  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.
