多组分动态系统模型的多级贝叶斯校准
Multi-Level Bayesian Calibration of a Multi-Component Dynamic System Model
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中文总结 AI 辅助
本文针对时变多组分系统，提出多级贝叶斯校准方法，融合异构信息并考虑不确定性，通过迭代策略校准参数，在离线和在线场景下，基于燃气涡轮发动机转子叶片数据验证了方法的有效性。
中文摘要 AI 辅助
本文提出一种多级贝叶斯校准方法，该方法融合来自异构源的信息，并考虑时变多组分系统的建模与测量不确定性。所开发的方法包含两个部分：通过融合所有可用信息量化组分级与系统级的不确定性，以及修正模型预测。该多级贝叶斯校准方法用于估计组分级与系统级参数，所使用的测量数据来自不同系统组分在不同时间实例下的采集结果。这类异构数据以顺序方式被处理，同时开发了一种迭代策略来校准两个层级的参数。该校准策略针对离线与在线两种场景实施：离线校准使用在所有时间步长内采集的数据，而在线校准则在每个时间步长获得新测量值时实时执行。本文利用燃气涡轮发动机转子叶片热机械行为的分析模型与观测数据，验证了所提方法的有效性。
英文摘要
This paper proposes a multi-level Bayesian calibration approach that fuses information from heterogeneous sources and accounts for uncertainties in modeling and measurements for time-dependent multi-component systems. The developed methodology has two elements: quantifying the uncertainty at component and system levels, by fusing all available information, and corrected model prediction. A multi-level Bayesian calibration approach is developed to estimate component-level and system-level parameters using measurement data that are obtained at different time instances for different system components. Such heterogeneous data are consumed in a sequential manner, and an iterative strategy is developed to calibrate the parameters at the two levels. This calibration strategy is implemented for two scenarios: offline and online. The offline calibration uses data that is collected over all the time-steps, whereas online calibration is performed in real-time as new measurements are obtained at each time-step. Analysis models and observation data for the thermo-mechanical behavior of gas turbine engine rotor blades are used to analyze the effectiveness of the proposed approach.
