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Multi-fidelity approach to dynamics model calibration

机译：动力学模型校准的多保真方法

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This paper investigates the use of structural dynamics computational models with multiple levels of fidelity in the calibration of system parameters. Different types of models may be available for the estimation of unmeasured system properties, with different levels of physics fidelity, mesh resolution and boundary condition assumptions. In order to infer these system properties, Bayesian calibration uses information from multiple sources (including experimental data and prior knowledge), and comprehensively quantifies the uncertainty in the calibration parameters. Estimating the posteriors is done using Markov Chain Monte Carlo sampling, which requires a large number of computations, thus making the use of a high-fidelity model for calibration prohibitively expensive. On the other hand, use of a low-fidelity model could lead to significant error in calibration and prediction. Therefore, this paper develops an approach for model parameter calibration with a low-fidelity model corrected using higher fidelity simulations, and investigates the trade-off between accuracy and computational effort. The methodology is illustrated for a curved panel located in the vicinity of a hypersonic aircraft engine, subjected to acoustic loading. Two models (a frequency response analysis and a full time history analysis) are combined to calibrate the damping characteristics of the panel.
机译：本文研究了具有多个保真度级别的结构动力学计算模型在系统参数校准中的使用。具有不同级别的物理保真度，网格分辨率和边界条件假设的不同类型的模型可能可用于估计未测系统属性。为了推断这些系统属性，贝叶斯校准使用来自多个来源的信息（包括实验数据和先验知识），并全面量化校准参数中的不确定性。后验估计是使用马尔可夫链蒙特卡洛采样完成的，这需要大量的计算，因此使用高保真模型进行校准非常昂贵。另一方面，使用低保真模型可能会导致校准和预测中的重大错误。因此，本文提出了一种使用高保真度模拟校正的低保真度模型进行模型参数校准的方法，并研究了精度与计算量之间的权衡。针对位于超声速飞行器发动机附近的弯曲面板的声学方法，说明了该方法。结合了两个模型（频率响应分析和全时历史分析）以校准面板的阻尼特性。

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