AI RESEARCH
A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems
arXiv CS.AI
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ArXi:2604.02483v1 Announce Type: cross Computational fluid dynamics (CFD) simulations of complex fluid flows in energy systems are prohibitively expensive due to strong nonlinearities and multiscale-multiphysics interactions. In this work, we present a transformer-based modeling framework for prediction of fluid flows, and nstrate it for high-pressure gas injection phenomena relevant to reciprocating engines. The approach employs a hierarchical Vision Transformer (SwinV2-UNet) architecture that processes multimodal flow datasets from multi-fidelity simulations.