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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2504.10119 |
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| _version_ | 1866908317964566528 |
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| author | Yao, Yinuo Noah Battiato, Ilenia |
| author_facet | Yao, Yinuo Noah Battiato, Ilenia |
| contents | 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 maintains errors 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10119 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-intrusive Auto-detecting and Adaptive Hybrid Scheme for Multiscale Heat Transfer: Thermal Runaway in a Battery Pack Yao, Yinuo Noah Battiato, Ilenia Computational Physics 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 maintains errors 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. |
| title | Non-intrusive Auto-detecting and Adaptive Hybrid Scheme for Multiscale Heat Transfer: Thermal Runaway in a Battery Pack |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2504.10119 |