000229403 001__ 229403
000229403 005__ 20181203035605.0
000229403 0247_ $$2doi$$a10.2514/6.2017-3329
000229403 037__ $$aCONF
000229403 245__ $$aA Multilevel Monte Carlo Evolutionary Algorithm for Robust Aerodynamic Shape Design
000229403 269__ $$a2017
000229403 260__ $$c2017
000229403 336__ $$aConference Papers
000229403 520__ $$aThe majority of problems in aircraft production and operation require decisions made in the presence of uncertainty. For this reason aerodynamic designs obtained with traditional deterministic optimization techniques seeking only optimality in a specific set of conditions may have very poor off-design performances or may even be unreliable. In this work we present a novel approach for robust optimization of aerodynamic shapes based on the combination of single and multi-objective Evolutionary Algorithms and a Continuation Multi Level Monte Carlo methodology to estimate robust designs, without relying on derivatives and meta-models. Detailed numerical studies are presented for a transonic airfoil design affected by geometrical and operational uncertainties.
000229403 6531_ $$aAerodynamics
000229403 6531_ $$aShape Design
000229403 6531_ $$aRobust Optimization
000229403 700__ $$0247665$$aPisaroni, Michele$$g239930
000229403 700__ $$0241873$$aNobile, Fabio$$g118353
000229403 700__ $$0242407$$aLeyland, Pénélope$$g105662
000229403 7112_ $$a18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference$$cDenver, Colorado, USA$$d5-9 June 2017
000229403 8564_ $$uhttps://arc.aiaa.org/doi/pdf/10.2514/6.2017-3329$$zURL
000229403 8564_ $$s1980431$$uhttps://infoscience.epfl.ch/record/229403/files/6.2017-3329.pdf$$yPublisher's version$$zPublisher's version
000229403 909C0 $$0252411$$pCSQI$$xU12495
000229403 909CO $$ooai:infoscience.tind.io:229403$$pconf$$pGLOBAL_SET$$pSB
000229403 917Z8 $$x239930
000229403 937__ $$aEPFL-CONF-229403
000229403 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000229403 980__ $$aCONF