arXiv:2610.19539v1 Announce Type: new Abstract: Aggregate performance metrics do not identify local modelling inaccuracies in machine learning (ML) surrogates used for thermal-system optimisation. Th...
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 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.
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.
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...
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....
Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns. Accurate wind turbine power predictions can ident...
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:2608. 13260v1 Announce Type: new Abstract: Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation.
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.
arXiv:2609. 08375v1 Announce Type: cross Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants.
arXiv:2510. 00831v2 Announce Type: replace Abstract: The increasing complexity of modern power systems, driven by the integration of inverter-based and distributed energy resources, challenges the reliability of conventional protection schemes and motivates the use of machine learning for protection tasks.