Using Artificial Intelligence for Transient Heat Transfer

During hypersonic re-entry, heat transfer throughout a vehicle is modeled as a transient time-dependent problem due to the constant deformation of the vehicle from aero-heating effects. Traditional numerical methods, including the finite difference method, have already been widely successful in modeling these transient heat transfer problems. Machine learning frameworks have also been recently proposed to solve problems in dynamic environments, and machine learning algorithms have been applied to stress simulations as quicker alternatives that produce comparable accuracy. To improve the simulation wall-time, this study examined the possible use of machine learning to emulate the finite difference method solver on the 2D heat equation. To generate test data the finite difference method was used to solve the 2D heat equation and generate test data usable by a neural network. Multiple machine learning models were then trained using this test data, and the results of each method were compared amongst themselves, and the data generated by the finite difference method. The feasibility of applying machine learning models to certain problems and whether machine learning models can serve as an alternative or improve current methods was also assessed.