Robust design using stochastic response surface and sensitivities

Collection of Technical Papers - 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

Volumn 2, Issue , 2006, Pages 1225-1239

  Wang, Haoyu ^a,b   Kim, Nam H ^a,b  

^a UNIVERSITY OF FLORIDA   (United States)

^b AIAA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DESIGN VARIABLES; LINEAR APPROXIMATION; SENSITIVITY INFORMATION;

CHAOS THEORY; MONTE CARLO METHODS; NUMERICAL METHODS; POLYNOMIAL APPROXIMATION; RANDOM PROCESSES; SENSITIVITY ANALYSIS;

PRODUCT DESIGN;

EID: 33846551865     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Taguchi, G., Introduction to Quality Engineering. 1986, White Plains, New York: UNIPUB.
2. Taguchi, G., System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Cost. 1987, White Plains, New York: UNlPUB/Kraus International.
3. Wang, L. and S. Kodiyalam. An Efficient Method for Probabilistic and Robust Design with Non-normal Distributions. in 43rd AIAA/ASME/ASCE/AHS/ ASC Structures, Structural Dynamics, and Materials Conference. 2002. Denver, Colorado.
4. Du. X. and W. Chen, Efficient Uncertainty Analysis Methods for Multidisciplinary Robust Design. AIAA Journal, 2002. 4(3): p. 545-552.
5. Chen, C.J. and K.K. Choi, Robust Design using Second-order Shape Design Sensitivity Analysis. 1996, Center for Computer-Aided Design, University of Iowa: Iowa City, IA.
6. Ghanem, R.G. and P.D. Spanos. Stochastic Finite Elements: A Spectral Approach. 1991: Springer-Verlag New York Inc.
7. Isukapalli, S.S., A. Roy, and P.G. Georgopoulos, Stochastic Response Surface Met hods (SRSMs) for Uncertainty Propagation: Application to Environmental and Biological Systems. Risk Analysis, 1998. 18(3): p. 351-363.
8. Webster, M.D., M.A. Tatang, and G. J. McRae, Application of the Probabilistic Collocation Method for an Uncertainty Analysis of a Simple Ocean Model. 1996, MIT Joint Program on the Science and Policy of Global Change.
9. Isukapalli, S.S., A. Roy, and P.G. Georgopoulos, Efficient Sensitivity/Uncertainty Analysis Using the Combined Stochastic Response Surface Method and Automated Differentiation: Application to Environmental and Biological Systems. Risk Analysis, 2000. 20(5): p. 591-602.
10. Kim, N.H., H. Wang, and N.V. Queipo, Efficient Shape Optimization under Uncertainty Using Polynomial Chaos Expansions and Local Sensitivities. AIAA Journal, 2006. 44(5): p. 1112-1116.
11. Koch, P.N., R.-J. Yang, and L. Gu, Design for Six Sigma through Robust Optimization. Structural and Multidisciplinary Optimization, 2004. 26: p. 235-248.
12. Haftka, R.T. and Z. Gurdal, Elements of Structural Optimization (Solid Mechanics and Its Applications). 3rd ed. 1991: Springer.
13. Choi, K.K. and N.H. Kim, Structural Sensitivity Analysis and Optimization I: Linear Systems. 2004: Springer.
14. Choi, K.K. and N.H. Kim, Structural Sensitivity Analysis and Optimization II: Nonlinear Systems and Applications. 2004: Springer.
15. Wu, Y.-T., et al. Safety-factor Based Approach for Probability-based Design Optimization, in 42nd AIAA/ASME/ASCE/AHS/ASC Structure, Structural Dynamics, and Materials Conference. 2001. Seattle, Washington.
16. Qu, X. and R.T. Haftka, Reliability-based Design Optimization using Probabilistic Sufficiency Factor. Structural and Multidisciplinary Optimization, 2004. 27(5): p. 314-325.
17. Ang, A.H.-S. and W.H. Tang, Probability Concepts in Engineering Planning and Design. Basic Principles. 1975: Wiley.
18. Saltelli, A., K. Chan, and E.M. Scott, Sensitivity Analysis. 2000: John Wiley & Sons, Ltd.
19. Saltelli, A., S. Tarantola, and K. Chan, Quantitative Model-independent Method for Global Sensitivity Analysis of Model Output. Technometrics, 1999.41(1): p. 39-56.
20. Sobol, I.M., Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo estimates. Mathematics and computers in simulation, 2001. 55: p. 271-280.
21. Sobol, I.M., Sensitivity Analysis for Nonlinear Mathematical Models. Mathematical modeling and computational experiment, 1993. 1: p. 407-144.
22. Chen, W., R. Jin, and A. Sudjianto, Analytical Variance-based Global Sensitivity Analysis in Simulation-based Design under Uncertainty. Journal of mechanical design, 2005.127: p. 875-886.
23. Jang, S.K. and C.W. Bert, Free Vibration of Stepped Beams: higher mode frequencies and effects of steps on frequency. Journal of Sound and Vibration, 1989. 132(1): p. 164-168.
24. De Rosa, M.A., Free Vibrations of Stepped Beams with Elastic Ends. Journal of Sound and Vibration, 1994. 173(4): p. 563-567.
25. Jang, S.K. and C.W. Bert, Free Vibration of Stepped Beams: exact and numerical solutions. Journal of Sound and Vibration, 1989.130(2): p. 342-346.
26. Li, Y., et al., Modeling and Optimization of A Side-implanted Piezoresi stive Shear Stress Sensor, in Proceeding of SPIE lith Annual International Symposium on Smart Structures and Materials. 2006: San Diego, CA.
27. Chase, K.W. and W.H. Greenwood, Design Issues in Mechanical Tolerance Analysis. Manufacturing Review, ASME. 1988. 1(1): p. 50-59.
28. Cattafesta, L., J. Mathew, and A. Kurdila. Modeling and Design of Piezoelectric Actuators for Fluid Control, in World Aviation Conference. 2000. San Diego, CA.