Fast Forward and Inverse Thermal Modeling for Parameter Estimation of Multi-Layer composites -- Part I: Forward Modeling

TL;DR

This paper introduces fast, analytical methods for transient thermal modeling in multi-layer composites, enabling rapid and accurate parameter estimation crucial for inverse modeling applications.

Contribution

It develops a combined separation of variables and orthogonal expansion approach, along with Green's function techniques, to efficiently solve complex thermal problems in layered composites.

Findings

Significantly faster than finite element methods

Maintains high accuracy in thermal predictions

Handles non-homogeneous and temperature-dependent properties

Abstract

This study presents fast and accurate analytical methods for transient thermal modeling in multi-layer composites with an arbitrary number of layers. The proposed approach accounts for internal heat generation and non-homogeneities in the heat diffusion equation. The separation of variables (SOV) method is employed to decouple spatial and temporal components, enabling the determination of eigenvalues. The orthogonal expansion (OE) technique is then applied to compute Fourier coefficients using 'natural' orthogonality. An analytical solution for composites with constant heat sources is developed by combining the SOV method and OE technique. Additionally, a Green's function (GF) based approach is formulated to handle transient heat sources and other non-homogeneous conditions, including temperature-dependent thermal conductivity. The results demonstrate that the proposed method offers significantly faster computations compared to finite element (FE) methods, while maintaining high accuracy. This forward modeling approach serves as an efficient basis for inverse modeling, aimed at estimating unknown material properties and geometric deformations, which are explored in Part II of this study.