Flow field prediction in bed configurations: A parametric spatio-temporal convolutional autoencoder approach

IF 1.7 4区工程技术 Q3 MECHANICS

Numerical Heat Transfer Part B-Fundamentals Pub Date : 2024-07-19 DOI:10.1080/10407790.2024.2379006

Ali Mjalled, Reza Namdar, Lucas Reineking, Mohammad Norouzi, Fathollah Varnik, Martin Mönnigmann

引用次数: 0

Abstract

The use of deep learning methods for modeling fluid flow has drawn a lot of attention in the past few years. Here we present a data-driven reduced-order model (ROM) for predicting flow fields in a ...

查看原文本刊更多论文

床配置中的流场预测：参数时空卷积自动编码器方法

在过去几年中，使用深度学习方法为流体流动建模引起了广泛关注。在这里，我们提出了一种数据驱动的降阶模型（ROM），用于预测流体流动中的流场。

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

Numerical Heat Transfer Part B-Fundamentals 工程技术-力学

CiteScore

2.40

自引率

0.00%

发文量

审稿时长

2.8 months

期刊介绍： Published 12 times per year, Numerical Heat Transfer, Part B: Fundamentals addresses all aspects of the methodology for the numerical solution of problems in heat and mass transfer as well as fluid flow. The journal’s scope also encompasses modeling of complex physical phenomena that serves as a foundation for attaining numerical solutions, and includes numerical or experimental results that support methodology development. All submitted manuscripts are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous expert referees. The Editor reserves the right to reject without peer review any papers deemed unsuitable.