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Research of Complex Parameter Solution of Stochastic Seepage and the Simulation of Random Field by Three-Dimensional Perturbation Stochastic Finite Element Method

Author(s): Meili Zhan; Huibin Song; Yulong Luo

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Keywords: Complex variable; Stochastic seepage flow; Perturbation stochastic finite element; Variation

Abstract: In the study of nondeterministic seepage, the hydraulic conductivity here is considered as a random variable and is then expressed in complex variable form, the real part of which is a deterministic value and the imaginary part is a variable value. Considering the spatial randomness of hydraulic conductivity, the mean value and variance of reduction the hydraulic conductivity can be obtained through the discrete of hydraulic conductivity random field. The head field and head field variation value distribution are directly calculated by self-compiling program according to the solution of linear equations with complex coefficient based on the finite element method, greatly improving the solving efficiency of the nondeterministic problem. The formulas of response variable (water level) in stochastic seepage analysis are deduced based on perturbation stochastic finite element method, and then the stochastic analysis of 3D steady seepage flow field is performed. And a corresponding program is developed. The hydraulic conductivity of infiltration domain is dealt with 3D isotropic random field, and vector separable local average method is adopted for discretization of random field. Finally, a numerical example is given and discussed, the random seepage flow fields are simulated by the two methods that mentioned in the above. And the feasibility of the two methods is proved by comparing the results with the results which are derived when the hydraulic conductivity is only dealt with random variable. Comparing the two kinds of methods, hydraulic conductivity expressed in complex variable has higher computational efficiency in the calculation of random seepage flow field when the hydraulic conductivity of variability larger. So this study provides a convenient method for solving large complex problem under large variability conditions.

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Year: 2013

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