Unsteady Radiative Maxwell Fluid Flow over an Expanding Sheet with Sodium Alginate Water-Based Copper-Graphene Oxide Hybrid Nanomaterial: An Application to Solar Aircraft

Chandrasekaran, S.; Gupta, Murlidhar; Jangid, Sanju; Loganathan, K.; Deepa, B.; Chaudhary, Dinesh Kumar

doi:10.1155/2022/8622510

Cited by 11 publications

(4 citation statements)

References 44 publications

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“…Some other tectonic industrial and engineering implementations of Maxwell fluids include manufacturing hot rolling papers, metal spinning, drilling muds, etc. Chandrasekaran et al (2022) analyzed the Maxwell HNF's flow through an expanding sheet. Several studies regarding the flow of non-Newtonian fluids across different geometries are discussed in Khan et al (2021a, b) and Paul et al (2023).…”

Section: Multidisciplinementioning

confidence: 99%

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Numerical assessment of viscoelastic tetra hybrid nanofluid flow across a stretchable rotatory disk under the Soret and Dufour aspects

Patgiri,

Paul,

Sarma

2024

MMMS

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PurposeFluid flows through rotatory disks are encountered in industrial and practical engineering processes, such as computer storage devices, gas turbine rotators, rotating machinery, air cleaning machines, etc. The primary purpose of this research is to examine the combined aspects of variable electrical conductivity, thermal radiation, Soret and Dufour effects on a magnetohydrodynamic Maxwell single-walled carbon nanotubes–graphene oxide–multi-walled carbon nanotubes–copper (SWCNT–GO–MWCNT–Cu)/sodium alginate tetra-hybrid nanofluid flow through a stretchable rotatory disk.Design/methodology/approachThe modeled administrative equations of the present flow problem are converted to a non-dimensional system of ordinary differential equations by applying suitable similarity conversion and then solved numerically by implementing the bvp4c method. The impressions of noteworthy dimensionless parameters on velocity, temperature, concentration distributions, Nusselt number, skin friction and Sherwood number are reported via graphs and tables.FindingsThe authors figured out that the developed values of the rotation parameter diminish the temperature but enhance both the radial and angular velocities. Further, the mass and heat transmission rates are better for tetra-hybrid nanofluids than for ternary and hybrid nanofluids.Originality/valueThe present study emphasizes a special type of fluid called the tetra-hybrid nanofluid. The existing literature has not discussed the Maxwell tetra hybrid nanofluid flow through a stretchable rotatory disk with variable electrical conductivity. Besides, the novel aspects of magnetohydrodynamics, thermal radiation, Soret and Dufour effects are also incorporated into the present flow problem.

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Section: Multidisciplinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical assessment of viscoelastic tetra hybrid nanofluid flow across a stretchable rotatory disk under the Soret and Dufour aspects

Patgiri,

Paul,

Sarma

2024

MMMS

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“…The ability to automatically segment separating MRI brain pictures into white matter-WM, grey matter-GM, & cerebrospinal fluid-CSF are difficult for scientific study. Clinical diagnostics that aim better to understand the brain anatomy and function [17]. Here, they illustrated an MRI brain image segmentation methodology based on multiresolution edge detection, area selection, and methods for spatial & intensity cutoffs [18].…”

Section: Literature Surveymentioning

confidence: 99%

Cloud-Based LeNet-5 CNN for MRI Brain Tumor Diagnosis and Recognition

Srinivasarao,

Rajesh,

Saikumar

et al. 2023

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Early and accurate diagnosis of brain tumors is crucial in the medical field, as undetected or misdiagnosed tumors can lead to sudden death. Traditional models for diagnosing brain tumors suffer from low Time of Conversion (ToC) and low accuracy, contributing to a high mortality rate among the 5 million people affected by brain disease annually, as reported by the World Health Organization (WHO). Previous methods, such as Elastic Net Regression (ENR), Logistic Regression (LR), and other machine learning models, struggle to accurately locate and identify brain lesions. Moreover, these models are not suited for cloud-based platforms. To address this issue, we developed a sophisticated, cloud-based brain abnormality detection application using the LeNet-5 Convolutional Neural Network (CNN) on the DriveHQ platform. The pre-trained LeNet-5 model extracts features from ADNI-1, ADNI-2, and MIRIAD datasets. Real-time MRI brain images were collected from Manipal Hospital in Vijayawada, Andhra Pradesh, India. The LeNet-5 model employs hidden layers, flattened layers, max-pooling layers, dense layers, and ReLu layers for optimal performance. Our 2D-LeNet-5 CNN approach preprocesses images using split and merge techniques of binary mask segmentation. The Python 3.7 software tool was used to train and test datasets to identify abnormalities in MRI brain images. The proposed application achieved remarkable performance metrics, including 99.65% accuracy, 99.59% sensitivity, 99.72% F1-score, 99.25% recall, 59.32 PSNR, and 0.9929 MCC. These results demonstrate the superiority of our methodology in comparison to existing models, making it a promising solution for cloud-based brain tumor diagnosis and recognition.

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“…Dynamics of Cu/H 2 O nanofluid with the consequences of prescribed surface temperature and prescribed heat flux have been scrutinized by Ahmed et al [28]. Some recent novel contributions related with the applications of hybrid and nanofluids are made by researchers [29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Nanoplatelets in Mixed Convective Radiative Flow of Hybridized Nanofluid Mobilized by Variable Thermal Conditions

Ahmad

Faisal

Zan-Ul-Abadin

et al. 2022

Mathematical Problems in Engineering

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The present mathematical model discloses the effects of Boussinesq and Rosseland approximations on unsteady 3D dynamics of water-driven hybridized nanomaterial with the movements of nanoplatelets (molybdenum disulfide, MoS 2 and graphene oxide, GO ). Variable thermal conditions, namely, VST (variable surface temperature) and VHF (variable heat flux), are opted to provide temperature to the surface. MHD effects have also been used additionally to make the study more versatile. In order to transmute the transportation equations into nondimensionlized forms, similarity transformations have been adopted. The Keller-Box technique has been applied to obtain a numerical simulation of the modeled problem. The convergence of the solution for both VST and VHF cases is presented via the grid independence tactic. Thermal setup against escalating choices of power indices and nonlinear thermal radiation parameter is discussed via graphical illustrations. The rate of heat transaction has been discussed with the growing choices of mixed convection, thermal radiation, and unsteady parameters through various tabular arrangements. It is observed through the present analysis that mixed convection parameter, radiation parameter, temperature maintaining indices r , s , and unsteady parameter magnify the rate of heat transference under the control of platelet-shaped nanoparticles.

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Unsteady Radiative Maxwell Fluid Flow over an Expanding Sheet with Sodium Alginate Water-Based Copper-Graphene Oxide Hybrid Nanomaterial: An Application to Solar Aircraft

Cited by 11 publications

References 44 publications

Numerical assessment of viscoelastic tetra hybrid nanofluid flow across a stretchable rotatory disk under the Soret and Dufour aspects

Numerical assessment of viscoelastic tetra hybrid nanofluid flow across a stretchable rotatory disk under the Soret and Dufour aspects

Cloud-Based LeNet-5 CNN for MRI Brain Tumor Diagnosis and Recognition

Dynamics of Nanoplatelets in Mixed Convective Radiative Flow of Hybridized Nanofluid Mobilized by Variable Thermal Conditions

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