Surface line integral convolution-based vortex detection using computer vision

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dc.contributor.authorAbolholl, Hazem Ashor Amran
dc.contributor.authorTeschner, Tom-Robin
dc.contributor.authorMoulitsas, Irene
dc.date.accessioned2023-01-25T14:55:51Z
dc.date.available2023-01-25T14:55:51Z
dc.date.issued2023-01-11
dc.description.abstractVortex cores in fluid mechanics are easy to visualise, yet difficult to detect numerically. Precise knowledge of these allow fluid dynamics researchers to study complex flow structures and allow for a better understanding of the turbulence transition process and the development and evolution of flow instabilities, to name but a few relevant areas. Various approaches such as the Q, delta and swirling strength criterion have been proposed to visualise vortical flows and these approaches can be used to detect vortex core locations. Using these methods can resulted in spuriously detected vortex cores and which can be balanced by a cut-off filter, making these methods lack robustness. To overcome this shortcoming, we propose a new approach using convolutional neural networks to detect flow structures directly from streamline plots, using the line integral convolution method. We show that our computer vision-based approach is able to reduce the number of false positives and negatives while removing the need for a cut-off. We validate our approach using the Taylor-Green vortex problem to generate input images for our network. We show that with an increasing number of images used for training, we are able to monotonically reduce the number of false positives and negatives. We then apply our trained network to a different flow problem where vortices are still reliably detected. Thus, our study presents a robust approach that allows for reliable vortex detection which is applicable to a wide range of flow scenarios.en_UK
dc.identifier.citationAbolholl HAA, Teschner T-R, Moulitsas I. (2023) Surface line integral convolution-based vortex detection using computer vision. Journal of Computing and Information Science in Engineering, Volume 23, Issue 5, October 2023, Article number 051002, Paper number JCISE-22-1222en_UK
dc.identifier.issn1530-9827
dc.identifier.urihttps://doi.org/10.1115/1.4056660
dc.identifier.urihttps://asmedigitalcollection.asme.org/computingengineering/article/doi/10.1115/1.4056660/1156034/SURFACE-LINE-INTEGRAL-CONVOLUTION-BASED-VORTEX
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19012
dc.language.isoenen_UK
dc.publisherAmerican Society of Mechanical Engineersen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMachine Learning for Engineering Applicationsen_UK
dc.subjectPhysics-Based Simulationsen_UK
dc.titleSurface line integral convolution-based vortex detection using computer visionen_UK
dc.typeArticleen_UK

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