Sensor Placement for Multi-Fidelity Dynamics Model Calibration

This paper studies a multi-fidelity resource optimization methodology for sensor location in the calibration of dynamics model parameters. Effective calibration can only be achieved if the information collection in the experiments is successful. This requires a thoughtful study of the sensor configuration to maximize information gain in the calibration of system parameters. This paper proposes a framework for optimizing the sensor number and locations to maximize information gain in the calibration of damping parameters for non-linear dynamics problems. Further, we extend the basic framework to the case of multi-fidelity modeling. In the presence of models of multiple fidelity, runs from the high-fidelity model can be used to correct the low-fidelity surrogate and result in stronger physics-informed priors for calibration with experimental data. This multi-fidelity calibration allows the fusion of information from low and high-fidelity models in inverse problems. The proposed sensor optimization methodology is illustrated for a curved panel subjected to acoustic and non-uniform thermal loading. Two models of different fidelity (a time history analysis and a frequency domain analysis) are employed to calibrate the structure's damping parameters and model errors. The optimization methodology considers two complicating factors: (1) the damping behavior is input-dependent, and (2) the sensor uncertainty is affected by temperature.