Work

Non-intrusive auto-detecting and adaptive hybrid scheme for multiscale heat transfer: Thermal runaway in a battery pack

Year: 2025

Type: article

Abstract: Accurately capturing and simulating multiscale systems is a formidable challenge, as both spatial and temporal scales can span many orders of magnitude. Rigorous upscaling methods not only ensure efficient computation, but also guarantee that errors remain within a priori prescribed limits. This provides a balance between computational costs and accuracy. However, the most significant difficulties arise when the conditions under which upscaled models can be applied cease to hold. To address this, we develop an automatic-detecting and adaptive, nonintrusive two-sided hybrid method for multiscale heat transfer and apply it to thermal runaway in a battery pack. To allow adaptive hybrid simulations, two kernels are developed to dynamically map the values between the fine-scale and the upscaled subdomains in a single simulation. The accuracy of the developed hybrid method is demonstrated through conducting a series of thermal runaway test cases in a battery pack. Our results show that the maximum spatial errors consistently remain below the threshold bounded by upscaling errors.

Source: International Journal of Heat and Mass Transfer

DOI: 10.1016/j.ijheatmasstransfer.2025.127003

Authors: Yinuo Noah Yao, Ilenia Battiato

Institutions: Texas A&M University, Stanford University

Language: English

Topic: Advanced Mathematical Modeling in Engineering

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,220