Intelligent computing technique based supervised learning for squeezing flow model

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.

MHD Hybrid Nanofluid Flow Due to Rotating Disk with Heat Absorption and Thermal Slip Effects: An Application of Intelligent Computing

“…Many of the researchers used traditional deterministic techniques to solve various fluid dynamics problems including Joule heating, entropy generation, nanofluid and viscous dissipation [31][32][33][34], Molecular Sensitivity of Near-Field Vibrational Infrared Imaging [35], Capillary driven flow in nanochannels [36], application of MnO 2 -Fe 3 O 4 /CuO hybrid catalysts [37] and Molecule-Plasmon Excitation Coupling [38], and the solution of such problems through modern stochastic solution methods based on the artificial intelligence algorithm is innovative. Stochastic solution techniques based on artificial intelligence (AI) algorithms are better and efficient alternatives for various linear and non-linear mathematical models representing a variety of fluidic problems [39][40][41][42][43][44]. These solution techniques are designed based on a modern computational paradigm to tackle the system of highly nonlinear ODEs representing the mathematical models of such fluid problems.…”