Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks

• URL: http://arxiv.org/abs/2308.08391v1
• Date: Wed, 16 Aug 2023 14:23:24 GMT
• Title: Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks
• Authors: Arnau Alb\`a, Andreas Adelmann, Lucas M\"unster, Dimitri Rochman, Romana Boiger
• Abstract summary: State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics. An NN is trained using data generated from CASMO5 lattice calculations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking into account the costs of generating training samples) are reduced by a factor of 10 or more. Our findings demonstrate the feasibility of using NNs as surrogate models for fast characterization of SNF, providing a promising avenue for improving computational efficiency in assessing nuclear fuel behavior and associated risks.

Related papers

• Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems [52.19558333652367]
  We present finite-range embeddings (FiRE) for accurate large-scale ab-initio electronic structure calculations. FiRE reduces the complexity of neural-network variational Monte Carlo (NN-VMC) by $sim ntextel$, the number of electrons. We validate our method's accuracy on various challenging systems, including biochemical compounds and organometallic compounds.
  arXiv  Detail & Related papers  (2025-04-08T14:28:54Z)
• A General Neural Network Potential for Energetic Materials with C, H, N, and O elements [0.9742644628669695]
  High-energy materials (HEMs) are constrained by the prohibitive computational expense and prolonged development cycles. We develop a general neural network potential (NNP) that efficiently predicts the structural, mechanical, and decomposition properties of HEMs.
  arXiv  Detail & Related papers  (2025-03-03T03:24:59Z)
• Comparison of CNN-based deep learning architectures for unsteady CFD acceleration on small datasets [0.0]
  This study compares advanced convolutional neural network (CNN) architectures for accelerating unsteady computational fluid dynamics (CFD) simulations. CNNs were evaluated under identical conditions to determine their predictive accuracy and robustness in autoregressive time-series predictions. ConvLSTM-UNet consistently outperformed other models, particularly in difference value calculation, achieving lower maximum errors and stable residuals.
  arXiv  Detail & Related papers  (2025-02-06T03:30:49Z)
• Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling [10.055838489452817]
  Deep ensembles (DEs) are efficient and scalable methods for uncertainty quantification (UQ) in Deep Neural Networks (DNNs) We propose a novel method that combines Bayesian optimization (BO) with DE, referred to as BODE, to enhance both predictive accuracy and UQ. We apply BODE to a case study involving a Densely connected Convolutional Neural Network (DCNN) trained on computational fluid dynamics (CFD) data to predict eddy viscosity in sodium fast reactor thermal stratification modeling.
  arXiv  Detail & Related papers  (2024-12-11T21:06:50Z)
• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval. A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks [0.0]
  KerNN is a combined kernel/neural network-based approach to represent molecular PESs. Compared to state-of-the-art neural network PESs the number of learnable parameters of KerNN is significantly reduced. KerNN shows excellent performance on test set statistics and observables including vibrational bands computed from classical and quantum simulations.
  arXiv  Detail & Related papers  (2024-11-27T08:01:21Z)
• Physics-informed neural networks need a physicist to be accurate: the case of mass and heat transport in Fischer-Tropsch catalyst particles [0.3926357402982764]
  Physics-Informed Neural Networks (PINNs) have emerged as an influential technology, merging the swift and automated capabilities of machine learning with the precision and dependability of simulations grounded in theoretical physics. However, wide adoption of PINNs is still hindered by reliability issues, particularly at extreme ends of the input parameter ranges. We propose a domain knowledge-based modifications to the PINN architecture ensuring its correct behavior.
  arXiv  Detail & Related papers  (2024-11-15T08:55:31Z)
• Physics-Informed Neural Networks with Hard Linear Equality Constraints [9.101849365688905]
  This work proposes a novel physics-informed neural network, KKT-hPINN, which rigorously guarantees hard linear equality constraints. Experiments on Aspen models of a stirred-tank reactor unit, an extractive distillation subsystem, and a chemical plant demonstrate that this model can further enhance the prediction accuracy.
  arXiv  Detail & Related papers  (2024-02-11T17:40:26Z)
• Quantum Algorithms for Simulating Nuclear Effective Field Theories [40.83664249192338]
  We use state-of-the-art Hamiltonian-simulation methods to estimate the qubit and gate costs to simulate low-energy effective field theories (EFTs) of nuclear physics. We demonstrate how symmetries of the low-energy nuclear Hamiltonians can be utilized to obtain tighter error bounds on the simulation algorithm.
  arXiv  Detail & Related papers  (2023-12-08T20:09:28Z)
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems [44.99833362998488]
  Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. We combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$$O and a MAPE of 7%.
  arXiv  Detail & Related papers  (2023-11-17T15:30:12Z)
• Accurate melting point prediction through autonomous physics-informed learning [52.217497897835344]
  We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making.
  arXiv  Detail & Related papers  (2023-06-23T07:53:09Z)
• Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning [9.234802409391111]
  We propose a novel method based on deep learning for timing analysis of modularized detectors. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model.
  arXiv  Detail & Related papers  (2023-04-24T09:16:31Z)
• Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano-particles in a contaminated aquifer [0.0]
  Engineered Nano-particles (ENPs) have emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. The complex transport and retention mechanisms of ENPs hinder the development of an efficient remediation strategy. This work uses a dynamic, weight-enabled Physics-Informed Neural Network (dw-PINN) framework to model the nano-particle behavior within an aquifer.
  arXiv  Detail & Related papers  (2022-10-25T07:55:20Z)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)
• Physics-constrained deep neural network method for estimating parameters in a redox flow battery [68.8204255655161]
  We present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium flow battery (VRFB) We show that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training.
  arXiv  Detail & Related papers  (2021-06-21T23:42:58Z)
• 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)
• GINNs: Graph-Informed Neural Networks for Multiscale Physics [1.1470070927586016]
  Graph-Informed Neural Network (GINN) is a hybrid approach combining deep learning with probabilistic graphical models (PGMs) GINNs produce kernel density estimates of relevant non-Gaussian, skewed QoIs with tight confidence intervals.
  arXiv  Detail & Related papers  (2020-06-26T05:47:45Z)

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.