The rapid generation of high-quality flow data and the development of increasingly powerful artificial intelligence methods foster novel highly fruitful research paradigms for solving big challenge problems in fluid m...The rapid generation of high-quality flow data and the development of increasingly powerful artificial intelligence methods foster novel highly fruitful research paradigms for solving big challenge problems in fluid mechanics.This paradigm change marks the birth of a novel field of research—intelligent fluid mechanics(IFM).展开更多
A key question in flow control is that of the design of optimal controllers when the control space is high-dimensional and the experimental or computational budget is limited.We address this formidable challenge using...A key question in flow control is that of the design of optimal controllers when the control space is high-dimensional and the experimental or computational budget is limited.We address this formidable challenge using a particular flavor of machine learning and present the first application of Bayesian optimization to the design of open-loop controllers for fluid flows.We consider a range of acquisition functions,including the recently introduced output-informed criteria of Blanchard and Sapsis(2021),and evaluate performance of the Bayesian algorithm in two iconic configurations for active flow control:computationally,with drag reduction in the fluidic pinball;and experimentally,with mixing enhancement in a turbulent jet.For these flows,we find that Bayesian optimization identifies optimal controllers at a fraction of the cost of other optimization strategies considered in previous studies.Bayesian optimization also provides,as a by-product of the optimization,a surrogate model for the latent cost function,which can be leveraged to paint a complete picture of the control landscape.The proposed methodology can be used to design open-loop controllers for virtually any complex flow and,therefore,has significant implications for active flow control at an industrial scale.展开更多
基金We appreciate the efforts made by all the contrib-125 utors for this thematic issue.We are grateful to Prof.Xiao-Jing Zheng,126 the Editor-in-Chief of Acta Mechanica Sinica(AMS)for inviting us as 127 the guest editors of this thematic issue.We also thank the staff of the 128 editorial office of AMS for managing,assistance,and support.\。
文摘The rapid generation of high-quality flow data and the development of increasingly powerful artificial intelligence methods foster novel highly fruitful research paradigms for solving big challenge problems in fluid mechanics.This paradigm change marks the birth of a novel field of research—intelligent fluid mechanics(IFM).
文摘A key question in flow control is that of the design of optimal controllers when the control space is high-dimensional and the experimental or computational budget is limited.We address this formidable challenge using a particular flavor of machine learning and present the first application of Bayesian optimization to the design of open-loop controllers for fluid flows.We consider a range of acquisition functions,including the recently introduced output-informed criteria of Blanchard and Sapsis(2021),and evaluate performance of the Bayesian algorithm in two iconic configurations for active flow control:computationally,with drag reduction in the fluidic pinball;and experimentally,with mixing enhancement in a turbulent jet.For these flows,we find that Bayesian optimization identifies optimal controllers at a fraction of the cost of other optimization strategies considered in previous studies.Bayesian optimization also provides,as a by-product of the optimization,a surrogate model for the latent cost function,which can be leveraged to paint a complete picture of the control landscape.The proposed methodology can be used to design open-loop controllers for virtually any complex flow and,therefore,has significant implications for active flow control at an industrial scale.
