[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125770-en":3,"doc-seo-125770-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125770,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A HYBRID COMPUTER VISION AND MACHINE LEARNING APPROACH FOR ROBUST VORTEX CORE DETECTION IN FLUID MECHANICS APPLICATIONS","Vortex core detection remains an unsolved problem in experimental and computational fluid dynamics due to existing Q, delta, and swirling-strength criteria producing unreliable results with false positives and false negatives. A hybrid machine learning framework is proposed using a convolutional neural network for vortex region detection from surface streamline plots, and a deep neural network for core identification. Automatic K-means labeling pre-processes training images. Evaluations on Taylor-Green vortex and rotating blades show up to 2.6× speedup versus a pure deep model and labeling error within 0.45% MSE.","Journal of Computing and Information Science in Engineering, Available online 12 January 2024  \nDOI:10.1115/1.4064478  \nA HYBRID COMPUTER VISION AND MACHINE LEARNING APPROACH FOR ROBUST VORTEX CORE DETECTION  \nIN FLUID MECHANICS APPLICATIONS  \nHazem Ashor Amran Abolholl1 , Tom-Robin Teschner1, 􀀃 , Irene Moulitsas1  \n1 Cran􀀜eld University, College Rd, Cran􀀜eld, Wharley End, Bedford MK43 0AL  \nABSTRACT  \nVortex core detection remains an unsolved problem in the 􀀜eld of experimental and computational 􀀝uid dynamics. Available methods such as the Q, delta, and swirling-strength criterion are based on a decomposed velocity gradient tensor but detect spurious vortices (false positives and false negatives), making these methods less robust. To overcome this, wepropose anew hybrid machine learning approach in which we use a convolutional neural network to detect vortex regions within surface streamline plots and an additional deep neural network to detect vortex cores within identi􀀜ed vortex regions. Furthermore, we propose an automatic labelling approach based on K-means clustering topre-process our input images. We show results for two classical test cases in 􀀝uid mechanics; the Taylor-Green vortex problem and two rotating blades. We show that our hybrid approach is up to 2. 6 times faster than a pure deep neural network-based approach and furthermore show that our automatic K-means clustering labelling approach achieves within 0.45% mean square error of the more labour-intensive, manual labelling approach. At the same time, using a su􀀞cient number of samples, we show that we are able to reduce false positives and negatives entirely and thus show that our hybrid machine learning approach is a viable alternative to currently used vortex detection tools in 􀀝uid mechanics applications.  \nKeywords: machine learning; computer vision; vortex core detection; 􀀝uid mechanics  \n2010 􀀢 􀀨􀀘 : 68T45, 76D17, 76F65, 76M27  \n1. INTRODUCTION  \nIt is widely considered that vortex dynamics play an essential role in determining the behaviour of ŕuid ŕows in practical applications. For instance, identifying the locations of vortices on the aircraft objects consigning to reduced drag and lift. It is also beneőcial to know the exact location of vortex cores when automated mesh reőnement in computational ŕuid dynamics simulations (CFD) . Therefore, during the last decades, researchers have intensively studied the identiőcation of vortices in ŕow őelds.  \n∗ Corresponding author: tom.teschner@cranő[eld.ac.uk](eld.ac.uk)  \nDocumentation for asmeconf .cls: Version 1.34, February 16, 2024 .  \nTwo types of algorithms are commonly used to detect vortices, local and global. The local method is based on the decomposition of the local velocity gradient tensor such as Q criterion [1], delta criterion [2], Lambda 2 criterion [3], and swirlingstrength criterion [4] . These techniques could acquire reasonable estimates, yet, at the same time, these techniques generatea substantial number of false positives and negatives. To control the number of these false positives and negatives, the user should set appropriate parameters which lead to poor robustness. The Global vortex detection methods are typically accomplished through streamlines to detect vortex regions. Generally, global methods are more robust than local methods, but they use neighbouring cells to identify vortical ŕows, which adds to their computational time. On the other hand, local methods require users’input who have speciőc knowledge about the őeld, making them inappropriate to detect vortex cores automatically. Due to this, both local and global detection methods have weaknesses and cannot provide fully robust and reliable results.  \nTo address this problem, we present a hybrid computer vision and artiőcial neural network (ANN) machine learning approach to detect vortex cores in this study. Initially, we employed computer vision to identify the areas of vortical structures [5]; then, we applied ANN to identi","cbCaimEgMmulYjgU","https://ap.wps.com/l/cbCaimEgMmulYjgU","pdf",3366353,1,19,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"Why are conventional vortex core detection criteria not robust?\",\"answer\":\"They rely on decomposed local velocity gradient tensors and can generate substantial false positives and false negatives, making results sensitive to parameter choices.\"},{\"question\":\"What is the hybrid approach proposed for vortex detection?\",\"answer\":\"It combines computer vision and machine learning: a convolutional neural network detects vortex regions from streamline plots, then a deep neural network identifies vortex cores within those regions.\"},{\"question\":\"How are training labels generated automatically in the proposed method?\",\"answer\":\"An automatic labeling approach based on K-means clustering pre-processes input images, reducing the need for labor-intensive manual labeling.\"}]","A HYBRID COMPUTER VISION AND MACHINE LEARNING APPROACH FOR ROBUST VORTEX CORE DETECTION IN FLUID MECHANICS APPLICATIONS | 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are conventional vortex core detection criteria not robust?","Question",{"text":75,"@type":76},"They rely on decomposed local velocity gradient tensors and can generate substantial false positives and false negatives, making results sensitive to parameter choices.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the hybrid approach proposed for vortex detection?",{"text":80,"@type":76},"It combines computer vision and machine learning: a convolutional neural network detects vortex regions from streamline plots, then a deep neural network identifies vortex cores within those regions.",{"name":82,"@type":73,"acceptedAnswer":83},"How are training labels generated automatically in the proposed method?",{"text":84,"@type":76},"An automatic labeling approach based on K-means clustering pre-processes input images, reducing the need for labor-intensive manual 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