Vortex and core detection using computer vision and machine learning methods

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dc.contributor.authorXu, Zhenguo
dc.contributor.authorMaria, Ayush
dc.contributor.authorChelli, Kahina
dc.contributor.authorDe Premare, Thibaut Dumouchel
dc.contributor.authorBilbao, Xabadin
dc.contributor.authorPetit, Christopher
dc.contributor.authorZoumboulis-Airey, Robert
dc.contributor.authorMoulitsas, Irene
dc.contributor.authorTeschner, Tom
dc.contributor.authorAsif, Seemal
dc.contributor.authorLi, Jun
dc.date.accessioned2024-03-14T13:41:07Z
dc.date.available2024-03-14T13:41:07Z
dc.date.issued2023-12-29
dc.description.abstractThe 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 to 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 variables and 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.en_UK
dc.identifier.citationXu Z, Maria A, Chelli K, et al., (2023) Vortex and core detection using computer vision and machine learning methods, European Journal of Computational Mechanics. Volume 32, Issue 5, December 2023, pp. 467-494en_UK
dc.identifier.eissn2642-2050
dc.identifier.issn2642-2085
dc.identifier.urihttps://doi.org/10.13052/ejcm2642-2085.3252
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20996
dc.language.isoenen_UK
dc.publisherRiver Publishersen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectComputational fluid dynamicsen_UK
dc.subjectrotor bladeen_UK
dc.subjectmeshen_UK
dc.subjectensemble learningen_UK
dc.subjecthard votingen_UK
dc.titleVortex and core detection using computer vision and machine learning methodsen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2023-06-19

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