{"id":9417,"date":"2013-05-19T22:12:25","date_gmt":"2013-05-19T19:12:25","guid":{"rendered":"http:\/\/hgpu.org\/?p=9417"},"modified":"2013-05-19T22:12:25","modified_gmt":"2013-05-19T19:12:25","slug":"generating-3d-topologies-with-multiple-constraints-on-the-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9417","title":{"rendered":"Generating 3D Topologies with Multiple Constraints on the GPU"},"content":{"rendered":"<p>The objective of this paper is to demonstrate a topology optimization method that can handle multiple constraints. The method relies on the concept of topological sensitivity that captures the first order change in any quantity of interest to a topological change. Specifically, in this paper, the topological sensitivity field for each of constraints is first computed. These fields are then dynamically combined to result in a single topological level-set. Finally, by relying on a fixed-point iteration, the topological level-set leads to optimal topologies (with decreasing volume fractions) that satisfy the constraints. Since the method relies on an assembly-free finite-element analysis, it is parallelization-friendly, and can be easily ported to the GPU, as demonstrated through examples in 3D.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The objective of this paper is to demonstrate a topology optimization method that can handle multiple constraints. The method relies on the concept of topological sensitivity that captures the first order change in any quantity of interest to a topological change. Specifically, in this paper, the topological sensitivity field for each of constraints is first [&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":[11,89,3],"tags":[1782,14,20,379,808],"class_list":["post-9417","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-topology-optimization-problem"],"views":2355,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9417","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=9417"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9417\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9417"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9417"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9417"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}