Browsing by Author "Rana, Zeeshan"
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Item Open Access Aeroelastic analysis of a single element composite wing in ground effect using Fluid Structure Interaction(American Society of Mechanical Engineers, 2021-11-20) Bang, Chris Sungkyun; Rana, Zeeshan; Könözsy, László Z.; Rodriguez, Veronica M.; Temple, CliveThe present work focuses on an advanced coupling of computational fluid dynamics (CFD) and structural analysis (FEA) on the aeroelastic behaviour of a single element inverted composite wing with the novelty of including the ground effect. The front wing of the Formula One (F1) car can become flexible under the fluid loading due to elastic characteristics of composite materials, resulting in changing the flow field and eventually altering overall aerodynamics. The purpose of this study is to setup an accurate fluid-structure interaction (FSI) modelling framework and to assess the influence of elastic behaviour of the wing in ground effect on the aerodynamic and structural performance. Different turbulence models are studied to better capture the changes of the flow field and variation of ride heights are considered to investigate the influence of ground effect on aerodynamic phenomena. A steady-state two-way coupling method is exploited to run the FSI numerical simulations using ANSYS, which enables simultaneous calculation by coupling CFD with FEA. The effect of various composite structures on the wing performance is extensively studied concerning structure configuration, ply orientation and core materials. The numerical results generally represent good agreement with the experimental data, however, discrepancy, especially in the aerodynamic force, is presented. This may be consequence of less effective angle of attack due to the wing deflection and deterioration of vortex-induced effect. For the structural analysis, the woven structure gives rise to more stable structural deflection than the unidirectional structure despite the associated weight penalty.Item Open Access An automatic image analysis methodology for the measurement of droplet size distributions in liquid–liquid dispersion: round object detection(ACTA Press, 2018-11-08) Gawryszewski, K.; Rana, Zeeshan; Jenkins, Karl W.; Ioannou, Phivos; Okonkwo, D.This article presents an efficient and economical automatic image analysis technique for the droplet characterization in a liquid–liquid dispersion. The methodology employs a combination of the Satoshi Suzuki's [Topological structural analysis of digitized binary images by border following. Comput Vis Graph Image Process. 1985;30:32–46] find contours algorithm and the method of minimal enclosing circle identification, proposed by Emo Welzl [Smallest enclosing disks (balls and ellipsoids). Berlin, Heidelberg: Springer; 1991. p. 359–370. chapter 24], to achieve the objectives. The round object detection algorithm has been designed for the identification and verification of correct droplets in the mixture which helped to increase the accuracy of automatic detection. Tests have been performed on various sets of images obtained during several emulsification processes and contain examples of droplets which differ in size, density, volume and appearance etc. An effective communication between the two methodologies and newly introduced algorithms demonstrated an accuracy of 90% or above in the measurement of droplet size distribution and Sauter mean diameters through an automatic vision-based system.Item Open Access Autonomous ground refuelling approach for civil aircrafts using computer vision and robotics(IEEE, 2021-11-15) Yildirim, Suleyman; Rana, Zeeshan; Tang, Gilbert3D visual servoing systems need to detect the object and its pose in order to perform. As a result accurate, fast object detection and pose estimation play a vital role. Most visual servoing methods use low-level object detection and pose estimation algorithms. However, many approaches detect objects in 2D RGB sequences for servoing, which lacks reliability when estimating the object’s pose in 3D space. To cope with these problems, firstly, a joint feature extractor is employed to fuse the object’s 2D RGB image and 3D point cloud data. At this point, a novel method called PosEst is proposed to exploit the correlation between 2D and 3D features. Here are the results of the custom model using test data; precision: 0,9756, recall: 0.9876, F1 Score(beta=1): 0.9815, F1 Score(beta=2): 0.9779. The method used in this study can be easily implemented to 3D grasping and 3D tracking problems to make the solutions faster and more accurate. In a period where electric vehicles and autonomous systems are gradually becoming a part of our lives, this study offers a safer, more efficient and more comfortable environment.Item Open Access Computational analysis and design of an aerofoil with morphing tail for improved aerodynamic performance in transonic regime(Cambridge University Press, 2022-01-10) Rana, Zeeshan; Mauret, F.; Sanchez-Gil, J. M.; Zeng, Kai; Hou, Z.; Dayyani, Iman; Könözsy, László Z.This article focuses on the aerodynamic design of a morphing aerofoil at cruise conditions using computational fluid dynamics (CFD). The morphing aerofoil has been analysed at a Mach number of 0.8 and Reynolds number of 3×106 , which represents the transonic cruise speed of a commercial aircraft. In this research, the NACA0012 aerofoil has been identified as the baseline aerofoil where the analysis has been performed under steady conditions at a range of angles of attack between 0∘ and 3.86∘ . The performance of the baseline case has been compared to the morphing aerofoil for different morphing deflections ( wte/c=[0.005−0.1] ) and start of the morphing locations ( xs/c=[0.65−0.80] ). Further, the location of the shock wave on the upper surface has also been investigated due to concerns about the structural integrity of the morphing part of the aerofoil. Based upon this investigation, a most favourable morphed geometry has been presented that offers both, a significant increase in the lift-to-drag ratio against its un-morphed counterpart and has a shock location upstream of the start of the morphing part.Item Open Access Computational investigations into heat transfer over a double wedge in hypersonic flows(Elsevier, 2019-07-12) Expósito, Diego; Rana, ZeeshanRecently developed OpenFOAM application hy2FOAM is employed to predict the aerodynamic heat transfer numerically and compared with the experimental data from the University of Illinois. Mach 7 nitrogen flow at 2.1 MJ/kg stagnation enthalpy, and Mach 7 nitrogen and air flows at 8 MJ/kg stagnation enthalpy over a double wedge geometry have been reproduced numerically assuming chemical and thermal non-equilibrium. Good agreement of mean heat transfer profiles has been observed, although none of the simulations achieved a steady-state. The reattachment heat transfer peak in the high enthalpy air case showed an improved agreement with the experimental data, which is due to the non-equilibrium in the flow field.Item Open Access Failure mode analysis (FMA) for visual-based navigation for UAVs in urban environment(UK-Robotics and Autonomous Systems (UK-RAS) Network, 2022-08-26) Tabassum, Tarafder Elmi; Petrunin, Ivan; Rana, ZeeshanVisual-based navigation systems for Unmanned Aerial vehicles (UAVs) have become an interesting research area focused on improving robustness and accuracy in the urban environment. However, a lack of integrity can damage UAVs, limiting their potential usage in civil applications. For safety reasons, integrity performance requirements must be met. In literature, such systems require significant attention for their ability to perform fault analysis, referred to as failure mode. In this paper, we have conducted a failure mode analysis in urban environments for UAVs to identify threats and faults presented in existing Visual-inertial Navigation Systems. In addition, we propose a federated-filter-based fault detection and execution system to improve navigation performance under faulted conditions.Item Open Access A fault tolerant multi-sensor fusion navigation system for drone in urban environment(German Institute of Navigation, 2022-11-04) Tabassum, Tarafder Elmi; Petrunin, Ivan; Rana, ZeeshanPrecise positioning becomes an attractive research area to enhance last-mile delivery with drones. However, the reliability of precise poisoning is significantly degraded in GNSS-denied environments such as urban canyons. In this case, the excellent performance of Visual Inertial Odometry (VIO) in local pose estimation makes visual navigation technology more feasible for researchers. However, the accuracy and robustness of VIO degrade in faulted conditions. This paper presents a fault-tolerant multisensor fusion navigation system for drones in urban environments. We first performed Failure Mode and Effect Analysis (FMEA) in the VIO system to identify potential failure mode, which is feature extraction errors. Then, an integrated, loosely coupled EKF-based VIO system is proposed for our GNSS/VINS/LIO reference system to mitigate visual and IMU faults. The performance of the proposed method was validated by a synthetic dataset created using MATLAB, and it has shown improved robustness over Visual odometry and state-of-art VINS systems.Item Open Access Investigation of numerical dissipation in classical and implicit large eddy simulations(MDPI, 2017-12-11) El Rafei, Moutassem; Könözsy, László Z.; Rana, ZeeshanThe quantitative measure of dissipative properties of different numerical schemes is crucial to computational methods in the field of aerospace applications. Therefore, the objective of the present study is to examine the resolving power of Monotonic Upwind Scheme for Conservation Laws (MUSCL) scheme with three different slope limiters: one second-order and two third-order used within the framework of Implicit Large Eddy Simulations (ILES). The performance of the dynamic Smagorinsky subgrid-scale model used in the classical Large Eddy Simulation (LES) approach is examined. The assessment of these schemes is of significant importance to understand the numerical dissipation that could affect the accuracy of the numerical solution. A modified equation analysis has been employed to the convective term of the fully-compressible Navier–Stokes equations to formulate an analytical expression of truncation error for the second-order upwind scheme. The contribution of second-order partial derivatives in the expression of truncation error showed that the effect of this numerical error could not be neglected compared to the total kinetic energy dissipation rate. Transitions from laminar to turbulent flow are visualized considering the inviscid Taylor–Green Vortex (TGV) test-case. The evolution in time of volumetrically-averaged kinetic energy and kinetic energy dissipation rate have been monitored for all numerical schemes and all grid levels. The dissipation mechanism has been compared to Direct Numerical Simulation (DNS) data found in the literature at different Reynolds numbers. We found that the resolving power and the symmetry breaking property are enhanced with finer grid resolutions. The production of vorticity has been observed in terms of enstrophy and effective viscosity. The instantaneous kinetic energy spectrum has been computed using a three-dimensional Fast Fourier Transform (FFT). All combinations of numerical methods produce a k−4 spectrum at t∗=4 , and near the dissipation peak, all methods were capable of predicting the k−5/3 slope accurately when refining the mesh.Item Open Access Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline-riser using doppler ultrasonic sensor and deep neural networks(Elsevier, 2020-07-26) Nnabuife, Somtochukwu Godfrey; Kuang, Boyu; Whidborne, James F.; Rana, ZeeshanThe problem of predicting the regime of a two-phase flow is considered. An approach is proposed that classifies the flow regime using Deep Neural Networks (DNNs) operating on features extracted from Doppler ultrasonic signals of the flow using the Fast Fourier Transform (FFT) is proposed. The features extracted are categorised into one of the four flow regime classes: the annular, churn, slug, and bubbly flow regimes. The scheme was tested on signals from an experimental facility. To increase the number of samples without losing key classification information, this paper proposes a Twin-window Feature Extraction (TFE) technique. To further distinguish the performance of the proposed approach, the classifier was compared to four conventional machine learning classifiers: namely, the AdaBoost classifier, bagging classifier, extra trees classifier, and decision tree classifier. Using the TFE features, the DNNs classifier achieved a higher recognition accuracy of 99.01% and greater robustness for the overfitting challenge, thereby showing the superiority of the DNNs in flow regime classification when compared to the four conventional machine-learning classifiers, which had classification accuracies of 55.35%, 86.21%, 82.41%, and 80.03%, respectively. This approach demonstrates the application of DNNs for flow regime classification in chemical and petroleum engineering fields, using a clamp-on Doppler ultrasonic sensor. This appears to be the first known successful attempt to identify gas-liquid flow regimes in an S-shaped riser using Continuous Wave Doppler Ultrasound (CWDU) and DNNsItem Open Access A novel aircraft wing inspection framework based on multiple view geometry and convolutional neural network(3AF and CEAS, 2020-02-28) Kuang, Boyu; Rana, Zeeshan; Zhao, YifanTo achieve greener and safer aeronautical operations, this paper considers the problem of reconstructing the three-dimensional (3D) geometric structure of aeronautical components. A novel framework that recovers the 3D shapes by means of convolutional neural network (ConvNets) and multiple view geometry (MVG) operating on Mask-R-CNN-segmented two-dimensional images is proposed. To achieve more accurate 3D aircraft’s surface and exclude the invalid background structures, this paper innovatively integrates the environmental robustness of ConvNets and geometric adaptation of Mask-R- CNN into the MVG theory. The preliminary experiments show that the proposed framework is visual-comfortable, and it also accurately reconstructs the regions with damage to catch up with the inspection purpose.Item Open Access Pseudo-image-feature-based identification benchmark for multi-phase flow regimes(Elsevier, 2020-12-08) Kuang, Boyu; Nnabuife, Somtochukwu Godfrey; Rana, ZeeshanMultiphase flow is a prevalent topic in many disciplines, and flow regime identification is an essential foundation in multiphase flow research. Computer vision and deep learning have achieved numerous excellent models, but many have not demonstrated satisfactory performance in fundamental research, including flow regime identification. This research proposes an advanced pseudo-image feature (PIF) as the flow regime descriptor and a benchmark of multiple deep learning classifiers. The PIF simulates the image format and compactly encodes the flow regime to a pseudo-image, which explicitly displays the implicit flow regime signals. This research further evaluates three proposed and five existing popular deep learning classifiers. The proposed benchmark provides a baseline for applying deep learning in flow regime identification. The proposed fully convolutional network (FCN) classifier achieved state-of-the-art performance, and the testing and verification accuracy respectively reached 99.95% and 99.54%. This research suggests that PIF has an excellent capability for flow regime representation, and the proposed deep learning classifiers achieve superior performance in flow regime identification compared to the existing classifiers. Industries can utilize the proposed multiphase flow identification technology to obtain greater production efficiency, productivity, and financial gainItem Open Access Reducing the reality gap using hybrid data for real-time autonomous operations(MDPI, 2023-04-02) Yildirim, Suleyman; Rana, ZeeshanThis paper presents an ablation study aimed at investigating the impact of a hybrid dataset, domain randomisation, and custom-designed neural network architecture on the performance of object localisation. In this regard, real images were gathered from the Boeing 737-400 aircraft while synthetic images were generated using the domain randomisation technique involved randomising various parameters of the simulation environment in a photo-realistic manner. The study results indicated that the use of the hybrid dataset, domain randomisation, and the custom-designed neural network architecture yielded a significant enhancement in object localisation performance. Furthermore, the study demonstrated that domain randomisation facilitated the reduction of the reality gap between the real-world and simulation environments, leading to a better generalisation of the neural network architecture on real-world data. Additionally, the ablation study delved into the impact of each randomisation parameter on the neural network architecture’s performance. The insights gleaned from this investigation shed light on the importance of each constituent component of the proposed methodology and how they interact to enhance object localisation performance. The study affirms that deploying a hybrid dataset, domain randomisation, and custom-designed neural network architecture is an effective approach to training deep neural networks for object localisation tasks. The findings of this study can be applied to a wide range of computer vision applications, particularly in scenarios where collecting large amounts of labelled real-world data is challenging. The study employed a custom-designed neural network architecture that achieved 99.19% accuracy, 98.26% precision, 99.58% recall, and 97.92% mAP@.95 trained using a hybrid dataset comprising synthetic and real images.