Investigating the rheological properties of fourth‐grade fluid using peristaltic phenomena: A report on artificial neural network simulation and sensitivity analysis utilizing response surface methodology

Abstract This work aims to evaluate the implications of pressure rise per wavelength and frictional forces on peristaltic circulation in a fourth‐grade fluid using sensitivity analysis. To accomplish this objective, the frictional forces and pressure increase per wavelength are modelled empirically, linking them to functions that vary with the governing parameters of the issue. First, we use artificial neural networks (ANN) and response surface methodology (RSM) to build an empirical model between responses and governing parameters. This allows us to observe the sensitivity of the frictional forces, rise in pressure per wavelength, and transport parameters. We use the analysis of variance (ANOVA) table to determine the coefficient of determination, which indicates how well the empirical model fits the data. The empirical model shows a 100% coefficient of determination for both frictional forces and pressure increase, indicating a great goodness of fit. After a thorough sensitivity analysis, it is clear that changes to the thermal buoyance parameter have a significant effect on the pressure rise and frictional forces at all levels (−1, 0, and +1). This demonstrates that the Deborah number has the least influence on these responses, and that the thermal buoyancy parameter is the most crucial component in determining how these forces behave in the system.