Interpolated spline method for a thermal distribution of a pipe with a turbulent heat flow

Abstract

This work presents an interpolated spline method to mathematically represent experimental data of a thermal distribution on a tube with heat flux. Linear regression was compared with the double linear interpolation process with an optimization algorithm and cubic spline curve method with the proposed problem. The results show that the interpolated experimental data can highly improve the efficiency of the cubic spline curves and lead to a smooth empirical equation for the experiments. The optimization algorithm chooses the interpolated points in a way that provides more minor errors.

Multi-objective optimization and simulation for multiple models of walls to estimate heat gain using Artificial Neural Network model

“…The choice factors take into account the whole range of potential measures that may be used for wall retrofitting (Hazim et al, 2021) (such as absorptivity, direction, material type, thickness, etc.). The collection of retrofit operations includes combinations of options for layer materials, including concrete, gypsum plaster, air gap, insulation, steel wall covering, and various thicknesses for each layer.…”

Random forest regression on pullout resistance of a pile

“…In geotechnical engineering applications, it is widely used, like designing stabilized earth walls [25], assessing landslide and slope stability [26], predicting soil compression coefficient [27], modeling bearing capacity [28] and among others. Within a mathematical framework, these techniques can optimize the relationship between multiple parameters [29], tailored to a specific problem. By taking a cost function, these algorithms perform intricate computations to maximize/minimize this function.…”