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In this chapter, optimization of the MHD peristaltic flow of third grade nanofluid in symmetric channel via ANNs is investigated. The leading equations are simplified into the couple nonlinear differential equations by assuming low Reynolds number and long wavelength conditions. Use MATLAB's built-in bvp4c routine to obtain the numerical solution for the coupled nonlinear differential equations. The findings are displayed in graphical form, with an analysis of how key parameters affect velocity profile, temperature profile, nanoparticle concentration profile and pumping characteristics. To construct the correlation among input quantities (the Hartman number (M), the Groshaf number (Gr), the Deborah number ( \(\Gamma \) ), thermophoresis parameter (Nt), and Brownian motion parameter (Nb)) and output responses ( \(u, \theta , \phi \) and \(\Delta {P}_{\lambda }\) ) via ANNs. The MSE, error histogram and regression analysis plots of output responses showed the validation and accuracy of the model.

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Flow Analysis of Third Grade NanoFluid Flow Using Artificial Neural Network

  • 章节

  • 发表日期：

    2025年

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  • Ahmed Zeeshan,
  • Amad ur Rehman,
  • Zaheer Asghar,
  • Nouman Ijaz

摘要

In this chapter, optimization of the MHD peristaltic flow of third grade nanofluid in symmetric channel via ANNs is investigated. The leading equations are simplified into the couple nonlinear differential equations by assuming low Reynolds number and long wavelength conditions. Use MATLAB's built-in bvp4c routine to obtain the numerical solution for the coupled nonlinear differential equations. The findings are displayed in graphical form, with an analysis of how key parameters affect velocity profile, temperature profile, nanoparticle concentration profile and pumping characteristics. To construct the correlation among input quantities (the Hartman number (M), the Groshaf number (Gr), the Deborah number ( \(\Gamma \) ), thermophoresis parameter (Nt), and Brownian motion parameter (Nb)) and output responses ( \(u, \theta , \phi \) and \(\Delta {P}_{\lambda }\) ) via ANNs. The MSE, error histogram and regression analysis plots of output responses showed the validation and accuracy of the model.