{"id":1482,"date":"2010-11-17T12:05:17","date_gmt":"2010-11-17T12:05:17","guid":{"rendered":"http:\/\/hgpu.org\/?p=1482"},"modified":"2010-11-17T12:05:17","modified_gmt":"2010-11-17T12:05:17","slug":"direct-numerical-simulation-of-sub-grid-structures-in-gas-solid-flow-gpu-implementation-of-macro-scale-pseudo-particle-modeling","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1482","title":{"rendered":"Direct numerical simulation of sub-grid structures in gas-solid flow &#8212; GPU implementation of macro-scale pseudo-particle modeling"},"content":{"rendered":"<p>Due to significant multi-scale heterogeneity, understanding sub-grid structures is critical to effective continuum-based description of gas-solid flow. However, it is challenging for both physical measurements and numerical simulations. In this article, with the macro-scale pseudo-particle method (MaPPM) implemented on a GPU-based HPC system, up to 30,000 fluidized solids are simulated using the N-S equation directly. The destabilization of uniform suspensions and the formation of solids clusters are reproduced in two-dimensional suspensions. Distinct scale-dependence of the statistical properties in the systems at moderate solid\/gas density ratio is observed. Obvious cluster formation and its effect on drag coefficient are shown in a system at high solid\/gas density ratio. On the computational side, about 19 folds speed-up is obtained on one GT200 GPU, as compared to a mainstream CPU core. The necessity for investigating even larger systems is prospected.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Due to significant multi-scale heterogeneity, understanding sub-grid structures is critical to effective continuum-based description of gas-solid flow. However, it is challenging for both physical measurements and numerical simulations. In this article, with the macro-scale pseudo-particle method (MaPPM) implemented on a GPU-based HPC system, up to 30,000 fluidized solids are simulated using the N-S equation directly. [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[66,104,3],"tags":[1790,1795,122,120,20],"class_list":["post-1482","post","type-post","status-publish","format-standard","hentry","category-chemistry","category-fluid-dynamics","category-paper","tag-chemistry","tag-fluid-dynamics","tag-navier-stokes-equations","tag-nses","tag-nvidia"],"views":2261,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1482","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=1482"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1482\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1482"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1482"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1482"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}