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Titel:

JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flows

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Bezgin, Deniz A.; Buhendwa, Aaron B.; Adams, Nikolaus A.
Abstract:
In our effort to facilitate machine learning-assisted computational fluid dynamics (CFD), we introduce the second iteration of JAX-Fluids. JAX-Fluids is a Python-based fully-differentiable CFD solver designed for compressible single- and two-phase flows. In this work, the first version is extended to incorporate high-performance computing (HPC) capabilities. We introduce a parallelization strategy utilizing JAX primitive operations that scales efficiently on GPU (up to 512 NVIDIA A100 graphics c...     »
Stichworte:
Computational fluid dynamics; Differential programming; Diffuse-interface; High performance computing; JAX; Level-set; Machine learning; Navier-Stokes equations; Turbulence; Two-phase flows
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
arXivLabs
Jahr:
2024
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.48550/ARXIV.2402.05193
Verlag / Institution:
arXiv
Publikationsdatum:
01.01.2024
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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