1,722,214 research outputs found
Optimal Design of Capsule Transporting Pipeline carrying Spherical Capsules
Full text linkA capsule pipeline transports material or cargo in capsules propelled by fluid flowing through a pipeline. The cargo may either be contained in capsules (such as wheat enclosed inside sealed cylindrical containers), or may itself be the capsules (such as coal compressed into the shape of a cylinder or sphere). As the concept of capsule transportation is relatively new, the capsule pipelines need to be designed optimally for commercial viability. An optimal design of such a pipeline would have minimum pressure drop due to the presence of the solid medium in the pipeline, which corresponds to minimum head loss and hence minimum pumping power required to drive the capsules and the transporting fluid. The total cost for the manufacturing and maintenance of such pipelines is yet another important variable that needs to be considered for the widespread commercial acceptance of capsule transporting pipelines. To address this, the optimisation technique presented here is based on the least-cost principle. Pressure drop relationships have been incorporated to calculate the pumping requirements for the system. The maintenance and manufacturing costs have been computed separately to analyse their effects on the optimisation process. A design example has been included to show the usage of the model presented. The results indicate that for a specific throughput, there exists an optimum diameter of the pipeline for which the total cost for the piping system is at its minimum
Capacity Testing and Local Flow Analysis of a Geometrically Complex Trim Installed within a Commercial Control Valve
No full textIndustrial control valves often handle flows with very high pressure drops (conditions often referred to as severe service). In order to cope with such pressure drops, geometrically complex valve trims with many stages of pressure drops, are designed to prevent undesirable side effects such as cavitation etc. There are many different product designs for control valve trims produced by different manufacturers. One such design uses cylindrical obstructions in the flow field to control the pressure drop. The design of these trims is based on their capacity (Cv) values. With the advent of advanced computational tools, such as Computational Fluid Dynamics based codes, it has become possible to numerically test these trims over a wide range of flow conditions. Hence, this study presents capacity testing and local flow analysis of a complex geometry trim, installed within a commercial control valve for severe service. The results show that the capacity of this particular design of trim decreases as the valve opening position decreases
Numerical Investigations on Vortical Structures in the Near Tongue Region of a Centrifugal Pump during Transient Operation
No full textCentrifugal pumps are considered to be an integral part of process industries around the world. The flow structure within centrifugal pumps is very complex due to the interaction between the rotating impeller and the geometric features around it, such as tongue. Researchers have been analysing the effects of the interactions between impeller blades and the tongue, however, most of these studies are based on steady-state approximations where the impeller blades are modelled using frozen-rotor approach which leads to discrepancies in the predicted flow fields. In the present study, fully transient numerical investigations, on the generation and dissipation of vortical structures in the vicinity of the tongue region, have been carried out using a commercial Computational Fluid Dynamics (CFD) based solver. The instantaneous behaviour of a centrifugal pump is studied using the Sliding Mesh technique. Simulations have been carried out on both a constant rotating speed and under decelerating conditions. The second invariant of the velocity gradient tensor i.e. Q-criterion, has been employed to identify the generation and dissipation of vortical structures near the tongue region of the pump. The results indicate that the Q-criterion is fairly non-uniform downstream the tongue region due to the complex interaction between the impeller blades and the tongue. Furthermore, it has been observed that as the rotational speed of the centrifugal pump decreases, the Q-criterion in the near tongue region remains constant. The generation, expansion and subsequent mixing of two distinct vortical structures have been noticed downstream the tongue (within the volute), whereby the strength of these structures has been observed to be decreasing as the distance from the tongue increases
Pressure Drop in Capsule Transporting Bends Carrying Spherical Capsules
Full text linkOne of the most important parameters in designing a capsule transporting pipeline is the pressure drop in the pipes carrying capsules and associated pipe fittings such as bends etc. Capsules are hollow containers with typically cylindrical or spherical shapes flowing in the pipeline along with the carrier fluid. The dynamic behavior of a long train of capsules depends on the behavior of each capsule in the train and the hydrodynamic influence of one capsule on another. Researchers so far have used rather simplified empirical and semi-empirical correlations for pressure drop calculations, the range and application of which are fairly limited. Computational Fluid Dynamics (CFD) based techniques have been used to analyze the effect of the presence of solid phase in hydraulic bends. A steady state numerical solution has been obtained from the equations governing turbulent flow in pipe bends carrying spherical capsule train consisting of one to four capsules. The bends under consideration are of 45⁰ and 90⁰ with an inner diameter of 0.1m. The investigation was carried out in the practical range of 0.2 ≤Vb≥ 1.6 m/sec. The computationally obtained data set over a wide range of flow conditions has been used to develop a rigorous model for pressure drop calculations. The pressure drop along the pipe bends, in combination with the pressure drop along the pipes, can be used to calculate the pumping requirements and hence design of the system
Mediapipe based Preprocessed VGGFace2 Dataset
No full textVGGFace2 Dataset and Face Mesh PreprocessingIntroductionThe VGGFace2 dataset is a large-scale face recognition dataset containing over 3.31 million images of 9,131 identities, with an average of 362 images per identity. The dataset is designed to include extensive variations in pose, age, illumination, ethnicity, and profession, making it one of the most diverse and challenging face recognition datasets available. For more details, please refer to the original publication:VGGFace2: A dataset for recognizing faces across pose and age - DOI: 10.48550/arXiv.1710.08092
Preprocessing Using MediaPipe 3D Face MeshOn this dataset, we applied the MediaPipe-based 3D face mesh algorithm to accurately detect faces while removing all background elements, including hair. Our preprocessing strictly retained facial landmarks, ensuring that only the essential facial features were preserved. This approach significantly enhanced the accuracy and generalization of our model, as the model was trained exclusively on landmark-based facial data.
Training and PerformanceThe preprocessed data was utilized to train Xception model, which resulted in remarkably accurate outcomes due to the strictly landmark-based facial representation. The model demonstrated robust performance including explainable-AI, proving that eliminating unnecessary background elements contributed positively to its efficiency and reliability.
CitationIf you use this dataset or the preprocessed version in your work, please cite both of the following:
VGGFace2 Dataset:
@article{Cao2018VGGFace2, title={VGGFace2: A dataset for recognizing faces across pose and age}, author={Cao, Qiong and Shen, Li and Xie, Weidi and Parkhi, Omkar M and Zisserman, Andrew}, journal={arXiv preprint arXiv:1710.08092}, year={2018}}
DOI: [10.48550/arXiv.1710.08092](https://doi.org/10.48550/arXiv.1710.08092) Preprocessed Dataset using MediaPipe:@dataset{Shah2025_MediaPipe_FaceMesh, title={MediaPipe-based 3D Face Mesh Preprocessed VGGFace2 Dataset}, author={Shah, Syed Taimoor Hussain and Shah, Syed Adil Hussain and Zamir, Ammara and Qayyum, Kainat and Shah, Syed Baqir Hussain and Fatima, Syeda Maryam and Deriu, Marco Agostino}, year={2025}, doi={10.5281/zenodo.15078557}} DOI: [10.5281/zenodo.15078557](https://doi.org/10.5281/zenodo.15078557)
ContactFor any questions or further details, please feel free to contact us.Syed Taimoor Hussain ShahPolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, ItalyEmail: [email protected]: 0000-0002-6010-677
Multimodal AI Tools for Predicting Neurological and Neurodevelopmental Trajectories
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A HVS-Inspired Attention to Improve Loss Metrics for CNN-Based Perception-Oriented Super-Resolution
No full textDeep Convolutional Neural Network (CNN) features have been demonstrated to be effective perceptual quality features. The perceptual loss, based on feature maps of pre-trained CNN's has proven to be remarkably effective for CNN based perceptual image restoration problems. In this work, taking inspiration from the the Human Visual System (HVS) and visual perception, we propose a spatial attention mechanism based on the dependency human contrast sensitivity on spatial frequency. We identify regions in input images, based on the underlying spatial frequency, which are not generally well reconstructed during Super-Resolution but are most important in terms of visual sensitivity. Based on this prior, we design a spatial attention map that is applied to feature maps in the perceptual loss and its variants, helping them to identify regions that are of more perceptual importance. The results demonstrate the our technique improves the ability of the perceptual loss and contextual loss to deliver more natural images in CNN based super-resolution
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