Joseph Farmer, Ethan Smith, William Bennett, Ryan McClarren
{"title":"High Energy Density Radiative Transfer in the Diffusion Regime with Fourier Neural Operators","authors":"Joseph Farmer, Ethan Smith, William Bennett, Ryan McClarren","doi":"10.1007/s10894-024-00470-3","DOIUrl":null,"url":null,"abstract":"<div><p>Radiative heat transfer is a fundamental process in high energy density physics and inertial fusion. Accurately predicting the behavior of Marshak waves across a wide range of material properties and drive conditions is crucial for design and analysis of these systems. Conventional numerical solvers and analytical approximations often face challenges in terms of accuracy and computational efficiency. In this work, we propose a novel approach to model Marshak waves using Fourier Neural Operators (FNO). We develop two FNO-based models: (1) a base model that learns the mapping between the drive condition and material properties to a solution approximation based on the widely used analytic model by Hammer & Rosen (2003), and (2) a model that corrects the inaccuracies of the analytic approximation by learning the mapping to a more accurate numerical solution. Our results demonstrate the strong generalization capabilities of the FNOs and show significant improvements in prediction accuracy compared to the base analytic model.</p></div>","PeriodicalId":634,"journal":{"name":"Journal of Fusion Energy","volume":"43 2","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10894-024-00470-3.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Fusion Energy","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10894-024-00470-3","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUCLEAR SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Radiative heat transfer is a fundamental process in high energy density physics and inertial fusion. Accurately predicting the behavior of Marshak waves across a wide range of material properties and drive conditions is crucial for design and analysis of these systems. Conventional numerical solvers and analytical approximations often face challenges in terms of accuracy and computational efficiency. In this work, we propose a novel approach to model Marshak waves using Fourier Neural Operators (FNO). We develop two FNO-based models: (1) a base model that learns the mapping between the drive condition and material properties to a solution approximation based on the widely used analytic model by Hammer & Rosen (2003), and (2) a model that corrects the inaccuracies of the analytic approximation by learning the mapping to a more accurate numerical solution. Our results demonstrate the strong generalization capabilities of the FNOs and show significant improvements in prediction accuracy compared to the base analytic model.
期刊介绍:
The Journal of Fusion Energy features original research contributions and review papers examining and the development and enhancing the knowledge base of thermonuclear fusion as a potential power source. It is designed to serve as a journal of record for the publication of original research results in fundamental and applied physics, applied science and technological development. The journal publishes qualified papers based on peer reviews.
This journal also provides a forum for discussing broader policies and strategies that have played, and will continue to play, a crucial role in fusion programs. In keeping with this theme, readers will find articles covering an array of important matters concerning strategy and program direction.