Ensemble Parameter Estimation for the Lumped Parameter Linear Superposition (LPLSP) Framework: A Rapid Approach to Reduced-Order Modeling for Transient Thermal Systems

TL;DR

This paper presents a rapid ensemble parameter estimation framework for the LPLSP method, enabling quick creation of reduced-order thermal models from a single dataset, significantly reducing computational time and maintaining accuracy.

Contribution

The paper introduces a novel ensemble parameter estimation approach with rank-reduction and two-stage decomposition strategies for efficient reduced-order modeling of thermal systems.

Findings

Achieves mean temperature prediction errors within 5% of CFD.

Reduces model development time to seconds to tens of seconds.

Enables real-time evaluation of transient thermal conditions.

Abstract

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.