Parallel optimum design of foil bearing using particle swarm optimization method

Numerical optimization of tribological elements usually demands extended computations. The particle swarm optimization (PSO) method is known for its simple implementation and high efficiency in solving multifactor optimization problems. In this study, several parallel computing schemes using PSO for air foil bearing design are compared. The parallel programming models applied are multicore computing by OpenMP and many-core graphics processing unit (GPU) computing using Compute Unified Device Architecture (CUDA) and OpenACC. The best case was obtained when the OpenMP coding was applied at the algorithm level of optimization. The performance of CUDA was found to be compatible with OpenMP when parallel computing was used to solve the bearing model. Due to excess data communications computing using OpenACC was significantly slower than the other approaches. The parallel computing scheme recommended in this study is independent of PSO, which is applicable to tribological studies requiring global optimization analysis.