Numerical study of heat transfer and pressure drop of nanofluids in a combined porous media of hydrophobic and hydrophilic surfaces

Babaei, Amir; Aminian, Ehsan; Saffari, Hamid

doi:10.1177/09576509231158668

Cited by 2 publications

(2 citation statements)

References 70 publications

(78 reference statements)

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“…This process is examined by Maneengam et al [19]. Other interesting conclusions regarding such fluids can be found in [20][21][22][23].…”

Section: Introductionmentioning

confidence: 94%

Computational Intelligence Approach for Optimising MHD Casson Ternary Hybrid Nanofluid over the Shrinking Sheet with the Effects of Radiation

Zeeshan,

Khan,

Ellahi

et al. 2023

Applied Sciences

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The primary goal of this research is to present a novel computational intelligence approach of the AI-based Levenberg–Marquardt scheme under the influence of backpropagated neural network (LMS-BPNN) for optimizing MHD ternary hybrid nanofluid using Casson fluid over a porous shrinking sheet in the existence of thermal radiation (Rd) effects. The governing partial differential equations (PDEs) showing the Casson ternary hybrid nanofluid are converted into a system of ordinary differential equations (ODEs) with suitable transformations. The numerical data is constructed as a reference with bvp4c (MATLAB built-in function used to solve a system of ODEs) by varying Casson fluid parameters (β), magnetic field (M), porosity (S), nanoparticle concentrations (ϕ1=ϕ2=ϕ3), and thermal radiation (Rd) effects across all LMS-BPNN scenarios. The numerical data-sheet is divided into 80% of training, 10% of testing, and 10% of validation for LMS-BPNN are used to analyze the estimated solution and its assessment with a numerical solution using bvp4c is discussed. The efficiency and consistency of LMS-BPNN are confirmed via mean squared error (MSE) based fitness curves, regression analysis, correlation index (R) and error histogram. The results show that velocity decreases as β grows, whereas velocity increase as M increases. The concentrations of nanoparticles and thermal radiations have increasing effects on θ0. To comprehend the dependability and correctness of the data gained from numerical simulations, error analysis is a key stage in every scientific inquiry. Error analysis is presented in terms of absolute error and it is noticed that the error between the numerical values and predicted values with AI is approximately 10−6. The error analysis reveals that the developed AI algorithm is consistent and reliable.

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“…This process is examined by Maneengam et al [19]. Other interesting conclusions regarding such fluids can be found in [20][21][22][23].…”

Section: Introductionmentioning

confidence: 94%

Computational Intelligence Approach for Optimising MHD Casson Ternary Hybrid Nanofluid over the Shrinking Sheet with the Effects of Radiation

Zeeshan,

Khan,

Ellahi

et al. 2023

Applied Sciences

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“…Aminian et al 28 and Saffari et al 29 have theoretically investigated the microstructured hybrid surface heat transfer characteristics with Marangoni convection effect and hydrophobic-hydrophilic tube respectively. Babaei et al 30 have worked on the numerical analysis of heat transfer and pressure drop of nanofluids in a combined porous media of hydrophobic and hydrophilic surfaces.…”

Section: Heat Transfer Enhancementmentioning

confidence: 99%

A numerical study on effect of intra and inter spaced winding configuration on performance of oil forced transformer cooling system

Muralikrishna,

Satyanarayana Reddy,

Sahu

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part A:

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The present work aims at investigating, numerically, the heat transfer characteristics of transformer cooling system under forced circulation for three different winding configurations. The purpose of a transformer cooling system is to keep the hot spot temperature within the critical value for a long life of transformer, which increases with enhanced cooling of transformer windings. Thus, two different winding configurations are modeled by providing space within the windings (an intra-space model) and in between the windings (an inter-space model) to improve the fluid-solid interaction. A conventional winding, without spacing, is also modeled for comparative study. A 315 kVA, 3-phase core type transformer is taken as the base model from which the winding and core specifications are obtained analytically. Parameters chosen to measure the influence of winding configuration are local temperature distribution in the windings and core and maximum transformer temperature. Results are obtained for all three models with varying space for various cooling mediums like naphthenic oil, silicon oil, and synthetic ester oil at flow velocities ranging from 0.72 m/s to 2.16 m/s. It is observed that the gap between the windings, interspacing, decreases the secondary winding temperatures, while it increases the core and primary winding temperatures. However, the intra spacing configuration finds a drop in local temperatures at all locations of the transformer windings and core. The enhanced heat dissipation to the cooling agent, with intra spacing winding configuration, reduces the maximum transformer temperature. Contrasting to the above, the inter-space model has an adverse effect on maximum temperature at all widths of space considered. Quantitatively, the space within the transformer reduces the maximum temperature by 5% in comparison with the space between the windings. The increased velocity of the cooling medium, Naphthenic oil, from 0.72 m/s to 2.16 m/s reduces the maximum temperature by 4.5 units. The study made to know the role of cooling medium discloses that the synthetic ester oil shows better performance for reducing temperature values due to its highly viscous nature.

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Numerical study of heat transfer and pressure drop of nanofluids in a combined porous media of hydrophobic and hydrophilic surfaces

Cited by 2 publications

References 70 publications

Computational Intelligence Approach for Optimising MHD Casson Ternary Hybrid Nanofluid over the Shrinking Sheet with the Effects of Radiation

Computational Intelligence Approach for Optimising MHD Casson Ternary Hybrid Nanofluid over the Shrinking Sheet with the Effects of Radiation

A numerical study on effect of intra and inter spaced winding configuration on performance of oil forced transformer cooling system

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