4.7 Article

MCBTE: A variance-reduced Monte Carlo solution of the linearized Boltzmann transport equation for phonons

Journal

COMPUTER PHYSICS COMMUNICATIONS
Volume 265, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.cpc.2021.108003

Keywords

Linearized Boltzmann transport equation; Phonon transport; Thermal conductivity

Funding

  1. IRCC-IITB
  2. DST [SRG/2019/001238]
  3. MHRD-STARS [MoE-STARS/STARS-1/345]

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MCBTE is an algorithm that solves the linearized Boltzmann transport equation for phonons in three dimensions, suitable for analyzing thermal transport in structured materials in both transient and steady-state scenarios. The program outputs temperature and heat flux, allowing the study of cumulative thermal conductivity.
MCBTE solves the linearized Boltzmann transport equation for phonons in three-dimensions using a variance-reduced Monte Carlo solution approach. The algorithm is suited for both transient and steady-state analysis of thermal transport in structured materials with size features in the nanometer to hundreds of microns range. The code is portable and integrated with both first-principles density functional theory calculations and empirical relations for the input of phonon frequency, group velocity, and mean free path required for calculating the thermal properties. The program outputs space- and time-resolved temperature and heat flux for the transient study. For the steady-state simulations, the frequency-resolved contribution of phonons to temperature and heat flux is written to the output files, thus allowing the study of cumulative thermal conductivity as a function of phonon frequency or mean free path. We provide several illustrative examples, including ballistic and quasi-ballistic thermal transport, the thermal conductivity of thin films and periodic nanostructures, to demonstrate the functionality and to benchmark our code against available theoretical/analytical/computational results from the literature. Moreover, we parallelize the code using the Matlab Distributed Computing Server, providing near-linear scaling with the number of processors. (C) 2021 Elsevier B.V. All rights reserved.

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