Browsing by Author "Abolholl, Hazem Ashor Amran"

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    A hybrid computer vision and machine learning approach for robust vortex core detection in fluid mechanics applications
    (American Society of Mechanical Engineers, 2024-06-01) Abolholl, Hazem Ashor Amran; Teschner, Tom-Robin; Moulitsas, Irene
    Vortex core detection remains an unsolved problem in the field of experimental and computational fluid 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, we propose a new hybrid machine learning approach in which we used a convolutional neural network to detect vortex regions within surface streamline plots and an additional deep neural network to detect vortex cores within identified vortex regions. Furthermore, we propose an automatic labelling approach based on K-means clustering to pre-process our input images. We show results for two classical test cases in fluid 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 is within 0.45% mean square error of the more labour-intensive, manual labelling approach. At the same time, using a sufficient 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 fluid mechanics applications.
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    A hybrid computer vision and machine learning approach for robust vortex core detection in fluid mechanics applications
    (Unconfirmed, 2022-04-22) Abolholl, Hazem Ashor Amran; Teschner, Tom-Robin; Moulitsas, Irene
    Vortex core detection remains a challenging topic within the field of computational fluid dynamics (CFD). Local methods, such as the Q, delta, or swirling-strength criterion, are commonly used to detect vortices and these methods are entirely based on the local velocity gradient tensor. Results have shown that reasonable estimates can be obtained with these methods, however, at the same time, these methods produce a significant number of false positives and negatives. User-defined tuning parameters are introduced to keep the number of false positives and negatives in balance, but this requires knowledge of the vortices and thus does not present a robust and self-contained approach. We recently proposed a novel computer vision approach where we have trained a convolutional neural network (CNN) to look at line integral convolution (LIC)-based streamline plots. We showed that this approach is capable of accurately predicting the regions where vortices reside, and we were able to reduce the false positives and negatives to zero. Furthermore, we showed the universality of this approach by successfully applying our trained CNN to a different test case for which it has not been trained and which featured different vortical structures (generated through a different physical process). The CNN-based approach is limited in the sense that it is only able to predict the bounding boxes of vortex cores, but not the exact location of the vortex core itself. Therefore, we propose a hybrid machine learning and computer vision approach in this study, where we first identify areas of vortical structures using computer vision to which we add a layer of machine learning to find the vortex core within the vortex region. We test different sets of input parameters for both the hybrid and pure machine learning approach, starting with just the primitive variables (velocity and pressure), and adding more derived quantities (velocity gradients, pressure gradients, Q-criterion, vorticity, and magnitude of vector quantities). Comparing the hybrid with the pure machine learning approach applied to the full flow field, we show that the hybrid approach reduces the training time for all tested cases up to a factor of 2. We also find that using the primitive variables along with their derivatives provide fewer false positives and negatives using the hybrid approach. At the same time, using the variable set with all possible inputs does not provide a more accurate prediction of vortex cores and thus we demonstrate that our hybrid computer vision and machine learning approach is an effective way to reduce false positives and negatives entirely using just the primitive variables and their derivatives.
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    Surface line integral convolution-based vortex detection using computer vision
    (American Society of Mechanical Engineers, 2023-01-11) Abolholl, Hazem Ashor Amran; Teschner, Tom-Robin; Moulitsas, Irene
    Vortex 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.
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    Vortex core detection in turbulent simulations based on machine learning approaches
    (Cranfield University, 2023-04) Abolholl, Hazem Ashor Amran; Teschner, Tom-Robin; Moulitsas, Irene
    The identification of vortex cores in fluid mechanics is a challenging task that re- quires 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 pa- rameters 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 ap- proach 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 negatives in flow-specific problems are reduced without the need for calibrating user-defined parameters. Furthermore, the trained CNN was successfully applied to three test cases, indicating its universal applicability. As such, computer vision offers a reli- able 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, pres- sure gradients, Q-criterion, vorticity, and magnitude of vector quantities. In addi- tion, 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 only 0.45%, comparable to the manual labelling approach. The hybrid approach significantly reduced training time for all tested cases and led to fewer false posi- tives 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.