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A stochastic model updating strategy-based improved response surface model and advanced Monte Carlo simulation

机译:基于随机模型更新策略的改进响应面模型和高级蒙特卡洛模拟

摘要

To improve the accuracy and efficiency of computation model for complex structures, the stochastic model updating (SMU) strategy was proposed by combining the improved response surface model (IRSM) and the advanced Monte Carlo (MC) method based on experimental static test, prior information and uncertainties. Firstly, the IRSM and its mathematical model were developed with the emphasis on moving least-square method, and the advanced MC simulation method is studied based on Latin hypercube sampling method as well. And then the SMU procedure was presented with experimental static test for complex structure. The SMUs of simply-supported beam and aeroengine stator system (casings) were implemented to validate the proposed IRSM and advanced MC simulation method. The results show that (1) the SMU strategy hold high computational precision and efficiency for the SMUs of complex structural system; (2) the IRSM is demonstrated to be an effective model due to its SMU time is far less than that of traditional response surface method, which is promising to improve the computational speed and accuracy of SMU; (3) the advanced MC method observably decrease the samples from finite element simulations and the elapsed time of SMU. The efforts of this paper provide a promising SMU strategy for complex structure and enrich the theory of model updating.
机译:为了提高复杂结构计算模型的准确性和效率,结合改进的响应面模型(IRSM)和先进的蒙特卡洛(MC)方法,基于实验静态测试,先验信息,提出了随机模型更新(SMU)策略。和不确定性。首先,以移动最小二乘法为重点,开发了IRSM及其数学模型,并基于拉丁超立方体采样方法研究了先进的MC仿真方法。然后对SMU程序进行了复杂结构的实验静态测试。实施了简单支撑梁和航空发动机定子系统(壳体)的SMU,以验证所提出的IRSM和先进的MC仿真方法。结果表明:(1)SMU策略对复杂结构系统的SMU具有较高的计算精度和效率; (2)由于IRSM的SMU时间远少于传统的响应面法,因此被证明是一种有效的模型,有望提高SMU的计算速度和准确性。 (3)先进的MC方法显着减少了有限元模拟和SMU耗时的样本。本文的工作为复杂结构的SMU策略提供了有希望的方法,并丰富了模型更新的理论。

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