The vortex core shed from rotorcraft blades maintains coherency-and thus dynamic relevance-many blade turns after its creation. This presents a challenge to traditional Eulerian computational methods, as fine grids are required to suppress numerical diffusion which would weaken the vortex cores after a small number of revolutions. Vortex methods have been used in the past to overcome these problems, as they require computational elements only in vorticity-containing regions, but suffer from greater computational cost per element. In the present work, we will solve these problems with a hybrid EulerianLagrangian method for modeling rotor wakes. An Eulerian OVERFLOW overset grid method computes the near-body flow, while a Lagrangian particle vortex method tracks the wake. The vortex method uses an anisotropic LES model to handle subgrid-scale dissipation explicitly. The computational cost of vortex methods is alleviated by using a parallel adaptive treecode on a cluster of machines each with multi-core CPUs and multiple costeffcient graphics processing units (GPUs). Simulations of a low-Re sphere, finite wing, and 4-bladed rotor model are presented and are validated by comparisons with computational and experimental data.<\/p>\n","protected":false},"excerpt":{"rendered":"
The vortex core shed from rotorcraft blades maintains coherency-and thus dynamic relevance-many blade turns after its creation. This presents a challenge to traditional Eulerian computational methods, as fine grids are required to suppress numerical diffusion which would weaken the vortex cores after a small number of revolutions. Vortex methods have been used in the past […]<\/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":[104,3],"tags":[1795,121,106,20,373,244],"class_list":["post-4237","post","type-post","status-publish","format-standard","hentry","category-fluid-dynamics","category-paper","tag-fluid-dynamics","tag-fluid-simulation","tag-gpu-cluster","tag-nvidia","tag-nvidia-geforce-gtx-275","tag-tesla-s1070"],"views":1936,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4237","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=4237"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4237\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4237"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4237"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4237"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}