Numerical Computation and Statistical Interpretations of Heat Transfer of Tween-20/ethyl Acetate Nanofluid Flow with Melting Rheological Quality and Activation Energy

Authors

  • Aamir Farooq Department of Mathematics, Zhejiang Normal University, Jinhua 321004, PR China
  • Sadique Rehman Division of Mathematical and Physical Sciences, Kanazawa University, Kakuma, Kanazawa 920-1192, Japan
  • Mujahid Ullah Department of Mathematics, University of Science and Technology, Bannu, Khyber Pakhtunkhwa, Pakistan
  • M. S. Abbas Administrative and Financial Science Department, Ranyah University College, Taif University, Taif 21944, Saudi Arabia
  • K. El-Rashidy Technology and Science Department, Ranyah University College, Taif University,Taif 21944, Saudi Arabia
  • M. Mamun Miah Division of Mathematical and Physical Sciences, Kanazawa University, Kakuma, Kanazawa 920-1192, Japan https://orcid.org/0009-0007-1211-7897
  • Mohammad Kanan Department of Industrial Engineering, College of Engineering, University of Business and Technology, Jed-dah, 21448, Saudi Arabia https://orcid.org/0000-0003-0526-0015

DOI:

https://doi.org/10.37256/cm.5420245398

Keywords:

statistical interpretations, tween-20/ethyl acetate nanofluid, melting rheology, activation energy, artificial neural computing, numerical analysis, fourth-order runge-kutta method

Abstract

Tween-20 plays a significant role in the biological, food, and pharmaceutical industries. Additionally, it plays a vital role in improving the quality of reverse mechanisms of multi-drug resistance. This paper uses artificial neural computing to examine Tween-20 nanoparticles’ behaviors in a base fluid called ethyl acetate to enhance the applications in nanomedicine, drug delivery and biotechnology. The study focuses on the nonlinear model of a viscous fluid at the stagnation point, where mixed convection processes and activation energy are present. The study incorporates slip velocity and melting boundary conditions to examine heat and mass transfer, taking into account thermal and solutal stratification. Various fields of research and technology, specially in industrial engineering that include hydrodynamics, panto-graph systems, and biomedical mathematics, extensively utilize artificial computing. The datasets are based on velocity, temperature, and concentration outlines. The fourth-order Runge-Kutta method is used to generate the datasets. To validate the LMM-ABNNs, we have compared them with a numerical solution, showing a high level of agreement. We utilize the error histogram and mean square error results to validate the performance, scrutinize the training and testing methods, and explore the validity of the approximate answer. Furthermore, the quality characteristics such as skin friction, rate of heat, and mass movement (Nusselt and Sherwood numbers) are statistically analyzed to forecast the model’s durability. This article is the best example of investigating different fluid parameters with the latest artificial neural computing.

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Published

2024-11-11

How to Cite

1.
Farooq A, Rehman S, Ullah M, Abbas MS, El-Rashidy K, Miah MM, Kanan M. Numerical Computation and Statistical Interpretations of Heat Transfer of Tween-20/ethyl Acetate Nanofluid Flow with Melting Rheological Quality and Activation Energy. Contemp. Math. [Internet]. 2024 Nov. 11 [cited 2024 Dec. 10];5(4):4949-74. Available from: https://ojs.wiserpub.com/index.php/CM/article/view/5398