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基于混合深度神经网络的鱼类运动对流场影响的新型时空预测方法

IF 5.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Engineering Applications of Computational Fluid Mechanics Pub Date : 2024-07-04 DOI:10.1080/19942060.2024.2370927
Jingyang Wang, Baiyinbaoligao, Xiangpeng Mu, Zhihong Qie, Fengran Xu, Xiaochen Li
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引用次数: 0

摘要

在鱼类通道设施设计中，了解水动力对鱼类行为的耦合影响尤为重要。然而，由鱼类运动引起的流场通常是在水流的作用下产生的。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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A novel spatial–temporal prediction method for the effects of fish movement on flow field based on hybrid deep neural network
In the fish passage facility design, understanding the coupled effects of hydrodynamics on fish behaviour is particularly important. The flow field caused by fish movement however are usually obtai...
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来源期刊
Engineering Applications of Computational Fluid Mechanics
Engineering Applications of Computational Fluid Mechanics ENGINEERING, MULTIDISCIPLINARY-ENGINEERING, MECHANICAL
CiteScore
10.60
自引率
14.80%
发文量
109
审稿时长
3.4 months
期刊介绍： The aim of Engineering Applications of Computational Fluid Mechanics is a continuous and timely dissemination of innovative, practical and industrial applications of computational techniques to solve the whole range of hitherto intractable fluid mechanics problems. The journal is a truly interdisciplinary forum and publishes original contributions on the latest advances in numerical methods in fluid mechanics and their applications to various engineering fields including aeronautic, civil, environmental, hydraulic and mechanical. The journal has a distinctive and balanced international contribution, with emphasis on papers addressing practical problem-solving by means of robust numerical techniques to generate precise flow prediction and optimum design, and those fostering the thorough understanding of the physics of fluid motion. It is an open access journal.
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