{"id":9538,"date":"2013-06-06T23:05:25","date_gmt":"2013-06-06T20:05:25","guid":{"rendered":"http:\/\/hgpu.org\/?p=9538"},"modified":"2013-06-06T23:10:26","modified_gmt":"2013-06-06T20:10:26","slug":"development-of-an-explicit-pressure-based-unstructured-solver-for-three-dimensional-incompressible-flows-with-graphics-hardware-acceleration","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9538","title":{"rendered":"Development of an explicit pressure-based unstructured solver for three-dimensional incompressible flows with graphics hardware acceleration"},"content":{"rendered":"<p>In this research, a numerical algorithm was developed to solve the incompressible Navier-Stokes equations using explicit time stepping. The goal of this research was to develop an unsteady SIMPLER based algorithm with lower computational overhead. The new explicit algorithm uses a four stage Runge-Kutta scheme to update the velocities and eliminates the need for the pressure correction equation and sub-iterations. This algorithm was derived for use on unstructured tetrahedral grids and was validated with the lid-driven cavity and unsteady rotor flows. This algorithm proved to be easily parallelized and was implemented in CUDA. As a result, accelerations of over 80x are observed compared to a CPU based implicit SIMPLER algorithm using a standard workstation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this research, a numerical algorithm was developed to solve the incompressible Navier-Stokes equations using explicit time stepping. The goal of this research was to develop an unsteady SIMPLER based algorithm with lower computational overhead. The new explicit algorithm uses a four stage Runge-Kutta scheme to update the velocities and eliminates the need for the [&hellip;]<\/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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,89,104,3],"tags":[1787,14,1795,122,120,20,1091,390],"class_list":["post-9538","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-algorithms","tag-cuda","tag-fluid-dynamics","tag-navier-stokes-equations","tag-nses","tag-nvidia","tag-nvidia-geforce-gtx-570","tag-thesis"],"views":2561,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9538","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=9538"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9538\/revisions"}],"predecessor-version":[{"id":9540,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9538\/revisions\/9540"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9538"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9538"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}