Data Assimilation for Online Calibration of Simulation Digital Twin - A Case Study With Multiple Model Parameters

Calibrating a simulation digital twin using real-time observation data is a key requirement for making the simulation model aligned with a physical system under study. This paper applies a particle filter-based data assimilation framework to online calibration of simulation digital twin, with a focus on calibrating multiple model parameters. An overview of the problem formulation and the particle filter-based data assimilation is provided. The combination effect of multiple model parameters and its impact on multi-parameter calibration is discussed. Experiment results based on a simulation case study example demonstrate the effectiveness of the data assimilation for online model calibration of simulation digital twin models.