Artificial intelligence-based analysis employing Levenberg Marquardt neural networks to study chemically reactive thermally radiative tangent hyperbolic nanofluid flow considering Darcy-Forchheimer theory

Journal of Radiation Research and Applied Sciences, Journal Year: 2025, Volume and Issue: 18(1), P. 101253 - 101253

Published: Jan. 5, 2025

Language: Английский

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Supervised machine learning computing paradigm to measure melting and dissipative effects in entropy induced Darcy–Forchheimer flow with ternary-hybrid nanofluids

Numerical Heat Transfer Part B Fundamentals, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 22

Published: July 14, 2024

Language: Английский

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A multi-layer neural network-based evaluation of MHD radiative heat transfer in Eyring–Powell fluid model

Heliyon, Journal Year: 2025, Volume and Issue: 11(3), P. e41800 - e41800

Published: Jan. 18, 2025

In the modern era, artificial intelligence (AI) has been applied as one of transformative factors for scientific research in many fields that could provide new solutions to extremely complicated and complex physical models. this paper, a multi-layer neural network combined with Bayesian regularization procedure (MNNs-BRP) is utilized evaluate model MHD radiative heat non-uniform heating Eyring Powell fluid (EPF-MHD-RHS). The mixed convection parameter, Prandtl number, emission or immersion parameter are studied relation momentum transfer. To facilitate analysis, governing partial differential equations (PDEs) converted into ordinary (ODEs) only aid similarity transformations. From there, dataset created later trained, tested, validated by MNNs-BRP efficient estimating robust demonstrates high accuracy, which compares well benchmark solutions. Performances confirmed metrics like error histograms, mean squared-error (MSE) check-ups, regression analysis MSEs all over interval [10-09 - 10-04] ensure sustained model. obtained outcomes indicate built implemented utilizing offers precise performance ranging from 3.25E-13 5.41E-13 around 3.25E-13, 1.56E-11, 5.41E-13, 2.97E-12, 1.03E-11, 2.05E-12, 1.49E-12, 5.01E-12, across eight different circumstances, suggesting improved capability reliability developed predictive After careful review we have found temperature decreases increase Prandl while an inverse result noticed emission. However, velocity increasing trend values generation absorption parameter. contrast profile decreasing magnetic field stratification This work on integrating AI classical dynamics problem entirely paradigm involves smart combination computational strategies advanced modeling techniques. Our investigation not just raising bar predicting but also showing how can truly transform entire domain mechanics related disciplines. All numerical graphical illustrations attained employing AI-based techniques authenticates solution methodology evaluation problems.

Language: Английский

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Artificial intelligence analysis of thermal energy for convectively heated ternary nanofluid flow in radiated channel considering viscous dissipations aspects

Engineering Science and Technology an International Journal, Journal Year: 2025, Volume and Issue: 62, P. 101955 - 101955

Published: Jan. 20, 2025

Language: Английский

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Artificial intelligence-based procedure to analyze heat transfer features for chemically reactive Darcy-Forchheimer flow of magnetized tetra-hybrid nanofluid capturing joule heating aspects through stenotic artery

Tribology International, Journal Year: 2025, Volume and Issue: 206, P. 110532 - 110532

Published: Feb. 7, 2025

Language: Английский

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Influence of activation energy in steady state hydro dynamic non-Newtonian nano fluid with mobile microorganisms

Results in Chemistry, Journal Year: 2024, Volume and Issue: 9, P. 101653 - 101653

Published: July 1, 2024

The purpose of this article is to investigate the activation energy associated with bioconvection nano flow propagation via Riga plate Arrhenius impact. Electricity flows through fluid a Lorentz force that alters in vertical direction. A considered when magnetic electro applied. Casson and are included governing equations. model based on stretchy sheet can represent bioconvection, movement microorganisms two-phase nano-liquid flows. Novelty research explore thermal mass transport across steady, laminar, MHD incompressible impacts. PDEs organized by application basic conservation laws, they subsequently transformed into equivalent ordinary differential An evaluation present analysis conducted using bvp4c, displayed graphically. Results indicates field Rayleigh wither velocity. Temperature profiles tend be increased Brownian motion.

Language: Английский

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Multilayer deep-learning intelligent computing for the numerical analysis of unsteady heat and mass transfer in MHD Carreau Nanofluid model

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 105369 - 105369

Published: Oct. 1, 2024

Language: Английский

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Paradigm on Levenberg–Marquardt neural algorithm analysis of heat conduction optimization for ternary hybrid nanofluid with entropy generation

ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 9, 2024

Abstract The significance of the present article is to enhance thermal management and energy efficiency complex engineering infrastructures such as storage systems, modern electric vehicles, insulations, heavy‐duty machinery, production units. This research aims understand intricate relationship between conductivity performance ternary () hybrid nanomaterial entropy generation optimize material design efficacy. A synergetic combination three distinct nanomaterials silicon dioxide, ferric oxide, titanium oxide with ethylene glycol water in ratio 3:2 a base solvent comprised contributing unique thermophysical properties. To elucidate impact this composition on conductivity, various factors are analyzed. advanced computational technique Artificial intelligent feed‐forward neural network (AIFFNN) utilized. problem governed system PDEs, which transformed into ODEs by dimensionless similarity. Adams method provided dataset filtered embedded Marquardt–Levenberg Algorithm (LMA). study examines role constituents, morphology, boundary conditions generation. Graphical analysis velocity, temperature, respect varying parameters, including surface absorption ( λ ), magnetic strength (Tesla M radiation parameter Rd Brownian motion Br Eckert number Ec ). findings have practical for optimizing industrial applications.

Language: Английский

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Machine learning investigation with neural network modelling for Sutterby Multi-hybrid fluid in Biomedical treatments

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104427 - 104427

Published: Feb. 1, 2025

Language: Английский

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Optimizing heat transportation features of magneto-Carreau nanofluid flow through novel machine learning algorithm with the immersion of chemical process for inclined cylindrical surface

Tribology International, Journal Year: 2025, Volume and Issue: unknown, P. 110619 - 110619

Published: March 1, 2025

Language: Английский

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