Structural reliability under non-Gaussian stochastic behavior

Computers and Structures

Volumn 82, Issue 13-14, 2004, Pages 1113-1121

  Choi, Seung Kyum ^a   Grandhi, Ramana V ^a   Canfield, Robert A ^b  

^a Wright State University   (United States)

^b AIR FORCE INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Joined wing; Latin hypercube; Polynomial chaos; Reliability; SFEM

Indexed keywords

AIRCRAFT; APPROXIMATION THEORY; COMPUTATIONAL METHODS; FINITE ELEMENT METHOD; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; POLYNOMIALS; PROBABILITY; STRUCTURAL ANALYSIS;

LATIN HYPERCUBE; POLYNOMIALS CHAOS; RELIABILITY METHOD; STOCHASTIC FINITE ELEMENT METHODS (SFEM);

MECHANICAL ENGINEERING;

EID: 2442505028     PISSN: 00457949     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compstruc.2004.03.015     Document Type: Conference Paper

References (13)

1. Schuëller G.I. A state-of-the-art report on computational stochastic mechanics. J. Probab. Eng. Mech. 12(4):1997;197-313.
2. Matthies H.G., Brenner C.E., Bucher C.G., Soares C.G. Uncertainties in probabilistic numerical analysis of structures and solids-stochastic finite elements. Struct. Safety. 19(3):1997;283-336.
3. Ghanem R., Spanos P.D. Stochastic finite elements: a spectral approach. 1991;Springer-Verlag, New York.
4. Tatang MA. Direct incorporation of uncertainty in chemical and environmental engineering systems. PhD Dissertation. Massachusetts Institute of Technology, Cambridge, MA, 1995.
5. Isukapalli SS. Uncertainty analysis of transport-transformation models. PhD Dissertation. Rutgers, the State University of New Jersey, New Brunswick, NJ, 1999.
6. Pettit CL, Canfield RA, Ghanem R. Stochastic analysis of an aeroelastic system. 15th ASCE Engineering Mechanics Conference, Columbia University, New York, 2002.
7. Xiu D., Karniadakis G. The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 24(2):2002;619-644.
8. Xiu D., Karniadakis G.E. Modeling uncertainty in flow simulations via generalized polynomial chaos. J. Comput. Phys. 187:2003;137-167.
9. Choi S, Grandhi RV, Canfield RA, Pettit CL. Polynomial chaos expansion with Latin hypercube sampling for estimating response variability. AIAA J, in press.
10. McKay M.D., Beckman R.J., Conover W.J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics. 21(2):1979;239-245.
11. Wyss GD, Jorgensen KH. A user's guide to LHS: Sandia's Latin Hypercube Sampling Software, SAND98-0210, Sandia National Lab. PO Box 5800, Albuquerque, New Mexico, available on-line at http://www.prod.sandia.gov/cgi-bin/techlib/ access-control.pl/1998/980210.pdf , 1998.
12. Montgomery D.C., Peck E.A. Introduction to linear regression analysis. 1992;Wiley, New York.
13. Devroye L. Non-uniform random variate generation. 1986;Springer-Verlag, New York.