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Intelligent computing technique based supervised learning for squeezing flow model.

Maryam Mabrook Almalki1,2, Eman Salem Alaidarous3, Dalal Adnan Maturi3

  • 1Department of Mathematics, Faculty of Science, Umm Al-Qura University, Makkah, 24211, Saudi Arabia. mmmalki@uqu.edu.sa.

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This summary is machine-generated.

This study introduces a novel approach using Levenberg-Marquardt backpropagation neural networks (LMBNN) to analyze unsteady squeezing flow between circular parallel plates (USF-CPP). The method accurately models complex fluid dynamics for various flow conditions.

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Area of Science:

  • Computational Fluid Dynamics (CFD)
  • Artificial Intelligence in Fluid Mechanics
  • Non-Newtonian Fluid Flow Modeling

Background:

  • Investigating unsteady squeezing flow between circular parallel plates (USF-CPP) is crucial for understanding complex fluid dynamics.
  • Traditional numerical methods can be computationally intensive for such problems.
  • Intelligent computing offers a promising alternative for efficient fluid flow analysis.

Purpose of the Study:

  • To develop and validate an intelligent computing approach for analyzing unsteady squeezing flow between circular parallel plates (USF-CPP).
  • To utilize Levenberg-Marquardt backpropagation neural networks (LMBNN) for modeling fluid dynamics.
  • To assess the accuracy and applicability of LMBNN across various flow scenarios.

Main Methods:

  • Governing partial differential equations were transformed into nonlinear ordinary differential equations using similarity transformation.
  • A dataset for the USF-CPP system was generated using the Runge-Kutta method with varying Reynolds numbers and volume flow rates.
  • Levenberg-Marquardt backpropagation neural networks (LMBNN) were trained, tested, and validated against reference data.

Main Results:

  • LMBNN successfully provided approximation solutions for unsteady squeezing flow between circular parallel plates (USF-CPP).
  • The model's accuracy was confirmed by comparing its outputs with the generated reference dataset.
  • Analysis of mean square error, state transition dynamics, error histograms, and regression illustrations validated the LMBNN's performance.

Conclusions:

  • Levenberg-Marquardt backpropagation neural networks (LMBNN) offer an effective and accurate method for simulating unsteady squeezing flow between circular parallel plates (USF-CPP).
  • The intelligent computing paradigm provides a robust framework for solving complex fluid dynamics problems.
  • This approach demonstrates significant potential for advancing the analysis of fluid flow systems.