Aggregate performance metrics do not identify local modelling inaccuracies in machine learning (ML) surrogates used for thermal-system optimisation. This study assesses local modelling strength throug...
The paper evaluates machine learning (ML) models for predicting critical heat flux (CHF) in square rod bundles using the CTF subchannel code and the EPRI rod bundle CHF database. It compares pure and hybrid residual correction models in local and semilocal forms, finding that tube-trained ML models transfer well to rod bundle geometries and outperform traditional empirical correlations and lookup tables. The local hybrid LUT model shows the best overall performance, while the semilocal pure ML model remains highly competitive, indicating that significant improvements in rod bundle CHF prediction are achievable even with tube-only training data.
The paper examines the reliability of data‑driven models for real‑time optimization (RTO) using a vinyl acetate monomer benchmark. Two models—a structured hybrid model and a fully data‑driven neural ODE—accurately reproduce plant measurements but yield economic optima that differ markedly from the plant’s true optimum, producing multiple phantom optima. The study shows that even with noise‑free data and correct initialization, stochastic gradient training can drift to weights that degrade RTO performance, indicating that predictive accuracy alone does not ensure reliable economic outcomes.
The paper compiles and digitizes a comprehensive dataset of critical heat flux (CHF) measurements for both uniform and non‑uniform axial heating conditions, preparing it for Phase II of the OECD/NEA AI/ML CHF benchmark. Heating profiles were extracted from technical reports, interpolated onto a consistent axial mesh, validated with energy‑balance checks, and encoded in machine‑readable formats. Baseline modeling results show that classical CHF correlations perform poorly, especially for non‑uniform profiles, and that neural networks trained only on uniform data fail to generalize to spatially varying scenarios, highlighting the need for models that explicitly incorporate axial power distributions.
arXiv:2609.33387v2 Announce Type: replace Abstract: Machine learning plays an increasingly vital role in engineering, but the corresponding increase in compute time is not without environmental cost....
arXiv:2607. 04450v1 Announce Type: cross Abstract: Integral codes like the Accident Source Term Evaluation Code (ASTEC) are powerful tools to study the physics of Severe Accidents (SAs) in nuclear reactors.
arXiv:2505. 05203v3 Announce Type: replace-cross Abstract: With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic efficiency, security, and robustness.
arXiv:2609. 08375v1 Announce Type: cross Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants.
arXiv:2609.06194v1 Announce Type: new Abstract: Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns....
arXiv:2608. 08048v1 Announce Type: cross Abstract: This paper presents, for the first time in power systems literature to our knowledge, analytical tools to explain the training performance of machine learning surrogate models for power system dynamics.
The paper explores global sensitivity analysis (GSA) when both physics-based models and experimental data are available, focusing on physics-informed machine learning to improve sensitivity estimates. It evaluates two ML approaches—deep neural networks (DNN) and Gaussian processes (GP)—and two physics integration strategies: physics-constrained loss functions and sequential pre‑training with simulation followed by experimental fine‑tuning. Four models per ML type are constructed, incorporating model uncertainties into Sobol index calculations, and results show DNNs yield tighter sensitivity bounds than GP models, demonstrated on additive manufacturing and lake temperature examples.
arXiv:2608.22076v1 Announce Type: cross Abstract: Rising energy demand, fossil fuel depletion and climate change highlight the need for more efficient energy production and consumption. Offshore oil...