GPU Accelerated Heat Transfer Simulation Supporting Heuristics To Solve The Inverse Heat Conduction Problem

There are numerous applications based on the simulation of heat transfer. A special kind of these tasks is the Inverse Heat Conduction Problem, where the thermal characteristics of the workpiece and the surrounding environment are known, and the objective of the search is to determine the Heat Transfer Coefficient. The most commonly-used methods for solving this problem are based on a discretized numerical analysis (finite-element or finite-difference based methods) combined with some kind of heuristics to find the optimal value. There are various computer-assisted numerical methods available, but these usually have unacceptably high computational demand. Using parallel algorithms and architectures (especially the massively parallel graphics accelerators) makes it possible to speed up the heat transfer simulation sub-procedure of the search. This paper presents a novel CUDA based data-parallel solution which gives the same accuracy as the traditional methods but with $32 \times $ smaller runtime. This method is able to support any kind of heuristic algorithms (Genetic Algorithm, Particle Swarm Optimization, Fireworks Algorithms, Gradient-based methods) in order to find the appropriate HTC value.