•Magnetohydrodynamics flow of viscous fluid is considered over a curved surface.•Buongiorno model is used in the mathematical modeling of flow problem.•Total entropy is calculated through second law ...of thermodynamics.•Homogeneous-heterogeneous reactions are considered at the stretchable surface.
Background:Magnetohydrodynamics or hydro-magnetics (MHD) is the study of dynamics in the presence of magnetic characteristics and impact of electrically conducting liquids which has a significant applications in engineering and biomedical sciences. Liquid metals, plasma, electrolytes and salt water are the examples of such magneto-fluids. MHD liquid flow in various geometries significant to engineering sciences is an interesting and noteworthy scientific area because of applications. The above applications of magnetohydrodynamics insist the engineers and analyst to develop new mathematical modeling in the field of fluid mechanics. Therefore, we considered electrical conducting viscous fluid flow over a curved surface with second order slip. The Buongiorno model is utilized in the modeling of flow problem with thermophoretic and Brownian diffusions. The effects of viscous dissipation, thermal radiation and Joule heating (Ohmic heating) is used in the modeling of energy equation. Homogeneous-heterogeneous reactions are further considered. The energy equation is modeled.
Method:The nonlinear ODE’s are obtained through utilization of appropriate transformations and numerical results are computed via NDSolve MATHEMATICA.
Results: Velocity field is decreasing function of first order slip parameter. Both Bejan number and entropy generation is upsurged versus heterogeneous reaction parameter.
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•Dissipative mixed convective Darcy-Forchheimer flow over a curved surface is addressed.•Ohmic heating and dissipation effects are considered.•Binary chemical reaction with activation energy is ...considered.•Numerical results are computed through ND-Solve technique.
Background: Mixed convection (forced+natural convection) is frequently observed in exceptionally high output devices where the forced convection isn’t sufficient to dissipate all of the heat essential. At this point, consolidating natural convection with forced convection will frequently convey the ideal outcomes. Nuclear reactor technology and a few features of electronic cooling are the examples of these processes. Mixed convection problems are categorized by Richardson number (Ri), which is the ratio of Grashof number (for natural convection) and Reynolds number (for forced convection). For buoyancy or mixed convection the relative effect can be addressed by Richardson number. Typically, the natural convection is negligible when Richardson number is less than 0.1 (Ri < 0.1), forced convection is negligible when Richardson number is greater than 10 (Ri > 10) and neither is negligible when (0.1 < Ri < 10). It might be noticed that generally the forced convection is large comparative with natural convection except in case of remarkably low forced flow velocities. The current work gives significant insights regarding dissipative mixed convective Darcy-Forchheimer flow with entropy generation over a stretched curved surface. The energy equation is developed with respect to nonlinear radiation, dissipation and Ohmic heating (Joule heating). Binary reaction via activation energy is accounted.
Method: Curvilinear transformations are utilized to change the nonlinear PDE’s into ordinary ones. Computational outcomes are obtained via NDSolve MATHEMATICA. The results are computed and discussed graphically.
Results: Velocity decays for Forchheimer number. Entropy generation enhances for diffusion parameter and chemical reaction parameter. Concentration profile reduces chemical reaction parameter and enhances for activation parameter.
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•Salient features of three dimensional flow of hybrid nanomaterial are discussed.•Thermophoresis and Brownian motion effects are considered.•Slip conditions on boundaries for velocity and temperature ...are implemented.•Special attention is given to the entropy rate and Bejan number.
Nanoliquids are dilute suspensions of nanoparticles with at least one of their principal dimensions smaller than 100 nm. Form literature, nanoliquids have been found to possess increased thermos-physical characteristics like thermal diffusivity, thermal conductivity, convective heat transport coefficients and viscosity associated to those of continuous phase liquids foe example oil, ethylene glycol and water. Nanoliquids have novel characteristics that make them possibly beneficial in numerous applications in heat transport like fuel cells, microelectronics, hybrid-powered engines, pharmaceutical processes, domestic refrigerator, engine cooling thermal management, chiller and heat exchanger. The above applications of nanofluids/hybrid nanofluids insist the researchers and engineers to develop new methodologies and technique in the field of heat transport. Therefore, we have considered mixed convective flow hybrid nanomaterial over a convectively heated surface of disk. Flow nature is discussed due to stretchable rotating surface of disk. Applied magnetic field is accounted. Ohmic heating and dissipation effects are utilized in the modeling of energy expression. Total entropy rate is calculated.
Suitable transformation leads to ordinary differential equations. Shooting method is implemented for numerical outcomes. Comparative analysis is made for the present result with published ones.
The effects of key parameters like magnetic parameter, mixed convection variable and Eckert and Biot numbers on the dimensionless velocity, surface drag force, temperature, (heat transfer rate) Nusselt number and entropy rate are discussed in detail and presented graphically. Furthermore, the outcomes demonstrate that velocity of liquid particles decline against magnetic parameter. Temperature and associated layer upsurge versus magnetic parameter and Eckert number. Skin friction coefficient (drag force) improves through higher values of stretching and magnetic variables. Heat transfer rate is more for higher Eckert number and magnetic parameter. Entropy rate is also enhances against Eckert number and Brickman number.
Magnitude of surface drag force increases for higher values of stretching and magnetic variables. Magnitude of heat transfer rate is more when magnetic variable and Eckert number attain the maximum values. Brinkman number is used to decrease the entropy rate. Furthermore, velocity and temperature show contrast behavior versus magnetic parameter i.e., velocity of fluid particles decreases.
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•Numerical investigation of vortex-induced vibration of an elastically mounted rigid cylinder is discussed.•The study is carried out for the range of reduced velocity = 2–16 which corresponds to ...Reynolds number 1700–14,000.•Maximum amplitude, other hydrodynamics coefficients are also discussed.•Current simulations are compared with previous experimental and numerical studies.
This study addresses vortex-induced vibration analysis by an elastic rigid cylinder oscillating free in cross flow direction subject to low mass damping ratio. Two-dimensional Reynold-Averaged Navier-Stokes equations (RAN) for this purpose are simulated. Analysis is carried out for range of reduced velocity = 2–16 which corresponds to Reynolds number 1700–14,000. The mass-ratio is 2.4 and mass-damping ratio 0.0013. Previously, similar studies have been performed numerically but are limited to achieve maximum cylinder response. In the current study, comparatively higher value of maximum amplitude of cylinder is computed. However delay in transition from ‘upper branch’ to ‘lower branch’ is noticed. In addition to maximum amplitude, other hydrodynamic coefficients are also discussed. The results extracted from current simulations are compared with previous experimental studies in literature.
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Carbon-fiber composites are considered to be one of the suitable materials for the fabrication of prosthetic feet. However, commercially available composites-based prosthetic foot designs present ...several problems for lower limb amputees, such as low tensile strength, reduced impact resistance, high cost, and weight structure. Modulating the mechanical properties of carbon-fiber composites using a simplified method can help reduce these issues. Therefore, our present research aims to identify the impact of increasing the concentration of carbon fiber in the fabrication of carbon-fiber composites by using the hand layup method without the vacuum bagging technique. To improve the mechanical strength of carbon-fiber laminates, an increasing number of carbon-fiber layers are used in sample preparation. This study aims to determine the tensile strength of the laminates with a different number of carbon-fiber laminations. For the preparation of the sample specimen, black 100% 3 K 200 gsm carbon fiber with a cloth thickness of 0.2 mm and tensile strength of 4380 Mpa was laminated with two parts of epoxy resin Araldite® LY556 and Aradur hardener at a ratio of 100:30 to make the test specimen. The results indicated an overall improvement in the tensile strength of carbon-fiber laminates owing to the increase in the number of carbon-fiber layers in successive samples. The maximum achieved tensile strength through the present experimental protocol is 576.079 N/mm2, depicted by a prepared specimen of 10 layers of carbon fiber. Secondly, an increase in the deformation rate has also been observed by increasing the loading rate from 2 mm/min to 5 mm/min during the tensile testing of fabricated samples. These sample carbon-fiber composites can be used in the fabrication of prosthetic feet by controlling the experimental conditions. The fabricated prosthetic foot will assist in rehabilitating lower-limb amputees.
The linear complementarity approach has been utilized as a systematic and unified numerical process for determining the response of a rigid-plastic structure subjected to impulsive loading. However, ...the popular Lemke Algorithm for solving linear complementarity problems (LCP) encounters numerical instability issues whilst tracing the response of structures under extreme dynamic loading. This paper presents an efficient LCP approach with an enhanced initiation subroutine for resolving the numerical difficulties of the solver. The numerical response of the impulsively loaded structures is affected by the initial velocity profile, which if not found correctly can undermine the overall response. In the current study, the initial velocity profile is determined by a Linear Programming (LP) subroutine minimizing the energy function. An example of a uniform impulsively loaded simply supported beam is adduced to show the validity and accuracy of the proposed approach. The beam is approximated with bending hinges having infinite resistance to shear. Comparison of the numerical results to the available closed-form solution confirms the excellent performance of the approach. However, a subsequent investigation into a beam having the same support conditions and the applied loading, but with bending and shear deformation, results in numerical instability despite optimizing the initial velocity profile. Thus a more generic description of kinetics and kinematics is proposed that can further enhance the numerical efficiency of the LCP formulation. The ensuing numerical results are compared with the available close form solution to assess the accuracy and efficiency of the developed approach.
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Diabetic retinopathy (DR) is one of the main causes of blindness in people around the world. Early diagnosis and treatment of DR can be accomplished by organizing large regular screening programs. ...Still, it is difficult to spot diabetic retinopathy timely because the situation might not indicate signs in the primary stages of the disease. Due to a drastic increase in diabetic patients, there is an urgent need for efficient diabetic retinopathy detecting systems. Auto-encoders, sparse coding, and limited Boltzmann machines were used as a few past deep learning (DL) techniques and features for the classification of DR. Convolutional Neural Networks (CNN) have been identified as a promising solution for detecting and classifying DR. We employ the deep learning capabilities of efficient net batch normalization (BNs) pre-trained models to automatically acquire discriminative features from fundus images. However, we successfully achieved F1 scores above 80% on all efficient net BNs in the EYE-PACS dataset (calculated F1 score for DeepDRiD another dataset) and the results are better than previous studies. In this paper, we improved the accuracy and F1 score of the efficient net BNs pre-trained models on the EYE-PACS dataset by applying a Gaussian Smooth filter and data augmentation transforms. Using our proposed technique, we have achieved F1 scores of 84% and 87% for EYE-PACS and DeepDRiD.
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Abstract
Brain–computer interfaces (BCIs) systems traditionally use machine learning (ML) algorithms that require extensive signal processing and feature extraction. Deep learning (DL)-based ...convolutional neural networks (CNNs) recently achieved state-of-the-art electroencephalogram (EEG) signal classification accuracy. CNN models are complex and computationally intensive, making them difficult to port to edge devices for mobile and efficient BCI systems. For addressing the problem, a lightweight CNN architecture for efficient EEG signal classification is proposed. In the proposed model, a combination of a convolution layer for spatial feature extraction from the signal and a separable convolution layer to extract spatial features from each channel. For evaluation, the performance of the proposed model along with the other three models from the literature referred to as EEGNet, DeepConvNet, and EffNet on two different embedded devices, the Nvidia Jetson Xavier NX and Jetson Nano. The results of the Multivariant 2-way ANOVA (MANOVA) show a significant difference between the accuracies of ML and the proposed model. In a comparison of DL models, the proposed models, EEGNet, DeepConvNet, and EffNet, achieved 92.44 ± 4.30, 90.76 ± 4.06, 92.89 ± 4.23, and 81.69 ± 4.22 average accuracy with standard deviation, respectively. In terms of inference time, the proposed model performs better as compared to other models on both the Nvidia Jetson Xavier NX and Jetson Nano, achieving 1.9 sec and 16.1 sec, respectively. In the case of power consumption, the proposed model shows significant values on MANOVA (p < 0.05) on Jetson Nano and Xavier. Results show that the proposed model provides improved classification results with less power consumption and inference time on embedded platforms.
Abstract
Time-dependent rotating flow in presence of heat source/sink, applied magnetic field, Joule heating, thermal radiation, and viscous dissipation is considered. Chemical reaction with ...Arrhenius activation energy is implemented. The governing partial differential equations have been reduced to ordinary differential systems. Shooting scheme is implemented for the computations of governing systems. Graphical results are arranged for velocity, temperature, and concentration, skin friction coefficients, and heat and mass transfer rates. Main results are mentioned in conclusion portion. It is analyzed that velocity decays in the presence of magnetic variable while temperature and concentration fields are enhanced via Eckert number and fitted rate constant. Moreover drag force and mass and heat transfer rates decrease through higher estimations of rotation rate variable, magnetic parameter, and Eckert number.
Graphical Abstract
Graphical Abstract
•ExoMechHand is a cost-effective rehabilitation device designed for patients with median and ulnar neuropathies for hand impairment.•After 20 days of usage, three out of ten subjects showed ...improvements.•Machine-learning algorithms, particularly the extra trees classifier, demonstrated an impressive 98 % accuracy on surface-electromyography signals obtained from the flexor carpi ulnaris and flexor carpi radialis muscles.
Rehabilitation is a major requirement to improve the quality of life and mobility of patients with disabilities. The use of rehabilitative devices without continuous supervision of medical experts is increasing manifold, mainly due to prolonged therapy costs and advancements in robotics. Due to ExoMechHand's inexpensive cost, high robustness, and efficacy for participants with median and ulnar neuropathies, we have recommended it as a rehabilitation tool in this study. ExoMechHand is coupled with three different resistive plates for hand impairment. For efficacy, ten unhealthy subjects with median or ulnar nerve neuropathies are considered. After twenty days of continuous exercise, three subjects showed improvement in their hand grip, range of motion of the wrist, or range of motion of metacarpophalangeal joints. The condition of the hand is assessed by features of surface-electromyography signals. A Machine-learning model based on these features of fifteen subjects is used for staging the condition of the hand. Machine-learning algorithms are trained to indicate the type of resistive plate to be used by the subject without the need for examination by the therapist. The extra-trees classifier came out to be the most effective algorithm with 98% accuracy on test data for indicating the type of resistive plate, followed by random-forest and gradient-boosting with accuracies of 95% and 93%, respectively. Results showed that the staging of hand condition could be analyzed by sEMG signal obtained from the flexor-carpi-ulnaris and flexor-carpi-radialis muscles in subjects with median and ulnar neuropathies.
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