[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126687-en":3,"doc-seo-126687-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126687,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine Learning-based optimization and PIV analysis of the active control of a cylinder wake via synthetic jets","The paper investigates synthetic-jet-based active control of a cylinder wake by combining Linear Genetic Programming with a flow-field study using Particle Image Velocimetry. A synthetic jet actuator is implemented using a loudspeaker coupled to a hollow cylinder, while the optimization focuses on the input signal to the device. A gradient-enriched ML control (gMLC) yields an optimized control law and reports a drag reduction of 7.6% versus the uncontrolled baseline. The result outperforms a reference sinusoidal signal at 44 Hz. PIV measurements compare the natural and both controlled cases.","Springer Nature 2021 LATEX template  \nMachine Learning-based optimization and PIV analysis of the active control of a cylinder wake via synthetic jets  \nAlessandro Scala 1*  \n1* Department of Industrial Engineering, University of Naples”Federico II”, via Claudio 21, Naples, 80125, Italy.  \nCorresponding author(s). E-mail(s): [alessandro.scala2@unina.it](alessandro.scala2@unina.it) ;  \nAbstract  \nThe aim of the present paper is the investigation of the Synthetic Jet (SJ) based control of a cylinder wake through Linear Genetic Programming (LGP) technique and the flow field via Particle Image Velocimetry (PIV) technique. Machine Learning is a branch of Artificial Intelligence aimed at extracting knowledge and experience from big volumes of data and it is the art of building models from data using optimization and regression algorithms. A SJ is an actuator used mainly for flow control and heat transfer performances. In this work, a loudspeaker attached to an hollow cylinder is used as SJ actuator device and the optimization procedure regards the input signal sent to the device. A gradient-enriched machine learning control, know as gMLC algorithm, is used as optimization tool. A preliminary phase of analysis of the ML algorithm, in which an optimal control law is found, is conducted. The latter allows to obtain a very complex control law which is able to give a percentage drag reduction of 7 .6 % with respect to the natural case and this reduction is found to be better than the one obtained by a reference sinusoidal signal characterized by a fundamental frequency of 44 Hz. After the ML analysis, an investigation via Particle Image Velocimetry is performed with the aim of of obtaining a comparison between the natural case, i.e. the uncontrolled configuration, and two different controlled cases: the optimal waveshape obtained via gMLC algorithm and the previous-mentioned sinusoidal waveshape.  \nKeywords: Machine Learning, Particle Image Velocimetry, Active Flow Control  \nSpringer Nature 2021 LATEX template  \n2 Machine Learning-based optimization and PIV analysis  \n1 Introduction  \nIn the few last years, the relationship between Machine Learning (ML) and fluid dynamics has become more and more important. Machine learning is theart of building model from data using optimization and regression algorithmsand many of the challenges in fluid dynamics may be posed as optimization problems, such as designing a wing to maximize the lift while minimizing the drag at the cruise velocities or simply minimizing the drag of a cylinder wake. These optimization tasks fit well with machine learning algorithms, which are designed to handle nonlinear and high-dimensional problems, such as the one which would be widely later discussed, i.e. the control of cylinder’s wake via Synthetic Jets (SJ) .  \nFlow control has always been a research topic of great interest for the entire scientific community. In the field of flow manipulation, the control of the wake of bluff bodies, characterized by the vortex shedding phenomenon, has a great relevance and especially, the wake of the circular cylinder and the possibility of controlling it, have attracted a large amount of research due to their importance in all aspects of engineering applications.  \nVortex shedding from a single circular cylinder, develops over a wide range of Reynolds number Re = DU∞ /ν (with D being the circular cylinder diameter, U∞ the free-stream velocity and ν the fluid kinematic viscosity) starting from a critical value (Re ≈ 49) above which the laminar symmetric bubble behind the body becomes unstable. This phenomenon is due to a wake instability associated with a rather sudden inception and growth in amplitude of wake fluctuations, as one increases the Reynolds number [1] .  \nVarious strategies have been adopted to control and prevent the von K´arm´an shedding and these can be divided into two different groups: passive and active flow control. The category of active flow control includes ","cbCaiemzYQRHzWkM","https://ap.wps.com/l/cbCaiemzYQRHzWkM","pdf",17633751,1,19,"English","en",105,"# Abstract\n# Introduction\n## Machine Learning and fluid dynamics as optimization\n## Vortex shedding and Reynolds number context\n## Passive vs active flow control\n## Synthetic jet device principles\n## Prior work on control parameter and signal shape\n# Problem formulation for functional optimization","[{\"question\":\"What control approach is used for the cylinder wake in the study?\",\"answer\":\"The study uses synthetic jets for active flow control, where the input signal to a loudspeaker-driven actuator is optimized.\"},{\"question\":\"How is the optimized control law obtained?\",\"answer\":\"A gradient-enriched machine learning control based on Linear Genetic Programming (gMLC) is used to search for an optimal control law during a preliminary analysis phase.\"},{\"question\":\"What measurements are used to evaluate the flow field and validate the control?\",\"answer\":\"Particle Image Velocimetry (PIV) is performed to compare the uncontrolled case with the optimized gMLC-controlled case and a sinusoidal reference signal.\"}]","Machine Learning-based optimization and PIV analysis of the active control of a cylinder wake via synthetic jets | 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control approach is used for the cylinder wake in the study?","Question",{"text":75,"@type":76},"The study uses synthetic jets for active flow control, where the input signal to a loudspeaker-driven actuator is optimized.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the optimized control law obtained?",{"text":80,"@type":76},"A gradient-enriched machine learning control based on Linear Genetic Programming (gMLC) is used to search for an optimal control law during a preliminary analysis phase.",{"name":82,"@type":73,"acceptedAnswer":83},"What measurements are used to evaluate the flow field and validate the control?",{"text":84,"@type":76},"Particle Image Velocimetry (PIV) is performed to compare the uncontrolled case with the optimized gMLC-controlled case and a sinusoidal reference 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