Ensemble Parameter Estimation for the Lumped Parameter Linear Superposition (LPLSP) Framework: A Rapid Approach to Reduced-Order Modeling for Transient Thermal Systems
This work addresses the need for rapid reduced-order modeling in transient thermal systems, enabling faster digital twin generation for engineering applications.
The paper presents an ensemble parameter estimation framework for the LPLSP method that generates reduced-order thermal models from a single transient dataset, achieving mean temperature-prediction errors within 5% of CFD simulations and reducing model-development times to seconds.
This work introduces an ensemble parameter estimation framework that enables the Lumped Parameter Linear Superposition (LPLSP) method to generate reduced order thermal models from a single transient dataset. Unlike earlier implementations that relied on multiple parametric simulations to excite each heat source independently, the proposed approach simultaneously identifies all model coefficients using fully transient excitations. Two estimation strategies namely rank-reduction and two-stage decomposition are developed to further reduce computational cost and improve scalability for larger systems. The proposed strategies yield ROMs with mean temperature-prediction errors within 5% of CFD simulations while reducing model-development times to O(10^0 s)-O(10^1 s). Once constructed, the ROM evaluates new transient operating conditions in O(10^0 s), enabling rapid thermal analysis and enabling automated generation of digital twins for both simulated and physical systems.