Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and climate adaptation.
Key points
- Large-eddy simulation (LES) effectively resolves these instantaneous fields, but its application is limited in iterative design of urban microclimate applications due to high computational cost.
- This paper adopts a novel generative framework of Conditional Flow Matching (CFM) that uses building geometry and mean flow as guidance to generate plausible three-dimensional instantaneous velocity and temperature fields for urban microclimate in seconds.
- Against reference LES data, the CFM surrogate can rapidly and accurately restore the first-order statistics with Normalized Root Mean Square Error (NRMSE) of 2.99% for wind and 1.77% for temperature, second-order turbulence metrics with NRMSE of 7.17% for wind and 8.84% for temperature, turbulent kinetic energy with NRMSE of 7%, probability density function and vertical profiles in representative locations.
- Wind engineering application of local gust prediction demonstrate that the speed and accuracy of CFM, supporting the use of generative AI for making turbulence-aware resilient urban design and climate adaptation more computationally feasible.
Sources (1)
- [1]Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate FieldsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 05:09 PM
Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and climate adaptation.
Large-eddy simulation (LES) effectively resolves these instantaneous fields, but its application is limited in iterative design of urban microclimate applications due to high computational cost.
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