[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122835-en":3,"doc-seo-122835-105":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},122835,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Vortex and Core Detection using Computer Vision and Machine Learning Methods - Abstract","The identification of vortices and cores is crucial for interpreting airflow motion in aerodynamics, especially around components such as rotor blades where turbulence and instability complicate analysis. Numerous computer vision and machine learning methods exist, yet their integration for robust core localization remains limited. This study proposes a unified framework that enhances a CNN-based pipeline with computer-vision feature engineering and applies ensemble learning for vortex core classification, reducing false positives, false negatives, and computational cost.","Vortex and Core Detection using Computer Vision and Machine Learning Methods  \nZhenguo Xu, Ayush Maria, Kahina Chelli, Thibaut Dumouchel De Premare, Xabadin Bilbao, Christopher Petit, Robert Zoumboulis-Airey, Irene Moulitsas,  \nTom Teschner, Seemal Asif and Jun Li∗  \nSchool of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK  \nE-mail: zhenguo.xu.545@cranfield.ac.uk; Ayush.Maria.256@cranfield.ac.uk; Kahina.Chelli.248@cranfield.ac.uk; t.dumoucheldepremare.684@cranfield.ac.uk; Xabadin.Bilbao.977@cranfield.ac.uk; c.d.petit.904@cranfield.ac.uk;  \nRobert.Zoumboulis-Airey@cranfield.ac.uk; i.moulitsas@cranfield.ac.uk;  \nTom.Teschner@cranfield.ac.uk; s.asif@cranfield.ac.uk; Jun.Li@cranfield.ac.uk  \n∗ Corresponding Author  \nReceived 15 March 2023; Accepted 19 June 2023; Publication 29 December 2023  \nAbstract  \nThe identification of vortices and cores is crucial for understanding airflow motion in aerodynamics. Currently, numerous methods in Computer Vision and Machine Learning exist for detecting vortices and cores. This research develops a comprehensive framework by combining classic Computer Vision and state-of-the-art Machine Learning techniques for vortex and core detection. It enhances a CNN-based method using Computer Vision algorithms for Feature Engineering and then adopts an Ensemble Learning approach for vortex core classification, through which false positives, false negatives, and computational costs are reduced. Specifically, four features, i.e., Contour Area, Aspect Ratio, Area Difference, and Moment Centre, are employed  \nEuropean Journal of Computational Mechanics, Vol. 32 5, 467–494. doi: 10.13052/ejcm2642-2085.3252  \n© 2023 River Publishers  \n468 Z. Xu et al.  \nto identify vortex regions using YOLOv5s, followed by a hard voting classifier based on Random Forest, Adaptive Boosting, and Xtreme Gradient Boosting algorithms for vortex core detection. This novel approach differs from traditional Computer Vision approaches using mathematical variablesand image features such as HAAR and SIFT for vortex core detection. The findings show that vortices are detected with a high degree of statistical confidence by a fine-tuned YOLOv5s model, and the integrated technique produces an accuracy score of 97.56% in detecting vortex cores conducted on a total of 133 images generated from a rotor blade NACA0012 simulation. Future work will focus on framework generalisation with a larger and more diverse dataset and intelligent threshold development for more efficient vortex and core detection.  \nKeywords: Computational fluid dynamics, rotor blade, mesh, ensemble learning, hard voting.  \n1 Introduction  \nUnderstanding the airflow around specific objects (e.g., cars, buildings, and turbo-machinery) known as aerodynamics is of great interest in many technical fields as the product performance strongly depends on it [11] . For instance, studying the airflow over a rotor blade is important because it improves the turbine’s performance, efficiency and longevity etc. Depending on the environment (e.g., in a wind farm) the airflow over blades can be unstable and turbulent. Being able to understand how vortices behave and move through space by identifying their formation may allow designers to improve the turbine performance. The detection of vortices and vortex cores is also the foundation of numerous aerodynamic subjects, serving as a catalyst for in-depth research that builds upon the study of vortices.  \nHowever, the detection of vortices and their cores in airflow with instability and turbulence is not straightforward. Sujudi and Haimes [15] develop an algorithm to identify vortex cores in 3D discrete vector fields. They use the distributed environment of pV3 and linear interpolation to find the trajectories of vortex cores by employing cell-by-cell processing. Unfortunately, the research only considers the vortex core trajectory, ignoring the effect of vortex strength and mesh roughness on t","cbCais7hby3Cyfzf","https://ap.wps.com/l/cbCais7hby3Cyfzf","pdf",1293345,1,29,"English","en",105,"# Introduction\n## Literature Review\n## Proposed Framework Overview\n# Methods\n## Feature Engineering\n## Vortex Core Classification","[{\"question\":\"Why is vortex and vortex core detection important in aerodynamics?\",\"answer\":\"Vortices strongly influence aerodynamic performance and can increase drag through interactions. Detecting vortex cores provides a foundation for deeper aerodynamic analysis and design improvements.\"},{\"question\":\"How does the proposed framework combine computer vision and machine learning?\",\"answer\":\"It uses computer vision algorithms for feature engineering to improve a CNN-based stage, then applies an ensemble learning approach for vortex core classification to better separate true cores from errors.\"},{\"question\":\"Which specific features and models are used for vortex and core detection?\",\"answer\":\"Four computer-vision features—Contour Area, Aspect Ratio, Area Difference, and Moment Centre—are used to identify vortex regions with YOLOv5s, followed by a hard voting classifier based on Random Forest, AdaBoost, and XGBoost.\"}]","Vortex and Core Detection using Computer Vision and Machine Learning Methods - Abstract | PDF",1785813159,73,{"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-and-core-detection-using-computer-vision-and-machine-learning-methods-abstract","",{"@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-and-core-detection-using-computer-vision-and-machine-learning-methods-abstract/122835/",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","2026-08-04",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 is vortex and vortex core detection important in aerodynamics?","Question",{"text":75,"@type":76},"Vortices strongly influence aerodynamic performance and can increase drag through interactions. Detecting vortex cores provides a foundation for deeper aerodynamic analysis and design improvements.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework combine computer vision and machine learning?",{"text":80,"@type":76},"It uses computer vision algorithms for feature engineering to improve a CNN-based stage, then applies an ensemble learning approach for vortex core classification to better separate true cores from errors.",{"name":82,"@type":73,"acceptedAnswer":83},"Which specific features and models are used for vortex and core detection?",{"text":84,"@type":76},"Four computer-vision features—Contour Area, Aspect Ratio, Area Difference, and Moment Centre—are used to identify vortex regions with YOLOv5s, followed by a hard voting classifier based on Random Forest, AdaBoost, and XGBoost.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]