防灾减灾工程学报2025，Vol.45Issue(2)：263-270,8.DOI:10.13409/j.cnki.jdpme.20241219002
基于人工智能技术的钝体尾流时空预报
Spatiotemporal Forecast of Bluff-body Wakes Using Artificial Intelligence Technologies
摘要
Abstract
Turbulent flow is ubiquitous in mechanical engineering,fluid mechanics,civil engineering,and other related disciplines.Traditionally,acquisition of turbulent flow data mainly depended on nu-merical simulations and wind tunnel tests.However,numerical simulations require substantial compu-tational time,and wind tunnel tests involve high economic costs.With the rapid development of mod-ern technologies,artificial intelligence technologies have attracted widespread attention in engineering fields due to their high efficiency,high precision,and reliability.This study developed an artificial in-telligence algorithm named Turbulent-Flow-Vision Transformer(TF-ViT),which enabled spatiotem-poral forecast of turbulent flow based on data-driven approaches.Specifically,the TF-ViT mainly con-sisted of two components:Transformer framework and UNet structure.In TF-ViT,each component had distinct functions.The Transformer framework served as the encoder,mainly responsible for pro-cessing and extracting spatiotemporal features of turbulent flow.Meanwhile,the UNet functioned as the decoder to decouple the encoded spatiotemporal turbulent flow information.The overall frame-work enabled the forecast of future spatiotemporal turbulent flow information.This study used the classical problem of the flow past rectangular cylinders to validate the developed TF-ViT algorithm.The open-source computational solver OpenFOAM was utilized to simulate the flow past rectangular cylinders,and the obtained wake flow field data was then used for the training and validation of the TF-ViT model.8 continuous frames of transient turbulent flow data were used to forecast the subse-quent 8 frames of turbulent flow information.The results showed that the developed TF-ViT algo-rithm in this study could accurately forecast the short-term spatiotemporal development of turbulent flow in the wake region.This study demonstrates the strong capability of TF-ViT in forecasting spa-tiotemporal turbulent flow,providing an effective method for turbulent wake field acquisition.关键词
深度学习/湍流/时空预报/人工智能Key words
deep learning/turbulent flow/spatiotemporal forecast/artificial intelligence分类
建筑与水利引用本文复制引用
刘军乐,沈其庆,谢锦添,胡钢..基于人工智能技术的钝体尾流时空预报[J].防灾减灾工程学报,2025,45(2):263-270,8.基金项目
国家自然科学基金项目(52278493)、香港特区政府大学教育资助委员会(16211821)资助 （52278493）
