Abstract
In this study, the unsteady squeezing flow between circular parallel plates (USF-CPP) is investigated through the intelligent computing paradigm of Levenberg–Marquard backpropagation neural networks (LMBNN). Similarity transformation introduces the fluidic system of the governing partial differential equations into nonlinear ordinary differential equations. A dataset is generated based on squeezing fluid flow system USF-CPP for the LMBNN through the Runge–Kutta method by the suitable variations of Reynolds number and volume flow rate. To attain approximation solutions for USF-CPP to different scenarios and cases of LMBNN, the operations of training, testing, and validation are prepared and then the outcomes are compared with the reference data set to ensure the suggested model’s accuracy. The output of LMBNN is discussed by the mean square error, dynamics of state transition, analysis of error histograms, and regression illustrations.
Highlights
In this study, the unsteady squeezing flow between circular parallel plates (USF-CPP) is investigated through the intelligent computing paradigm of Levenberg–Marquard backpropagation neural networks (LMBNN)
This study is inspired by a series of studies on squeezing flow system investigated by many researchers
The numerical application based on LMBNN is presented here for the squeezing flow model obtained in Eqs. [6,7,8,9]
Summary
The unsteady squeezing flow between circular parallel plates (USF-CPP) is investigated through the intelligent computing paradigm of Levenberg–Marquard backpropagation neural networks (LMBNN). A dataset is generated based on squeezing fluid flow system USF-CPP for the LMBNN through the Runge–Kutta method by the suitable variations of Reynolds number and volume flow rate. Abbreviations NN Neural network LMB Levenberg–Marquard backpropagation ρ Fluid density μ Dynamic viscosity w Axial velocities 2l(t) Distance between the plates at any time t Q The volume flow rate p The pressure η Dimensionless variable u Radial velocities ν The velocity of the circular plates Re Reynolds number. In the previous research, squeezing flow has been studied using different numerical methods, but stochastic numerical computing that is dealing with artificial intelligence is utilized to analyze the fluidic systems recently
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
