Aggregation of Published Non-Uniform Axial Power Data for Phase II of the OECD/NEA AI/ML Critical Heat Flux Benchmark

Reece Bourisaw; Reid McCants; Jean-Marie Le Corre; Anna Iskhakova; Arsen S. Iskhakov

cs.LGJun 18, 2025

Aggregation of Published Non-Uniform Axial Power Data for Phase II of the OECD/NEA AI/ML Critical Heat Flux Benchmark

Authors: Reece Bourisaw, Reid McCants, Jean-Marie Le Corre, Anna Iskhakova, Arsen S. Iskhakov

Organizations: Department of Mechanical and Nuclear Engineering, Kansas State University, Manhattan, KS · Westinghouse Electric Sweden AB, 72163 Västerås, Sweden

Abstract

Critical heat flux (CHF) marks the onset of boiling crisis in light-water reactors, defining safe thermal-hydraulic operating limits. To support Phase II of the OECD/NEA AI/ML CHF benchmark, which introduces spatially varying power profiles, this work compiles and digitizes a broad CHF dataset covering both uniform and non-uniform axial heating conditions. Heating profiles were extracted from technical reports, interpolated onto a consistent axial mesh, validated via energy-balance checks, and encoded in machine-readable formats for benchmark compatibility. Classical CHF correlations exhibit substantial errors under uniform heating and degrade markedly when applied to non-uniform profiles, while modern tabular methods offer improved but still imperfect predictions. A neural network trained solely on uniform data performs well in that regime but fails to generalize to spatially varying scenarios, underscoring the need for models that explicitly incorporate axial power distributions. By providing these curated datasets and baseline modeling results, this study lays the groundwork for advanced transfer-learning strategies, rigorous uncertainty quantification, and design-optimization efforts in the next phase of the CHF benchmark.

Figures & tables

Heating	Pressure, MPa	Mass flux, kg/(m ${}^{2}\cdot$ s)	Outlet equilibrium quality
Uniform	0.43 – 18	335.0 – 9561.9	$-0.582$ – 0.901
Non-uniform	0.43 – 18	328.2 – 8916.0	$-0.714$ – 1.091

Table 1: Summary of the standardized datasets.

Figure 1: Examples of the collected heat flux profiles. $q^{\prime\prime}$ denotes the heat flux, $q^{\prime\prime}_{\text{av}}$ denotes the average heat flux.

Parameter	Keyword	Units / Note
Test #	<TestID>	Sequential (e.g., from 1 to 651 for uniform)
Internal tube diameter	<Diameter>	[m]
Wetted perimeter	<Perimeter>	[m]
Flow area	<Area>	[m 2 ]
Total heated length	<Length>	[m]
Pressure	<Pressure>	[Pa]

Table 2: Quantities saved for each test case.

Panel of Figure 2 — Figure 2: Predicted vs. measured CHF using LUT for ( 2(a) ) uniform and ( 2(b) ) non-uniform data.

Parameter	Value / Method
Number of layers and neurons	5 – 61 – 51 – 28 – 39 – 26 – 21 – 20 – 14 – 1
Activation	ReLU
Optimizer	Adam
Loss function	Mean squared error
Regularization	Dropout 1% after the first layer
Learning rate	0.01 with decay on plateau

Table 3: Hyperparameters and NN architecture.

Panel of Figure 3 — Figure 3: Predicted vs. measured CHF using NN for ( 3(a) ) uniform and ( 3(b) ) non-uniform data.

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Department of Nuclear Engineering, North Carolina State University, Burlington Engineering Laboratories, 2500 Stinson Drive, Raleigh, NC 27695 · Nuclear Engineering & Engineering Physics Department, University of Wisconsin–Madison, Engineering Research Building, 1500 Engineering Drive, Madison, WI 53711 · Nuclear Energy and Fuel Cycle Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37830
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Université Paris Saclay, Cea, Service Thermo-hydraulique et Mécanique des Fluides (Stmf), Gif-sur-Yvette, F-91191, France · Université Paris Saclay, Cea, Service de Génie Logiciel pour la Simulation (Sgls), Gif-sur-Yvette, F-91191, France
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Mar 15, 2026Anirudh Tunga, Michael J. Mueterthies, Jonathan Nistor
Blue Wave AI Labs, 1284 Win Hentschel Blvd, West Lafayette, IN 47906 · Department of Physics & Astronomy, 525 Northwestern Avenue, Purdue University, West Lafayette, IN 47907

Sep 18, 2026J/K move · Enter open · S save

Assessment of Machine Learning-Based Critical Heat Flux Models in the CTF Subchannel Code for Square Rod Bundle Prediction

Aidan Furlong, Vinicius de Melo Monteiro, Robert Salko, Juliana Pacheco Duarte+1

Department of Nuclear Engineering, North Carolina State University, Burlington Engineering Laboratories, 2500 Stinson Drive, Raleigh, NC 27695 · Nuclear Engineering & Engineering Physics Department, University of Wisconsin–Madison, Engineering Research Building, 1500 Engineering Drive, Madison, WI 53711 · Nuclear Energy and Fuel Cycle Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37830

Cross-Domain Generalization Surrogate Modeling

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The prediction of critical heat flux (CHF), a key safety-related quantity in nuclear thermal hydraulics, remains an important challenge due to its direct relationship with fuel performance and reactor safety. Recent studies have demonstrated that relative to traditional empirical correlations and lookup tables (LUTs), machine learning (ML) methods can substantially improve CHF prediction accuracy. Most ML-based CHF models, however, have been developed and evaluated using tube databases, leaving their applicability to reactor-relevant rod bundle geometries largely unexplored. This study evaluates ML-based CHF models deployed within the CTF subchannel code using the Electric Power Research Institute (EPRI) rod bundle CHF database. Both pure and hybrid residual correction models are considered in local and semilocal formulations. The tube-trained ML CHF models generally transferred favorably to rod bundle applications and outperformed traditional CHF methods across most geometries and operating conditions. The local hybrid LUT model produced the strongest overall performance, and the semilocal pure ML model remained highly competitive. Comparison against the Bowring correlation, W-3 correlation, and 2006 Groeneveld LUT demonstrated that substantial improvements in rod bundle CHF prediction are possible even when models are trained exclusively on tube data. These findings provide one of the first large-scale assessments of ML-based CHF models in square rod bundles within a production-level subchannel analysis environment and support their broader application in reactor thermal hydraulic analysis.

Abstract

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