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:1.8010
- @PhdThesis{2017ESMA0014_li,
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author = "Ruiying Li",
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title = "Aerodynamic Drag Reduction of a Square-Back Car Model
Using Linear Genetic Programming and Physic-Based
Control",
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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",
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school = "ISAE-ENSMA Ecole Nationale Superieure de Mecanique et
d'Aerotechique",
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year = "2017",
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address = "Poitiers, France",
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month = "13 " # dec,
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keywords = "genetic algorithms, genetic programming, linear
genetic programming control, lgpc-3 aerodynamic drag,
wake, flow control, feedback control",
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bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
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language = "en",
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oai = "oai:HAL:tel-01685306v1",
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URL = "https://tel.archives-ouvertes.fr/tel-01685306",
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URL = "https://tel.archives-ouvertes.fr/tel-01685306/document",
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URL = "https://tel.archives-ouvertes.fr/tel-01685306/file/2017ESMA0014_li.pdf",
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URL = "http://www.theses.fr/2017ESMA0014",
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publisher = "HAL CCSD",
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identifier = "NNT : 2017ESMA0014; tel-01685306",
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size = "170 pages",
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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.",
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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