MODELING NUCLEAR DATA UNCERTAINTIES USING DEEP NEURAL NETWORKS

Majdi I Radaideh,Dean Price,Tomasz Kozlowski,M Margulis,P Blaise

doi:10.1051/epjconf/202124715016

Majdi I Radaideh, Dean Price + Show 3 more

Open Access

https://doi.org/10.1051/epjconf/202124715016

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Abstract

A new concept using deep learning in neural networks is investigated to characterize the underlying uncertainty of nuclear data. Analysis is performed on multi-group neutron cross-sections (56 energy groups) for the GODIVA U-235 sphere. A deep model is trained with cross-validation using 1000 nuclear data random samples to fit 336 nuclear data parameters. Although of the very limited sample size (1000 samples) available in this study, the trained models demonstrate promising performance, where a prediction error of about 166 pcm is found for keff in the test set. In addition, the deep model’s sensitivity and uncertainty are validated. The comparison of importance ranking of the principal fast fission energy groups with adjoint methods shows fair agreement, while a very good agreement is observed when comparing the global keff uncertainty with sampling methods. The findings of this work shall motivate additional efforts on using machine learning to unravel complexities in nuclear data research.

Highlights

Artificial intelligence and data sciences are thriving in many scientific disciplines to solve intractable problems
Machine learning was used in different areas to resolve the high-dimensionality and computational cost issues associated with nuclear reactor simulations
The methods are applied on the common GODIVA benchmark (HEU-MET-FAST-001), which is a bare sphere of highly enriched uranium

Summary

INTRODUCTION

Artificial intelligence and data sciences are thriving in many scientific disciplines to solve intractable problems. Application of deep neural networks (DNN) trained by high-order polynomials (known as group method of data handling) was performed on nuclear reactor simulations [4]. The method was applied on a nuclear data problem featuring homogenized neutron cross-sections, and it showed very good performance. We will focus on the other side of nuclear data, the covariances and uncertainties, where we will implement deep learning in the form of DNNs to characterize the nuclear data uncertainty in neutron multigroup cross-sections. The 56-energy-group covariance library will be used in this work, where the sensitivity and uncertainty of the trained model will be validated against other tools. The nuclear data, covariance libraries, neutronic solver, as well as the sensitivity/uncertainty validation tools will be used based on the SCALE code system [8], version 6.2.3

Nuclear Data Processing

RESULTS

Validation of Sensitivity Ranking

Validation of Global Uncertainty

CONCLUSIONS