Fast Forward and Inverse Thermal Modeling for Parameter Estimation of Multi-Layer Composites -- Part II: Inverse Modeling and Applications

TL;DR

This paper introduces a fast inverse heat conduction model for multi-layer composites that accurately estimates thermal properties and detects internal defects using minimal temperature data, enabling real-time diagnostics.

Contribution

It develops a novel inverse modeling approach based on transient temperature data, validated through FE simulations, for non-invasive thermal property estimation and defect detection in composites.

Findings

Accurately estimates thermal properties and internal defects.

Validated through finite element simulations.

Demonstrates potential for real-time thermal diagnostics.

Abstract

A fast inverse heat conduction model (IHCM) is developed for estimating unknown properties of multi-layer composites considering internal heat generation. This work builds on the validated analytical forward models presented in Part I. Transient temperature at a single point is used as input, with the objective function minimized through an interior-point optimization algorithm. The IHCM accurately estimates thermal properties such as thermal conductivity, specific heat capacity, density, and heat transfer coefficient. It also identifies internal geometric variations and their locations, such as delamination caused by thermal expansion or mechanical motion. These predictions are validated through finite element (FE) simulations. Additionally, a sensorless strategy is introduced, providing a non-invasive inverse modeling approach. The feasibility, sensitivity and limitations of the proposed IHCM are evaluated across various scenarios. The results demonstrate strong potential for applications such as thermal performance monitoring, online defect detection, and real-time diagnostics in multi-layer composite systems.