Scalable adaptive algorithms for next-generation multiphase flow simulations

K. Saurabh, Masado Ishii, Makrand A. Khanwale, H. Sundar, B. Ganapathysubramanian
{"title":"Scalable adaptive algorithms for next-generation multiphase flow simulations","authors":"K. Saurabh, Masado Ishii, Makrand A. Khanwale, H. Sundar, B. Ganapathysubramanian","doi":"10.1109/IPDPS54959.2023.00065","DOIUrl":null,"url":null,"abstract":"High-fidelity flow simulations are indispensable when analyzing systems exhibiting multiphase flow phenomena. The accuracy of multiphase flow simulations is strongly contingent upon the finest mesh resolution used to represent the fluid-fluid interfaces. However, the increased resolution comes at a higher computational cost. In this work, we propose algorithmic advances that aim to reduce the computational cost without compromising on the physics by selectively detecting key regions of interest (droplets/filaments) that require significantly higher resolution. The framework uses an adaptive octree–based meshing framework that is integrated with PETSc’s linear algebra solvers. We demonstrate scaling of the framework up to 114,688 processes on TACC’s Frontera. Finally, we deploy the framework to simulate one of the most resolved simulations of primary jet atomization. This simulation – equivalent to 35 trillion grid points on a uniform grid – is 64× larger than current state–of–the–art simulations and provides unprecedented insights into an important flow physics problem with a diverse array of engineering applications.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS54959.2023.00065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

High-fidelity flow simulations are indispensable when analyzing systems exhibiting multiphase flow phenomena. The accuracy of multiphase flow simulations is strongly contingent upon the finest mesh resolution used to represent the fluid-fluid interfaces. However, the increased resolution comes at a higher computational cost. In this work, we propose algorithmic advances that aim to reduce the computational cost without compromising on the physics by selectively detecting key regions of interest (droplets/filaments) that require significantly higher resolution. The framework uses an adaptive octree–based meshing framework that is integrated with PETSc’s linear algebra solvers. We demonstrate scaling of the framework up to 114,688 processes on TACC’s Frontera. Finally, we deploy the framework to simulate one of the most resolved simulations of primary jet atomization. This simulation – equivalent to 35 trillion grid points on a uniform grid – is 64× larger than current state–of–the–art simulations and provides unprecedented insights into an important flow physics problem with a diverse array of engineering applications.
下一代多相流模拟的可扩展自适应算法
在分析具有多相流动现象的系统时,高保真的流动模拟是必不可少的。多相流模拟的准确性很大程度上取决于用于表示流体-流体界面的最佳网格分辨率。然而,提高的分辨率带来了更高的计算成本。在这项工作中,我们提出了算法的进步,旨在通过选择性地检测需要更高分辨率的关键区域(液滴/细丝)来降低计算成本,同时不影响物理特性。该框架使用了一个基于八叉树的自适应网格框架,该框架与PETSc的线性代数求解器相结合。我们在TACC的Frontera上演示了将框架扩展到114,688个进程。最后,我们部署了该框架来模拟一次射流雾化的最精确的模拟之一。该模拟相当于统一网格上的35万亿个网格点,比目前最先进的模拟大64倍,并为各种工程应用的重要流动物理问题提供了前所未有的见解。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信