Reduced-Order Model to Predict Thermal Conductivity of Dimensionally-Confined Materials

TL;DR

This paper introduces an analytic reduced-order model using the Ballistic Correction Model to efficiently predict thermal conductivity in various dimensionally confined nanostructures, validated against BTE simulations.

Contribution

The paper presents a novel, computationally efficient reduced-order model for nanoscale thermal transport that accurately accounts for phonon boundary scattering in complex geometries.

Findings

The model shows excellent agreement with BTE simulations.

It provides a fast and accurate prediction of thermal conductivity in nanostructures.

The approach facilitates rapid material screening for energy and thermal management applications.

Abstract

Predicting nanoscale thermal transport in dielectrics requires models, such as the Boltzmann transport equation (BTE), that account for phonon boundary scattering in structures with complex geometries. Although the BTE has been validated against several key experiments, its computational expense limits its applicability. Here, we demonstrate the use of an analytic reduced-order model for predicting the thermal conductivity in dimensionally confined materials, i.e., monolithic and porous thin films, and rectangular and cylindrical nanowires. The approach uses the recently developed "Ballistic Correction Model" (BCM) which accounts for materials' full distribution of phonon mean-free-paths. The model is validated against BTE simulations for a selection of base materials, obtaining excellent agreement. By furnishing a precise yet easy-to-use prediction of thermal transport in nanostructures, our work strives to accelerate the identification of materials for energy-conversion and thermal-management applications.