A Quantitative Study of Non-Linear Convective Heat Transfer Model by Novel Hybrid Heuristic Driven Neural Soft Computing

Abstract

Heat transfer has a vital role in material selection, machinery efficacy, and energy consumption. The notion of heat transfer is essential in understanding many phenomena related to several engineering fields. Particularly, Mechanical, civil and chemical engineering. The presentation of the heat transfer model in this manuscript is a dedication to the heat transfer characteristics such as conduction, convection, and radiation. The heat energy consumption mainly depends on these characteristics. A better conductive and convective paradigm is required for miniaturization of heat loss or transfer. The phenomenon is mathematically assumed with the required parameters. A new mathematical strategy is also designed and implemented in the manuscript to evaluate the dynamics of heat transfer model. The mathematical approach is the hybrid structure of the Sine-Cosine algorithm and Interior point algorithm. The validation of new technique is evaluated by mean absolute deviation, root mean square errors, and error in Nash–Sutcliffe efficiency. For better illustration, an extensive data set executed by the proposed mathematical strategy is also drawn graphically with convergence plots.

Keywords

• Interior point technique
• machine learning
• heat transfer
• differential equation
• quantitative analysis
• neural network