Universal Effective Medium Theory to Predict the Thermal Conductivity in Nanostructured Materials
S. Aria Hosseini, Sarah Khanniche, P. Alex Greaney, Giuseppe, Romano

TL;DR
This paper introduces the Ballistic Correction Model (BCM), an effective medium theory that accurately predicts thermal conductivity in nanostructured materials by incorporating mean-free-path distribution and boundary effects, validated against full BTE simulations.
Contribution
The paper presents a novel, computationally efficient model (BCM) that accounts for complex boundary effects and MFP distribution, filling gaps left by previous simplified models.
Findings
BCM shows excellent agreement with full BTE simulations.
The model effectively captures boundary and size effects on thermal conductivity.
Parameters derived can be used for various materials and geometries.
Abstract
Nanostructured materials enable high thermal transport tunability, holding promises for thermal management and heat harvesting applications. Predicting the effect that nanostructuring has on thermal conductivity requires models, such as the Boltzmann transport equation (BTE), that capture the non-diffusive transport of phonons. Although the BTE has been well validated against several key experiments, notably those on nanoporous materials, its applicability is computationally expensive. Several effective model theories have been put forward to estimate the effective thermal conductivity; however, most of them are either based on simple geometries, e.g., thin films, or simplified material descriptions such as the gray-model approximation. To fill this gap, we propose a model that takes into account the whole mean-free-path (MFP) distribution as well as the complexity of the material's…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
