Problem
Research questions and friction points this paper is trying to address.
Bayesian calibration
multi-component systems
uncertainty quantification
heterogeneous data
Multi-Level Bayesian Calibration of a Multi-Component Dynamic System Model
🤖 AI Summary
本文提出了一种多级贝叶斯校准方法，通过融合异构数据来解决时间依赖的多组件系统的建模和测量不确定性问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-level Bayesian calibration
heterogeneous data fusion
uncertainty quantification
iterative parameter estimation
online and offline calibration
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2023-08-02arXiv.orgCitations: 54
