Direct numerical simulation (DNS) and large eddy simulation (LES) were performed on the wall-bounded flow at Re_tau = 180 using lattice Boltzmann method (LBM) and multiple Graphic Processing Units (GPUs). In the DNS, 8 K20M GPUs were adopted. The maximum number of meshes is 6.7×10^7, which results in the non-dimensional mesh size of Delta+=1.41 for the whole solution domain. It took 24 hours for GPU-LBM solver to simulate 3×10^6 LBM steps. The aspect ratio of resolution domain was tested to obtain accurate results for DNS. As a result, both the mean velocity and turbulent variables, such as Reynolds stress and velocity fluctuations, perfectly agree with the results of Moser et al [5] when the aspect ratios in streamwise and spanwise directions are 8 and 2 respectively. As for the LES, the local grid refinement technique was tested and then used. Using 1.76×10^6 grids and Smagorinsky const (Cs) =0.13, good results were obtained. The ability and validity of LBM on simulating turbulent flow were verified.<\/p>\n","protected":false},"excerpt":{"rendered":"
Direct numerical simulation (DNS) and large eddy simulation (LES) were performed on the wall-bounded flow at Re_tau = 180 using lattice Boltzmann method (LBM) and multiple Graphic Processing Units (GPUs). In the DNS, 8 K20M GPUs were adopted. The maximum number of meshes is 6.7×10^7, which results in the non-dimensional mesh size of Delta+=1.41 for […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,104,3],"tags":[14,1795,108,242,285,20,1390],"class_list":["post-11412","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-cuda","tag-fluid-dynamics","tag-lattice-boltzmann-model","tag-mpi","tag-numerical-simulation","tag-nvidia","tag-tesla-k20"],"views":2270,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11412","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=11412"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11412\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11412"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11412"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11412"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}