Toward Developing Machine-Learning-Aided Tools for the Thermomechanical Monitoring of Nuclear Reactor Components

• URL: http://arxiv.org/abs/2507.09443v1
• Date: Sun, 13 Jul 2025 01:32:46 GMT
• Title: Toward Developing Machine-Learning-Aided Tools for the Thermomechanical Monitoring of Nuclear Reactor Components
• Authors: Luiz Aldeia Machado, Victor Coppo Leite, Elia Merzari, Arthur Motta, Roberto Ponciroli, Lander Ibarra, Lise Charlot,
• Abstract summary: We explore the use of a Convolutional Neural Network (CNN) architecture combined with a computational thermomechanical model.<n>CNN was trained for over 1,000 epochs without signs of overfitting, achieving highly accurate temperature distribution predictions.<n>These were then used in a thermomechanical model to determine the stress and strain distribution within the fuel rod.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Proactive maintenance strategies, such as Predictive Maintenance (PdM), play an important role in the operation of Nuclear Power Plants (NPPs), particularly due to their capacity to reduce offline time by preventing unexpected shutdowns caused by component failures. In this work, we explore the use of a Convolutional Neural Network (CNN) architecture combined with a computational thermomechanical model to calculate the temperature, stress, and strain of a Pressurized Water Reactor (PWR) fuel rod during operation. This estimation relies on a limited number of temperature measurements from the cladding's outer surface. This methodology can potentially aid in developing PdM tools for nuclear reactors by enabling real-time monitoring of such systems. The training, validation, and testing datasets were generated through coupled simulations involving BISON, a finite element-based nuclear fuel performance code, and the MOOSE Thermal-Hydraulics Module (MOOSE-THM). We conducted eleven simulations, varying the peak linear heat generation rates. Of these, eight were used for training, two for validation, and one for testing. The CNN was trained for over 1,000 epochs without signs of overfitting, achieving highly accurate temperature distribution predictions. These were then used in a thermomechanical model to determine the stress and strain distribution within the fuel rod.

Related papers

• Inverse Modeling of Dielectric Response in Time Domain using Physics-Informed Neural Networks [1.680048515550978]
  Dielectric response (DR) of insulating materials is key input information for designing electrical insulation systems and defining safe operating conditions of HV devices.<n>This paper examines the use of physics-informed neural networks (PINNs) for inverse modeling of DR in time domain using parallel RC circuits.
  arXiv  Detail & Related papers  (2025-04-28T11:36:49Z)
• A machine learning platform for development of low flammability polymers [42.758516311179534]
  Flammability index (FI) and cone calorimetry outcomes, such as maximum heat release rate, time to ignition, total smoke release, and fire growth rate, are critical factors in evaluating the fire safety of polymers.<n>In this work, we investigate the use of machine learning (ML) techniques to predict these flammability metrics.
  arXiv  Detail & Related papers  (2025-03-31T20:50:29Z)
• Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks [3.422016133670755]
  This paper considers as a test case the Molten Salt Fast Reactor (MSFR), a Generation-IV reactor concept, characterised by strong coupling between the neutronics and the thermal hydraulics.<n>The accurate reconstruction of every characteristic field in real-time makes this approach suitable for monitoring and control purposes in the framework of a reactor digital twin.
  arXiv  Detail & Related papers  (2025-03-11T21:32:28Z)
• Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation [0.0]
  Monitoring the temperature of a power transformer is fundamental to ensure a long-term operational life.<n>Models presented in the IEC 60076-7 and IEEE standards, for example, monitor the temperature by calculating the top-oil and the hot-spot temperatures.<n>This paper focuses on finding an alternative method to predict the top-oil temperatures given previous measurements.
  arXiv  Detail & Related papers  (2025-01-28T10:21:49Z)
• Deployment of ARX Models for Thermal Forecasting in Power Electronics Boards Using WBG Semiconductors [37.69303106863453]
  ARX parametric models provide accurate temperature predictions without detailed understanding of component thickness disparities or material physical properties.<n>These models emerge as a reliable alternative to FEM simulations and conventional thermal models.
  arXiv  Detail & Related papers  (2024-11-25T14:01:17Z)
• Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants [0.6223528900192875]
  This article introduces a-temporal model for transformer winding temperature and ageing. It uses physics-based partial differential equations with data-driven Neural Networks. Results are validated with a distribution transformer operating on a floating photovoltaic power plant.
  arXiv  Detail & Related papers  (2024-05-10T12:48:57Z)
• A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators [46.348283638884425]
  We propose a two-step unsupervised deep learning framework named as Latent Autoregressive Recurrent Model (CLARM) for learning dynamics of charged particles in accelerators. The CLARM can generate projections at various accelerator sampling modules by capturing and decoding the latent space representation. The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-19T22:05:17Z)
• Introducing a Deep Neural Network-based Model Predictive Control Framework for Rapid Controller Implementation [41.38091115195305]
  This work presents the experimental implementation of a deep neural network (DNN) based nonlinear MPC for Homogeneous Charge Compression Ignition (HCCI) combustion control. Using the acados software package to enable the real-time implementation of the MPC on an ARM Cortex A72, the optimization calculations are completed within 1.4 ms. The IMEP trajectory following of the developed controller was excellent, with a root-mean-square error of 0.133 bar, in addition to observing process constraints.
  arXiv  Detail & Related papers  (2023-10-12T15:03:50Z)
• Bayesian inference of composition-dependent phase diagrams [47.79947989845143]
  We develop a method in which Bayesian inference is employed to combine thermodynamic data from molecular dynamics (MD), melting point simulations, and phonon calculations, process these data, and yield a temperature-concentration phase diagram. The developed algorithm was successfully tested on two binary systems, Ge-Si and K-Na, in the full range of concentrations and temperatures.
  arXiv  Detail & Related papers  (2023-09-03T20:57:10Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning [0.0]
  Thermal models for electric power systems are required to be both, real-time capable and of high estimation accuracy. In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based lumped- parameter models. A TNN has physically interpretable states through its state-space representation, is end-to-end trainable, and requires no material, geometry, nor expert knowledge for its design.
  arXiv  Detail & Related papers  (2021-03-30T13:15:48Z)
• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features [42.96944345045462]
  textscOrbNet is shown to outperform existing methods in terms of learning efficiency and transferability. For applications to datasets of drug-like molecules, textscOrbNet predicts energies within chemical accuracy of DFT at a computational cost that is thousand-fold or more reduced.
  arXiv  Detail & Related papers  (2020-07-15T22:38:41Z)
• Reservoir engineering with arbitrary temperatures for spin systems and quantum thermal machine with maximum efficiency [50.591267188664666]
  Reservoir engineering is an important tool for quantum information science and quantum thermodynamics. We employ this technique to engineer reservoirs with arbitrary (effective) negative and positive temperatures for a single spin system.
  arXiv  Detail & Related papers  (2020-01-28T00:18:00Z)

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.