{"id":67,"date":"2018-12-16T01:59:23","date_gmt":"2018-12-16T05:59:23","guid":{"rendered":"http:\/\/sites.nd.edu\/jianxun-wang\/?page_id=67"},"modified":"2019-11-04T17:08:32","modified_gmt":"2019-11-04T21:08:32","slug":"projects","status":"publish","type":"page","link":"https:\/\/sites.nd.edu\/jianxun-wang\/research\/projects\/","title":{"rendered":"Data-Driven Turbulence Modeling"},"content":{"rendered":"<ul>\n<li>\n<h2>Physics-informed machine learning (PIML) approach for predictive RANS modeling<\/h2>\n<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-224\" src=\"http:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.48.19-300x165.png\" alt=\"\" width=\"354\" height=\"194\" srcset=\"https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.48.19-300x165.png 300w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.48.19-768x422.png 768w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.48.19-1024x562.png 1024w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.48.19.png 1380w\" sizes=\"auto, (max-width: 354px) 100vw, 354px\" \/><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-226\" src=\"http:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.49.31-300x114.png\" alt=\"\" width=\"511\" height=\"194\" srcset=\"https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.49.31-300x114.png 300w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.49.31-768x292.png 768w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.49.31-1024x390.png 1024w, https:\/\/sites.nd.edu\/jianxun-wang\/files\/2018\/12\/Screen-Shot-2018-12-17-at-23.49.31.png 1518w\" sizes=\"auto, (max-width: 511px) 100vw, 511px\" \/><\/p>\n<ol>\n<li>J.-X. Wang, J. Huang, L. Duan,\u00a0H. Xiao. Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers with physics-informed machine learning. <em>Theoretical and Computational Fluid Dynamics<\/em>, 33 (1), 1-19, 2019 [<a href=\"https:\/\/arxiv.org\/pdf\/1808.07752.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Arxiv<\/a>, <a href=\"https:\/\/link.springer.com\/article\/10.1007%2Fs00162-018-0480-2\" target=\"_blank\" rel=\"noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<li>X. Yang, S. Zafar, J.-X. Wang, X. Heng. Predictive large-eddy-simulation wall modeling via physics-informed neural network.<em>Physical Review Fluids<\/em>, 4 (3), 034602, 2019. [Arxiv, <a href=\"https:\/\/doi.org\/10.1103\/PhysRevFluids.4.034602\">DOI<\/a>, bib]<\/li>\n<li>J.-X. Wang, J.-L. Wu, and H. Xiao. A physics informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data.\u00a0<em>Physical Review Fluids<\/em>. 2 (3), 034603, 1-22, 2017. [<a href=\"https:\/\/arxiv.org\/abs\/1606.07987\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Arxiv<\/a>,\u00a0<a href=\"https:\/\/journals.aps.org\/prfluids\/abstract\/10.1103\/PhysRevFluids.2.034603\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<li>J.-L. Wu, J.-X. Wang, H. Xiao, J. Ling.\u00a0A Priori\u00a0assessment of prediction confidence for data-driven turbulence modeling.\u00a0<em>Flow, Turbulence and Combustion<\/em>. 99(1), 25-46, 2017. [<a href=\"https:\/\/arxiv.org\/abs\/1607.04563\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Arxiv<\/a>,\u00a0<a href=\"https:\/\/doi.org\/10.1007\/s10494-017-9807-0\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<li>J.-X. Wang, J.-L. Wu, J. Ling, G. Iaccarino and H. Xiao. Towards a complete framework of physics-informed machine learning for predictive turbulence modeling. <em>In Proceedings of the Center for Turbulence Research (CTR) Summer Program (Stanford University)<\/em>, 2016. [Arxiv, <a href=\"https:\/\/stanford.app.box.com\/s\/62mlizpx8s67mlhh8ls9u3i3rtgto6fn\" target=\"_blank\" rel=\"noopener noreferrer\">Link<\/a>]<\/li>\n<li>J. Huang, L. Duan,\u00a0J.-X. Wang, R. Sun and H. Xiao. High-Mach-number turbulence modeling using machine learning and direct numerical simulation database. In\u00a0AIAA SciTech, 2017. [Arxiv, <a href=\"https:\/\/arc.aiaa.org\/doi\/10.2514\/6.2017-0315\" target=\"_blank\" rel=\"noopener noreferrer\">Link<\/a>]<\/li>\n<li>H. Xiao, J.-L. Wu,\u00a0J.-X. Wang, and E.G. Paterson. Physics-informed machine learning for predictive turbulence modeling: progress and perspectives. In\u00a0AIAA SciTech, 2017. [Arxiv, <a href=\"https:\/\/arc.aiaa.org\/doi\/abs\/10.2514\/6.2017-1712\" target=\"_blank\" rel=\"noopener noreferrer\">Link<\/a>]<\/li>\n<li>J.-L. Wu,\u00a0J.-X. Wang, H. Xiao and E.G. Paterson, Visualization of high dimensional turbulence simulation data using t-SNE, In\u00a0AIAA SciTech, 2017.\u00a0[Arxiv, <a href=\"https:\/\/arc.aiaa.org\/doi\/10.2514\/6.2017-1770\" target=\"_blank\" rel=\"noopener noreferrer\">Link<\/a>]<\/li>\n<li>J.-X. Wang, J.-L. Wu, J. Ling, G. Iaccarino, H. Xiao. A comprehensive physics-informed machine learning framework for predictive turbulence modeling, 2017. (Unpublished) [<a href=\"https:\/\/arxiv.org\/abs\/1701.07102\">Arxiv<\/a>]<\/li>\n<\/ol>\n<ul>\n<li>\n<h2>Hybrid LES\/RANS Simulations<\/h2>\n<\/li>\n<\/ul>\n<ol>\n<li>H. Xiao, J.-X. Wang and P. Jenny. Dynamic evaluation of mesh resolution and its application in hybrid LES\/RANS methods.\u00a0Flow, Turbulence and Combustion,\u00a093(1), 141-170, 2014. [Arxiv, <a href=\"http:\/\/dx.doi.org\/10.1007\/s10494-014-9541-9\" target=\"_blank\" rel=\"noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<li>H. Xiao, J.-X. Wang and P. Jenny. An Implicitly Consistent Formulation of a Dual-Mesh Hybrid LES\/RANS Method. <em>Communications in Computational Physics<\/em>, 21(2) 2017. [Arxiv, <a href=\"https:\/\/www.cambridge.org\/core\/journals\/communications-in-computational-physics\/article\/div-classtitlean-implicitly-consistent-formulation-of-a-dual-mesh-hybrid-lesrans-methoddiv\/7636A61E17F88B5552DD33BFD894EE6A\" target=\"_blank\" rel=\"noopener noreferrer\">DOI<\/a>, bib]<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Physics-informed machine learning (PIML) approach for predictive RANS modeling J.-X. Wang, J. Huang, L. Duan,\u00a0H. Xiao. Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers with physics-informed machine learning. Theoretical and Computational Fluid Dynamics, 33 (1), 1-19, 2019 [Arxiv, DOI, bib] X. Yang, S. Zafar, J.-X. Wang, X. Heng. Predictive large-eddy-simulation wall modeling via physics-informed [&hellip;]<\/p>\n","protected":false},"author":3220,"featured_media":0,"parent":2,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-67","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/67","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=67"}],"version-history":[{"count":16,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/67\/revisions"}],"predecessor-version":[{"id":422,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/pages\/67\/revisions\/422"}],"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=67"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}