{"id":11934,"date":"2014-04-24T10:31:39","date_gmt":"2014-04-24T07:31:39","guid":{"rendered":"http:\/\/hgpu.org\/?p=11934"},"modified":"2014-04-24T10:31:39","modified_gmt":"2014-04-24T07:31:39","slug":"galerkin-based-multi-scale-time-integration-for-nonlinear-structural-dynamics","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11934","title":{"rendered":"Galerkin-based multi-scale time integration for nonlinear structural dynamics"},"content":{"rendered":"<p>This paper deals with a Galerkin-based multi-scale time integration of a viscoelastic rope model. Using Hamilton&#8217;s dynamical formulation, Newton&#8217;s equation of motion as a second-order partial differential equation is transformed into two coupled first order partial differential equations in time. The considered finite viscoelastic deformations are described by means of a deformation-like internal variable determined by a first order ordinary differential equation in time. The corresponding multi-scale time-integration is based on a Petrov-Galerkin approximation of all time evolution equations, leading to a new family of time stepping schemes with different accuracy orders in the state variables. The resulting nonlinear algebraic time evolution equations are solved by a multi-level Newton-Raphson method. Realizing this transient numerical simulation, we also demonstrates a parallelized solution of the viscous evolution equation in CUDA.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper deals with a Galerkin-based multi-scale time integration of a viscoelastic rope model. Using Hamilton&#8217;s dynamical formulation, Newton&#8217;s equation of motion as a second-order partial differential equation is transformed into two coupled first order partial differential equations in time. The considered finite viscoelastic deformations are described by means of a deformation-like internal variable determined [&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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,157,3],"tags":[14,810,37,1796,285,20,923,922,550,551,1226],"class_list":["post-11934","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-mathematics","category-paper","tag-cuda","tag-differential-equations","tag-linear-algebra","tag-mathematics","tag-numerical-simulation","tag-nvidia","tag-odes","tag-ordinary-differential-equations","tag-partial-differential-equations","tag-pdes","tag-tesla-c2075"],"views":2474,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11934","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=11934"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11934\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11934"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11934"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11934"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}