{"id":1410,"date":"2010-11-11T13:42:05","date_gmt":"2010-11-11T13:42:05","guid":{"rendered":"http:\/\/hgpu.org\/?p=1410"},"modified":"2010-11-11T13:42:05","modified_gmt":"2010-11-11T13:42:05","slug":"cuda-simulations-of-active-dumbbell-suspensions","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1410","title":{"rendered":"CUDA simulations of active dumbbell suspensions"},"content":{"rendered":"<p>We describe and analyze CUDA simulations of hydrodynamic interactions in active dumbbell suspensions. GPU-based parallel computing enables us not only to study the time-resolved collective dynamics of up to a several hundred active dumbbell swimmers but also to test the accuracy of effective time-averaged models. Our numerical results suggest that the stroke-averaged model yields a relatively accurate description down to distances of only a few times the dumbbell&#8217;s length. This is remarkable in view of the fact that the stroke-averaged model is based on a far-field expansion. Thus, our analysis confirms that stroke-averaged far-field equations of motion may provide a useful starting point for the derivation of hydrodynamic field equations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We describe and analyze CUDA simulations of hydrodynamic interactions in active dumbbell suspensions. GPU-based parallel computing enables us not only to study the time-resolved collective dynamics of up to a several hundred active dumbbell swimmers but also to test the accuracy of effective time-averaged models. Our numerical results suggest that the stroke-averaged model yields a [&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":[89,3,12],"tags":[196,14,20,436,1783,358,199],"class_list":["post-1410","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-condensed-matter","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-295","tag-physics","tag-soft-condensed-matter","tag-tesla-c1060"],"views":2231,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1410","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=1410"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1410\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1410"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1410"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1410"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}