Simultaneous heat and mass enhancement in mixed convective flow of power-law fluid using hybrid nanoparticles

The presented article aims to analyze the three‐dimensional electrically conducting flow induced by a rotating stretchable disk. A hybrid nanomaterial is generated by adding copper and graphene oxide nanoparticles to kerosene oil, which saturates through a Darcy–Forchheimer porous medium. The article also reports on entropy generation and the Bejan number. To solve the associated partial differential equations, appropriate transformations are applied to convert them into ordinary differential equations. These equations are then solved numerically to obtain the desired solutions. The significance of these nanoparticles in kerosene oil is due to their ability to enhance heat transfer and thermal conductivity, thereby optimizing the performance of the rotating stretchable disk system. The synergistic effects of these components lead to improved energy efficiency and overall system effectiveness. The results for various quantities of interest, such as velocity, entropy, temperature, and the Bejan number, are presented graphically and analyzed considering the influence of relevant parameters. Moreover, the comparative analysis indicated that hybrid nanofluid have dominating effect than base liquid (kerosene oil).

“…Recent analyses on entropy generation can be found in Refs. [24][25][26][27][28][29], providing updated insights into this fascinating field of research.…”

Section: Thermophysicalmentioning

confidence: 99%

Entropy generation in MHD Darcy–Forchheimer flow of hybrid nanomaterial: A numerical study of local similar solution

Hayat,

Yazman,

Muhammad

et al. 2023

“…Different researchers produced important results related to heat transfer phenomenon which are illustrated in refs. [16][17][18][19][20][21][22][23][24][25]. Magnetohydrodynamic (MHD) is the study of magnetically illustrated characteristics amongst those fluids that are electrically conducting like liquid metals, plasmas and electrolytes.…”

Section: Introductionmentioning

confidence: 99%

“…Different researchers produced important results related to heat transfer phenomenon which are illustrated in refs. [16–25].…”

Section: Introductionmentioning

confidence: 99%

Integrated intelligence of inverse multiquadric radial base neuro‐evolution for radiative MHD Prandtl–Eyring fluid flow model with convective heating

Butt,

Ahmad,

Shoaib

et al. 2024

In this communication, a new stochastic numerical paradigm is introduced for thorough scrutinization of the magnetohydrodynamic (MHD) boundary layer flow of Prandtl–Eyring fluidic model along a stretched sheet impact on thermal radiation as well as convective heating scenarios by exploiting the accurate approximation knacks of ANNs (artificial‐neural‐networks) modeling by using the inverse multiquadric (IMQ) function optimized/trained with well‐reputed global search with genetic algorithms (GAs) and local search efficacy of sequential quadratic programming (SQP), that is, ANNs‐IMQ‐GA‐SQP. The mathematical model of the Prandtl–Eyring fluid flow is portrayed in the form of PDEs and then converted in the form of a nonlinear system of ODEs by utilizing suitable similarity transformation to calculate/analyze the solution dynamics. The kinematic and thermodynamic properties of MHD Prandtl–Eyring fluid flow are examined/observed by varying the sundry physical parameters. The obtained intelligent computing designed solver‐based numerical results are consistently found in good agreement with reference results obtained through the Adams numerical technique. First and second‐order cumulant‐based statistical assessments are extensively exploited to endorse trustily the efficacy of ANNs‐IMQ‐GA‐SQP solver based on exhaustive simulations for the Prandtl–Eyring fluidic system.

Intelligent adaptive nonlinear autoregressive eXogeneous neuro‐structure for ferromagnetic Powell‐Eyring fluidic involving cubic autocatalysis chemical reaction

Khan,

Chang,

Raja

et al. 2024

The scope of artificial intelligence in the field of fluid mechanics has been expanded with the development sophisticated technology to enhance the efficiency, reliability, solve complexities, introduced alternate transformation and enabling more dependable solutions with their analysis. The goal of this study is to investigate the ferromagnetic Powell‐Eyring fluids (FMPEFs) model with non‐Fourier heat flux by using artificial intelligence‐based scheme by exploiting the adaptive nonlinear autoregressive eXogenous (NARX) neuro‐architecture with backpropagation of Levenberg Marquart (LM), that is, NARX‐LM. The developed NARX‐LM methodology applied on synthetic datasets acquired with the help of Adams numerical method for FMPEF system by prudently changing physical quantities that is, material parameters of Eyring Powell, homogeneous reaction, heterogeneous reaction, dimensionless thermal relaxation time, Prandtl number, Schmidt number with fixed values parameter of ferrohydrodynamic interaction, rate of diffusion coefficient. Outcomes of NARX‐LM are regularly overlapping with the numerical results for the FMPEFs system having reasonable small error magnitude for each variant. The proficiency of intelligent computing anticipated on FMPEFs is depicted exhaustively with iterative mean squared error based iconvergence curves, analysis of adaptive controlling factors, error frequency distribution on the histograms, auto‐correlation, and correlation measures.