Hong Zhang¹, Lukai Song^1,2,*, Guangchen Bai¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1871-1897, 2023, DOI:10.32604/cmes.2022.021880

Abstract

The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like high-nonlinearity, multi-failure regions, and small failure probability, which brings in unacceptable computing efficiency and accuracy of the current analysis methods. In this case, by fitting the implicit limit state function (LSF) with active Kriging (AK) model and reducing candidate sample pool with adaptive importance sampling (AIS), a novel AK-AIS method is proposed. Herein, the AK model and Markov chain Monte Carlo (MCMC) are first established to identify the most probable failure region(s) (MPFRs), and the adaptive kernel density estimation (AKDE) importance sampling function is constructed to… More >

D. C. Knupp^1,2, A. J. Silva Neto³

CMES-Computer Modeling in Engineering & Sciences, Vol.96, No.5, pp. 339-360, 2013, DOI:10.3970/cmes.2013.096.339

Abstract Inverse radiative transfer problems in heterogeneous participating media applications include determining gas properties in combustion chambers, estimating environmental and atmospheric conditions, and remote sensing, among others. In recent papers the spatially variable single scattering albedo has been estimated by expanding this unknown function as a series of known functions, and then estimating the expansion coefficients with parameter estimation techniques. In the present work we assume that there is no prior information on the functional form of the unknown spatially variable albedo and, making use of the Bayesian approach, we propose the development of a posterior probability density, which is explored… More >