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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.08157 |
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Table of 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.