Uncertainty-based robust aerodynamic optimization of rotor blades

International Journal for Numerical Methods in Engineering

Volumn 94, Issue 2, 2013, Pages 111-127

  Wang, Xiaodong ^a,c   Hirsch, Charles ^b   Liu, Zhiyi ^a   Kang, Shun ^a   Lacor, Chris ^c  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

^b NUMECA Int SA   (Belgium)

^c VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

Author keywords

Compressor; Genetic algorithm; Pareto front; Polynomial chaos; Robust optimization; Uncertainty quantification

Indexed keywords

AERO-DYNAMIC PERFORMANCE; DETERMINISTIC OPTIMIZATION; MULTI-OBJECTIVE GENETIC ALGORITHM; PARETO FRONT; POLYNOMIAL CHAOS; PRACTICAL ENGINEERING APPLICATIONS; ROBUST OPTIMIZATION; UNCERTAINTY QUANTIFICATIONS;

AERODYNAMICS; COMPRESSORS; GENETIC ALGORITHMS; MACHINE DESIGN; MONTE CARLO METHODS; NASA; PARETO PRINCIPLE;

COMPUTATIONAL FLUID DYNAMICS;

EID: 84875590919     PISSN: 00295981     EISSN: 10970207     Source Type: Journal    
DOI: 10.1002/nme.4438     Document Type: Article

References (33)

1. Zang TA, Hemsch MJ, Hilburger MW, Kenny SP, Luckring JM, Maghami P, Padula SL, Stroud WF. Needs and Opportunities for Uncertainty-Based Multidisciplinary Design Methods for Aerospace Vehicles, NASA/TM-2002-211462, NASA Langley Research Center: Hampton, USA, 2002.
2. Tsai JT. Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Transactions on Evolutionary Computation 2004; 8:365-377.
3. Gunawan S, Azarm S. Non-gradient based parameter sensitivity estimation of single objective robust design optimization. Journal of Mechanical Design 2004; 126:395-402.
4. Ong Y, Nair P, Lum K. Max-min surrogate assisted evolutionary algorithm for robust design. IEEE Transactions on Evolutionary Computation 2006; 10:392-404.
5. Liang Y, Cheng XQ, Li ZN, Xiang JW. Robust multi-objective wing design optimization via CFD approximation model. Engineering Applications of Computational Fluid Mechanics 2011; 5:286-300.
6. Park GJ, Hwang KH, Lee TH, Lee KH. Robust design: an overview. AIAA Journal 2006; 44:181-191.
7. Beyer H-G, Sendhoff B. Robust optimization - a comprehensive survey. Computer Methods in Applied Mechanics and Engineering 2007; 196:3190-3218.
8. Hosder S, Walters RW, Perez R. A Non-intrusive polynomial chaos method for uncertainty propagation in CFD simulations. Proceedings of the 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, USA, January 2006.
9. Bert JD, Habib NN, Philippe PP, Omar MK, Roger GG, Olivier PLM. Numerical challenges in the use of polynomial chaos representations for stochastic processes. SIAM Journal on Scientific Computing 2004; 26:698-719.
10. Wiener N. The homogeneous chaos. American Journal of Mathematics 1938; 60:897-936.
11. Xiu D, Karniadakism GE. Modeling uncertainty in flow simulations via generalized polynomial chaos. Journal of Computational Physics 2003; 187:137-167.
12. Le Maître OP, Knio O, Habib NN, Ghanem RG. A stochastic projection method for fluid flow-I. Basic formulation. Journal of Computational Physics 2001; 173:481-511.
13. Lacor C, Smirnov S. Uncertainty propagation in the solution of compressible Navier-Stokes equations using polynomial chaos decomposition. Proceeding of NATO AVT symposium, Athens, Greece, December 2007.
14. Wang XD, Kang S. Application of polynomial chaos on numerical simulation of stochastic cavity flow. Science in China Series E: Technological Sciences 2010; 53:2853-2861.
15. Parussini L, Pediroda V, Poloni C. Prediction of geometric uncertainty effects on fluid dynamics by polynomial chaos and fictitious domain method. Computers and Fluids 2010; 39:137-151.
16. Reagan MT, Najm HN, Ghanem RG, Knio OM. Uncertainty quantification in reacting-flow simulations through nonintrusive spectral projection. Combustion and Flame 2003; 132:545-555.
17. Mathelin L, Hussaini MY, Zang TA. Stochastic approaches to uncertainty qualification in CFD simulations. Numerical Algorithms 2005; 38:209-236.
18. Loeven A, Witteveen J, Bijl H. Probabilistic collocation: an efficient non-intrusive approach for arbitrarily distributed parametric uncertainties. Proceedings of the 45th AIAA Aerospace Sciences Meeting and Exhibit, Nevada, USA, January 2007.
19. Benini E. Three-dimensional multi-objective design optimization of a transonic compressor rotor. Journal of Propulsion and Power 2004; 20:559-565.
20. Voutchkov I, Keane AJ. Multi-objective optimization using surrogates. Proceedings of the 7th International Conference on Adaptive Computing in Design and Manufacture, Bristol, 2006; 167-175.
21. Poloni C, Giurgevich A, Onesti L, Pediroda V. Hybridization of a multi-objective genetic algorithm, a neural network and a classical optimizer for a complex design problem in fluid dynamics. Computer Methods in Applied Mechanics and Engineering 2000; 186:403-420.
22. Wang XD, Hirsch C, Kang S, Lacor C. Multi-objective optimization of turbomachinery using improved NSGA-II and approximation model. Computer Methods in Applied Mechanics and Engineering 2011; 200:883-895.
23. Cinnella P, Hercus SJ. Robust optimization of dense gas flows under uncertain operating conditions. Computers and Fluids 2010; 39:1893-1908.
24. Congedo PM, Corre C, Martinez JM. Shape optimization of an airfoil in a BZT flow with multiple-source uncertainties. Computer Methods in Applied Mechanics and Engineering 2011; 200:216-232.
25. Ghanem RG, Spanos PD. Stochastic Finite Elements: A Spectral Approach, 2nd edition. Dover Publications: New York, USA, 2003.
26. Najm HN. Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics. Annual Review of Fluid Mechanics 2009; 41:35-52.
27. Paul GC, Michael SE, Eric TP. Sparse pseudospectral approximation method. Computer Methods in Applied Mechanics and Engineering 2012; 229-232:1-12.
28. Dunham J. CFD validation for propulsion system components. Advisory Report, AGARD, 1998.
29. Tartinville B, Hirsch C. Rotor 37. Notes in Numerical Fluid Mechanics and Multidisciplinary Design 2007; 94:193-202. FLOMANIA - A European Initiative on Flow Physics Modelling.
30. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transaction Evolutionary Computation 2002; 6:182-197.
31. Demeulenaere A, Ligout A, Hirsch C. Application of multipoint optimization to the design of turbomachinery blades. Proceedings of ASME Turbo Expo 2004. ASME-GT2004-53110.
32. Deb K, Jain S. Running performance metrics for evolutionary multi-objective optimization. Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, Singapore, November 2002.
33. Schott JR. Fault tolerant design using single and multicriteria genetic algorithm optimization. Ph.D. Thesis, Massachusetts Institute of Technology, 1995.