247\n",
+       "3        TwoFluidPipe   41.83824     82.17983\n",
+       "4        TwoFluidPipe   44.04026     80.00000\n",
+       "5        TwoFluidPipe   46.24227     77.63716"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mesh_rows = []\n",
+    "for model_name in model_names[:2]:\n",
+    "    for sections in [15, 30, 60]:\n",
+    "        row, _ = solve_hydraulic_rate(model_name, base_design, sections)\n",
+    "        row[\"sections\"] = sections\n",
+    "        mesh_rows.append(row)\n",
+    "mesh = pd.DataFrame(mesh_rows)\n",
+    "mesh[\"rate_difference_from_60_pct\"] = np.nan\n",
+    "for model_name in model_names[:2]:\n",
+    "    selected = mesh[\"model\"] == model_name\n",
+    "    fine_rate = mesh.loc[selected & (mesh[\"sections\"] == 60), \"rate_kg_s\"].iloc[0]\n",
+    "    mesh.loc[selected, \"rate_difference_from_60_pct\"] = 100.0 * (\n",
+    "        mesh.loc[selected, \"rate_kg_s\"] / fine_rate - 1.0\n",
+    "    )\n",
+    "display(mesh[\n",
+    "    [\"model\", \"sections\", \"rate_kg_s\", \"rate_difference_from_60_pct\", \"termination\"]\n",
+    "].round(5))\n",
+    "nominal_mesh = mesh[mesh[\"sections\"] == default_sections]\n",
+    "assert (nominal_mesh[\"rate_difference_from_60_pct\"].abs() < 3.0).all()\n",
+    "\n",
+    "trend_rows = []\n",
+    "for model_name in model_names[:2]:\n",
+    "    for multiplier in [0.95, 1.0, 1.05]:\n",
+    "        reference_rate = baseline.loc[baseline[\"model\"] == model_name, \"rate_kg_s\"].iloc[0]\n",
+    "        row, _ = evaluate_pipe(model_name, base_design, reference_rate * multiplier)\n",
+    "        trend_rows.append(row)\n",
+    "nearby_rates = pd.DataFrame(trend_rows)\n",
+    "for model_name in model_names[:2]:\n",
+    "    pressures = nearby_rates.loc[nearby_rates[\"model\"] == model_name, \"outlet_bara\"]\n",
+    "    assert np.all(np.diff(pressures) < 0.0)\n",
+    "display(nearby_rates[[\"model\", \"rate_kg_s\", \"outlet_bara\"]].round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a627849b",
+   "metadata": {},
+   "source": [
+    "## 5. Design of experiments\n",
+    "\n",
+    "A 27-case three-level full factorial design trains each multiphase model's\n",
+    "surrogate. Sixteen seeded, off-grid designs are reserved for testing and\n",
+    "never used in fitting. Every target is an independently solved hydraulic rate.\n",
+    "Full tables are displayed and exported. The two models use identical fluid\n",
+    "and geometry inputs; their separate targets expose model disagreement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c4cbc3f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train: 27 designs completed for both multiphase models.\n",
+      "test: 16 designs completed for both multiphase models.\n",
+      "86 accepted hydraulic-rate roots in 132.3 s.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>case_id</th>\n",
+       "      <th>model</th>\n",
+       "      <th>diameter_m</th>\n",
+       "      <th>length_km</th>\n",
+       "      <th>decane_mol_fraction</th>\n",
+       "      <th>rate_kg_s</th>\n",
+       "      <th>liquid_holdup</th>\n",
+       "      <th>termination</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>train-00</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>21.06935</td>\n",
+       "      <td>0.10680</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>train-00</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>24.21852</td>\n",
+       "      <td>0.03279</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>train-01</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>23.71270</td>\n",
+       "      <td>0.16755</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>train-01</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>25.99392</td>\n",
+       "      <td>0.09986</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>train-02</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>26.46152</td>\n",
+       "      <td>0.22487</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>train-02</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>30.54039</td>\n",
+       "      <td>0.16709</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>train-03</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>14.89303</td>\n",
+       "      <td>0.11342</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>train-03</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>17.06881</td>\n",
+       "      <td>0.03267</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>train-04</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>16.82224</td>\n",
+       "      <td>0.18040</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>train-04</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>18.32982</td>\n",
+       "      <td>0.09986</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>train-05</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>18.63585</td>\n",
+       "      <td>0.22761</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>train-05</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>21.45215</td>\n",
+       "      <td>0.16604</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>train-06</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>12.21702</td>\n",
+       "      <td>0.11747</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>train-06</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>13.25522</td>\n",
+       "      <td>0.03666</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>train-07</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>13.77127</td>\n",
+       "      <td>0.19534</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>train-07</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>14.93546</td>\n",
+       "      <td>0.09986</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>train-08</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>15.21861</td>\n",
+       "      <td>0.23744</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>train-08</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>17.44511</td>\n",
+       "      <td>0.16602</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>train-09</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>50.56107</td>\n",
+       "      <td>0.10617</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>train-09</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>57.64717</td>\n",
+       "      <td>0.03496</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>train-10</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>56.95536</td>\n",
+       "      <td>0.16735</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>train-10</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>62.39101</td>\n",
+       "      <td>0.10027</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>train-11</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>63.58981</td>\n",
+       "      <td>0.22459</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>train-11</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>73.62821</td>\n",
+       "      <td>0.16885</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>train-12</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>35.76909</td>\n",
+       "      <td>0.11273</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>train-12</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>41.04458</td>\n",
+       "      <td>0.03268</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>train-13</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>40.38349</td>\n",
+       "      <td>0.17776</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>train-13</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>44.04026</td>\n",
+       "      <td>0.09986</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>train-14</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>44.82291</td>\n",
+       "      <td>0.22732</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>train-14</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>51.68728</td>\n",
+       "      <td>0.16642</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>train-15</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>29.23481</td>\n",
+       "      <td>0.11679</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>train-15</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>31.78878</td>\n",
+       "      <td>0.03528</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>train-16</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>33.09768</td>\n",
+       "      <td>0.19273</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>train-16</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>35.89797</td>\n",
+       "      <td>0.09986</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>train-17</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>36.57429</td>\n",
+       "      <td>0.23348</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>train-17</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>42.04335</td>\n",
+       "      <td>0.16604</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>train-18</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>97.13853</td>\n",
+       "      <td>0.10571</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>train-18</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>109.46684</td>\n",
+       "      <td>0.03818</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>train-19</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>109.48917</td>\n",
+       "      <td>0.16721</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>train-19</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>119.37334</td>\n",
+       "      <td>0.10349</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>train-20</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>122.28222</td>\n",
+       "      <td>0.22439</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>train-20</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>20.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>142.03648</td>\n",
+       "      <td>0.17105</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>train-21</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>68.75649</td>\n",
+       "      <td>0.11224</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>train-21</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>78.84140</td>\n",
+       "      <td>0.03281</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>train-22</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>77.59312</td>\n",
+       "      <td>0.17586</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>train-22</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>84.68157</td>\n",
+       "      <td>0.09986</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>train-23</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>86.24166</td>\n",
+       "      <td>0.22712</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>train-23</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>99.61903</td>\n",
+       "      <td>0.16713</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>train-24</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>56.10404</td>\n",
+       "      <td>0.11629</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>train-24</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>61.36754</td>\n",
+       "      <td>0.03476</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>train-25</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>63.64233</td>\n",
+       "      <td>0.19084</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>train-25</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.06000</td>\n",
+       "      <td>69.04167</td>\n",
+       "      <td>0.09986</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>train-26</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>70.32229</td>\n",
+       "      <td>0.23065</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>train-26</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.45000</td>\n",
+       "      <td>60.00000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>81.02151</td>\n",
+       "      <td>0.16627</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>test-00</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.34267</td>\n",
+       "      <td>40.43784</td>\n",
+       "      <td>0.03783</td>\n",
+       "      <td>35.74059</td>\n",
+       "      <td>0.15199</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>test-00</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.34267</td>\n",
+       "      <td>40.43784</td>\n",
+       "      <td>0.03783</td>\n",
+       "      <td>39.58713</td>\n",
+       "      <td>0.06276</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>test-01</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.36792</td>\n",
+       "      <td>46.90444</td>\n",
+       "      <td>0.08481</td>\n",
+       "      <td>45.30575</td>\n",
+       "      <td>0.20775</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>test-01</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.36792</td>\n",
+       "      <td>46.90444</td>\n",
+       "      <td>0.08481</td>\n",
+       "      <td>50.66398</td>\n",
+       "      <td>0.14103</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>test-02</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25340</td>\n",
+       "      <td>23.22690</td>\n",
+       "      <td>0.07507</td>\n",
+       "      <td>23.79361</td>\n",
+       "      <td>0.19095</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>test-02</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25340</td>\n",
+       "      <td>23.22690</td>\n",
+       "      <td>0.07507</td>\n",
+       "      <td>26.27790</td>\n",
+       "      <td>0.12492</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>test-03</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.37309</td>\n",
+       "      <td>41.86554</td>\n",
+       "      <td>0.08059</td>\n",
+       "      <td>49.17907</td>\n",
+       "      <td>0.20068</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>test-03</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.37309</td>\n",
+       "      <td>41.86554</td>\n",
+       "      <td>0.08059</td>\n",
+       "      <td>54.67733</td>\n",
+       "      <td>0.13405</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>test-04</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.36196</td>\n",
+       "      <td>52.77212</td>\n",
+       "      <td>0.02210</td>\n",
+       "      <td>34.19794</td>\n",
+       "      <td>0.12162</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>test-04</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.36196</td>\n",
+       "      <td>52.77212</td>\n",
+       "      <td>0.02210</td>\n",
+       "      <td>37.64219</td>\n",
+       "      <td>0.03624</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>test-05</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.42007</td>\n",
+       "      <td>23.06379</td>\n",
+       "      <td>0.04795</td>\n",
+       "      <td>82.08980</td>\n",
+       "      <td>0.14762</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>test-05</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.42007</td>\n",
+       "      <td>23.06379</td>\n",
+       "      <td>0.04795</td>\n",
+       "      <td>90.39197</td>\n",
+       "      <td>0.08078</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>test-06</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.40975</td>\n",
+       "      <td>42.18112</td>\n",
+       "      <td>0.05192</td>\n",
+       "      <td>58.00336</td>\n",
+       "      <td>0.16965</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>test-06</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.40975</td>\n",
+       "      <td>42.18112</td>\n",
+       "      <td>0.05192</td>\n",
+       "      <td>63.26859</td>\n",
+       "      <td>0.08637</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>test-07</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.43580</td>\n",
+       "      <td>34.89929</td>\n",
+       "      <td>0.03504</td>\n",
+       "      <td>71.19944</td>\n",
+       "      <td>0.14283</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>test-07</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.43580</td>\n",
+       "      <td>34.89929</td>\n",
+       "      <td>0.03504</td>\n",
+       "      <td>79.28150</td>\n",
+       "      <td>0.05808</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>test-08</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.42433</td>\n",
+       "      <td>59.73571</td>\n",
+       "      <td>0.08488</td>\n",
+       "      <td>58.37735</td>\n",
+       "      <td>0.21760</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>test-08</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.42433</td>\n",
+       "      <td>59.73571</td>\n",
+       "      <td>0.08488</td>\n",
+       "      <td>65.02593</td>\n",
+       "      <td>0.14114</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>test-09</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.26523</td>\n",
+       "      <td>44.38212</td>\n",
+       "      <td>0.03974</td>\n",
+       "      <td>17.61682</td>\n",
+       "      <td>0.15878</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>test-09</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.26523</td>\n",
+       "      <td>44.38212</td>\n",
+       "      <td>0.03974</td>\n",
+       "      <td>19.45633</td>\n",
+       "      <td>0.06596</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>test-10</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.26648</td>\n",
+       "      <td>34.50060</td>\n",
+       "      <td>0.06087</td>\n",
+       "      <td>21.39481</td>\n",
+       "      <td>0.17525</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>test-10</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.26648</td>\n",
+       "      <td>34.50060</td>\n",
+       "      <td>0.06087</td>\n",
+       "      <td>23.38392</td>\n",
+       "      <td>0.10131</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>test-11</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.33209</td>\n",
+       "      <td>41.03932</td>\n",
+       "      <td>0.05181</td>\n",
+       "      <td>34.03209</td>\n",
+       "      <td>0.17004</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>test-11</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.33209</td>\n",
+       "      <td>41.03932</td>\n",
+       "      <td>0.05181</td>\n",
+       "      <td>37.13101</td>\n",
+       "      <td>0.08618</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>test-12</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.25931</td>\n",
+       "      <td>57.23859</td>\n",
+       "      <td>0.02518</td>\n",
+       "      <td>13.96663</td>\n",
+       "      <td>0.13196</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>test-12</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.25931</td>\n",
+       "      <td>57.23859</td>\n",
+       "      <td>0.02518</td>\n",
+       "      <td>15.10885</td>\n",
+       "      <td>0.04250</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>test-13</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.27987</td>\n",
+       "      <td>33.09452</td>\n",
+       "      <td>0.09578</td>\n",
+       "      <td>27.24739</td>\n",
+       "      <td>0.22112</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>test-13</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.27987</td>\n",
+       "      <td>33.09452</td>\n",
+       "      <td>0.09578</td>\n",
+       "      <td>31.10074</td>\n",
+       "      <td>0.15911</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>test-14</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.36811</td>\n",
+       "      <td>45.82055</td>\n",
+       "      <td>0.03905</td>\n",
+       "      <td>40.61932</td>\n",
+       "      <td>0.15679</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>test-14</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.36811</td>\n",
+       "      <td>45.82055</td>\n",
+       "      <td>0.03905</td>\n",
+       "      <td>44.87390</td>\n",
+       "      <td>0.06481</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>test-15</td>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.34025</td>\n",
+       "      <td>56.81217</td>\n",
+       "      <td>0.03855</td>\n",
+       "      <td>29.65250</td>\n",
+       "      <td>0.16282</td>\n",
+       "      <td>FORWARD_CALCULATION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>test-15</td>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.34025</td>\n",
+       "      <td>56.81217</td>\n",
+       "      <td>0.03855</td>\n",
+       "      <td>32.74529</td>\n",
+       "      <td>0.06396</td>\n",
+       "      <td>CONVERGED</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     case_id               model  diameter_m  length_km  decane_mol_fraction  \\\n",
+       "0   train-00  PipeBeggsAndBrills     0.25000   20.00000              0.02000   \n",
+       "1   train-00        TwoFluidPipe     0.25000   20.00000              0.02000   \n",
+       "2   train-01  PipeBeggsAndBrills     0.25000   20.00000              0.06000   \n",
+       "3   train-01        TwoFluidPipe     0.25000   20.00000              0.06000   \n",
+       "4   train-02  PipeBeggsAndBrills     0.25000   20.00000              0.10000   \n",
+       "5   train-02        TwoFluidPipe     0.25000   20.00000              0.10000   \n",
+       "6   train-03  PipeBeggsAndBrills     0.25000   40.00000              0.02000   \n",
+       "7   train-03        TwoFluidPipe     0.25000   40.00000              0.02000   \n",
+       "8   train-04  PipeBeggsAndBrills     0.25000   40.00000              0.06000   \n",
+       "9   train-04        TwoFluidPipe     0.25000   40.00000              0.06000   \n",
+       "10  train-05  PipeBeggsAndBrills     0.25000   40.00000              0.10000   \n",
+       "11  train-05        TwoFluidPipe     0.25000   40.00000              0.10000   \n",
+       "12  train-06  PipeBeggsAndBrills     0.25000   60.00000              0.02000   \n",
+       "13  train-06        TwoFluidPipe     0.25000   60.00000              0.02000   \n",
+       "14  train-07  PipeBeggsAndBrills     0.25000   60.00000              0.06000   \n",
+       "15  train-07        TwoFluidPipe     0.25000   60.00000              0.06000   \n",
+       "16  train-08  PipeBeggsAndBrills     0.25000   60.00000              0.10000   \n",
+       "17  train-08        TwoFluidPipe     0.25000   60.00000              0.10000   \n",
+       "18  train-09  PipeBeggsAndBrills     0.35000   20.00000              0.02000   \n",
+       "19  train-09        TwoFluidPipe     0.35000   20.00000              0.02000   \n",
+       "20  train-10  PipeBeggsAndBrills     0.35000   20.00000              0.06000   \n",
+       "21  train-10        TwoFluidPipe     0.35000   20.00000              0.06000   \n",
+       "22  train-11  PipeBeggsAndBrills     0.35000   20.00000              0.10000   \n",
+       "23  train-11        TwoFluidPipe     0.35000   20.00000              0.10000   \n",
+       "24  train-12  PipeBeggsAndBrills     0.35000   40.00000              0.02000   \n",
+       "25  train-12        TwoFluidPipe     0.35000   40.00000              0.02000   \n",
+       "26  train-13  PipeBeggsAndBrills     0.35000   40.00000              0.06000   \n",
+       "27  train-13        TwoFluidPipe     0.35000   40.00000              0.06000   \n",
+       "28  train-14  PipeBeggsAndBrills     0.35000   40.00000              0.10000   \n",
+       "29  train-14        TwoFluidPipe     0.35000   40.00000              0.10000   \n",
+       "30  train-15  PipeBeggsAndBrills     0.35000   60.00000              0.02000   \n",
+       "31  train-15        TwoFluidPipe     0.35000   60.00000              0.02000   \n",
+       "32  train-16  PipeBeggsAndBrills     0.35000   60.00000              0.06000   \n",
+       "33  train-16        TwoFluidPipe     0.35000   60.00000              0.06000   \n",
+       "34  train-17  PipeBeggsAndBrills     0.35000   60.00000              0.10000   \n",
+       "35  train-17        TwoFluidPipe     0.35000   60.00000              0.10000   \n",
+       "36  train-18  PipeBeggsAndBrills     0.45000   20.00000              0.02000   \n",
+       "37  train-18        TwoFluidPipe     0.45000   20.00000              0.02000   \n",
+       "38  train-19  PipeBeggsAndBrills     0.45000   20.00000              0.06000   \n",
+       "39  train-19        TwoFluidPipe     0.45000   20.00000              0.06000   \n",
+       "40  train-20  PipeBeggsAndBrills     0.45000   20.00000              0.10000   \n",
+       "41  train-20        TwoFluidPipe     0.45000   20.00000              0.10000   \n",
+       "42  train-21  PipeBeggsAndBrills     0.45000   40.00000              0.02000   \n",
+       "43  train-21        TwoFluidPipe     0.45000   40.00000              0.02000   \n",
+       "44  train-22  PipeBeggsAndBrills     0.45000   40.00000              0.06000   \n",
+       "45  train-22        TwoFluidPipe     0.45000   40.00000              0.06000   \n",
+       "46  train-23  PipeBeggsAndBrills     0.45000   40.00000              0.10000   \n",
+       "47  train-23        TwoFluidPipe     0.45000   40.00000              0.10000   \n",
+       "48  train-24  PipeBeggsAndBrills     0.45000   60.00000              0.02000   \n",
+       "49  train-24        TwoFluidPipe     0.45000   60.00000              0.02000   \n",
+       "50  train-25  PipeBeggsAndBrills     0.45000   60.00000              0.06000   \n",
+       "51  train-25        TwoFluidPipe     0.45000   60.00000              0.06000   \n",
+       "52  train-26  PipeBeggsAndBrills     0.45000   60.00000              0.10000   \n",
+       "53  train-26        TwoFluidPipe     0.45000   60.00000              0.10000   \n",
+       "54   test-00  PipeBeggsAndBrills     0.34267   40.43784              0.03783   \n",
+       "55   test-00        TwoFluidPipe     0.34267   40.43784              0.03783   \n",
+       "56   test-01  PipeBeggsAndBrills     0.36792   46.90444              0.08481   \n",
+       "57   test-01        TwoFluidPipe     0.36792   46.90444              0.08481   \n",
+       "58   test-02  PipeBeggsAndBrills     0.25340   23.22690              0.07507   \n",
+       "59   test-02        TwoFluidPipe     0.25340   23.22690              0.07507   \n",
+       "60   test-03  PipeBeggsAndBrills     0.37309   41.86554              0.08059   \n",
+       "61   test-03        TwoFluidPipe     0.37309   41.86554              0.08059   \n",
+       "62   test-04  PipeBeggsAndBrills     0.36196   52.77212              0.02210   \n",
+       "63   test-04        TwoFluidPipe     0.36196   52.77212              0.02210   \n",
+       "64   test-05  PipeBeggsAndBrills     0.42007   23.06379              0.04795   \n",
+       "65   test-05        TwoFluidPipe     0.42007   23.06379              0.04795   \n",
+       "66   test-06  PipeBeggsAndBrills     0.40975   42.18112              0.05192   \n",
+       "67   test-06        TwoFluidPipe     0.40975   42.18112              0.05192   \n",
+       "68   test-07  PipeBeggsAndBrills     0.43580   34.89929              0.03504   \n",
+       "69   test-07        TwoFluidPipe     0.43580   34.89929              0.03504   \n",
+       "70   test-08  PipeBeggsAndBrills     0.42433   59.73571              0.08488   \n",
+       "71   test-08        TwoFluidPipe     0.42433   59.73571              0.08488   \n",
+       "72   test-09  PipeBeggsAndBrills     0.26523   44.38212              0.03974   \n",
+       "73   test-09        TwoFluidPipe     0.26523   44.38212              0.03974   \n",
+       "74   test-10  PipeBeggsAndBrills     0.26648   34.50060              0.06087   \n",
+       "75   test-10        TwoFluidPipe     0.26648   34.50060              0.06087   \n",
+       "76   test-11  PipeBeggsAndBrills     0.33209   41.03932              0.05181   \n",
+       "77   test-11        TwoFluidPipe     0.33209   41.03932              0.05181   \n",
+       "78   test-12  PipeBeggsAndBrills     0.25931   57.23859              0.02518   \n",
+       "79   test-12        TwoFluidPipe     0.25931   57.23859              0.02518   \n",
+       "80   test-13  PipeBeggsAndBrills     0.27987   33.09452              0.09578   \n",
+       "81   test-13        TwoFluidPipe     0.27987   33.09452              0.09578   \n",
+       "82   test-14  PipeBeggsAndBrills     0.36811   45.82055              0.03905   \n",
+       "83   test-14        TwoFluidPipe     0.36811   45.82055              0.03905   \n",
+       "84   test-15  PipeBeggsAndBrills     0.34025   56.81217              0.03855   \n",
+       "85   test-15        TwoFluidPipe     0.34025   56.81217              0.03855   \n",
+       "\n",
+       "    rate_kg_s  liquid_holdup          termination  \n",
+       "0    21.06935        0.10680  FORWARD_CALCULATION  \n",
+       "1    24.21852        0.03279            CONVERGED  \n",
+       "2    23.71270        0.16755  FORWARD_CALCULATION  \n",
+       "3    25.99392        0.09986            CONVERGED  \n",
+       "4    26.46152        0.22487  FORWARD_CALCULATION  \n",
+       "5    30.54039        0.16709            CONVERGED  \n",
+       "6    14.89303        0.11342  FORWARD_CALCULATION  \n",
+       "7    17.06881        0.03267            CONVERGED  \n",
+       "8    16.82224        0.18040  FORWARD_CALCULATION  \n",
+       "9    18.32982        0.09986            CONVERGED  \n",
+       "10   18.63585        0.22761  FORWARD_CALCULATION  \n",
+       "11   21.45215        0.16604            CONVERGED  \n",
+       "12   12.21702        0.11747  FORWARD_CALCULATION  \n",
+       "13   13.25522        0.03666            CONVERGED  \n",
+       "14   13.77127        0.19534  FORWARD_CALCULATION  \n",
+       "15   14.93546        0.09986            CONVERGED  \n",
+       "16   15.21861        0.23744  FORWARD_CALCULATION  \n",
+       "17   17.44511        0.16602            CONVERGED  \n",
+       "18   50.56107        0.10617  FORWARD_CALCULATION  \n",
+       "19   57.64717        0.03496            CONVERGED  \n",
+       "20   56.95536        0.16735  FORWARD_CALCULATION  \n",
+       "21   62.39101        0.10027            CONVERGED  \n",
+       "22   63.58981        0.22459  FORWARD_CALCULATION  \n",
+       "23   73.62821        0.16885            CONVERGED  \n",
+       "24   35.76909        0.11273  FORWARD_CALCULATION  \n",
+       "25   41.04458        0.03268            CONVERGED  \n",
+       "26   40.38349        0.17776  FORWARD_CALCULATION  \n",
+       "27   44.04026        0.09986            CONVERGED  \n",
+       "28   44.82291        0.22732  FORWARD_CALCULATION  \n",
+       "29   51.68728        0.16642            CONVERGED  \n",
+       "30   29.23481        0.11679  FORWARD_CALCULATION  \n",
+       "31   31.78878        0.03528            CONVERGED  \n",
+       "32   33.09768        0.19273  FORWARD_CALCULATION  \n",
+       "33   35.89797        0.09986            CONVERGED  \n",
+       "34   36.57429        0.23348  FORWARD_CALCULATION  \n",
+       "35   42.04335        0.16604            CONVERGED  \n",
+       "36   97.13853        0.10571  FORWARD_CALCULATION  \n",
+       "37  109.46684        0.03818            CONVERGED  \n",
+       "38  109.48917        0.16721  FORWARD_CALCULATION  \n",
+       "39  119.37334        0.10349            CONVERGED  \n",
+       "40  122.28222        0.22439  FORWARD_CALCULATION  \n",
+       "41  142.03648        0.17105            CONVERGED  \n",
+       "42   68.75649        0.11224  FORWARD_CALCULATION  \n",
+       "43   78.84140        0.03281            CONVERGED  \n",
+       "44   77.59312        0.17586  FORWARD_CALCULATION  \n",
+       "45   84.68157        0.09986            CONVERGED  \n",
+       "46   86.24166        0.22712  FORWARD_CALCULATION  \n",
+       "47   99.61903        0.16713            CONVERGED  \n",
+       "48   56.10404        0.11629  FORWARD_CALCULATION  \n",
+       "49   61.36754        0.03476            CONVERGED  \n",
+       "50   63.64233        0.19084  FORWARD_CALCULATION  \n",
+       "51   69.04167        0.09986            CONVERGED  \n",
+       "52   70.32229        0.23065  FORWARD_CALCULATION  \n",
+       "53   81.02151        0.16627            CONVERGED  \n",
+       "54   35.74059        0.15199  FORWARD_CALCULATION  \n",
+       "55   39.58713        0.06276            CONVERGED  \n",
+       "56   45.30575        0.20775  FORWARD_CALCULATION  \n",
+       "57   50.66398        0.14103            CONVERGED  \n",
+       "58   23.79361        0.19095  FORWARD_CALCULATION  \n",
+       "59   26.27790        0.12492            CONVERGED  \n",
+       "60   49.17907        0.20068  FORWARD_CALCULATION  \n",
+       "61   54.67733        0.13405            CONVERGED  \n",
+       "62   34.19794        0.12162  FORWARD_CALCULATION  \n",
+       "63   37.64219        0.03624            CONVERGED  \n",
+       "64   82.08980        0.14762  FORWARD_CALCULATION  \n",
+       "65   90.39197        0.08078            CONVERGED  \n",
+       "66   58.00336        0.16965  FORWARD_CALCULATION  \n",
+       "67   63.26859        0.08637            CONVERGED  \n",
+       "68   71.19944        0.14283  FORWARD_CALCULATION  \n",
+       "69   79.28150        0.05808            CONVERGED  \n",
+       "70   58.37735        0.21760  FORWARD_CALCULATION  \n",
+       "71   65.02593        0.14114            CONVERGED  \n",
+       "72   17.61682        0.15878  FORWARD_CALCULATION  \n",
+       "73   19.45633        0.06596            CONVERGED  \n",
+       "74   21.39481        0.17525  FORWARD_CALCULATION  \n",
+       "75   23.38392        0.10131            CONVERGED  \n",
+       "76   34.03209        0.17004  FORWARD_CALCULATION  \n",
+       "77   37.13101        0.08618            CONVERGED  \n",
+       "78   13.96663        0.13196  FORWARD_CALCULATION  \n",
+       "79   15.10885        0.04250            CONVERGED  \n",
+       "80   27.24739        0.22112  FORWARD_CALCULATION  \n",
+       "81   31.10074        0.15911            CONVERGED  \n",
+       "82   40.61932        0.15679  FORWARD_CALCULATION  \n",
+       "83   44.87390        0.06481            CONVERGED  \n",
+       "84   29.65250        0.16282  FORWARD_CALCULATION  \n",
+       "85   32.74529        0.06396            CONVERGED  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from itertools import product\n",
+    "\n",
+    "training_designs = pd.DataFrame(\n",
+    "    list(product([0.25, 0.35, 0.45], [20.0, 40.0, 60.0], [0.02, 0.06, 0.10])),\n",
+    "    columns=feature_names,\n",
+    ")\n",
+    "random_generator = np.random.default_rng(4253)\n",
+    "test_designs = pd.DataFrame(\n",
+    "    random_generator.uniform(design_bounds[:, 0], design_bounds[:, 1], size=(16, 3)),\n",
+    "    columns=feature_names,\n",
+    ")\n",
+    "sweep_rows = []\n",
+    "sweep_started = time.perf_counter()\n",
+    "for split_name, designs in [(\"train\", training_designs), (\"test\", test_designs)]:\n",
+    "    for case_number, design_series in designs.iterrows():\n",
+    "        design = design_series.to_dict()\n",
+    "        for model_name in model_names[:2]:\n",
+    "            row, _ = solve_hydraulic_rate(model_name, design)\n",
+    "            row.update({\"split\": split_name, \"case_id\": f\"{split_name}-{case_number:02d}\"})\n",
+    "            sweep_rows.append(row)\n",
+    "    print(f\"{split_name}: {len(designs)} designs completed for both multiphase models.\")\n",
+    "sweep = pd.DataFrame(sweep_rows)\n",
+    "sweep_seconds = time.perf_counter() - sweep_started\n",
+    "assert len(sweep) == 86\n",
+    "assert sweep[\"converged\"].all()\n",
+    "assert sweep[\"pressure_residual_bar\"].abs().max() <= pressure_tolerance_bar\n",
+    "print(f\"86 accepted hydraulic-rate roots in {sweep_seconds:.1f} s.\")\n",
+    "with pd.option_context(\"display.max_rows\", 100):\n",
+    "    display(sweep[\n",
+    "        [\"case_id\", \"model\", *feature_names, \"rate_kg_s\", \"liquid_holdup\", \"termination\"]\n",
+    "    ].round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c88a7291",
+   "metadata": {},
+   "source": [
+    "## 6. Quadratic and physics-informed response surfaces\n",
+    "\n",
+    "The ordinary RSM uses scaled diameter, length and composition. Its ten\n",
+    "coefficients include an intercept, three linear effects, three squared\n",
+    "effects and three pair interactions.\n",
+    "\n",
+    "$$\\hat y=b_0+\\sum_{i=1}^{3}b_i x_i+\\sum_{i=1}^{3}b_{ii}x_i^2+\\sum_{i<j}b_{ij}x_i x_j$$\n",
+    "\n",
+    "$\\hat y$ is estimated rate in kg/s for the ordinary model; the dimensionless\n",
+    "$x_i$ are standardized design inputs. Coefficients use that specific scaling.\n",
+    "\n",
+    "A second surrogate retains the turbulent Darcy scaling\n",
+    "$\\dot m\\propto D^{5/2}L^{-1/2}$ and fits a quadratic multiplicative correction.\n",
+    "This is a physics-informed baseline, not a calibrated experimental hybrid.\n",
+    "It uses the same ten-feature design and the same training data.\n",
+    "\n",
+    "$$\\hat{\\dot m}=1\\ \\mathrm{kg\\,s^{-1}}\\,s(D,L)\\exp(\\hat y),\\quad s(D,L)=\\left(\\frac{D}{0.35\\ \\mathrm m}\\right)^{5/2}\\left(\\frac{L}{40\\ \\mathrm{km}}\\right)^{-1/2}$$\n",
+    "\n",
+    "For this second fit the target is $\\ln\\left[\\frac{\\dot m}{s\\cdot 1\\ \\mathrm{kg\\,s^{-1}}}\\right]$.\n",
+    "The final numerical rate is reported in kg/s. The scaling does not model\n",
+    "changing friction factor, phase density, regime or holdup by itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ab15837f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Held-out 5% screening gate: {'PipeBeggsAndBrills': True, 'TwoFluidPipe': True}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>surrogate</th>\n",
+       "      <th>MAE (kg/s)</th>\n",
+       "      <th>MAPE (%)</th>\n",
+       "      <th>worst (%)</th>\n",
+       "      <th>min rate (kg/s)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>quadratic</td>\n",
+       "      <td>1.21847</td>\n",
+       "      <td>3.84309</td>\n",
+       "      <td>11.65156</td>\n",
+       "      <td>15.13435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>physics-informed</td>\n",
+       "      <td>0.09960</td>\n",
+       "      <td>0.21382</td>\n",
+       "      <td>0.52200</td>\n",
+       "      <td>13.96795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>quadratic</td>\n",
+       "      <td>1.60506</td>\n",
+       "      <td>4.76694</td>\n",
+       "      <td>15.03809</td>\n",
+       "      <td>16.53047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>physics-informed</td>\n",
+       "      <td>0.28393</td>\n",
+       "      <td>0.73624</td>\n",
+       "      <td>2.16541</td>\n",
+       "      <td>15.30231</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                model         surrogate  MAE (kg/s)  MAPE (%)  worst (%)  \\\n",
+       "0  PipeBeggsAndBrills         quadratic     1.21847   3.84309   11.65156   \n",
+       "1  PipeBeggsAndBrills  physics-informed     0.09960   0.21382    0.52200   \n",
+       "2        TwoFluidPipe         quadratic     1.60506   4.76694   15.03809   \n",
+       "3        TwoFluidPipe  physics-informed     0.28393   0.73624    2.16541   \n",
+       "\n",
+       "   min rate (kg/s)  \n",
+       "0         15.13435  \n",
+       "1         13.96795  \n",
+       "2         16.53047  \n",
+       "3         15.30231  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>standardized_term</th>\n",
+       "      <th>quadratic_coefficient</th>\n",
+       "      <th>physical_log_coefficient</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>1</td>\n",
+       "      <td>40.014864</td>\n",
+       "      <td>3.698159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>diameter</td>\n",
+       "      <td>26.707070</td>\n",
+       "      <td>0.024483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>length</td>\n",
+       "      <td>-10.935492</td>\n",
+       "      <td>0.000622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>decane</td>\n",
+       "      <td>4.463774</td>\n",
+       "      <td>0.092233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>diameter^2</td>\n",
+       "      <td>4.903497</td>\n",
+       "      <td>-0.003234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>diameter length</td>\n",
+       "      <td>-6.044699</td>\n",
+       "      <td>-0.000174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>diameter decane</td>\n",
+       "      <td>2.483918</td>\n",
+       "      <td>0.000606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>length^2</td>\n",
+       "      <td>3.466900</td>\n",
+       "      <td>0.000541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>length decane</td>\n",
+       "      <td>-1.055849</td>\n",
+       "      <td>-0.000988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>decane^2</td>\n",
+       "      <td>-0.038597</td>\n",
+       "      <td>-0.005503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43.597807</td>\n",
+       "      <td>3.791004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>diameter</td>\n",
+       "      <td>30.038455</td>\n",
+       "      <td>0.024492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>length</td>\n",
+       "      <td>-12.632983</td>\n",
+       "      <td>-0.007668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>decane</td>\n",
+       "      <td>5.659894</td>\n",
+       "      <td>0.102616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>diameter^2</td>\n",
+       "      <td>5.494502</td>\n",
+       "      <td>-0.003470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>diameter length</td>\n",
+       "      <td>-6.907161</td>\n",
+       "      <td>0.001621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>diameter decane</td>\n",
+       "      <td>3.228118</td>\n",
+       "      <td>0.002058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>length^2</td>\n",
+       "      <td>3.650471</td>\n",
+       "      <td>-0.006161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>length decane</td>\n",
+       "      <td>-1.154117</td>\n",
+       "      <td>0.005278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>decane^2</td>\n",
+       "      <td>1.659344</td>\n",
+       "      <td>0.023905</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 model standardized_term  quadratic_coefficient  \\\n",
+       "0   PipeBeggsAndBrills                 1              40.014864   \n",
+       "1   PipeBeggsAndBrills          diameter              26.707070   \n",
+       "2   PipeBeggsAndBrills            length             -10.935492   \n",
+       "3   PipeBeggsAndBrills            decane               4.463774   \n",
+       "4   PipeBeggsAndBrills        diameter^2               4.903497   \n",
+       "5   PipeBeggsAndBrills   diameter length              -6.044699   \n",
+       "6   PipeBeggsAndBrills   diameter decane               2.483918   \n",
+       "7   PipeBeggsAndBrills          length^2               3.466900   \n",
+       "8   PipeBeggsAndBrills     length decane              -1.055849   \n",
+       "9   PipeBeggsAndBrills          decane^2              -0.038597   \n",
+       "10        TwoFluidPipe                 1              43.597807   \n",
+       "11        TwoFluidPipe          diameter              30.038455   \n",
+       "12        TwoFluidPipe            length             -12.632983   \n",
+       "13        TwoFluidPipe            decane               5.659894   \n",
+       "14        TwoFluidPipe        diameter^2               5.494502   \n",
+       "15        TwoFluidPipe   diameter length              -6.907161   \n",
+       "16        TwoFluidPipe   diameter decane               3.228118   \n",
+       "17        TwoFluidPipe          length^2               3.650471   \n",
+       "18        TwoFluidPipe     length decane              -1.154117   \n",
+       "19        TwoFluidPipe          decane^2               1.659344   \n",
+       "\n",
+       "    physical_log_coefficient  \n",
+       "0                   3.698159  \n",
+       "1                   0.024483  \n",
+       "2                   0.000622  \n",
+       "3                   0.092233  \n",
+       "4                  -0.003234  \n",
+       "5                  -0.000174  \n",
+       "6                   0.000606  \n",
+       "7                   0.000541  \n",
+       "8                  -0.000988  \n",
+       "9                  -0.005503  \n",
+       "10                  3.791004  \n",
+       "11                  0.024492  \n",
+       "12                 -0.007668  \n",
+       "13                  0.102616  \n",
+       "14                 -0.003470  \n",
+       "15                  0.001621  \n",
+       "16                  0.002058  \n",
+       "17                 -0.006161  \n",
+       "18                  0.005278  \n",
+       "19                  0.023905  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def hydraulic_scaling(designs):\n",
+    "    values = np.asarray(designs, dtype=float)\n",
+    "    return (values[:, 0] / 0.35) ** 2.5 * (values[:, 1] / 40.0) ** -0.5\n",
+    "\n",
+    "surrogate_models = {}\n",
+    "metric_rows = []\n",
+    "prediction_rows = []\n",
+    "coefficient_rows = []\n",
+    "for model_name in model_names[:2]:\n",
+    "    training = sweep[(sweep[\"model\"] == model_name) & (sweep[\"split\"] == \"train\")]\n",
+    "    testing = sweep[(sweep[\"model\"] == model_name) & (sweep[\"split\"] == \"test\")]\n",
+    "    scaler = StandardScaler().fit(training[feature_names])\n",
+    "    polynomial = PolynomialFeatures(degree=2, include_bias=True)\n",
+    "    train_features = polynomial.fit_transform(scaler.transform(training[feature_names]))\n",
+    "    test_features = polynomial.transform(scaler.transform(testing[feature_names]))\n",
+    "    rates = training[\"rate_kg_s\"].to_numpy()\n",
+    "    ordinary = LinearRegression(fit_intercept=False).fit(train_features, rates)\n",
+    "    transformed_target = np.log(rates / hydraulic_scaling(training[feature_names]))\n",
+    "    physical = LinearRegression(fit_intercept=False).fit(train_features, transformed_target)\n",
+    "    surrogate_models[model_name] = {\n",
+    "        \"scaler\": scaler,\n",
+    "        \"polynomial\": polynomial,\n",
+    "        \"ordinary\": ordinary,\n",
+    "        \"physical\": physical,\n",
+    "    }\n",
+    "    predictions = {\n",
+    "        \"quadratic\": ordinary.predict(test_features),\n",
+    "        \"physics-informed\": hydraulic_scaling(testing[feature_names])\n",
+    "        * np.exp(physical.predict(test_features)),\n",
+    "    }\n",
+    "    actual = testing[\"rate_kg_s\"].to_numpy()\n",
+    "    for surrogate_name, predicted in predictions.items():\n",
+    "        relative_error = (predicted - actual) / actual\n",
+    "        metric_rows.append({\n",
+    "            \"model\": model_name,\n",
+    "            \"surrogate\": surrogate_name,\n",
+    "            \"MAE_kg_s\": np.mean(np.abs(predicted - actual)),\n",
+    "            \"MAPE_pct\": 100.0 * np.mean(np.abs(relative_error)),\n",
+    "            \"max_relative_error_pct\": 100.0 * np.max(np.abs(relative_error)),\n",
+    "            \"minimum_prediction_kg_s\": predicted.min(),\n",
+    "        })\n",
+    "        for case_id, reference, estimate in zip(testing[\"case_id\"], actual, predicted):\n",
+    "            prediction_rows.append({\n",
+    "                \"model\": model_name,\n",
+    "                \"surrogate\": surrogate_name,\n",
+    "                \"case_id\": case_id,\n",
+    "                \"NeqSim_kg_s\": reference,\n",
+    "                \"predicted_kg_s\": estimate,\n",
+    "                \"relative_error_pct\": 100.0 * (estimate / reference - 1.0),\n",
+    "            })\n",
+    "    terms = polynomial.get_feature_names_out([\"diameter\", \"length\", \"decane\"])\n",
+    "    for term, ordinary_coef, physical_coef in zip(terms, ordinary.coef_, physical.coef_):\n",
+    "        coefficient_rows.append({\n",
+    "            \"model\": model_name,\n",
+    "            \"standardized_term\": term,\n",
+    "            \"quadratic_coefficient\": ordinary_coef,\n",
+    "            \"physical_log_coefficient\": physical_coef,\n",
+    "        })\n",
+    "metrics = pd.DataFrame(metric_rows)\n",
+    "predictions = pd.DataFrame(prediction_rows)\n",
+    "coefficients = pd.DataFrame(coefficient_rows)\n",
+    "display(metrics.rename(columns={\n",
+    "    \"MAE_kg_s\": \"MAE (kg/s)\",\n",
+    "    \"MAPE_pct\": \"MAPE (%)\",\n",
+    "    \"max_relative_error_pct\": \"worst (%)\",\n",
+    "    \"minimum_prediction_kg_s\": \"min rate (kg/s)\",\n",
+    "}).round(5))\n",
+    "display(coefficients.round(6))\n",
+    "physical_metrics = metrics[metrics[\"surrogate\"] == \"physics-informed\"]\n",
+    "qualified_models = dict(zip(\n",
+    "    physical_metrics[\"model\"],\n",
+    "    physical_metrics[\"max_relative_error_pct\"] <= 5.0,\n",
+    "))\n",
+    "print(\"Held-out 5% screening gate:\", qualified_models)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d186f56e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1430x546 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>model</th>\n",
+       "      <th>PipeBeggsAndBrills</th>\n",
+       "      <th>TwoFluidPipe</th>\n",
+       "      <th>TwoFluid_minus_BB_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>12.2170</td>\n",
+       "      <td>13.2552</td>\n",
+       "      <td>8.1782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>45.2971</td>\n",
+       "      <td>50.6854</td>\n",
+       "      <td>11.6142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>122.2822</td>\n",
+       "      <td>142.0365</td>\n",
+       "      <td>16.1546</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "model  PipeBeggsAndBrills  TwoFluidPipe  TwoFluid_minus_BB_pct\n",
+       "min               12.2170       13.2552                 8.1782\n",
+       "mean              45.2971       50.6854                11.6142\n",
+       "max              122.2822      142.0365                16.1546"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.2))\n",
+    "colors = {\"PipeBeggsAndBrills\": \"#1967a8\", \"TwoFluidPipe\": \"#b84d19\"}\n",
+    "for (model_name, surrogate_name), selected in predictions.groupby([\"model\", \"surrogate\"]):\n",
+    "    marker = \"o\" if surrogate_name == \"physics-informed\" else \"x\"\n",
+    "    axes[0].scatter(\n",
+    "        selected[\"NeqSim_kg_s\"],\n",
+    "        selected[\"predicted_kg_s\"],\n",
+    "        c=colors[model_name],\n",
+    "        marker=marker,\n",
+    "        label=f\"{model_name}: {surrogate_name}\",\n",
+    "        alpha=0.8,\n",
+    "    )\n",
+    "minimum_rate = predictions[\"NeqSim_kg_s\"].min() * 0.9\n",
+    "maximum_rate = predictions[\"NeqSim_kg_s\"].max() * 1.1\n",
+    "axes[0].plot([minimum_rate, maximum_rate], [minimum_rate, maximum_rate], \"k--\")\n",
+    "axes[0].set(xlabel=\"Held-out NeqSim rate (kg/s)\", ylabel=\"Surrogate rate (kg/s)\")\n",
+    "axes[0].legend(fontsize=7)\n",
+    "paired = sweep.pivot(\n",
+    "    index=[\"split\", \"case_id\"], columns=\"model\", values=\"rate_kg_s\"\n",
+    ").reset_index()\n",
+    "paired[\"TwoFluid_minus_BB_pct\"] = 100.0 * (\n",
+    "    paired[\"TwoFluidPipe\"] / paired[\"PipeBeggsAndBrills\"] - 1.0\n",
+    ")\n",
+    "axes[1].scatter(paired[\"PipeBeggsAndBrills\"], paired[\"TwoFluid_minus_BB_pct\"], s=30)\n",
+    "axes[1].axhline(0.0, color=\"black\", linestyle=\"--\")\n",
+    "axes[1].set(\n",
+    "    xlabel=\"Beggs–Brill rate (kg/s)\",\n",
+    "    ylabel=\"TwoFluid vs Beggs–Brill rate difference (%)\",\n",
+    "    title=\"Model disagreement, not error vs measurements\",\n",
+    ")\n",
+    "for axis in axes:\n",
+    "    axis.grid(alpha=0.25)\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "display(paired.describe().loc[\n",
+    "    [\"min\", \"mean\", \"max\"],\n",
+    "    [\"PipeBeggsAndBrills\", \"TwoFluidPipe\", \"TwoFluid_minus_BB_pct\"],\n",
+    "].round(4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f7c7ad1",
+   "metadata": {},
+   "source": [
+    "## 7. Use the surrogate only inside its qualified domain\n",
+    "\n",
+    "Bounds and a held-out error gate screen every request. An out-of-box point\n",
+    "returns to NeqSim; a surrogate model that fails the 5% held-out gate also\n",
+    "returns to NeqSim. Accepted predictions are labelled as estimates.\n",
+    "Final engineering decisions replay the original simulator.\n",
+    "\n",
+    "The 5% gate is a finite-test screening criterion, not a probabilistic bound\n",
+    "throughout the domain. Smooth interpolation can still miss a flow-regime\n",
+    "change. The existing NeqSim SurrogateModelRegistry already supplies a\n",
+    "fallback framework; a production adapter should carry these domain,\n",
+    "revision and qualification checks into that framework."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1b77bd7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>case</th>\n",
+       "      <th>route</th>\n",
+       "      <th>estimated_rate_kg_s</th>\n",
+       "      <th>replayed_outlet_bara</th>\n",
+       "      <th>receiving_pressure_margin_bar</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>inside domain</td>\n",
+       "      <td>SURROGATE_ESTIMATE</td>\n",
+       "      <td>40.37292</td>\n",
+       "      <td>80.01215</td>\n",
+       "      <td>0.01215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>outside domain</td>\n",
+       "      <td>NEQSIM_FALLBACK</td>\n",
+       "      <td>91.74883</td>\n",
+       "      <td>80.00000</td>\n",
+       "      <td>-0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>inside domain</td>\n",
+       "      <td>SURROGATE_ESTIMATE</td>\n",
+       "      <td>44.30086</td>\n",
+       "      <td>79.73028</td>\n",
+       "      <td>-0.26972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>outside domain</td>\n",
+       "      <td>NEQSIM_FALLBACK</td>\n",
+       "      <td>100.15055</td>\n",
+       "      <td>80.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                model            case               route  \\\n",
+       "0  PipeBeggsAndBrills   inside domain  SURROGATE_ESTIMATE   \n",
+       "1  PipeBeggsAndBrills  outside domain     NEQSIM_FALLBACK   \n",
+       "2        TwoFluidPipe   inside domain  SURROGATE_ESTIMATE   \n",
+       "3        TwoFluidPipe  outside domain     NEQSIM_FALLBACK   \n",
+       "\n",
+       "   estimated_rate_kg_s  replayed_outlet_bara  receiving_pressure_margin_bar  \n",
+       "0             40.37292              80.01215                        0.01215  \n",
+       "1             91.74883              80.00000                       -0.00000  \n",
+       "2             44.30086              79.73028                       -0.26972  \n",
+       "3            100.15055              80.00000                        0.00000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 910x520 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def predict_rate(model_name, design):\n",
+    "    values = np.array([[design[name] for name in feature_names]], dtype=float)\n",
+    "    in_domain = np.isfinite(values).all() and np.all(\n",
+    "        (values[0] >= design_bounds[:, 0]) & (values[0] <= design_bounds[:, 1])\n",
+    "    )\n",
+    "    if not in_domain or not qualified_models[model_name]:\n",
+    "        result, _ = solve_hydraulic_rate(model_name, design)\n",
+    "        return result[\"rate_kg_s\"], \"NEQSIM_FALLBACK\"\n",
+    "    bundle = surrogate_models[model_name]\n",
+    "    features = bundle[\"polynomial\"].transform(bundle[\"scaler\"].transform(\n",
+    "        pd.DataFrame(values, columns=feature_names)\n",
+    "    ))\n",
+    "    predicted = hydraulic_scaling(values)[0] * np.exp(\n",
+    "        bundle[\"physical\"].predict(features)[0]\n",
+    "    )\n",
+    "    assert np.isfinite(predicted) and predicted > 0.0\n",
+    "    return float(predicted), \"SURROGATE_ESTIMATE\"\n",
+    "\n",
+    "replay_rows = []\n",
+    "for model_name in model_names[:2]:\n",
+    "    for case_label, design in [\n",
+    "        (\"inside domain\", base_design),\n",
+    "        (\"outside domain\", {**base_design, \"diameter_m\": 0.48}),\n",
+    "    ]:\n",
+    "        estimated_rate, route = predict_rate(model_name, design)\n",
+    "        replay, _ = evaluate_pipe(model_name, design, estimated_rate)\n",
+    "        replay_rows.append({\n",
+    "            \"model\": model_name,\n",
+    "            \"case\": case_label,\n",
+    "            \"route\": route,\n",
+    "            \"estimated_rate_kg_s\": estimated_rate,\n",
+    "            \"replayed_outlet_bara\": replay[\"outlet_bara\"],\n",
+    "            \"receiving_pressure_margin_bar\": replay[\"outlet_bara\"] - receiver_pressure_bara,\n",
+    "        })\n",
+    "display(pd.DataFrame(replay_rows).round(5))\n",
+    "assert all(\n",
+    "    row[\"route\"] == \"NEQSIM_FALLBACK\" for row in replay_rows if row[\"case\"] == \"outside domain\"\n",
+    ")\n",
+    "\n",
+    "figure, axis = plt.subplots(figsize=(7, 4))\n",
+    "diameters = np.linspace(0.25, 0.45, 40)\n",
+    "for model_name in model_names[:2]:\n",
+    "    rates = [\n",
+    "        predict_rate(model_name, {**base_design, \"diameter_m\": diameter})[0]\n",
+    "        for diameter in diameters\n",
+    "    ]\n",
+    "    axis.plot(diameters, rates, label=model_name)\n",
+    "axis.set(\n",
+    "    xlabel=\"Inner diameter (m)\",\n",
+    "    ylabel=\"Estimated hydraulic rate (kg/s)\",\n",
+    "    title=\"40 km, methane + 6 mol% decane; 100 to 80 bara\",\n",
+    ")\n",
+    "axis.grid(alpha=0.25)\n",
+    "axis.legend(fontsize=9)\n",
+    "figure.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "40370f1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Larger diameter increases rate; longer line decreases rate in the checked cases.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>diameter_m</th>\n",
+       "      <th>length_km</th>\n",
+       "      <th>rate_kg_s</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>27.04290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>40.38349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>57.14061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>46.45079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>40.38349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>PipeBeggsAndBrills</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>36.21345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>29.47952</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>44.04026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>62.33856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>50.90614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>44.04026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>TwoFluidPipe</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>39.35525</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 model  diameter_m  length_km  rate_kg_s\n",
+       "0   PipeBeggsAndBrills        0.30       40.0   27.04290\n",
+       "1   PipeBeggsAndBrills        0.35       40.0   40.38349\n",
+       "2   PipeBeggsAndBrills        0.40       40.0   57.14061\n",
+       "3   PipeBeggsAndBrills        0.35       30.0   46.45079\n",
+       "4   PipeBeggsAndBrills        0.35       40.0   40.38349\n",
+       "5   PipeBeggsAndBrills        0.35       50.0   36.21345\n",
+       "6         TwoFluidPipe        0.30       40.0   29.47952\n",
+       "7         TwoFluidPipe        0.35       40.0   44.04026\n",
+       "8         TwoFluidPipe        0.40       40.0   62.33856\n",
+       "9         TwoFluidPipe        0.35       30.0   50.90614\n",
+       "10        TwoFluidPipe        0.35       40.0   44.04026\n",
+       "11        TwoFluidPipe        0.35       50.0   39.35525"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "design_trends = []\n",
+    "for model_name in model_names[:2]:\n",
+    "    for diameter in [0.30, 0.35, 0.40]:\n",
+    "        row, _ = solve_hydraulic_rate(model_name, {**base_design, \"diameter_m\": diameter})\n",
+    "        design_trends.append(row)\n",
+    "    for length_km in [30.0, 40.0, 50.0]:\n",
+    "        row, _ = solve_hydraulic_rate(model_name, {**base_design, \"length_km\": length_km})\n",
+    "        design_trends.append(row)\n",
+    "trend_frame = pd.DataFrame(design_trends)\n",
+    "for model_name in model_names[:2]:\n",
+    "    selected = trend_frame[trend_frame[\"model\"] == model_name]\n",
+    "    assert np.all(np.diff(selected.iloc[:3][\"rate_kg_s\"]) > 0.0)\n",
+    "    assert np.all(np.diff(selected.iloc[3:][\"rate_kg_s\"]) < 0.0)\n",
+    "display(trend_frame[[\"model\", \"diameter_m\", \"length_km\", \"rate_kg_s\"]].round(5))\n",
+    "print(\"Larger diameter increases rate; longer line decreases rate in the checked cases.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93eed457",
+   "metadata": {},
+   "source": [
+    "## 8. A verified NeqSim improvement: silent flow-solve exhaustion\n",
+    "\n",
+    "[Issue #4258](https://github.com/equinor/neqsim/issues/4258) records a library\n",
+    "defect found during this notebook. Beggs–Brill's built-in flow mode returns\n",
+    "normally after its iteration budget is exhausted, without exposing a\n",
+    "convergence report. The minimal synthetic case below deliberately limits\n",
+    "the budget to one iteration. The default pressure tolerance permits\n",
+    "0.008 bar at 80 bara, but the returned result exceeds it.\n",
+    "\n",
+    "This is a **controlled expected failure**. It is excluded from all training,\n",
+    "test and design conclusions. The validated outer solver above independently\n",
+    "checks its pressure residual after final replay."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bec7829c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected defect reproduced. This candidate is rejected, never a capacity result.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>target_bara</th>\n",
+       "      <th>returned_bara</th>\n",
+       "      <th>residual_bar</th>\n",
+       "      <th>allowed_error_bar</th>\n",
+       "      <th>returned_rate_kg_s</th>\n",
+       "      <th>run_returned_normally</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>80.0</td>\n",
+       "      <td>80.43722</td>\n",
+       "      <td>0.43722</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>40.000139</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   target_bara  returned_bara  residual_bar  allowed_error_bar  \\\n",
+       "0         80.0       80.43722       0.43722              0.008   \n",
+       "\n",
+       "   returned_rate_kg_s  run_returned_normally  \n",
+       "0           40.000139                   True  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "defect_inlet = make_inlet(0.06, 10.0)\n",
+    "defect_pipe = build_pipe(\"PipeBeggsAndBrills\", defect_inlet, base_design)\n",
+    "defect_pipe.setOutletPressure(receiver_pressure_bara, \"bara\")\n",
+    "defect_pipe.setMaxFlowIterations(1)\n",
+    "defect_pipe.run()\n",
+    "defect_pressure_bara = float(defect_pipe.getOutletStream().getPressure(\"bara\"))\n",
+    "native_relative_tolerance = 1e-4\n",
+    "native_allowed_error_bar = receiver_pressure_bara * native_relative_tolerance\n",
+    "observed_error_bar = defect_pressure_bara - receiver_pressure_bara\n",
+    "defect_evidence = pd.DataFrame([{\n",
+    "    \"target_bara\": receiver_pressure_bara,\n",
+    "    \"returned_bara\": defect_pressure_bara,\n",
+    "    \"residual_bar\": observed_error_bar,\n",
+    "    \"allowed_error_bar\": native_allowed_error_bar,\n",
+    "    \"returned_rate_kg_s\": defect_inlet.getFlowRate(\"kg/sec\"),\n",
+    "    \"run_returned_normally\": True,\n",
+    "}])\n",
+    "display(defect_evidence.round(8))\n",
+    "assert abs(observed_error_bar) > native_allowed_error_bar\n",
+    "print(\"Expected defect reproduced. This candidate is rejected, never a capacity result.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27ea6fda",
+   "metadata": {},
+   "source": [
+    "## 9. Improvement priorities and acceptance evidence\n",
+    "\n",
+    "| Priority | Finding / improvement | NeqSim tracking and acceptance |\n",
+    "|---|---|---|\n",
+    "| P1 | Native Beggs–Brill flow-solve termination must be explicit and fail closed. | [#4258](https://github.com/equinor/neqsim/issues/4258): add residuals, bracket, iterations and termination reason; test iteration exhaustion, inner errors and restoration. |\n",
+    "| P1 | Model disagreement needs a redistributable experimental pressure-drop/holdup basis. | [#2907](https://github.com/equinor/neqsim/issues/2907), public-validation workstream, and [#4253](https://github.com/equinor/neqsim/issues/4253): ingest traceable cases with uncertainty, units, geometry and fluid provenance; compare both models without tuning to one trace. |\n",
+    "| P2 | Network/design results should carry common model, unit, convergence and validity evidence. | [#4228](https://github.com/equinor/neqsim/issues/4228): reuse existing pipe adapters and surrogate registry; retain exact revision, phase basis, thermal/terrain assumptions and replay evidence. |\n",
+    "| P2 | Real subsea-to-shore operability needs thermal, terrain and transient qualification. | [#3298](https://github.com/equinor/neqsim/issues/3298) and [#2907](https://github.com/equinor/neqsim/issues/2907): establish mesh/time convergence, phase conservation, liquid accumulation and sustained-slug benchmarks before making operational claims. |\n",
+    "\n",
+    "Numerical limits and response-surface accuracy above are measured notebook\n",
+    "findings. The latter three items are engineering follow-ups under existing\n",
+    "roadmaps; this study does not establish an additional library defect for them.\n",
+    "Do not create duplicate implementations or infer transient qualification\n",
+    "from a successful steady calculation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f8f00d0",
+   "metadata": {},
+   "source": [
+    "## 10. Add a published dataset without confusing its meaning\n",
+    "\n",
+    "The adapter below distinguishes a measurement from an independent-simulator\n",
+    "output and checks required units/provenance. Supply one row per reported\n",
+    "measurement at a **specified mass rate**, rather than silently solving a\n",
+    "different rate. The current runner supports only this notebook's binary,\n",
+    "horizontal, isothermal setup. Other fluids, elevations and heat-transfer\n",
+    "conditions must be implemented before their data can be evaluated.\n",
+    "\n",
+    "Required columns include DOI/URL, reuse permission, temperature and absolute\n",
+    "pressure basis, uncertainty, geometry, composition and rate. Experimental\n",
+    "uncertainty is reported as one standard deviation in bar. Do not fill\n",
+    "missing observations or uncertainties with simulator predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b8807c2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty-data guard passed: No public numerical observations have been supplied.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>required CSV field</th>\n",
+       "      <th>purpose</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>source_url</td>\n",
+       "      <td>Source URL / DOI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>reuse_permission</td>\n",
+       "      <td>Reuse permission or license</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>evidence_kind</td>\n",
+       "      <td>measurement or independent_simulator</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>diameter_m</td>\n",
+       "      <td>Inner diameter, m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>length_km</td>\n",
+       "      <td>Line length, km</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>decane_mol_fraction</td>\n",
+       "      <td>Decane mole fraction</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>rate_kg_s</td>\n",
+       "      <td>Mass rate, kg/s</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>inlet_bara</td>\n",
+       "      <td>Absolute inlet pressure, bara</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>temperature_c</td>\n",
+       "      <td>Isothermal temperature, Celsius</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>roughness_m</td>\n",
+       "      <td>Absolute roughness, m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>elevation_change_m</td>\n",
+       "      <td>Net elevation change, m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>thermal_mode</td>\n",
+       "      <td>Must be isothermal for this adapter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>observed_outlet_bara</td>\n",
+       "      <td>Observed outlet pressure, bara</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>standard_uncertainty_bar</td>\n",
+       "      <td>One-standard-deviation uncertainty, bar</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          required CSV field                                  purpose\n",
+       "0                 source_url                         Source URL / DOI\n",
+       "1           reuse_permission              Reuse permission or license\n",
+       "2              evidence_kind     measurement or independent_simulator\n",
+       "3                 diameter_m                        Inner diameter, m\n",
+       "4                  length_km                          Line length, km\n",
+       "5        decane_mol_fraction                     Decane mole fraction\n",
+       "6                  rate_kg_s                          Mass rate, kg/s\n",
+       "7                 inlet_bara            Absolute inlet pressure, bara\n",
+       "8              temperature_c          Isothermal temperature, Celsius\n",
+       "9                roughness_m                    Absolute roughness, m\n",
+       "10        elevation_change_m                  Net elevation change, m\n",
+       "11              thermal_mode      Must be isothermal for this adapter\n",
+       "12      observed_outlet_bara           Observed outlet pressure, bara\n",
+       "13  standard_uncertainty_bar  One-standard-deviation uncertainty, bar"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "benchmark_columns = [\n",
+    "    \"source_url\", \"reuse_permission\", \"evidence_kind\", \"diameter_m\", \"length_km\",\n",
+    "    \"decane_mol_fraction\", \"rate_kg_s\", \"inlet_bara\", \"temperature_c\",\n",
+    "    \"roughness_m\", \"elevation_change_m\", \"thermal_mode\",\n",
+    "    \"observed_outlet_bara\", \"standard_uncertainty_bar\",\n",
+    "]\n",
+    "benchmark_template = pd.DataFrame(columns=benchmark_columns)\n",
+    "display(pd.DataFrame({\n",
+    "    \"required CSV field\": benchmark_columns,\n",
+    "    \"purpose\": [\n",
+    "        \"Source URL / DOI\", \"Reuse permission or license\",\n",
+    "        \"measurement or independent_simulator\", \"Inner diameter, m\",\n",
+    "        \"Line length, km\", \"Decane mole fraction\", \"Mass rate, kg/s\",\n",
+    "        \"Absolute inlet pressure, bara\", \"Isothermal temperature, Celsius\",\n",
+    "        \"Absolute roughness, m\", \"Net elevation change, m\",\n",
+    "        \"Must be isothermal for this adapter\", \"Observed outlet pressure, bara\",\n",
+    "        \"One-standard-deviation uncertainty, bar\",\n",
+    "    ],\n",
+    "}))\n",
+    "\n",
+    "def compare_public_cases(data):\n",
+    "    missing = set(benchmark_columns) - set(data.columns)\n",
+    "    if missing:\n",
+    "        raise ValueError(f\"Missing benchmark columns: {sorted(missing)}\")\n",
+    "    if data.empty:\n",
+    "        raise ValueError(\"No public numerical observations have been supplied.\")\n",
+    "    records = []\n",
+    "    for _, observation in data.iterrows():\n",
+    "        assert str(observation[\"source_url\"]).startswith(\"https://\")\n",
+    "        assert str(observation[\"reuse_permission\"]).strip()\n",
+    "        assert observation[\"evidence_kind\"] in [\"measurement\", \"independent_simulator\"]\n",
+    "        assert observation[\"thermal_mode\"] == \"isothermal\"\n",
+    "        assert observation[\"elevation_change_m\"] == 0.0\n",
+    "        assert observation[\"inlet_bara\"] == inlet_pressure_bara\n",
+    "        assert observation[\"temperature_c\"] == inlet_temperature_c\n",
+    "        assert observation[\"roughness_m\"] == roughness_m\n",
+    "        assert observation[\"standard_uncertainty_bar\"] > 0.0\n",
+    "        design = {name: float(observation[name]) for name in feature_names}\n",
+    "        for model_name in model_names[:2]:\n",
+    "            calculated, _ = evaluate_pipe(model_name, design, observation[\"rate_kg_s\"])\n",
+    "            residual = calculated[\"outlet_bara\"] - observation[\"observed_outlet_bara\"]\n",
+    "            records.append({\n",
+    "                \"source_url\": observation[\"source_url\"],\n",
+    "                \"evidence_kind\": observation[\"evidence_kind\"],\n",
+    "                \"model\": model_name,\n",
+    "                \"prediction_bara\": calculated[\"outlet_bara\"],\n",
+    "                \"observed_bara\": observation[\"observed_outlet_bara\"],\n",
+    "                \"residual_bar\": residual,\n",
+    "                \"residual_in_standard_uncertainties\": residual\n",
+    "                / observation[\"standard_uncertainty_bar\"],\n",
+    "            })\n",
+    "    return pd.DataFrame(records)\n",
+    "\n",
+    "try:\n",
+    "    compare_public_cases(benchmark_template)\n",
+    "except ValueError as error:\n",
+    "    print(\"Empty-data guard passed:\", str(error))\n",
+    "else:\n",
+    "    raise AssertionError(\"Empty benchmark data must never be presented as validation.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "976abccd",
+   "metadata": {},
+   "source": [
+    "## 11. Results, reproducibility and limitations\n",
+    "\n",
+    "Results are summarized from the executed cells below. The analytical limit,\n",
+    "mass closure, pressure-root residual, mesh sensitivity and trend checks are\n",
+    "explicit. Sixteen off-grid cases assess approximation to each simulator,\n",
+    "rather than accuracy against measurements. Model disagreement, finite mesh\n",
+    "effects, surrogate error and experimental uncertainty are different quantities.\n",
+    "\n",
+    "On the pinned source, the 40 km, 0.35 m, 6 mol% decane baseline gives\n",
+    "**40.38 kg/s** with Beggs–Brill and **44.04 kg/s** with TwoFluidPipe.\n",
+    "The homogeneous diagnostic gives 63.70 kg/s, demonstrating why it should\n",
+    "not replace a slip/holdup calculation for this two-phase design.\n",
+    "Across the design cases, TwoFluidPipe rates exceed Beggs–Brill by up to\n",
+    "16.15%. No experimental basis here decides which rate is more accurate.\n",
+    "\n",
+    "The physics-informed surrogate reduces the worst held-out rate difference\n",
+    "from 11.65% to 0.52% for Beggs–Brill and from 15.04% to 2.17% for\n",
+    "TwoFluidPipe. This supports bounded screening and simulator replay;\n",
+    "it does not reduce the underlying model disagreement.\n",
+    "\n",
+    "The files exported below contain all 86 hydraulic roots, coefficients,\n",
+    "held-out predictions, model differences, checks and source/dependency\n",
+    "provenance. They complement the inline outputs, which remain in this notebook.\n",
+    "The notebook was inspected through Jupyter HTML and locally rendered MathJax\n",
+    "SVG; validation evidence is recorded in the companion maintenance ledger.\n",
+    "\n",
+    "Hosted Colab downloads/source compilation are a setup route. Local validation\n",
+    "uses the same exact source-built JAR and a fresh Python process; the hosted\n",
+    "download route has not been exercised here.\n",
+    "\n",
+    "**Exercises:** add a sourced experimental dataset; extend the fluid basis\n",
+    "to a characterized reservoir fluid; add explicit heat transfer and shore\n",
+    "elevation; compare design points with two-phase regime changes; repeat\n",
+    "the 30-to-60 section check at design-box corners before using the entire\n",
+    "surrogate as a design tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4cd7f3d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"baseline_rates_kg_s\": {\n",
+      "    \"PipeBeggsAndBrills\": 40.383490984789795,\n",
+      "    \"TwoFluidPipe\": 44.040257414113945,\n",
+      "    \"AdiabaticPipe\": 63.70336041052979\n",
+      "  },\n",
+      "  \"max_model_rate_difference_pct\": 16.15464595296976,\n",
+      "  \"held_out_metrics\": [\n",
+      "    {\n",
+      "      \"model\": \"PipeBeggsAndBrills\",\n",
+      "      \"surrogate\": \"quadratic\",\n",
+      "      \"MAE_kg_s\": 1.2184734077353012,\n",
+      "      \"MAPE_pct\": 3.8430859028461244,\n",
+      "      \"max_relative_error_pct\": 11.651558293580367,\n",
+      "      \"minimum_prediction_kg_s\": 15.134350951513966\n",
+      "    },\n",
+      "    {\n",
+      "      \"model\": \"PipeBeggsAndBrills\",\n",
+      "      \"surrogate\": \"physics-informed\",\n",
+      "      \"MAE_kg_s\": 0.09960390773485572,\n",
+      "      \"MAPE_pct\": 0.21382231758902337,\n",
+      "      \"max_relative_error_pct\": 0.5219953859960703,\n",
+      "      \"minimum_prediction_kg_s\": 13.967951244093472\n",
+      "    },\n",
+      "    {\n",
+      "      \"model\": \"TwoFluidPipe\",\n",
+      "      \"surrogate\": \"quadratic\",\n",
+      "      \"MAE_kg_s\": 1.6050611641125843,\n",
+      "      \"MAPE_pct\": 4.766937484878066,\n",
+      "      \"max_relative_error_pct\": 15.038090643660587,\n",
+      "      \"minimum_prediction_kg_s\": 16.53046593869791\n",
+      "    },\n",
+      "    {\n",
+      "      \"model\": \"TwoFluidPipe\",\n",
+      "      \"surrogate\": \"physics-informed\",\n",
+      "      \"MAE_kg_s\": 0.2839253847517623,\n",
+      "      \"MAPE_pct\": 0.7362376039481943,\n",
+      "      \"max_relative_error_pct\": 2.165412836619108,\n",
+      "      \"minimum_prediction_kg_s\": 15.30231320298312\n",
+      "    }\n",
+      "  ],\n",
+      "  \"surrogate_screening_qualification\": {\n",
+      "    \"PipeBeggsAndBrills\": true,\n",
+      "    \"TwoFluidPipe\": true\n",
+      "  },\n",
+      "  \"validation_checks\": {\n",
+      "    \"source_and_class_location_verified\": true,\n",
+      "    \"86_converged_hydraulic_roots\": true,\n",
+      "    \"receiving_pressure_residual_qualified\": true,\n",
+      "    \"mass_balance_qualified\": true,\n",
+      "    \"single_liquid_Darcy_limit_within_15pct\": true,\n",
+      "    \"baseline_30_to_60_mesh_rate_within_3pct\": true,\n",
+      "    \"all_surrogate_predictions_positive\": true,\n",
+      "    \"out_of_domain_fallback_verified\": true,\n",
+      "    \"native_flow_iteration_defect_reproduced\": true,\n",
+      "    \"no_experimental_accuracy_claim\": true\n",
+      "  },\n",
+      "  \"evidence\": \"synthetic simulation, analytical hydraulic limit and numerical checks\",\n",
+      "  \"experimental_validation\": false,\n",
+      "  \"related_issues\": [\n",
+      "    4253,\n",
+      "    4258,\n",
+      "    2907,\n",
+      "    4228,\n",
+      "    3298\n",
+      "  ],\n",
+      "  \"provenance\": {\n",
+      "    \"source_repository\": \"equinor/neqsim\",\n",
+      "    \"source_ref\": \"1768fc1fec78ff974fe9ff4232582f09ed504686\",\n",
+      "    \"jar_sha256\": \"c9ca3a016a9b1e524e53e22e2ff9bb0b8fc98ef629df566e261ec5a3c084f9c8\",\n",
+      "    \"python\": \"3.12.14\",\n",
+      "    \"java\": \"17.0.20\",\n",
+      "    \"packages\": {\n",
+      "      \"neqsim\": \"3.23.0\",\n",
+      "      \"jpype1\": \"1.7.1\",\n",
+      "      \"numpy\": \"2.5.3\",\n",
+      "      \"pandas\": \"3.0.6\",\n",
+      "      \"matplotlib\": \"3.11.2\",\n",
+      "      \"scipy\": \"1.18.1\",\n",
+      "      \"scikit-learn\": \"1.9.1\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "Exported files:\n",
+      " - coefficients.csv\n",
+      " - held_out_predictions.csv\n",
+      " - hydraulic_roots.csv\n",
+      " - mesh_refinement.csv\n",
+      " - model_disagreement.csv\n",
+      " - public_benchmark_template.csv\n",
+      " - summary.json\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>check</th>\n",
+       "      <th>passed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>source_and_class_location_verified</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>86_converged_hydraulic_roots</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>receiving_pressure_residual_qualified</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>mass_balance_qualified</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>single_liquid_Darcy_limit_within_15pct</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>baseline_30_to_60_mesh_rate_within_3pct</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>all_surrogate_predictions_positive</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>out_of_domain_fallback_verified</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>native_flow_iteration_defect_reproduced</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>no_experimental_accuracy_claim</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     check  passed\n",
+       "0       source_and_class_location_verified    True\n",
+       "1             86_converged_hydraulic_roots    True\n",
+       "2    receiving_pressure_residual_qualified    True\n",
+       "3                   mass_balance_qualified    True\n",
+       "4   single_liquid_Darcy_limit_within_15pct    True\n",
+       "5  baseline_30_to_60_mesh_rate_within_3pct    True\n",
+       "6       all_surrogate_predictions_positive    True\n",
+       "7          out_of_domain_fallback_verified    True\n",
+       "8  native_flow_iteration_defect_reproduced    True\n",
+       "9           no_experimental_accuracy_claim    True"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "checks = {\n",
+    "    \"source_and_class_location_verified\": True,\n",
+    "    \"86_converged_hydraulic_roots\": len(sweep) == 86 and bool(sweep[\"converged\"].all()),\n",
+    "    \"receiving_pressure_residual_qualified\": bool(\n",
+    "        sweep[\"pressure_residual_bar\"].abs().max() <= pressure_tolerance_bar\n",
+    "    ),\n",
+    "    \"mass_balance_qualified\": bool(sweep[\"mass_balance_relative\"].max() < 1e-9),\n",
+    "    \"single_liquid_Darcy_limit_within_15pct\": bool(\n",
+    "        (analytical[\"relative_difference_pct\"].abs() < 15.0).all()\n",
+    "    ),\n",
+    "    \"baseline_30_to_60_mesh_rate_within_3pct\": bool(\n",
+    "        (nominal_mesh[\"rate_difference_from_60_pct\"].abs() < 3.0).all()\n",
+    "    ),\n",
+    "    \"all_surrogate_predictions_positive\": bool(\n",
+    "        (metrics[\"minimum_prediction_kg_s\"] > 0.0).all()\n",
+    "    ),\n",
+    "    \"out_of_domain_fallback_verified\": True,\n",
+    "    \"native_flow_iteration_defect_reproduced\": bool(\n",
+    "        abs(observed_error_bar) > native_allowed_error_bar\n",
+    "    ),\n",
+    "    \"no_experimental_accuracy_claim\": True,\n",
+    "}\n",
+    "assert all(checks.values())\n",
+    "display(pd.DataFrame([{\"check\": name, \"passed\": passed} for name, passed in checks.items()]))\n",
+    "summary = {\n",
+    "    \"baseline_rates_kg_s\": dict(zip(baseline[\"model\"], baseline[\"rate_kg_s\"])),\n",
+    "    \"max_model_rate_difference_pct\": float(paired[\"TwoFluid_minus_BB_pct\"].abs().max()),\n",
+    "    \"held_out_metrics\": metrics.to_dict(orient=\"records\"),\n",
+    "    \"surrogate_screening_qualification\": qualified_models,\n",
+    "    \"validation_checks\": checks,\n",
+    "    \"evidence\": \"synthetic simulation, analytical hydraulic limit and numerical checks\",\n",
+    "    \"experimental_validation\": False,\n",
+    "    \"related_issues\": [4253, 4258, 2907, 4228, 3298],\n",
+    "    \"provenance\": provenance,\n",
+    "}\n",
+    "print(json.dumps(summary, indent=2))\n",
+    "output_directory = Path(\"subsea_to_shore_4253_results\")\n",
+    "output_directory.mkdir(exist_ok=True)\n",
+    "for filename, frame in [\n",
+    "    (\"hydraulic_roots.csv\", sweep),\n",
+    "    (\"held_out_predictions.csv\", predictions),\n",
+    "    (\"coefficients.csv\", coefficients),\n",
+    "    (\"model_disagreement.csv\", paired),\n",
+    "    (\"mesh_refinement.csv\", mesh),\n",
+    "    (\"public_benchmark_template.csv\", benchmark_template),\n",
+    "]:\n",
+    "    frame.to_csv(output_directory / filename, index=False)\n",
+    "(output_directory / \"summary.json\").write_text(json.dumps(summary, indent=2))\n",
+    "print(\"Exported files:\")\n",
+    "for path in sorted(output_directory.iterdir()):\n",
+    "    print(\" -\", path.name)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "subsea_to_shore_response_surface_4253.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12"
+  },
+  "neqsim": {
+   "notebook_version": "1.0.0",
+   "related_issue": "https://github.com/equinor/neqsim/issues/4253",
+   "source_ref": "1768fc1fec78ff974fe9ff4232582f09ed504686"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/maintenance_ledger/subsea_to_shore_response_surface_4253_20261007.json b/notebooks/maintenance_ledger/subsea_to_shore_response_surface_4253_20261007.json
new file mode 100644
index 00000000..fb59525b
--- /dev/null
+++ b/notebooks/maintenance_ledger/subsea_to_shore_response_surface_4253_20261007.json
@@ -0,0 +1,153 @@
+{
+  "schema_version": 1,
+  "updated_at": "2026-10-07",
+  "notebooks": [
+    {
+      "path": "notebooks/fluidflow/subsea_to_shore_response_surface_4253.ipynb",
+      "execution_status": "passed",
+      "code_cells": 15,
+      "validation_date": "2026-10-07",
+      "source_commit": "1768fc1fec78ff974fe9ff4232582f09ed504686",
+      "source_jar_sha256": "c9ca3a016a9b1e524e53e22e2ff9bb0b8fc98ef629df566e261ec5a3c084f9c8",
+      "related_issues": [
+        "https://github.com/equinor/neqsim/issues/4253",
+        "https://github.com/equinor/neqsim/issues/4258",
+        "https://github.com/equinor/neqsim/issues/2907",
+        "https://github.com/equinor/neqsim/issues/4228",
+        "https://github.com/equinor/neqsim/issues/3298"
+      ],
+      "engineering_validation": {
+        "hydraulic_roots": 86,
+        "training_designs_per_model": 27,
+        "held_out_designs_per_model": 16,
+        "checks": {
+          "source_and_class_location_verified": true,
+          "86_converged_hydraulic_roots": true,
+          "receiving_pressure_residual_qualified": true,
+          "mass_balance_qualified": true,
+          "single_liquid_Darcy_limit_within_15pct": true,
+          "baseline_30_to_60_mesh_rate_within_3pct": true,
+          "all_surrogate_predictions_positive": true,
+          "out_of_domain_fallback_verified": true,
+          "native_flow_iteration_defect_reproduced": true,
+          "no_experimental_accuracy_claim": true
+        },
+        "data_basis": "Synthetic methane/decane; no experimental accuracy validation",
+        "metrics": [
+          {
+            "model": "PipeBeggsAndBrills",
+            "surrogate": "quadratic",
+            "MAE_kg_s": 1.2184734077353012,
+            "MAPE_pct": 3.8430859028461244,
+            "max_relative_error_pct": 11.651558293580367,
+            "minimum_prediction_kg_s": 15.134350951513966
+          },
+          {
+            "model": "PipeBeggsAndBrills",
+            "surrogate": "physics-informed",
+            "MAE_kg_s": 0.09960390773485572,
+            "MAPE_pct": 0.21382231758902337,
+            "max_relative_error_pct": 0.5219953859960703,
+            "minimum_prediction_kg_s": 13.967951244093472
+          },
+          {
+            "model": "TwoFluidPipe",
+            "surrogate": "quadratic",
+            "MAE_kg_s": 1.6050611641125843,
+            "MAPE_pct": 4.766937484878066,
+            "max_relative_error_pct": 15.038090643660587,
+            "minimum_prediction_kg_s": 16.53046593869791
+          },
+          {
+            "model": "TwoFluidPipe",
+            "surrogate": "physics-informed",
+            "MAE_kg_s": 0.2839253847517623,
+            "MAPE_pct": 0.7362376039481943,
+            "max_relative_error_pct": 2.165412836619108,
+            "minimum_prediction_kg_s": 15.30231320298312
+          }
+        ],
+        "model_rate_difference_max_pct": 16.15464595296976,
+        "pressure_residual_max_bar": 7.9e-05,
+        "baseline_mesh_30_to_60_rate_difference_pct": {
+          "PipeBeggsAndBrills": 0.09612,
+          "TwoFluidPipe": 0.98509
+        }
+      },
+      "visual_validation": {
+        "status": "passed",
+        "renderer": "nbconvert classic HTML + MathJax 3.2.2 SVG + WeasyPrint 70.0 paginated QA",
+        "markdown_cells": 12,
+        "figures": 3,
+        "math_expressions": 16,
+        "coverage": "Every Markdown cell, result-bearing cell, equation, table and plot",
+        "paginated_qa_pages": 13
+      },
+      "execution": {
+        "method": "Fresh IPython process, scripts/execute_notebook_inprocess.py",
+        "python": "3.12.14",
+        "java": "17.0.20",
+        "packages": {
+          "neqsim": "3.23.0",
+          "jpype1": "1.7.1",
+          "numpy": "2.5.3",
+          "pandas": "3.0.6",
+          "matplotlib": "3.11.2",
+          "scipy": "1.18.1",
+          "scikit-learn": "1.9.1"
+        },
+        "setup_limit": "Hosted-Colab download/build route not exercised; exact source-built JAR used locally",
+        "exit_code": 0,
+        "runs": "Three clean top-to-bottom runs; final numerical targets reproduce earlier runs"
+      },
+      "integrity_validation": {
+        "scope": "Target notebook, checker unit tests and complete catalog target graph",
+        "repository_wide_scan": "Pending GitHub CI; historical notebooks were not downloaded locally",
+        "catalog_relative_targets_verified": 329,
+        "readability_and_math": "AST, <=100-character code lines, no compressed statements, compact single-backslash Colab math",
+        "notebook_schema": "passed",
+        "checker_unit_tests": "6 passed",
+        "targeted_main_source_checker": "passed"
+      },
+      "documentation_impact": {
+        "updated": [
+          "README.md",
+          "notebooks/examples_of_NeqSim_in_Colab.ipynb",
+          "notebooks/notebook_maintenance_ledger.json"
+        ],
+        "assessment": "New source-pinned hydraulic/RSM teaching notebook; setup, assumptions, limitations and upstream evidence documented"
+      },
+      "improvement_assessment": "Verified flow-solve exhaustion defect filed as #4258. Experimental multiphase validation, common network evidence and transient qualification reference existing #2907/#4228/#3298. #4253 remains open pending paper/public numerical data.",
+      "source_validation_tests": {
+        "TwoFluidPipeSteadyStateConvergenceTest": {
+          "tests": 5,
+          "failures": 0,
+          "errors": 0
+        },
+        "PipeBeggsAndBrillsBoundaryTest": {
+          "tests": 1,
+          "failures": 0,
+          "errors": 0
+        }
+      },
+      "validation_commands": [
+        {
+          "command": "Maven -DskipTests -Dmaven.javadoc.skip=true package",
+          "exit_code": 0
+        },
+        {
+          "command": "Maven -Dtest=TwoFluidPipeSteadyStateConvergenceTest,PipeBeggsAndBrillsBoundaryTest test",
+          "exit_code": 0
+        },
+        {
+          "command": "scripts/execute_notebook_inprocess.py (15 code cells, source-built JAR)",
+          "exit_code": 0
+        },
+        {
+          "command": "python -m unittest discover -s scripts -p test_check_notebook.py",
+          "exit_code": 0
+        }
+      ]
+    }
+  ]
+}
diff --git a/notebooks/notebook_maintenance_ledger.json b/notebooks/notebook_maintenance_ledger.json
index f2f6ba06..2e55d20b 100644
--- a/notebooks/notebook_maintenance_ledger.json
+++ b/notebooks/notebook_maintenance_ledger.json
@@ -1,5 +1,5 @@
 {
-  "active_notebook_count": 321,
+  "active_notebook_count": 322,
   "retired_notebooks": [
     {
       "follow_up": "Replace with the planned NeqSim-master data-reconciliation and Bayesian digital-twin notebook.",
@@ -34,5 +34,5 @@
   ],
   "schema_version": 3,
   "shards_glob": "maintenance_ledger/*.json",
-  "updated_at": "2026-10-04"
+  "updated_at": "2026-10-07"
 }