Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields

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.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
  arXiv (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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