Entropy Minimization Analysis of a Partially Ionized Casson Nanofluid Flow over a Bidirectional Stretching Sheet with Surface Catalyzed Reaction

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%

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.

“…Additionally, it considers surface catalytic reactions occurring on the sheet. The goal is to understand how these factors affect the flow and heat transfer characteristics in this complex scenario [28].…”

Section: Introductionmentioning

confidence: 99%

Intelligent computing technique to study heat and mass transport of Casson nanofluidic flow model on a nonlinear slanted extending sheet

Hussain,

Islam,

Raja

et al. 2023

This paper explains the importance and benefits of using AI in fluid mechanics, emphasizing its capability of finding the solution of fluid flow systems through iterative optimization techniques. It explores how AI can enhance the understanding of fluid dynamics to improve engineering processes. In the presented studies, the heat and mass transport of the Casson nanofluidic flow model on a nonlinear slanted extending sheet (HMT‐CNFM) via AI‐based Levenberg Marquard methodology with backpropagated trained neural networks (TNN‐BLMM) is studied. By applying an appropriate transformation, the governing PDEs representing HMT‐CNFM are converted into a system of nonlinear ODEs. The dataset for Levenberg Marquard methodology with backpropagated trained neural network (TNN‐BLMM) for all six scenarios of this proposed model via computational power of the Lobatto IIIA scheme using the “bvp4c” package in MATLAB and then graphically illustrate all these six scenarios through nftool to attain mean square error, regression, error histogram, performance, and fit curve. Training, testing, and validation processes of NN‐BLMM are organized for the investigation of the HMT‐CNFM model.