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

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Hauptverfasser:	Soria, Julio, Karami, Shahram, Atkinson, Callum, Li, Minghang
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Fluid Dynamics
Online-Zugang:	Acceder al recurso
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author	Soria, Julio Karami, Shahram Atkinson, Callum Li, Minghang
author_facet	Soria, Julio Karami, Shahram Atkinson, Callum Li, Minghang
contents	Direct numerical simulation of a turbulent thermal boundary layer (TTBL) can perform the role of an analogy to simulate bushfires that can serve as a testbed for artificial intelligence (AI) enhanced remote sensing of bushfire propagation. By solving the Navier-Stokes equations for a turbulent flow, DNS predicts the flow field and allows for a detailed study of the interactions between the turbulent flow and thermal plumes. In addition to potentially providing insights into the complex bushfire behaviour, direct numerical simulation (DNS) can generate synthetic remote sensing data to train AI algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which can process large amounts of remotely sensed data associated with bushfire. Using the results of DNS as training data can improve the accuracy of AI remote sensing in predicting firefront propagation of bushfires. DNS can also test the accuracy of the AI remote sensing algorithms by generating synthetic remote sensing data that allows their performance assessment and uncertainty quantification in predicting the evolution of a bushfire. The combination of DNS and AI can improve our understanding of bushfire dynamics, develop more accurate prediction models, and aid in bushfire management and mitigation.
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institution	arXiv
publishDate	2024
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spellingShingle	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 Soria, Julio Karami, Shahram Atkinson, Callum Li, Minghang Fluid Dynamics Direct numerical simulation of a turbulent thermal boundary layer (TTBL) can perform the role of an analogy to simulate bushfires that can serve as a testbed for artificial intelligence (AI) enhanced remote sensing of bushfire propagation. By solving the Navier-Stokes equations for a turbulent flow, DNS predicts the flow field and allows for a detailed study of the interactions between the turbulent flow and thermal plumes. In addition to potentially providing insights into the complex bushfire behaviour, direct numerical simulation (DNS) can generate synthetic remote sensing data to train AI algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which can process large amounts of remotely sensed data associated with bushfire. Using the results of DNS as training data can improve the accuracy of AI remote sensing in predicting firefront propagation of bushfires. DNS can also test the accuracy of the AI remote sensing algorithms by generating synthetic remote sensing data that allows their performance assessment and uncertainty quantification in predicting the evolution of a bushfire. The combination of DNS and AI can improve our understanding of bushfire dynamics, develop more accurate prediction models, and aid in bushfire management and mitigation.
title	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
topic	Fluid Dynamics
url	https://arxiv.org/abs/2402.08157

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