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

• URL: http://arxiv.org/abs/2507.00034v2
• Date: Mon, 03 Nov 2025 20:04:57 GMT
• Title: 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,
• Abstract summary: Critical heat flux (CHF) marks the onset of boiling crisis in light-water reactors.<n>This work compiles and digitizes a broad CHF dataset covering both uniform and non-uniform axial heating conditions.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

Related papers

• Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage [65.51149575007149]
  We present Fun-DDPS, a generative framework that combines function-space diffusion models with differentiable neural operator surrogates for both forward and inverse modeling.<n>Fun-DDPS produces physically consistent realizations free from the high-frequency artifacts observed in joint-state baselines.
  arXiv  Detail & Related papers  (2026-02-12T18:58:12Z)
• Robust Machine Learning Framework for Reliable Discovery of High-Performance Half-Heusler Thermoelectrics [0.0]
  Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis.<n>ML models often suffer from poor experimental generalizability despite high metrics.<n>This study presents a robust workflow, applied to the half-Heusler (hH) structural prototype, for figure of merit (zT) prediction.
  arXiv  Detail & Related papers  (2026-02-01T10:50:42Z)
• Making Tunable Parameters State-Dependent in Weather and Climate Models with Reinforcement Learning [0.5131152350448099]
  This study presents a framework that learns components of parametrisation schemes online.<n>It evaluates the resulting RL-driven parameter updates across a hierarchy of idealised testbeds.<n>Results highlight RL to deliver skilful state-dependent, and regime-aware parametrisations.
  arXiv  Detail & Related papers  (2026-01-07T11:19:16Z)
• Learning to Reconstruct Temperature Field from Sparse Observations with Implicit Physics Priors [22.013633764284936]
  High cost of measurement acquisition and substantial distributional shifts in temperature field present challenges for developing reconstruction models.<n>We propose IPTR, an implicit physics-guided temperature field reconstruction framework.<n>We show that IPTR consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy and strong generalization capability.
  arXiv  Detail & Related papers  (2025-12-01T02:22:30Z)
• Score Distillation of Flow Matching Models [67.86066177182046]
  We extend Score identity Distillation (SiD) to pretrained text-to-image flow-matching models.<n>SiD works out of the box across these models, in both data-free and data-aided settings.<n>This provides the first systematic evidence that score distillation applies broadly to text-to-image flow matching models.
  arXiv  Detail & Related papers  (2025-09-29T17:45:48Z)
• Composable Score-based Graph Diffusion Model for Multi-Conditional Molecular Generation [85.58520120011269]
  We propose Composable Score-based Graph Diffusion model (CSGD), which extends score matching to discrete graphs via concrete scores.<n>We show that CSGD achieves state-of-the-art performance with a 15.3% average improvement in controllability over prior methods.<n>Our findings highlight the practical advantages of score-based modeling for discrete graph generation and its capacity for flexible, multi-property molecular design.
  arXiv  Detail & Related papers  (2025-09-11T13:37:56Z)
• Solving Inverse Problems with FLAIR [59.02385492199431]
  Flow-based latent generative models are able to generate images with remarkable quality, even enabling text-to-image generation.<n>We present FLAIR, a novel training free variational framework that leverages flow-based generative models as a prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems [51.15230303652732]
  FLEX (F Low EXpert) is a backbone architecture for generative modeling of-temporal physical systems.<n>It reduces the variance of the velocity field in the diffusion model, which helps stabilize training.<n>It achieves accurate predictions for super-resolution and forecasting tasks using as few features as two reverse diffusion steps.
  arXiv  Detail & Related papers  (2025-05-23T00:07:59Z)
• Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal Hydraulics Code [4.538224798436768]
  Critical heat flux (CHF) prediction is essential for efficiency, safety, and preventing equipment damage.<n>Traditional machine learning approaches have suffered from limited interpretability, data scarcity, and insufficient knowledge of physical principles.<n>This study integrated a purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code.
  arXiv  Detail & Related papers  (2025-05-12T13:14:33Z)
• Data-Driven Optical To Thermal Inference in Pool Boiling Using Generative Adversarial Networks [1.0499611180329804]
  We present a data-driven framework that infers temperature fields from geometric phase in a canonical pool boiling configuration.<n>Our results highlight the potential of deep generative models to bridge the gap between observable multiphase phenomena and underlying thermal transport.
  arXiv  Detail & Related papers  (2025-05-01T19:26:01Z)
• Decrypting the temperature field in flow boiling with latent diffusion models [1.9190568044682759]
  This paper presents an innovative method using Latent Diffusion Models (LDMs) to generate temperature fields from phase indicator maps.<n>By leveraging the BubbleML dataset from numerical simulations, the LDM phase field data translates into corresponding temperature distributions.<n>The resulting model effectively reconstructs complex temperature fields at interfaces.
  arXiv  Detail & Related papers  (2025-01-27T21:18:05Z)
• PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing [66.27103948750306]
  PearSAN is a machine learning-assisted optimization algorithm applicable to inverse design problems with large design spaces.<n>It uses a Pearson correlated surrogate model to predict the figure of merit of the true design metric.<n>It achieves a state-of-the-art maximum design efficiency of 97%, and is at least an order of magnitude faster than previous methods.
  arXiv  Detail & Related papers  (2024-12-26T17:02:19Z)
• Rectified Diffusion Guidance for Conditional Generation [62.00207951161297]
  We revisit the theory behind CFG and rigorously confirm that the improper configuration of the combination coefficients (i.e., the widely used summing-to-one version) brings about expectation shift of the generative distribution. We propose ReCFG with a relaxation on the guidance coefficients such that denoising with ReCFG strictly aligns with the diffusion theory. That way the rectified coefficients can be readily pre-computed via traversing the observed data, leaving the sampling speed barely affected.
  arXiv  Detail & Related papers  (2024-10-24T13:41:32Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Efficient mapping of phase diagrams with conditional Boltzmann Generators [4.437335677401287]
  We develop deep generative machine learning models based on the Boltzmann Generator approach for entire phase diagrams. By training a single normalizing flow to transform the equilibrium distribution sampled at only one reference thermodynamic state to a wide range of target temperatures and pressures, we can efficiently generate equilibrium samples. We demonstrate our approach by predicting the solid-liquid coexistence line for a Lennard-Jones system in excellent agreement with state-of-the-art free energy methods.
  arXiv  Detail & Related papers  (2024-06-18T08:05:04Z)
• Critical heat flux diagnosis using conditional generative adversarial networks [0.0]
  The critical heat flux (CHF) is an essential safety boundary in boiling heat transfer processes employed in high heat flux thermal-hydraulic systems. This study presents a data-driven, image-to-image translation method for reconstructing thermal data of a boiling system at CHF.
  arXiv  Detail & Related papers  (2023-05-04T07:53:04Z)
• Efficient CDF Approximations for Normalizing Flows [64.60846767084877]
  We build upon the diffeomorphic properties of normalizing flows to estimate the cumulative distribution function (CDF) over a closed region. Our experiments on popular flow architectures and UCI datasets show a marked improvement in sample efficiency as compared to traditional estimators.
  arXiv  Detail & Related papers  (2022-02-23T06:11:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.