Aerodynamic Drag Reduction of a Square-Back Car Model Using Linear Genetic Programming and Physic-Based Control

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@PhdThesis{2017ESMA0014_li,

• author = "Ruiying Li",
• title = "Aerodynamic Drag Reduction of a Square-Back Car Model Using Linear Genetic Programming and Physic-Based Control",
• titletranslation = "R{\'e}duction de la tra{\^i}n{\'e}e a{\'e}rodynamique d'un v{\'e}hicule {\`a} culot droit en utilisant un contr{\^o}le bas{\'e} sur la programmation g{\'e}n{\'e}tique lin{\'e}aire et sur la physique",
• school = "ISAE-ENSMA Ecole Nationale Superieure de Mecanique et d'Aerotechique",
• year = "2017",
• address = "Poitiers, France",
• month = "13 " # dec,
• keywords = "genetic algorithms, genetic programming, linear genetic programming control, lgpc-3 aerodynamic drag, wake, flow control, feedback control",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• language = "en",
• oai = "oai:HAL:tel-01685306v1",
• URL = "https://tel.archives-ouvertes.fr/tel-01685306",
• URL = "https://tel.archives-ouvertes.fr/tel-01685306/document",
• URL = "https://tel.archives-ouvertes.fr/tel-01685306/file/2017ESMA0014_li.pdf",
• URL = "http://www.theses.fr/2017ESMA0014",
• publisher = "HAL CCSD",
• identifier = "NNT : 2017ESMA0014; tel-01685306",
• size = "170 pages",
• abstract = "The thesis aims to develop effective active flow control strategies for aerodynamic drag reduction of road vehicles.We experimentally examine the effects of fluidic actuation on the wake past a simplified square-back car model.The actuation is performed with pulsed jets at trailing edges and the flow is monitored with 16 pressure sensors distributed at the rear side. We address the challenging nonlinear turbulence control---which is often beyond the capabilities of model-oriented approach---by developing a simple yet powerful model-free control strategy: the data-driven linear genetic programming control (LGPC). This method explores and exploits strongly nonlinear dynamics in an unsupervised manner with no or little prior knowledge about the system. The control problem is to find a control logic which optimises a given cost function by employing linear genetic programming as an easy and simple regression solver in a high-dimensional control search space. In particular, the present work advances and generalises the previous studies of genetic programming control by comprising multi-frequency forcing, sensor-based feedback including also time-history information feedback and combinations thereof in the control search space. The performance of LGPC is successfully demonstrated on the drag control experiments of the car model where the investigated turbulent wake exhibits a spanwise symmetry and a wall-normal asymmetry. Approximately 33percent base pressure recovery associated with 22percent drag reduction is achieved in all considered classes of control laws. The consumed actuation energy accounts for only 30percent of the aerodynamic power saving. In this research, we also study the turbulent wakes having a lateral asymmetry: an intermittent bi-modal wake at zero yaw and an asymmetric wake at a moderate yaw angle of 5 degree. For the bimodal wake exhibiting are flectional symmetry-breaking, a physics-based opposition feedback control is inferred from the previous open loop control tests. The controller successfully suppresses the bi-modality of the wake and renders a symmetrized wake with a concomitant drag reduction. For the asymmetric wake at yaw, we infer from the single-frequency forcing results a bi-frequency control at the windward edge comprising two frequencies having one order of magnitude difference. This bi-frequency actuation combines the favourable effects of fluidic boat-tailing and balance control of the shear layers. Importantly, LGPC is also applied to this yawed situation and converges to the same bi-frequency actuation. The control strategies proposed in the present study open promising new paths for the control of drag reduction in more complex conditions such as the varying oncoming velocity and wind gust.",
• resume = "Le but de la th{\`e}se est de d{\'e}velopper des strat{\'e}gies de contr{\^o}le efficaces pour la r{\'e}duction de la train{\'e}e a{\'e}rodynamique des v{\'e}hicules terrestres. Nous examinons exp{\'e}rimentalement les effets d{'}un for{\c c}age fluidique sur le sillage d{'}un mod{\`e}le de v{\'e}hicule simplifi{\'e} {\`a} culot droit. Le for{\c c}age est effectu{\'e} par des jets puls{\'e}s aux ar{\^e}tes et16 capteurs de pression r{\'e}partis {\`a} la surface arri{\`e}re permettent d{'}estimer la tra{\^i}n{\'e}e instantan{\'e}e. Nous abordons le probl{\`e}me difficile du contr{\^o}le de l{'}{\'e}coulement turbulent non lin{\'e}aire---qui est souvent au-del{\`a} des capacit{\'e}s de la mod{\'e}lisation r{\'e}duite---par le d{\'e}veloppement d'une strat{\'e}gie de contr{\^o}le sans mod{\`e}le: le contr{\^o}le via la programmation g{\'e}n{\'e}tique lin{\'e}aire (LGPC) dirig{\'e} par les donn{\'e}es. Cette m{\'e}thode explore et exploite la dynamique fortement non lin{\'e}aire d'une mani{\`e}re non supervis{\'e}e avec pas ou peu de connaissances ant{\'e}rieures sur le syst{\`e}me.Le probl{\`e}me est de trouver une logique de contr{\^o}le qui optimise une fonction de co{\^u}t donn{\'e}e. Cette optimisation est r{\'e}alis{\'e}e par la programmation g{\'e}n{\'e}tique lin{\'e}aire comme un solveur de r{\'e}gression simple dans un espace de recherche de grande dimension. En particulier, cette recherche fait progresser et g{\'e}n{\'e}ralise les {\'e}tudes ant{\'e}rieures sur le contr{\^o}le via la programmation g{\'e}n{\'e}tique en incluant le for{\c c}age multi-fr{\'e}quences, le signal des capteurs,l{'}historique des informations temporelles et leurs combinaisons dans l'espace de recherche de contr{\^o}le. La performance de LGPC est d{\'e}montr{\'e}e avec succ{\`e}s sur les exp{\'e}riences de contr{\^o}le de tra{\^i}n{\'e}e du mod{\`e}le de v{\'e}hicule simplifi{\'e} o{\`u} le sillage turbulent pr{\'e}sente une sym{\'e}trie lat{\'e}rale et une asym{\'e}trie normale {\`a} la paroi. Environ 33percent de r{\'e}cup{\'e}ration de pression au culot associ{\'e}e {\`a} 22percent de r{\'e}duction de train{\'e}e est obtenue dans toutes les classes de loisde contr{\^o}le consid{\'e}r{\'e}es. L'{\'e}nergie consomm{\'e}e du for{\c c}age ne repr{\'e}sente que 30percent de l'{\'e}nergie a{\'e}rodynamique r{\'e}cup{\'e}r{\'e}e. Dans ce travail, nous {\'e}tudions {\'e}galement les sillages turbulents ayant une asym{\'e}trie lat{\'e}rale: un sillage intermittent et bi-modal {\`a} d{\'e}rapage nul et un sillage asym{\'e}trique avec un angle de d{\'e}rapage mod{\'e}r{\'e} de 5 degr{\'e}s.Pour le sillage intermittent, un contr{\^o}le de r{\'e}troaction en opposition bas{\'e} sur la physique est d{\'e}duit {\`a} partir des essais pr{\'e}c{\'e}dents de contr{\^o}le en boucle ouverte. Le contr{\^o}leur supprime avec succ{\`e}s la bi-modalit{\'e} du sillage et rend le sillage sym{\'e}trique avec une r{\'e}duction de tra{\^i}n{\'e}e concomitante. Pour le sillage asym{\'e}trique en d{\'e}rapage,nous construisons un contr{\^o}le bi-fr{\'e}quence {\`a} l{'}ar{\^e}te au vent {\`a} partir des r{\'e}sultats de for{\c c}age {\`a} fr{\'e}quence unique. Ce for{\c c}age bi-fr{\'e}quentiel comprend deux fr{\'e}quences ayant une diff{\'e}rence d'un ordre de grandeur. Il combine les effets favorables de la vectorisation du sillage et le contr{\^o}le de l'{\'e}quilibre des couches de cisaillement. Il est important de noter que la strat{\'e}gie LGPC est {\'e}galement appliqu{\'e} {\`a} cette situation en d{\'e}rapage et converge vers le m{\^e}me for{\c c}age bi-fr{\'e}quentiel. Les strat{\'e}gies de contr{\^o}le propos{\'e}es dans cette BibTeX entry too long. Truncated

Genetic Programming entries for Ruiying Li

Citations