Chaarane et al 2023a - Revision history

http://www.colloquiam.com/wd/index.php?action=history&feed=atom&title=Chaarane_et_al_2023a Chaarane et al 2023a - Revision history 2026-05-11T11:43:10Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 http://www.colloquiam.com/wd/index.php?title=Chaarane_et_al_2023a&diff=272360&oldid=prev Avicene: Avicene moved page Draft CHAARANE 561773564 to Chaarane et al 2023a 2023-03-18T11:43:45Z

<p>Avicene moved page <a href="/public/Draft_CHAARANE_561773564" class="mw-redirect" title="Draft CHAARANE 561773564">Draft CHAARANE 561773564</a> to <a href="/public/Chaarane_et_al_2023a" title="Chaarane et al 2023a">Chaarane et al 2023a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 11:43, 18 March 2023</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

Avicene http://www.colloquiam.com/wd/index.php?title=Chaarane_et_al_2023a&diff=272358&oldid=prev Avicene at 11:18, 18 March 2023 2023-03-18T11:18:47Z

<p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 11:18, 18 March 2023</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l16" >Line 16:</td> <td colspan="2" class="diff-lineno">Line 16:</td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Abstract==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Abstract==</div></td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">'''keywords'''</del></div></td><td colspan="2"> </td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Keywords''': ''Deep Neural Network, unsteady aerodynamics, limit-cycle-oscillations''<br/></div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Keywords''': ''Deep Neural Network, unsteady aerodynamics, limit-cycle-oscillations''<br/></div></td></tr> </table>

Avicene http://www.colloquiam.com/wd/index.php?title=Chaarane_et_al_2023a&diff=272357&oldid=prev Avicene: Created page with "<!-- metadata commented in wiki content ==Deep-learning based aeroelastic modeling for transient flows and non-linear dynamics== '''A. Chaarane<math>^{*1}</math>, M. Casanova..." 2023-03-18T11:16:05Z

<p>Created page with "<br /> <br /> ==Abstract==<br /> <br /> '''keywords'''<br /> <br /> '''Keywords''': ''Deep Neural Network, unsteady aerodynamics, limit-cycle-oscillations''<br/><br /> <br /> Supervised deep learning algorithms are investigated as reduced order models for the prediction of unsteady forces resulting from flow-induced vibrations of an elastically mounted structures. Several machine learning architectures, namely convolutional, echo state, and long-term memory networks, are deployed to model the transient aerodynamic force and the motion of the structure in the presence of nonlinear dynamics due to cubic structural forces or due to aerodynamic nonlinearities such as moving shock waves and vortex-shedding.<br /> <br /> First, machine learning architectures are designed to handle the regression problem where the motion of the structure is learned only in the transient phase. A comparative study is performed to evaluate the accuracy of the prediction of limit cycle oscillations obtained for the case of the pitch and plunge airfoil problem[1], where a nonlinear cubic torsional stiffness is considered in order to observe sub- or super-critical Hopf bifurcations in the airfoil motion[2]. Then, a hybrid neural network architecture is proposed in order to parametrize the bifurcation diagram of the aeroelastic system across multiple coefficients in the nonlinear restoring force.<br /> <br /> Finally, the present deep learning algorithm is employed as a meta-model to perform ensemble-based variational data assimilation[3] where pressure sensors, located in the wake flow, are used to identify the structural parameters of the spring-mass system subjected to flow-induced vibrations such as gallop and flutter mechanisms at low Reynolds number.<br /> <br /> ===BIBLIOGRAPHY===<br /> <br /> <div id="cite-1"></div><br /> '''[1]''' J-C. Chassaing, C-T. Nitschke, D. Lucor, P. Cinnella, A. Vincenti, Advances in parametric and model-form uncertainty quantification in canonical aeroelastic systems, ''AerospaceLab Journal'', AL14-07, Issue 14, Sep. 2018<br /> <br /> <div id="cite-2"></div><br /> '''[2]''' K. Li, J. Kou, W. Zhang, Deep Neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers , ''Nonlinear Dynamics'', Vol. '''96(5)''', pp. 1&#8211;21, 2019.<br /> <br /> <div id="cite-3"></div><br /> '''[3]''' V. Mons, J.-C. Chassaing, T. Gomez, P. Sagaut, Reconstruction of unsteady viscous flows using data assimilation schemes, ''Journal of Computational Physics'', Vol. '''316''', pp. 255&#8211;280, 2016.</div>

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