A Noniterative Approach for Simulating Multivariate NonGaussian#br#
 Fluctuating Wind Pressure

Abstract

Abstract:

Based on the correlativity of multivariate nonGaussian random processes, the proposed noniterative algorithm used for the univariate nonGaussian stochastic processes is expanded to simulate the multivariate nonGaussian processes. The coherency functions of underlying multivariate nonGaussian processes are dealt with by resorting to the correlativity of multivariate Gaussian processes, a noniteration based algorithm is developed for simulating the multivariate nonGaussian processes. The numerical simulations of the multivariate nonGaussian fluctuating wind pressure indicate that the noniteration based algorithms can effectively simulate the multivariate nonGaussian processes with low, middle, and high skewnesses, respectively.

Key words: multivariate nonGaussian random processes, nonGaussian fluctuating wind pressure, coherency functions, noniteration, skewness

CLC Number:

TU 311

LI Jinhua1,LI Chunxiang2，JIANG Lei2,DENG Ying2. A Noniterative Approach for Simulating Multivariate NonGaussian#br# Fluctuating Wind Pressure[J]. Journal of Shanghai Jiaotong University, 2017, 51(4): 462-.

References

［1］HUANG M F, LOU W, PAN X, et al. Hermite extreme value estimation of nonGaussian wind load process on a longspan roof structure ［J］. Journal of Structural Engineering, 2014, 140(9):04014061.
［2］YANG Q, TIAN Y. A model of probability density function of nonGaussian wind pressure with multiple samples ［J］. Journal of Wind Engineering and Industrial Aerodynamics, 2015, 140: 6778.
［3］DEODATIS G, MICALETTI R C. Simulation of highly skewed nonGaussian stochastic processes ［J］. Journal of Engineering Mechanics, 2001, 127(12): 12841295.
［4］LI J, LI C. Simulation of nonGaussian stochastic process with target power spectral density and lowerorder moments ［J］. Journal of Engineering Mechanics, 2012, 138(5): 391404.
［5］STEFANOU G. The stochastic finite element method: Past, present and future ［J］. Computer Methods in Applied Mechanics and Engineering, 2009, 198(9/10/11/12): 10311051.
［6］YAMAZAKI F, SHINOZUKA M. Digital generation of nonGaussian stochastic fields ［J］. Journal of Engineering Mechanics, 1988, 114(7):11831197.
［7］MASTER F, GURLEY K R. NonGaussian simulation: cumulative distribution function mapbased spectral correction ［J］. Journal of Engineering Mechanics, 2003, 129(12):14181428.
［8］GRIGORIU M. Spectral representation for a class of nongaussian processes ［J］. Journal of Engineering Mechanics, 2004, 130(5): 541546.
［9］SHI Y. Simulation of stationary nonGaussian stochastic processes or fields with application in suspension bridge cable strength estimation ［D］. New York: Columbia University, 2006.
［10］BOCCHINIA P, DEODATIS G. Critical review and latest developments of a class of simulation algorithms for strongly nonGaussian random fields ［J］. Probabilistic Engineering Mechanics, 2008, 23(4):393407.
［11］GURLEY K R, KAREEM A, TOGNARELLI M A. Simulation of a class of nonnormal random processes ［J］. International Journal of NonLinear Mechanics, 1996, 31(5): 601617.
［12］PUIG B, AKIAN J L. NonGaussian simulation using Hermite polynomials expansion and maximum entropy principle ［J］. Probabilistic Engineering Mechanics, 2004, 19(4): 293305.
［13］DEODATIS G. Simulation of ergodic multivariate stochastic processes ［J］. Journal of Engineering Mechanics, 1996, 122(8): 778787.

A Noniterative Approach for Simulating Multivariate NonGaussian#br# Fluctuating Wind Pressure

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 1

Recommended Articles

Metrics

Comments