{"id":1536,"date":"2022-08-18T02:00:44","date_gmt":"2022-08-18T09:00:44","guid":{"rendered":"https:\/\/gantovnik.com\/bio-tips\/?p=1536"},"modified":"2022-08-18T02:00:44","modified_gmt":"2022-08-18T09:00:44","slug":"210-parametric-curve-in-3d-2-2-2-2-2-2-2-2-2-2-2-2-2-3-2-2-2-2-2-2-2-2-2-3-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2","status":"publish","type":"post","link":"https:\/\/gantovnik.com\/bio-tips\/2022\/08\/210-parametric-curve-in-3d-2-2-2-2-2-2-2-2-2-2-2-2-2-3-2-2-2-2-2-2-2-2-2-3-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2\/","title":{"rendered":"#294 DOPTPRM, OBJTOL"},"content":{"rendered":"<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\nDOPTPRM, OBJTOL\r\nRelative convergence criterion.\r\nIf relative change in the objective function between two design iterations is less than OBJTOL, then optimization stops. \r\nA relative change in the objective function of 0.005 is the same as a 0.5% change in the objective function.\r\nDefault = 0.005\r\n<\/pre>\n","protected":false},"excerpt":{"rendered":"<p>DOPTPRM, OBJTOL Relative convergence criterion. If relative change in the objective function between two design iterations is less than OBJTOL, then optimization stops. A relative change in the objective function of 0.005 is the same as a 0.5% change in the objective function. Default = 0.005<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_lmt_disableupdate":"yes","_lmt_disable":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[28],"tags":[25,44],"class_list":["post-1536","post","type-post","status-publish","format-standard","hentry","category-optistruct","tag-optimization","tag-optistruct"],"modified_by":"gantovnik","jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p8bH0k-oM","jetpack_likes_enabled":true,"jetpack-related-posts":[{"id":541,"url":"https:\/\/gantovnik.com\/bio-tips\/2020\/05\/doptprm-discrete\/","url_meta":{"origin":1536,"position":0},"title":"DOPTPRM, DISCRETE","author":"gantovnik","date":"2020-05-11","format":false,"excerpt":"DOPTPRM, DISCRETE Bulk Data Entry Discreteness parameter. Influences the tendency for elements in a topology optimization to converge to a material density of 0 or 1. Default DISCRETE=1. Recommended bounds are 0.0 and 2.0 for shells, or 3.0 for solids. Improving Discreteness. There are numerous ways to improve the discreteness\u2026","rel":"","context":"In &quot;HyperMesh&quot;","block_context":{"text":"HyperMesh","link":"https:\/\/gantovnik.com\/bio-tips\/category\/hypermesh\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":1534,"url":"https:\/\/gantovnik.com\/bio-tips\/2022\/08\/210-parametric-curve-in-3d-2-2-2-2-2-2-2-2-2-2-2-2-2-3-2-2-2-2-2-2-2-2-2-3-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2\/","url_meta":{"origin":1536,"position":1},"title":"#293 DOPTPRM, DESMAX","author":"gantovnik","date":"2022-08-18","format":false,"excerpt":"[code language=\"python\"] DOPTPRM, DESMAX Maximum number of design iterations. Default = 30 [\/code]","rel":"","context":"In &quot;OptiStruct&quot;","block_context":{"text":"OptiStruct","link":"https:\/\/gantovnik.com\/bio-tips\/category\/optistruct\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":1538,"url":"https:\/\/gantovnik.com\/bio-tips\/2022\/08\/210-parametric-curve-in-3d-2-2-2-2-2-2-2-2-2-2-2-2-2-3-2-2-2-2-2-2-2-2-2-3-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2\/","url_meta":{"origin":1536,"position":2},"title":"#295 DOPTPRM, OPTMETH","author":"gantovnik","date":"2022-08-18","format":false,"excerpt":"[code language=\"python\"] DOPTPRM, OPTMETH Options to choose the optimization algorithm. MFD = Method of feasible directions. MFD is the default optimizer for problems with a large number of constraints (but smaller number of design variables) such as size\/gauge and shape. SQP = Sequential Quadratic Programming. DUAL = Enhanced Dual Optimizer\u2026","rel":"","context":"In &quot;OptiStruct&quot;","block_context":{"text":"OptiStruct","link":"https:\/\/gantovnik.com\/bio-tips\/category\/optistruct\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":536,"url":"https:\/\/gantovnik.com\/bio-tips\/2020\/05\/optistruct-during-topology-optimization-can-i-use-stress-constraints-through-dresp1-in-the-design-space\/","url_meta":{"origin":1536,"position":3},"title":"OptiStruct: During topology optimization, can I use stress constraints through DRESP1 in the design space?","author":"gantovnik","date":"2020-05-09","format":false,"excerpt":"OptiStruct: During topology optimization, can I use stress constraints through DRESP1 in the design space? Defining local stress constraints through DRESP1 in the design region for Topology Optimization is allowed. The Stress-Norm method is used to aggregate stress responses.","rel":"","context":"In &quot;HyperMesh&quot;","block_context":{"text":"HyperMesh","link":"https:\/\/gantovnik.com\/bio-tips\/category\/hypermesh\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":642,"url":"https:\/\/gantovnik.com\/bio-tips\/2020\/09\/102-what-is-maximum-value-of-constraints-column-in-iteration-history-for-sol200\/","url_meta":{"origin":1536,"position":4},"title":"#102: What is &#8220;Maximum Value of Constraints&#8221; column in iteration history for SOL200?","author":"gantovnik","date":"2020-09-25","format":false,"excerpt":"#102: What is \"Maximum Value of Constraints\" column in iteration history for SOL200? Answer: This refers to the maximum normalized constraint over all constraints in the design model. A positive value indicates constraint violation, a negative value constraint satisfaction, and a near zero value a critical constraint. Normalized constraints are\u2026","rel":"","context":"In &quot;nastran&quot;","block_context":{"text":"nastran","link":"https:\/\/gantovnik.com\/bio-tips\/category\/nastran\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":1095,"url":"https:\/\/gantovnik.com\/bio-tips\/2021\/11\/190-run-options-for-optistruct\/","url_meta":{"origin":1536,"position":5},"title":"#190 Run options for OptiStruct","author":"gantovnik","date":"2021-11-13","format":false,"excerpt":"List of possible run options for OptiStrut -analysis | submit an analysis run. Check the optimization data. -optskip | submit an analysis run without optimization. -check | submit a check job through the command line. -nt X | number of threads\/cores (X) to be used for multiprocessor (SMP) run. -np\u2026","rel":"","context":"In &quot;OptiStruct&quot;","block_context":{"text":"OptiStruct","link":"https:\/\/gantovnik.com\/bio-tips\/category\/optistruct\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/gantovnik.com\/bio-tips\/wp-json\/wp\/v2\/posts\/1536","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gantovnik.com\/bio-tips\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gantovnik.com\/bio-tips\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gantovnik.com\/bio-tips\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/gantovnik.com\/bio-tips\/wp-json\/wp\/v2\/comments?post=1536"}],"version-history":[{"count":0,"href":"https:\/\/gantovnik.com\/bio-tips\/wp-json\/wp\/v2\/posts\/1536\/revisions"}],"wp:attachment":[{"href":"https:\/\/gantovnik.com\/bio-tips\/wp-json\/wp\/v2\/media?parent=1536"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gantovnik.com\/bio-tips\/wp-json\/wp\/v2\/categories?post=1536"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gantovnik.com\/bio-tips\/wp-json\/wp\/v2\/tags?post=1536"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}