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Direct numerical simulation (DNS) of a thermal turbulent boundary layer (TTBL) can simulate bushfire analogies to serve as a testbed for artificial intelligence (AI)-enhanced remote sensing using infrared sensors of bushfire propagation. Solving the Navier–Stokes equations for a turbulent flow using DNS yields the flow and thermal field and, hence, generates synthetic remote sensing data to train AI algorithms that can process large amounts of remote sensing data associated with bushfire. Thus, DNS training data can aid the development and improvement of the accuracy of AI remote sensing by predicting fire-front propagation of bushfires, as well as test the accuracy of the AI remote sensing algorithms via uncertainty quantification.

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Direct Numerical Simulation of a Thermal Turbulent Boundary Layer: An Analogy to Simulate Bushfires and a Testbed for Artificial Intelligence Remote Sensing of Bushfire Propagation

  • 会议论文

  • 发表日期：

    2025年

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  • Julio Soria,
  • Shahram Karami,
  • Callum Atkinson,
  • Minghang Li

摘要

Direct numerical simulation (DNS) of a thermal turbulent boundary layer (TTBL) can simulate bushfire analogies to serve as a testbed for artificial intelligence (AI)-enhanced remote sensing using infrared sensors of bushfire propagation. Solving the Navier–Stokes equations for a turbulent flow using DNS yields the flow and thermal field and, hence, generates synthetic remote sensing data to train AI algorithms that can process large amounts of remote sensing data associated with bushfire. Thus, DNS training data can aid the development and improvement of the accuracy of AI remote sensing by predicting fire-front propagation of bushfires, as well as test the accuracy of the AI remote sensing algorithms via uncertainty quantification.