LAUSR

Several efforts have been performed to improve LBM defects related to its computational performance. In this work, a new algorithm has been introduced to reduce memory consumption. In the past, most LBM developers have not paid enough attention to retain LBM simplicity in their modified version, while it has been one of the main concerns in developing of the present algorithm. Note, there is also a deficiency in our new algorithm. Besides the memory reduction, because of high memory call back from the main memory, some computational efficiency reduction occurs. To overcome this difficulty, an optimization approach has been introduced, which has recovered this efficiency to the original two-steps two-lattice LBM. This is accomplished by a trade-off between memory reduction and computational performance. To keep a suitable computational efficiency, memory reduction has reached to about 33% in D2Q9 and 42% in D3Q19. In addition, this approach has been implemented on graphical processing unit (GPU) as well. In regard to onboard memory limitation in GPU, the advantage of this new algorithm is enhanced even more (39% in D2Q9 and 45% in D3Q19). Note, because of higher memory bandwidth in GPU, computational performance of our new algorithm using GPU is better than CPU.