[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120308-en":3,"doc-seo-120308-105":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},120308,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Vortex core detection in turbulent simulations based on Machine Learning approaches - PhD Thesis","Vortex core identification in fluid mechanics is a difficult problem that local criteria such as Q, delta, or swirling-strength often address with limited robustness. These methods can generate false positives and negatives and require user tuning to keep errors acceptable, while also assuming prior knowledge of vortices. A hybrid computer vision and machine learning framework is proposed: computer vision locates vortex regions and a convolutional neural network, trained on LIC streamline plots, then detects the core location. K-means labeling and multiple feature sets reduce training time and improve accuracy.","Cranfield University  \nHazem Ashor Amran Abolholl  \nVortex core detection in turbulent  \nsimulations based on Machine Learning  \napproaches  \nSchool of Aerospace Transport and Manufacturing  \nCentre for Computational Engineering Sciences  \nPhD Thesis  \nAcademic Year: 2022 – 2023  \nSupervisor: Dr Tom-Robin Teschner Associate Supervisor: Dr Irene Moulitsas  \nApril, 2023  \nCranfield University  \nSchool of Aerospace  \nTransport and Manufacturing  \nCentre for Computational Engineering Sciences  \nPhD Thesis  \nAcademic Year: 2022 – 2023  \nHazem Ashor Amran Abolholl  \nVortex core detection in turbulent  \nsimulations based on Machine Learning  \napproaches  \nSupervisor: Dr Tom-Robin Teschner  \nAssociate Supervisor: Dr Irene Moulitsas  \nApril, 2023  \nThis thesis is submitted in partial fulfilment of the requirements for the degree of  \nDoctor in Philosophy in Aerospace  \n©Cranfield Univeristy, 2023 . All rights reserved. No part of this publication maybe reproduced without the written permission of the copyright holder  \nAbstract  \nThe identification of vortex cores in fluid mechanics is a challenging task that requires sophisticated techniques. Commonly employed local detection methods, such as the Q, delta, or swirling-strength criterion, rely on the local velocity gradient tensor to locate the vortex cores. Despite their reasonable accuracy, these methods tend to produce false positives and negatives, necessitating user-defined tuning parameters to maintain an acceptable error level. Moreover, this method presupposes prior knowledge of the vortices, limiting its robustness and self-contained nature. To overcome this shortcoming, a hybrid computer vision and machine learning approach is proposed to enhance the detection features of vortex cores and reduce false positives and negatives. Initially, computer vision was employed to identify the areas of vortex structures, followed by machine learning to identify the vortex core within the vortex region. A convolutional neural network (CNN) was trained to analyse streamline plots based on line integral convolution (LIC) for the computer vision process. Through the use of computer vision, false positives and negativesin flow-specific problems are reduced without the need for calibrating user-defined parameters. Furthermore, the trained CNN was successfully applied to three testcases, indicating its universal applicability. As such, computer vision offers a reliable convolutional neural network approach to detect vortex areas, which requires training once and is suitable for a broad range of flow scenarios. To identify the exact location of vortex cores, machine learning was combined with the computer vision approach. Various sets of input features were tested for both hybrid and pure machine learning approaches, starting with primitive variables such as velocity and pressure and expanding to more derived quantities such as velocity gradients, pressure gradients, Q-criterion, vorticity, and magnitude of vector quantities. In addition, a method for automatically labelling the dataset using K-means clustering was proposed to preprocess input images for machine learning. Results demonstrated that the K-means clustering-based labelling approach had a mean square error of  \nonly 0 .45%, comparable to the manual labelling approach. The hybrid approach significantly reduced training time for all tested cases and led to fewer false positives and negatives when using primitive variables and their derivatives compared to pure machine learning using the Artificial Neural Network approach applied to the entire flow. At the same time, using the variable set with all possible inputs does not provide a more accurate prediction of vortex cores and thus the hybrid approach is demonstrated as an effective way to reduce false positives and negatives entirely using just the primitive variables and their derivatives.  \nKeywords  \nmachine learning; computer vision; vortex core detection; fluid mechanics; line inte","cbCair2C7LHpaOBI","https://ap.wps.com/l/cbCair2C7LHpaOBI","pdf",22249680,1,163,"English","en",105,"# 1 Introduction\n## 1.1 Motivation\n## 1.2 Aim and Objectives\n## 1.3 Contributions to the Scientific Knowledge\n## 1.4 List of Publications\n## 1.5 Thesis Structure\n# 2 LITERATURE REVIEW\n## 2.1 Vortex definition\n## 2.2 Artificial Intelligence (AI)\n## 2.2.1 Computer vision (CV)\n## 2.2.1.1 Overview\n## 2.2.1.2 Object Detection methods","[{\"question\":\"Why are conventional local vortex-core detection methods challenging in practice?\",\"answer\":\"Common criteria based on local velocity-gradient information can produce both false positives and false negatives. They also often require user-defined tuning parameters to maintain acceptable error levels and may depend on prior knowledge of vortex properties.\"},{\"question\":\"How does the proposed hybrid approach improve vortex-core detection?\",\"answer\":\"Computer vision first identifies vortex-structure regions, then machine learning determines the vortex core within those regions. A CNN trained on LIC streamline plots reduces false positives and negatives without calibrating user-defined parameters.\"},{\"question\":\"What role does K-means clustering play in the machine learning workflow?\",\"answer\":\"K-means clustering is used to automatically label the dataset as preprocessing for training. The reported mean square error is about 0.45%, comparable to manual labeling.\"}]","Vortex core detection in turbulent simulations based on Machine Learning approaches - PhD Thesis | PDF",1785729377,411,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"vortex-core-detection-in-turbulent-simulations-based-on-machine-learning-approaches-phd-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/vortex-core-detection-in-turbulent-simulations-based-on-machine-learning-approaches-phd-thesis/120308/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are conventional local vortex-core detection methods challenging in practice?","Question",{"text":75,"@type":76},"Common criteria based on local velocity-gradient information can produce both false positives and false negatives. They also often require user-defined tuning parameters to maintain acceptable error levels and may depend on prior knowledge of vortex properties.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed hybrid approach improve vortex-core detection?",{"text":80,"@type":76},"Computer vision first identifies vortex-structure regions, then machine learning determines the vortex core within those regions. A CNN trained on LIC streamline plots reduces false positives and negatives without calibrating user-defined parameters.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does K-means clustering play in the machine learning workflow?",{"text":84,"@type":76},"K-means clustering is used to automatically label the dataset as preprocessing for training. 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