Entropy Optimized Second Grade Fluid with MHD and Marangoni Convection Impacts: An Intelligent Neuro-Computing Paradigm
Source abstract
Abstract
Artificial intelligence applications based on soft computing and machine learning algorithms have recently become the focus of researchers’ attention due to their robustness, precise modeling, simulation, and efficient assessment. The presented work aims to provide an innovative application of Levenberg Marquardt Technique with Artificial Back Propagated Neural Networks (LMT-ABPNN) to examine the entropy generation in Marangoni convection Magnetohydrodynamic Second Grade Fluidic flow model (MHD-SGFM) with Joule heating and dissipation impact. The PDEs describing MHD-SGFM are reduced into ODEs by appropriate transformation. The dataset is determined through Homotopy Analysis Method by the variation of physical parameters for all scenarios of proposed LMT-ABPNN. The reference data samples for training/validation/testing processes are utilized as targets to determine the approximated solution of proposed LMT-ABPNN. The performance of LMT-ABPNN is validated by MSE based fitness, error histogram scrutiny, and regression analysis. Furthermore, the influence of pertinent parameters on temperature, concentration, velocity, entropy generation, and Bejan number is also deliberated. The study reveals that the larger β and Ma, the higher f′(η) while M has the reverse influence on f′(η). For higher values of β, M, Ma, and Ec, θ(η) boosts. The concentration ϕ(η) drops as Ma and Sc grow. An augmentation is noticed for NG for higher estimations of β,M, and Br. Larger β,M and Br decays the Bejan number.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fluid Dynamics and Turbulent Flows · Nanofluid Flow and Heat Transfer · Nuclear Engineering Thermal-Hydraulics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mathematics, COMSATS University Islamabad, Attock Campus, Punjab 43600, Pakistan
Department of Mathematics, College of Arts and Sciences, Prince Sattam Bin Abdulaziz University, Wadi Aldawaser 11991, Saudi Arabia
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Yunlin 64002, Taiwan
Department of Applied Mathematics and Statistics, Institute of Space Technology, Islamabad 44000, Pakistan
Department of Mathematics and Statistic, Riphah International University, Islamabad 46000, Pakistan
Department of Mathematics, Capital University of Science and Technology (CUST), Islamabad 44000, Pakistan
Department of Physics, College of Sciences, University of Bisha, Bisha 61922, Saudi Arabia
Physics Department, Faculty of Science, Al-Azhar University, Assiut 71524, Egypt
Laboratory of Nano-Smart Materials for Science and Technology (LNSMST), Department of Physics, Faculty of Science, King Khalid University, Abha 61413, Saudi Arabia
Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha 61413, Saudi Arabia
Nanoscience Laboratory for Environmental and Biomedical Applications (NLEBA), Semiconductor Lab, Department of Physics, Faculty of Education, Ain Shams University, Cairo 11757, Egypt
Department of Mathematics and Statistics, College of Science, Taif University, Taif 21944, Saudi Arabia
References (53)
Show source-provided references
1. On convection cells induced by surface tension10.1017/S0022112058000616 ↗
2. The marangoni effects10.1038/187186a0 ↗
3. Dynamics of marangoni convection in hybrid nanofluid flow submerged in ethylene glycol and water base fluids10.1016/j.icheatmasstransfer.2020.104962 ↗
4. Analysis of boundary layer MHD darcy-forchheimer radiative nanofluid flow with soret and dufour effects by means of marangoni convection10.1016/j.csite.2020.100792 ↗
5. MHD Thin film flow of kerosene oil based CNTs nanofluid under the influence of marangoni convection10.1088/1402-4896/ab47c4 ↗
6. The impact of the Marangoni convection and magnetic field versus blood-based carbon nanotube nanofluids
7. Marangoni forced convective casson type nanofluid flow in the presence of lorentz force generated by riga plate
8. Rasool, G., Zhang, T., Chamkha, A.J., Shafiq, A., Tlili, I., and Shahzadi, G. (2020). Entropy generation and consequences of binary chemical reaction on MHD Darcy–Forchheimer Williamson nanofluid flow over non-linearly stretching surface. Entropy, 22.10.3390/e22010018 ↗
9. An Analytical study of MHD heat and mass transfer oscillatory flow of a micropolar fluid over a vertical permeable plate in a porous medium
10. Hall and ion slip effects on MHD rotating boundary layer flow of nanofluid past an infinite vertical plate embedded in a porous medium10.1016/j.rinp.2019.102652 ↗
11. Natural convection from an inclined plate embedded in a variable porosity porous medium due to solar radiation10.1016/S1290-0729(01)01305-9 ↗
12. Non-darcy fully developed mixed convection in a porous medium channel with heat generation/absorption and hydromagnetic effects10.1080/10407789708913911 ↗
13. MHD Flow over a moving plate in a rotating fluid with magnetic field, hall currents and free stream velocity10.1016/S0020-7225(02)00016-2 ↗
14. Unsteady flow and heat transfer on a semi-infinite flat plate with an aligned magnetic field10.1016/S0020-7225(98)00144-X ↗
15. Hydromagnetic three-dimensional free convection on a vertical stretching surface with heat generation or absorption10.1016/S0142-727X(98)10032-2 ↗
16. Significance of suction/injection, gravity modulation, thermal radiation, and magnetohydrodynamic on dynamics of micropolar fluid subject to an inclined sheet via finite element approach10.1016/j.csite.2021.101537 ↗
17. Ali, B., Thumma, T., Habib, D., and Riaz, S. (2021). Finite element analysis on transient MHD 3D rotating flow of Maxwell and tangent hyperbolic nanofluid past a bidirectional stretching sheet with Cattaneo Christov heat flux model. Therm. Sci. Eng. Prog., 101089. (In press).10.1016/j.tsep.2021.101089 ↗
18. Ali, B., Siddique, I., Ahmadian, A., Senu, N., Ali, L., and Haider, A. (2021). Significance of Lorentz and Coriolis forces on dynamics of water based silver tiny particles via finite element simulation. Ain Shams Eng. J., in press.10.1016/j.asej.2021.08.014 ↗
19. Motion of hybrid nanofluid (MnZnFe2O4–NiZnFe2O4–H2O) with homogeneous–heterogeneous reaction: Marangoni convection10.1016/j.matcom.2021.07.017 ↗
20. Marangoni convective flow of hybrid nanofluid (MnZnFe2O4-NiZnFe2O4-H2O) with Darcy Forchheimer medium10.1016/j.asej.2021.01.028 ↗
21. Transportation of marangoni convection and irregular heat source in entropy optimized dissipative flow10.1016/j.icheatmasstransfer.2020.105031 ↗
22. Dynamics of Marangoni convection in radiative flow of power-law fluid with entropy optimization10.1142/S0217979221502805 ↗
23. Thermosolutal Marangoni convection of bingham non-Newtonian fluids within inclined lid-driven enclosures full of porous media10.1002/htj.22259 ↗
24. Khan, M.I., Qayyum, S., Chu, Y.M., and Kadry, S. (2020). Numerical simulation and modeling of entropy generation in Marangoni convective flow of nanofluid with activation energy. Numer. Methods Partial. Differ. Equ., in press.10.1002/num.22610 ↗
25. Irreversibility analysis in Marangoni forced convection flow of second grade fluid10.1088/2399-6528/aba8db ↗
26. State-of-the-art in artificial neural network applications: A survey10.1016/j.heliyon.2018.e00938 ↗
27. An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings10.1016/j.energy.2019.116370 ↗
28. Prediction of interfacial interactions related with membrane fouling in a membrane bioreactor based on radial basis function artificial neural network (ANN)10.1016/j.biortech.2019.03.044 ↗
29. A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: Theory and applications10.1007/s00521-019-04109-9 ↗
30. Neuro-fuzzy modeling and prediction of summer precipitation with application to different meteorological stations10.1016/j.aej.2019.12.011 ↗
31. Data-driven prognosis method using hybrid deep recurrent neural network10.1016/j.asoc.2020.106351 ↗
32. Heat transfer between two porous parallel plates of steady nano fludis with Brownian and thermophoretic effects: A new stochastic numerical approach10.1016/j.icheatmasstransfer.2021.105436 ↗
33. Intelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model10.1038/s41598-021-88499-8 ↗
34. Computational intelligence of Levenberg-Marquardt backpropagation neural networks to study the dynamics of expanding/contracting cylinder for cross magneto-nanofluid flow model10.1088/1402-4896/abe068 ↗
35. Intelligent computing Levenberg Marquardt approach for entropy optimized single-phase comparative study of second grade nanofluidic system10.1016/j.icheatmasstransfer.2021.105544 ↗
36. Intelligent computing for the dynamics of entropy optimized nanofluidic system under impacts of MHD along thick surface10.1142/S0217979221502696 ↗
37. Raja, M.A.Z., Shoaib, M., Khan, Z., Zuhra, S., Saleel, C.A., Nisar, K.S., Islam, S., and Khan, I. (2021). Supervised neural networks learning algorithm for three dimensional hybrid nanofluid flow with radiative heat and mass fluxes. Ain Shams Eng. J., in press.10.1016/j.asej.2021.08.015 ↗
38. Neuro-intelligent mappings of hybrid hydro-nanofluid Al2O3–Cu–H2O model in porous medium over rotating disk with viscous dissolution and JOULE heating10.1016/j.ijhydene.2021.06.065 ↗
39. MHD boundary layer flow over a stretching sheet: A new stochastic method10.1155/2021/9924593 ↗
40. Stochastic numerical computing with Levenberg–Marquardt backpropagation for performance analysis of heat sink of functionally graded material of the porous fin10.1016/j.surfin.2021.101403 ↗
41. Falkner–Skan equation with heat transfer: A new stochastic numerical approach10.1155/2021/3921481 ↗
42. Neuro-swarm intelligent computing paradigm for nonlinear HIV infection model with CD4+ T-cells10.1016/j.matcom.2021.04.008 ↗
43. Meyer Wavelet neural networks to solve a novel design of fractional order pantograph lane-emden differential model10.1016/j.chaos.2021.111404 ↗
44. Ali, W., Li, Y., Raja, M.A.Z., Khan, W.U., and He, Y. (2021). State estimation of an underwater markov chain maneuvering target using intelligent computing. Entropy, 23.10.3390/e23091124 ↗
45. Shercliff, J.A. (1965). Textbook of Magnetohydrodynamics, Pergamon Press.
46. Cramer, K.R., and Pai, S.I. (1973). Magnetofluid Dynamics for Engineers and Applied Physicists, McGrawhill.10.1002/eej.4390930120 ↗
47. Simulation and modeling of entropy optimized MHD flow of second grade fluid with dissipation effect10.1016/j.jmrt.2020.07.067 ↗
48. Exact analytical results for the thermosolutal MHD Marangoni boundary layers10.1016/j.ijthermalsci.2007.07.004 ↗
49. Influence of Lorentz forces on nanofluid forced convection considering Marangoni convection10.1016/j.molliq.2016.11.001 ↗
50. A novel design of three-dimensional mhd flow of second-grade fluid past a porous plate10.1155/2019/2584397 ↗
51. The effect of slip condition on the three-dimensional flow of Jeffrey fluid along a plane wall with periodic suction10.1007/s40430-017-0721-0 ↗
52. Three-dimensional flow of Jeffrey fluid along an infinite plane wall with periodic suction10.1007/s11012-017-0649-6 ↗
53. Heat and mass transfer phenomenon for the dynamics of casson fluid through porous medium over shrinking wall subject to Lorentz force and heat source/sink10.1016/j.aej.2020.10.056 ↗