ADAPTIVE WING AEROELASTICITY CFD ANALYSIS USING ARTIFICIAL INTELLIGENCE TECHNIQUES (ANN).pdf

Morphing wing technology has been part of the aviation industry since the time of the Wright Brothers. Wright Brothers
used morphing wing for Wright flyer, and type of morphing wing used then was twisting the wing prepared with the help of
bicycle tubes and cardboards. But to enhance the morphing wing technology and to produce more effective results
aerodynamically, and increase lift performances and lower drag performances, better materials with better
mechanism had to be used. This research is directed towards morphing a Supersonic aero elastic wing in
Computational Fluid Dynamics (CFD) analysis using Artificial Neural Networks (ANN). Here a CFD analysis of National
Advisory Committee for Aeronautics (NACA)0012 airfoil, made of a morphing material is done in the initial part. It has low
and transonic regions of CFD analysis in the said airfoil. The problems persisting with the high speed aerodynamic CFD
analysis is brought out in detail. Later part of the work is on designing of a self-trainable and multilayer Artificial Deep
Neural Network and its programming using Python deep learning (Python Anaconda). It also explains how these Artificial
Intelligence (AI) techniques help immensely in supersonic aerodynamic and aeroelastic analysis of the aerofoils. The
data from designing the aerofoil and its CFD analysis will be used for training the deep neural networks, which is done by
the Python code

1. RESEARCH PAPERS.

ADAPTIVE WING AEROELASTICITY CFD ANALYSIS USING
ARTIFICIAL NEURAL NETWORK (ANN)

by
M. SUNIL KUMAR * RM. MENGHAL *

+ fau o Areoute Engrocg. EME tr Colego o Eoctones cra Machen Engraain. socorro ange no.
REN ern MCE tater Caso Esos does Ingreso Screen Torre Poo.

Do cono 22022022 Dot Rasect 2702022
ABSRACT
‘Morphing wing technology hos been par ofthe vation industry since he eof Me High romees, Wight Brothers
useamorphinguinglorWign te andype of morphing win used Men was sing ne wingprepereduthine Help of
"eyes tubes anc corcboart. Bu fo enhonce the morphing wing technology and o produce mere eect resus
cerogmameai. ond increase lit perlomances and lower dog performances, beter materia wih better
mechanism hod 19 Be used. Tis exearch s dected towards marphng a Susoronie aero east wing In
Computational Fd Dynamics (CFD) anoyas using Ari Neural Networks (ANN). Hero a CFD ana of Nato!
Acs Committe or Aeronauites(NACAIOO 12 aol, mace ofamerphing materia sdonein the lnNalpart. hos ow
‘end ronsonic regions of CFD anat in he said rol. The problems persisting wih Ie high speed aerodynamic CFD
‘nays is brought Our n deta Later por of mo work son designing of o salranatse and mulver AI Doo
Nowa! Network aalitsprogramming using Python dem earring{ihon Anaconda). aso explains how these AIO!
Intligence (A) techniques hoj» immansay In supersonic aerodynamic ond aeroolstic anos of he asrofals. me
oto tom designing he cerotoiand ts CFO ana} wil used for raining ne deep neural networks which s done by

thePritoncode.

Keywords Supersonic, Aro Aeroelastic, AriciNewo!Networs, CompurotonalflucDynamies.

Dot Accents 02082022

INTRODUCTION
Me caro lose bohoworwalbe sgnicanty changed by
Mouse of odoptablo wings. which wal commute the wing
sections ond confguations according to the load
condo at waiousstoges ol tight. Morphing wings. con
be detinodosthe wings which con adapt the fi nom
ound them (both stuctualy and goomhicom
Matoby changing the wing shui parameters ond the
Ccontows on the suroco 10 prove best possible wing

avcidance of nonbrupt shape changes in mo flow
tection. eso wingsneedno contolsufaces, sce me
‘doptoble camber performs the function of adtiacted
cp. The fly adoptable wing otra sucio demandas
the tnowedge of mo performance of an oxtemoy
fente wing sucre, which ls interacting win mo
onda 10008 that aro constant changing. while
In fight wih varying load conations. us. he anays of
fluc stuctue Interaction effect on an adaptive wing

Coniguaion under at wing oad conditions (Gein eo.
2001). Those wings hove som contour ensuing

úlcratsthopúme goalinmanetat. 2001}
1 UlerotreReviow

The ION review involves the aeroelostlc anoysos
trom he bogmning doy tothe present times. Since 1916,
when © fatter Incksent on Handley Rage 01400 we
engine biplone bomber had occunad, futter
phenomenon has foscinotos the 1esoorchens dealing

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2. RESEARCH PAPERS.

win tho alert stobaty and. In botwoon 19805 and
19705, mony alerfts were instoled win excitation
systems and occotoramotar for seeping a lequoncy
rongetocheckresonance whore domping wosmanuay.
otomined usng sip chats. Some intial ana of
ere ntablty ofan otcratt goes bockto 19208
staring win the resecicho theta (2001) conducted
‘tNctionaPhsicalLaborotoy. UK

Mott method ond numerico! techniques used by nom
‘ee very fomilr ond prominent. Hond operated desk
colcvotng machines were used to perlom mati
oporaons. Extonsve effort was put In to soto the
{equation of motion of a semi ga aa employing
OS sOy soto oorosmamie back Actowbackot his
‘method iso be inapt ot righ speed regres, Duo 10 me
development regorcing to high-speed fight and ow
expect ato ting suloces the 1olabity o2.0 unsteady
cotodimamie fermusation become insuffeiont in timo.
Tre Invention stand 2nd generation gta computers
mode tusworhy solutons of 30 for subsonic ond
Supersonic regimes possible. Advisory Group for
‘Aerospace Resecach and Development (AGARD) hos
publsheda user manual in skvolumes ali focusedion
0010 clostciy predicament In 1968, in which
conbusons hom several experts in the Mela of
cetoetastery were shored long win on extenso
‘optodoste Meoieteaibockgound.

Furmor forseaching suveys ondieferences. dooing wih
the ste of heat in mo problem formulation, sucia
modeing, cetoemomic modeling, vatous phenomeno
in cocolostely ond factual ifomason on fuer
chorsctonsies ond emetimental methods wore oso
expresso. Recenty coIoclastic simulation wos
‘performed by Ramanolah ond Sichar (2014) of vary ight
‘ond Bee otra wih im of having next generation of
fight vehicles such of Unmanned Aerial Vehicle (UAVS
High Aude Long Encizonce (MALE and thing wings
Also, they porforned a nonlnear osroslasic anayas of
fon otcraf! in subsonic regime via loworder fte
temen. Enomous deformations ro ospoñencos by
HAE craft wgsin a immed fight Raney tal. 2000.
Hence,enamousdeflechons nd coupling between the

vehicle fight mois and wing vibrations. and body:
freedom ter occus. Richard examined the body:
reer tor performance of HALE hing wing rat
‘ond senativty of he behovior depencing on some
parameters such os stuctuat coeffcients ange
thickness, center au scoton fuselage mass factor
fond fuselage. Inet factor. NASA developed Active
‘Aeroetasic Wing (AAW) program which ams to of
manewerng contol by wing Neue wing, The test
erat wos chosen asomodifed FA-18A Hom US. Naw
onctestswerecompleted success

ote et ol (2018) define that Aia Neural Networks.
[ANNs ro a dominant erchetype in machine learning
[whicnincluses deoplearing ase subst neptecby the
Copatattos of omputtion ond communication ofthe
bra. Tey hove been the basis for many pool
Ooritms win opplications in pattem recognition,
‘memory, mopping. tc. hough he Ino oppiance moy
vary, the Iwo components of ANN (ANNs remain the
some: in ondogy wih bologied! systems, they are
referato as neurons and synapses. and conespona to
verices ond the edges of graph respectively The
Cotogotaaton of ANN Is possible in many ways Bo
suponvised learing ond unsupervised leaning. om a
stuctua standpoint, ANNs con be divided into two main
categories, fecc-tomors neotis, in which Mo
Coicuiaton s done Ia loyer loyer mode from the
Inputto he output he network ond recurentnetwons.
which ave on Interconnected network stuctue
nobeingeycies

Togeer ond Bay (2003) proved Mot ML (Machino-
Learning) hos the potential o comatealy eccoleate
igh stroughout epproaches to motets design. os
verte by aiceestesin blo molecular design and hora
motoras design. However, Inthe search for now soft
‘materia exiting properties and performance beyond
those previously achieved, machine learning
pprogcnes ae requonty mic by two shortcomings
Pay hey oe Inherniy nerpokıtvo, ond cre batter
ea 1 he option of propor winin tho known
Lange of accessible behowor than o the innovation of
‘new motetolswih acute beaver Secondly they equie

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3. RESEARCH PAPERS.

largo pre-existing olasets, which ore frequently
navoloble ond proiotiveyexporsiveto produce. Hore
pra new sratogem. he Nour NM OI Bo
Genetic Agente (NSGA). ls proposed fer combing
genetic cigoims, machine leorring. and high
‘rroughput computation oF expetment to determine
matelas wth ntonse properties inthe non-existence of
pro ostng dota. presents cnoratve speed estimation
‘methedology of on induction motor using Elman Neus
Notwotts (ENN). The proposed ENN-bosed speed
simota, which replaces the speed sensor in vector
contol scheme, reves tobe success under 1000)
“toto function os we! 6s orient functions undemoaih
sut 1006. tonsfornation ond inconsistent speed
2.Adophive Sutoce Goometical Modeling

Acopio corctoll asroclstty equis mo geometical
moding o te Dorfes. Tiss done by using ANSIS
FLUENT Goometic Modulo (Basu & Hancock. 1978). For
Mo simploly of onolis NACA 0012 20 oo fol ls
esignedasshowninFigue |.

3 Aerootostic Analysis Nowal Noworks

The modem methodology of corodinamlc anal of
response surface sy using neural networks and other Al
methods (Han. 1999), The response sulaces ao
Polnomicl soles approximations of the dota uncer
coraideraon, On he herhand naureinemens are me
response surfaces hat cre moresophisicotedsince. Pay
Ihave non-inoar functions, opereing on pahmamicl
‘exponsions Noteroen. 2002.

E

—

Pe 1. 1ACOI Aro Dos Ara (ice. 2000

4. Fam of Development: Morphing Wing Technology in
LostiwoDecader

Amophing wing competent of optimized performance
In four tight regimes, nematy launch, maneuver, ler
ondes developed. Ihe wntunnettestwoscatiod
UOC! atoptmeaipartomance demonstioxon for
og wing oct In esecrch mood the posa
of ectuation design, which she most mporon sion
parameter loro morphing wing. In the yor 2005, he
resecich wos continued by nother team ond they
publenea ne article "Vigna Tech Morphing Wing Team
Sping 2005 Fnalopor In wrich Mo 12 comprehensive
porrajas ofthe new achievements n maphing wings
wore considered (Prkanon. 1997; Tora et dl, 2001;
Jacob, 1998; Chenetol, 2000: aig et 2001).

6 Fluid Dynamic ModelstorFlowover Adaptive Wings
Inespeciho of edopteble or ronodoptable wing. the
sporty exits in boundory condtions ana exisence of
sirptying colculatons wih respect o he tid aynornc
model! o ued wing and 8 mosphing wing okcrat The
Ccoroetastesolversdonotuse smpletiic owelomentcry
equation for ooroelostcty, thot Is’ Novier stotos
‘equation: Is because of these equations basic non
Incor exstonce (Domol ot ol, 1981) In ardor to mode
Unstable ooroaynomies. many Hana ceroelasic
solvers use a technique Inown os me "Double Latice
Method he menos of doublet laicos essen do:
run wth he assumption of ‘itetational low ond the
ovoming mathematics enables the use of a doublers
ccoleraien potential os a basis for measuing the
Unstable pressure over an oscilating wing, Sovoral
Improvernens recenty hove been made fo the ofginal
Double Late method that willow E o be used even
under tonsonle condtions (Natrajan. 2002).
Computational Fluc Dynamics (CFD) approumates
ferai equations of motion o Ihe governing por
ilorontol equations. Ths esiontialy suggest thot
‘mode equations composed of tuncated Tovors
Hofes tpresontations of he dorhaties in mo otginal
equation ore now JUDE for parar afercetot
equations. The govering equations in 20 Carteson

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4. RESEARCH PAPERS.

coordinates wih comespondng i cynomics can be
dofredinéquation os

39,98 ‚96a Uy

Ht at ay a o

° pu ov
fea] Jos + »| pue
wre pl FU pur “| S| pve + p
4 yout pal [pve ml
o
ma
se my

max + yy E
ty
dy Dy
tay vty + wy #

in on extremar turbulent flow wos modeled using
CCFO, some concessions regarding the precisen of the
oucomes have to bo mode, only in the cose of
Comprssto tow or where wacous forces pay ©
Prevating tien Me uso of CFO calculations. e uy of
panel motnods gie © quick estimation of the
oerodimamie forces for Incompress In vcs How os
showninFlgure2.

6.Aero losticy

-Aerooosicty eos wth esccrch that tests an oorospace
vohicles reciprocal rtotonshp between Doria
fond oloste forces. ler contol one of the main
research area in the ooroeosticty dusty Fer ls 0
OmemIe voaly of 0 non conserve force uen

= fas
Faye 2.Computoton Rectang Gua Garmsnsuung mo
Sse ARS ue Soong me Mesh unsung SO
‘el in Ps fora borda Core &
Tres Chong tence Neen 7002)

body in which the ocllatons ccceleate without ei,
msisoko adevosngoceurence. andacuckiraen
1 auoidance, However, mos moteros have such non
Ineaties. such 05 hardening oF infernal Gaming
sociale with the behav. Sructues subject o ol
compresores often ei bucking‘elcted
gecmetie nonineciis. Moteovet. due 10 cision of
flow oF due to the presence of o shock, even thi
mechones may be nomineot Petcher eta. 2012: Ka,
1998; Poing Toon. 1986)
7. heroeisictyot Morphing Surface
Mo bes fight nahe hs cow a to esa as
hove a lot weight stleton sin-covered sory frome
(Dhawan, 1991) Acoge wing lat may be smi to
basin het design. nema! spars ons noedto be sit
and study enough o suppat range of maneweing
ood
{ime Voontstuctu Omamies
To stuekzolmecharics oft ng, such os osoin
hetcopters.requke otferntial equoñons win Going
cooticions of te, the aitorenco boing ecınoy
ood. Mis toot diflentolequons selon 1003
Me Motu HI tterentatequatons ond the Equation 2
isgvenhby

x+ (a-qeos@)x=0 MI
neo. 1010 consoms Bkpinghotet al. 1990.
9-Aoroslase Ancias Using our Networks
Sistoce response methods and neual networks have
been intocuced by some schon, such os Ral onc
Mocovan. Suloce responses are pkaly polmomial
sees approumatonsto the date refered above. Nauta!
news, on the oiher hand, ate mote sophisieated
reaction sufoces os hey have nor-Ins funcions that
run on paroi eıponsons (Mak. 2019, However
ater the model has Been obtained, neural nes Wl
tole mote Smucion toting pom operations thon
response sufoces. Both pes of madelshovebeen sed
[or000CjmamI cptimaation, Snce nea networscon
model wide 1ange of sem behaviors Ike incor ond
orina they ae paticuaty eben modeling non
incor oeste responses. Futhemoe. complex

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5. RESEARCH PAPERS.

Loco ens in commercial packages hat ore easy
cesstle. such as Pyhon ortho stucluing of a range
of neural nee. Mo erchlechse of ANN const of
gonna 'neuons in various lay, ageing on thee
connect, spectving the inputs and outputs of the
system ond shoping ‘he stengh of each relation
‘between the neurons (Deep Neural Networks, 2020. The
ANN taining ls coed out using © CFD soler
cerodinamie data or an approach called pane!
‘pproach stuctuttdta tema tine elomentsoner.

10.CFD Analysis Structural Mad

Anote model of cexoeostic problem of an adoptive
stuctue that & enalzed her, Is the proa contol ofan
ape bump located on leading edge of he oot
Mot bum ls sutoble for atoning contol of flow
separation for horzontel moneuveing of an ara!
Consequenty octuctr ar being used to generat this
bump on ho wing sutaco, an the energy necossayto
ce so needs tobe reduced. The oppoeing pressue og
son effect of his bump needs tobe precise so mar me
lessinittdbove mo wingis mode negligible. he proposal
lof such o "spolerbump" on he surface o the fois on
photon problemot mauimizng pressure tog custo
‘ow separation while reducing the os in it and energy
needed to deform the bump. One new netwot ls
onedbymeonsofthe CFD codeFWUENTtocharactaive
mo otocimomc loosing over the bump 1 (Gein et al
2001: Hoyhin, 1999)

‘One of the major problems ln compustonal Mit
‘dmmomics is tubulenco madelng since o constat is
lor a tubulence model tat represents the turbulont
performance and at the same sme ls computationally
efficient There ere numerous tubulence model eng
‘nowadays. Méindicatos a corn compromises bout
the preckion othe resutshavelobemadewhenahighyy
abuse flow ls CFD modeled. The usage of CFO
Ccccultions remains essential only in no cose of
SOLES tow or while wscoustorcespiayamajttole
‘osshouninFlgure 3 (Bauer. 1998)

11. NowolNotworkimplemeniaion

Nour Neterts which provided por dataonbehavorof

que 3 etalon Mechenam ot iso on Asoc Bane
‘ite Long Lage oon itt ear to 20!"
Face 201 Neon 2008

system, te © very Noble method fe system response
precicton. For the onayse of wing stuctues using
fonalogous sin onchis oplmbaion procedures on
newalnetwoiks were used. nthe research, using toned
deep neural networks, an sroslaste optimization fools
developed. The package of ANN Is used in the Python
‘Anaconda platom. The data needed for me neural
networstahing include

+ Porometestorbumps scale, shopeandposton

+ Aorodimamie forces ing, ping, stale overbump
+ To power toques forthe actuar the loads ter na
These detols were car shown in Figure 4 (Notation,
2002}

12, Dynamic Roroolostcanalysis

An afl showing two degrees of eedom. the motion n.

oe ost Loor De Nel ans wih ip mag
Taverns clone o end la, News Bare Sd
iit Save cocon Armor Nacken. 2003

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6. RESEARCH PAPERS.

plc end pine placer cs sewn Roe 8
Intenso cion perico
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1020 atom scoped heh arg peed ame
(bles tne ube oscar bos ov ho a
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aca estrato ueno ns rari cet
mamá Porfeppoecet erat
amapage ove onal pta
tient gma verte nd gous Tene gt
ou plots ap donned by he bouncy
conctonontnoungerote
Au motor ede. Hu conato low mo ow
webct tope te penta target Tat
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open omg coe mule costs pc
MARS mo ale och poneten we oe peel
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in Copbity Stes na bem ie Seam |

aS

ue à ute ot Conon Song Cot! ones on At
toco ora We oa ne Do from
‘aan a nie 20082)

unsteady calculations, À time marching sytem i wed
‘ond a doublet panels hed rom the ool sing age
into o woke ot each poht For each time phase, he
combined ect of al the wale panes on the clot
panels ls determined. For panel methods, the
mathematical detvaion comes tom the governing
Lopiacion equation for ncompressbie potensal ow ln
Equatona, nome

ve-0 a
oro © the vloety potential. Ts equation is vn.
stooay and unsteody incompressible fon. UsuaM, a
Soul represents the Ing portion in the thin cio
Principe, ond he afolthicinesisdetined bya source.
on consigas doublet representations over he aol
crdertorender he ana ear He, 1974)
13.FuiterSuppresion

Mi slection of cuota neural netwons now ote on
‘odepive representation of the tearing snoss of
MO ootoolesesystom. As tho ANY ls able 0 respond
conec to a number of towonal changes. the
representation I so 10 be adoptve. the numerica!
Sever does not need to be Usod each Ime, ond the
sttinss deviaton roto or magnitude vaes. The neural
ao scheme, on ho other hand, may bo used. This
tows for the constucton of © toobox for fier
suppression The schematic alagiemsshowninFigu 7.
Mshignlgnis ie tree conneciedneurainehwonsbultio
represent the performance ofthe ecoles system. A
feedback lop hat assesses the systems elas so
shown. Me stably ls Based on the systems response

ancora |

owe 7.Anay Nowe Netos tr Caco a Oyromi
arcs pon 020 Aces Sem

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7. RESEARCH PAPERS.

¿tag one peroo of oxcitaton. The oscilan pero
hee 1 ond Me zon E meosued a phate 1 of
"e to. Ms mos 8 me became Mis a teat
tem genera, netectnique fo etectngfuteristo
mece he extemal non consevate faces función
condctedoverapetodolosctaion Hence o system
Isora boundary conan name Equoton Sgen
>

TORE 6
hor Mo non-consevetve cetosynamic 108 4)
ana sine vector contoning Mo veloc ot sor.
"e soVo songs ty over oe ositon cie me
Aus ae sles Mo adopte made 1 efecto
such response Le, ANN on ANN, whee ey WA
represent a me changing tinal ses. mo tol
amram oe response sdetaminsd uing ne
cal neu newots of constant FI; ANNT on
AS, e vo sins ope neutres ce
tunes 1 ON mode fre suctu domi 10 od
so Un Me em 8 checled 10 be unse.
(freon. 2011)
14.SofWwor CFD Vacation: na as Systems ANS)
The ANSIS FLUENT CED sota vero 15 ued for
ang the fic ow on the selected oo using mo
computational gid shown in Fue 2. Fate low peed
IAE eee steamiloctysassunediobeot Mech
03. Me farfels presu Boundary conctons ae
speciteden ame ou faces e bourdon: he bu
loco aerine leading edge 0.4% ote oil
chan ne upper sutoce of me ool Te se of me
bump s Bown up tom a y co-aanat ole of 0.2% of
the olf chor va of 4% of he orto chown
Fue 8 (a 6). Me eect fhe vaio of he bum
20 où ei ond cog E sc. Figue 8 shows me
‘ebetymogntuse cortos toon be seen Rot Beyond
cebump ste onthe ocerot0 35H01 Me cho hares
tetalsoperatenotne ton
sey fumar marce In eight of Bump eus in on
unable tegin before the occurence of a al
parao O fhe fow over he ofl fp store, Shee
the purpose o is bump 810 active on tect yaw

@

Be
o
CE aa
‘J 41
ta

11900 à vc orton Gerne ung e ATS LEAT
note ont ne io Sato dior Edo
Banana 904% a he NACA O12 Acheron
‘ro Sr ot Wen 03 (a oan tage Baro
Mog 02 Choe I Lang tage Km Hehe
OS

contol mechanism, needs o be osvssed whetne he
drogproducedby tne specifecibumpsuelsccequote.
Tho Below ee figures. Figure 9 (a to) show static

presse, Volooty tle OA Pros on NACAODI2
Aston bump atteading edge on top suce wih inet

A AT FR Io ey TNT =

8. RESEARCH PAPERS

och number 080.7. Figure Star esse on on
10.7 Mach presu ct bump has reduced whieh in um
changes the monewvering moments lke Young
moment lateral moment and longtudnal moment
que 9b shows Velocty pro at bump where flow is
reattachedto the eo sufoce, which changes hele!
flowporametos Absolut Pressure on foi up ot
07 Mach reduced abupty and ended wi the
rminkizedvolueosshowninFigure ve.

The Valves of Magnitude, State Pressure ond Absolute
Prssweuelociy winBumpat0.8 Mach sclealy pictues
Inbelow twee gues Figure 1O(otoc).

e

owe 90) ot Messe on A 107 Moca Ven
en oooO? bain Eon

InFgue 10astae Pose on orfolctO.6Mach presu
Cotbump hos reduced which um changes Figure 100
showsVeoct prot wih Bump ot 0.8 Mach whee Hows
teattoched lo the alfa sufoce, which changes wih
deeper extent Absolute Pressure on al wih burp ot
08 Mach reduced cbuptly and ended win the
‘minimized volue senor figure 106.

Ins noticeably diran! when compared 10 inet Mach
number 010.3 and 07, os nt Mach number reaches
Intolronge of arsoni lo

te

que 10 (a) Motu o ul np ot 08 Moch,
Foie mare in D oi 08 ac Asco Pero

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9. RESEARCH PAPERS.

15. Aeroelastic Analysis Using NouralNetwors
Thelncompresiteindscldaor0-cynomics wasmadeled
¡sing scattered doublet panel components over the
morphing ofl surface. The non‘penetation condón
‘onc Me Kuna condition ate the boundary conations mot
ole obligated on the oll. The non-penoraton
Cconcton specs hat he Muss nomolvelociy ot the
wal ofcifolshoulabeequattotne sxtoce veloctyotthe
Coto The sun of he nora uid veloct at Me sutaco
of ext ovng othe ais toto. the teesteam ond
Petubotion ofthe doutle!mustbe 2010

At te noting 0090, the Kuto Condon cows the Now
wo dsappear atthe point of he taling edge os
shonin que 11, Thee cific eco numero othe
locaton and scale of he panel athe tating edge must
be cotetulypickea.

16. Deep Neural Network Aerodynamic Analysis
Subsone, amont, Supersonic) and Comparative
Analysis

The ANN ones wing the deep warning have me
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representation ofthe sus ANN which wos produced
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dornonsoted. The hoy contbutons of this work a the
representation by the uso af rowgine wor of rar ana
onto cetoetast reaction using the latest Pyhon.
‘Anaconda (verson 2019.03) deep earning smuaion
ena Its cppcañon o adaptie wings and the effectve
computation of slutons fe abri tme-varyng near
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‘tempted for morphing wing oe. anal ond
Mo some con be voldoled, which hos not been
atempiegtnon.

Roteronces

ID}, Anderson, 4. (2011). EBOOK: Functmentals of
‘Aerocynamics{SIunts). MG.

[21 Bollunos,S.L, Home, LH. Poe A, Duncan, D.K.
Frazer, 4, Lanning, MH... & Wooderd, L (1985. Temo:
series measurements of chromosphere CA IH ond K
mission in cool stas ond the worch for chant
rotation. Me AsropnsicaJoumo 294, 310-325,

[BL Bas, 8. €. & Hancock, 6. J (1978). mo unsteady
‘motion of «two-dimensional aan Incompiessbio
sci ow. Journal of utd Mechanics, 6711), 159-178
Soie 10.1017/0022112078002980

Al. Bauer. S. (1998, January), An aerodynamic
assessment micto-<tag goneratos(MOGs) In 16" AAA
Applied Aerodynamics Conference (p. 2621)
hpsiidoloxg/10.251.416, 1998-2621

[SL Bspinghot RL, Ashley... & Haïman RL. (1996)
‘Aoroolosticy. DoverPubications.

(61. Chen. PC. Sara D, na... Us, D. 0, Gif, K..
&rkovch,R. (2000) Vaioblestiinessspar approach for
‘otctat maneuver enhancement ung ASTROS Jounaiot
Atcrot3X5.865-371.Nps.IGoLo1g/10514/2.2682
IN. Deep Neural Networks. (2020). Retieved tom
tips: ww kdnuggets.com/2020102/deep-neurc!
tuo mA

{81 Ohowan, $. (1991. As lo. Sognono, 1614) 275
362

[9] Dowell .H. Scanlon, RH, Sklo.F. Cuts, H.C...
Saunders, H (1981), A modem couse in cercelstay.

Journal of Mechanica Design, 1032), 261. psc.
oO. 1EN 322896

NO}. Garg. D. P, Zry, M. A, & Anderson, G. L. (2001)
Curent and poten fue research cc In
dope statues: on ARO perspective, Smart Materias
onasivctres, 10%, 610.

[111-Gom, , rman, D. 8 Kaponia, R. (2001. Sta!
fond corola modeling of general plantorm UCAY
wags wih morphing ofl In 19" ARA APS
‘Aerodynamics Conference (p. 1369) hps1dblrg/10
2514/6.2001-1369
[N2}-Hoykin.s.(1999).Nouinetworks:Aguedtour Sot
Computing and Inteligent Systems: Theory ond
‘Applications, 71

(03). Heu. CS. (1974). On approximating a generalineor
potlode som. Jounal of Mathematica Anahi ond
Applications, 4511 284-251

[14 Inman, 0.1, Gem, FH, Roberiaw.H.H, Kapania,
R.K, Pott, G., Natarajan, A. & Sulaeman, E. (2001.
June}. Comments on prospects of uly adoptive ara!
Wings in Smart Suctures ond Materias 2001: meer
nd Commercial Applicaton: of Smart Sructuos
Technologies Vol. 4332, pp. 19} SPE. ntpsiféoLorg0.
mızn2a20043

SL Jacob, 4. D. (1998, Novamban. On the tid
oynemics of copie alfa. In ASVE Intemational
Mechanical Engnoeing Congress and Exposition (vo.
16035, pp. 167-178), American Society of Mechanical
Engnoon.mMpe:ldoLorg 10.11 15AMECE 998-0950,
116} Ki... (1998), Recent experience win cios
once model applied tothe caleulsionot tow over
Greta components. Progress in Aerospace Sciences,
34{7-8), 481-541. MDS:/G0L.o19/10.1016/50376.
(0421;98100009-8,

117] Maik, E (2019. Nou! Networks Bias and Weighs
Retieved tom nipsiimecium comitntechexploned!
uranetwots-bias ond weights 100836828500

118} Notorojon, A. (2002). Acrootstcty of Morphing
Wings Usng Nowra! Nomorks, (Doctora issoaton,
\ignioPaytechaicinsiute ndStote Univers

EE =

12. RESEARCH PAPERS.

9]. Noyton, AH. & Balachandran, 8. (2008. Apolo!
Nonlinear Dynamics: Anaÿical. Computational, ond
ErpormentoiMetock. Jen Vey & Sons.

{201 Para, L K, Znang, E. Schulman, D. $. Chan. H..
Cherukore. M. 1, Terones, M.… 8 Sankaranorayanen
5K, (2018. Defect aynamies in 2.0 MoS2 probed by
using mochine leaning, ctor simulations, and high
resclulon microscopy, ACS Nano, 128. 8006-016
Nps/idoLorg/0.1021/ocsnon0 8802848

{21} Pinkerton .L (1997) A FoGsiy Stuy fo Contra
AO! Snape using Thunder (VOL. 4767). NASA. Longley
Rosoareh Conte

122} Pletcher, RH, Tonnen 4. C., & Anderson, D.
(2012). Computational uid Mechanics and Hoot
Toner. CRC press

123} Poting, OR. A toons, D. (1984. Ie rosponso of
oi to polo cisuboncesMe unsteady Kuo
Concton. AMA Journal, 242, 193:199. ntpsiicotorg
102514139244

[24]. Ramanoion,K.V, 8 Sfar S.I.R.LPU.R-A. PU
2014), Hardware Implementation of afila! neural

homo. managers Journal on Embocaod Systems
a), 31-34.rpsifdolorg)10.26684h053.4.3814
1251. Raney. D. Montgomery, R. Green, L. & Pak. M
(2000, Apı). Fight contol uing_dembured shape
chonge efector onoys. In 41" Sructues. Sutra!
Dynamics. ond Materias Conference ond Ex Ip
1560) PS:/COL01/10.251446.2000-1560
[26).Smi.M. PON... &Hodges D. (2001). CFD based
Omaha O1 noninoar aeroosie behavior oFhigh-ospect
In 19° ARA Applied Aorooynomics
Conference (p. 1882}. It ps.IdoLo1g/10251446.2001-
1982
(27). Toqeer, R. $. & Bayndi N. S. (2008). speed
estmation of an induction motor using Eman neural
network. Neurocomputing, S(3-4). 727-730.
hpsifdotorg/10.101680925-251203}00388-9
(28) Toro, v,tolgue, A. & Lovey. F (2001). Guaranteed
Behavior In Shope Memory Alloys. Shor-ond longtime
ect altea to temperature ond phase coexistence.
umaï of Mema Any ond Calorimetry, 6411) 7-16
hitpsifooicxg/10.102510:1012408825770

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