{"id":222,"date":"2018-12-18T00:56:19","date_gmt":"2018-12-18T04:56:19","guid":{"rendered":"http:\/\/sites.nd.edu\/jianxun-wang\/?page_id=222"},"modified":"2019-11-04T16:51:13","modified_gmt":"2019-11-04T20:51:13","slug":"bayesian-uncertainty-quantification-and-reduction-in-turbulence-model","status":"publish","type":"page","link":"https:\/\/sites.nd.edu\/jianxun-wang\/research\/bayesian-uncertainty-quantification-and-reduction-in-turbulence-model\/","title":{"rendered":"Bayesian uncertainty quantification and reduction in turbulence model"},"content":{"rendered":"<ul>\n<li>\n<h2>RANS model-form uncertainty estimation<\/h2>\n<h4>Random matrix, Max-entropy theory; Physics-based perturbation; Multi-model uncertainty propagation<\/h4>\n<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-209\" src=\"http:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.14.51-300x117.png\" alt=\"\" width=\"375\" height=\"146\" srcset=\"https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.14.51-300x117.png 300w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.14.51-768x299.png 768w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.14.51.png 917w\" sizes=\"auto, (max-width: 375px) 100vw, 375px\" \/><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-239\" src=\"http:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.06.12-300x138.png\" alt=\"\" width=\"317\" height=\"146\" srcset=\"https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.06.12-300x138.png 300w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.06.12-768x354.png 768w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.06.12-1024x472.png 1024w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.06.12.png 1054w\" sizes=\"auto, (max-width: 317px) 100vw, 317px\" \/><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-238\" src=\"http:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.06.25-300x214.png\" alt=\"\" width=\"203\" height=\"145\" srcset=\"https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.06.25-300x214.png 300w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.06.25-768x547.png 768w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.06.25-1024x730.png 1024w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.06.25.png 1034w\" sizes=\"auto, (max-width: 203px) 100vw, 203px\" \/><\/p>\n<ol>\n<li>H. Xiao,\u00a0J.-X. Wang\u00a0and Roger G. Ghanem. A random matrix approach for quantifying model-form uncertainties in turbulence modeling.\u00a0<em>Computer Methods in Applied Mechanics and Engineering<\/em>, 313, 941-965, 2017. [<a href=\"https:\/\/arxiv.org\/pdf\/1603.09656.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Arxiv<\/a>, <a href=\"http:\/\/dx.doi.org\/10.1016\/j.cma.2016.10.025\" target=\"_blank\" rel=\"noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<li>J.-X. Wang, C. J. Roy and H. Xiao.\u00a0Propagation of Input Uncertainty in Presence of Model-Form Uncertainty: A Multi-fidelity Approach for CFD Applications.\u00a0<em>ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering<\/em>,\u00a04 (1), 01100, 2017. \u00a0[<a href=\"http:\/\/arxiv.org\/abs\/1501.03189\" target=\"_blank\" rel=\"noopener noreferrer\">Arxiv<\/a>,\u00a0<a href=\"http:\/\/risk.asmedigitalcollection.asme.org\/article.aspx?articleid=2647606\" target=\"_blank\" rel=\"noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<li>J.-X. Wang, R. Sun, H. Xiao. Quantification of uncertainty in RANS models: a comparison of physics-based and random matrix theoretic approaches.\u00a0\u00a0<em>International Journal of Heat and Fluid Flow<\/em>, 62 (B): 577-592, 2016. [<a href=\"https:\/\/arxiv.org\/pdf\/1603.05549.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Arxiv<\/a>,\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.ijheatfluidflow.2016.07.005\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<\/ol>\n<ul>\n<li>\n<h2>Bayesian RANS model-form uncertainty reduction<\/h2>\n<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-250\" src=\"http:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.23.33-300x127.png\" alt=\"\" width=\"387\" height=\"164\" srcset=\"https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.23.33-300x127.png 300w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.23.33-768x326.png 768w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.23.33-1024x434.png 1024w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.23.33.png 1104w\" sizes=\"auto, (max-width: 387px) 100vw, 387px\" \/> <img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-251\" src=\"http:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.24.35-300x217.png\" alt=\"\" width=\"232\" height=\"168\" srcset=\"https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.24.35-300x217.png 300w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.24.35-768x554.png 768w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-18-at-00.24.35.png 906w\" sizes=\"auto, (max-width: 232px) 100vw, 232px\" \/><\/p>\n<ol>\n<li>H. Xiao,\u00a0J.-L. Wu,\u00a0J.-X. Wang,\u00a0R. Sun, and\u00a0C. J. Roy.\u00a0Quantifying and reducing model-form uncertainties in Reynolds averaged Navier\u2013Stokes equations: a data-driven, physics-informed, Bayesian approach. <em>Journal of Computational Physics<\/em>,\u00a0324, 115-136, 2016. [<a href=\"https:\/\/arxiv.org\/pdf\/1508.06315.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Arxiv<\/a>,\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.jcp.2016.07.038\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<li>J.-X. Wang, J.-L. Wu, and H. Xiao. Incorporating prior knowledge for quantifying and reducing model-form uncertainty in RANS simulations.\u00a0<em>International Journal of Uncertainty Quantification,<\/em> 6 (2): 109-126, 2016. [<a href=\"http:\/\/arxiv.org\/abs\/1512.01750\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Arxiv<\/a>,\u00a0<a href=\"http:\/\/www.dl.begellhouse.com\/journals\/52034eb04b657aea,3cc9ec274644f0dc,36d54fb408753c29.html\" target=\"_blank\" rel=\"noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<li>J.-L. Wu,\u00a0J.-X. Wang, and H. Xiao. A Bayesian calibration-prediction method for reducing model-form uncertainties with application in RANS simulations.\u00a0Flow, Turbulence and Combustion, 97, 761-786, 2016. [<a href=\"http:\/\/arxiv.org\/abs\/1510.06040\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Arxiv<\/a>,\u00a0<a href=\"https:\/\/doi.org\/10.1007\/s10494-016-9725-6\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>RANS model-form uncertainty estimation Random matrix, Max-entropy theory; Physics-based perturbation; Multi-model uncertainty propagation H. Xiao,\u00a0J.-X. Wang\u00a0and Roger G. Ghanem. A random matrix approach for quantifying model-form uncertainties in turbulence modeling.\u00a0Computer Methods in Applied Mechanics and Engineering, 313, 941-965, 2017. [Arxiv, DOI, bib] J.-X. Wang, C. J. Roy and H. Xiao.\u00a0Propagation of Input Uncertainty in Presence [&hellip;]<\/p>\n","protected":false},"author":3220,"featured_media":0,"parent":2,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-222","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/222","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/users\/3220"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/comments?post=222"}],"version-history":[{"count":22,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/222\/revisions"}],"predecessor-version":[{"id":390,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/222\/revisions\/390"}],"up":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/2"}],"wp:attachment":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/media?parent=222"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}