000232672 001__ 232672
000232672 005__ 20190619041447.0
000232672 0247_ $$2doi$$a10.5075/epfl-thesis-8082
000232672 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis8082-9
000232672 02471 $$2nebis$$a11086851
000232672 037__ $$aTHESIS
000232672 041__ $$aeng
000232672 088__ $$a8082
000232672 245__ $$aMulti Level Monte Carlo Methods for Uncertainty Quantification and Robust Design Optimization in Aerodynamics
000232672 260__ $$bEPFL$$c2017$$aLausanne
000232672 269__ $$a2017
000232672 300__ $$a230
000232672 336__ $$aTheses
000232672 502__ $$aProf. Christophe Ancey (président) ; Dr Pénélope Leyland, Prof. Fabio Nobile (directeurs) ; Prof. Jean-François Molinari, Prof. Frederick Stern, Dr Domenico Quagliarella (rapporteurs)
000232672 520__ $$aThe vast majority of problems that arise in aircraft production and operation require decisions to be made in the presence of uncertainty. An effective and accurate quantification and control of the level of uncertainty introduced in the design phase and during the manufacturing and operation of aircraft vehicles is imperative in order to design robust and risk tolerant systems. Indeed, the geometrical and operational parameters, that characterize aerodynamic systems, are naturally affected by aleatory uncertainties due to the intrinsic variability of the manufacturing processes and the surrounding environment. Reducing the geometrical uncertainties due to manufacturing tolerances can be prohibitively expensive while reducing the operational uncertainties due to atmospheric variability is simply impossible. The quantification of those two type of uncertainties should be available in reasonable time in order to be effective and practical in an industrial environment. The objective of this thesis is to develop efficient and accurate approaches for the study of aerodynamic systems affected by geometric and operating uncertainties. In order to treat this class of problems we first adapt the Multi Level Monte Carlo probabilistic approach to tackle aerodynamic problems modeled by Computational Fluid Dynamics simulations. Subsequently, we propose and discuss different strategies and extensions of the original technique to compute statistical moments, distributions and risk measures of random quantities of interest. We show on several numerical examples, relevant in compressible inviscid and viscous aerodynamics, the effectiveness and accuracy of the proposed approach. We also consider the problem of optimization under uncertainties. In this case we leverage the flexibility of our Multi Level Monte Carlo approach in computing different robust and reliable objective functions and probabilistic constraints. By combining our approach with single and multi objective evolutionary strategies, we show how to optimize the shape of transonic airfoils in order to obtain designs whose performances are as insensitive as possible to uncertain conditions.
000232672 6531_ $$aUncertainty Quantification
000232672 6531_ $$aMulti Level Monte Carlo
000232672 6531_ $$aContinuation Multi Level Monte Carlo
000232672 6531_ $$aRobust Design Optimization
000232672 6531_ $$aReliability-based Design Optimization
000232672 6531_ $$aOptimization Under Uncertainties
000232672 6531_ $$aAeronautics
000232672 6531_ $$aAerodynamics
000232672 700__ $$0247665$$g239930$$aPisaroni, Michele
000232672 720_2 $$aLeyland, Pénélope$$edir.$$g105662$$0242407
000232672 720_2 $$aNobile, Fabio$$edir.$$g118353$$0241873
000232672 8564_ $$uhttps://infoscience.epfl.ch/record/232672/files/EPFL_TH8082.pdf$$zn/a$$s17328512$$yn/a
000232672 909C0 $$0252589$$xU12151$$pIAG
000232672 909C0 $$0252411$$xU12495$$pCSQI
000232672 909CO $$pDOI$$ooai:infoscience.tind.io:232672$$qGLOBAL_SET$$pSB$$pthesis$$pSTI$$pthesis-bn2018$$pthesis-public$$qDOI2
000232672 917Z8 $$x108898
000232672 917Z8 $$x108898
000232672 918__ $$dEDME$$cIGM$$aSTI
000232672 919__ $$aIAG
000232672 919__ $$aCSQI
000232672 920__ $$b2017$$a2017-12-15
000232672 970__ $$a8082/THESES
000232672 973__ $$sPUBLISHED$$aEPFL
000232672 980__ $$aTHESIS