High Energy Density Radiative Transfer in the Diffusion Regime with Fourier Neural Operators

IF 1.9 4区 工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY Journal of Fusion Energy Pub Date : 2024-10-24 DOI:10.1007/s10894-024-00470-3
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,&nbsp;Ethan Smith,&nbsp;William Bennett,&nbsp;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 &amp; 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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用傅立叶神经算子实现扩散状态下的高能量密度辐射传输
辐射传热是高能量密度物理学和惯性聚变的基本过程。准确预测马沙克波在各种材料特性和驱动条件下的行为对这些系统的设计和分析至关重要。传统的数值求解器和分析近似往往在精度和计算效率方面面临挑战。在这项工作中,我们提出了一种使用傅立叶神经算子 (FNO) 建立马沙克波模型的新方法。我们开发了两个基于 FNO 的模型:(1) 一个基础模型,该模型根据 Hammer & Rosen (2003) 广泛使用的分析模型,学习驱动条件与材料属性之间的映射,以获得近似解;(2) 一个模型,该模型通过学习映射,获得更精确的数值解,从而纠正分析近似解的不准确性。我们的研究结果证明了 FNOs 强大的泛化能力,与基础分析模型相比,预测精度有了显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Fusion Energy
Journal of Fusion Energy 工程技术-核科学技术
CiteScore
2.20
自引率
0.00%
发文量
24
审稿时长
2.3 months
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.
期刊最新文献
Research on Insulation Technology for Nb3Sn Layer Coil of Superconducting Conductor Testing Facility Preliminary Control-Oriented Modeling of the ITER Steering Mirror Assembly and Local Control System in the Electron Cyclotron Heating & Current Drive Actuator High Energy Density Radiative Transfer in the Diffusion Regime with Fourier Neural Operators Retraction Note: Determination of the Plasma Internal Inductance and Evaluation of its Effects on Plasma Horizontal Displacement in IR-T1 Tokamak Effects of Injected Current Streams on MHD Equilibrium Reconstruction of Local Helicity Injection Plasmas in a Spherical Tokamak
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1