WAN Huaping, TAI Yonggan, ZHONG Jian, REN Weixin. Computation of High-Order Moments of Structural Dynamic Characteristics Based on Polynomial Chaos Expansion[J]. Applied Mathematics and Mechanics, 2018, 39(12): 1331-1342. doi: 10.21656/1000-0887.390165
Citation: WAN Huaping, TAI Yonggan, ZHONG Jian, REN Weixin. Computation of High-Order Moments of Structural Dynamic Characteristics Based on Polynomial Chaos Expansion[J]. Applied Mathematics and Mechanics, 2018, 39(12): 1331-1342. doi: 10.21656/1000-0887.390165

Computation of High-Order Moments of Structural Dynamic Characteristics Based on Polynomial Chaos Expansion

doi: 10.21656/1000-0887.390165
Funds:  The National Natural Science Foundation of China(51878235; 51508144);China Postdoctoral Science Foundation(2015M581981)
  • Received Date: 2018-06-13
  • Rev Recd Date: 2018-10-16
  • Publish Date: 2018-12-01
  • Uncertainty of structural parameters leads to uncertainty of structural dynamic characteristics. Quantification of uncertainty of dynamic characteristics provides accurate dynamic information for structural dynamic analysis. Statistical moments (e.g., mean and variance) mainly represent the uncertainty of structural dynamic properties. The MonteCarlo simulation (MCS) requires a large number of model evaluations to ensure the convergence of the results, which hinders its application to the largescale, complex engineering structures. The polynomial chaos expansion (PCE) surrogate model was used to replace the computationally expensive finite element model (FEM), and then the statistical moments of structural dynamic characteristics were efficiently calculated. The presented PCEbased method only needs a small set of model runs before the model formulation and subsequently does not require the FEM for calculations of the statistical moments. Therefore, the issue of the high computational cost associated with the computations of dynamic characteristic statistical moments was solved. The method is suitable for parameters with arbitrary probability distribution and has high computational efficiency in calculating the highorder statistical moments. Finally, the effectiveness of the developed method was verified through an example of an aluminum plate.
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