A cost‐effective approach to generate accurate correlations via analyzing a minimum number of data points; Case study on the convection heat transfer problem

Abstract

It is very common in the heat transfer area to analyze and design heat equipment using the past available heat correlations. Basically, demanding higheraccuracy correlations enforces the heat laboratories to test and collect larger banks of laboratory data. However, this conversely affects the laboratory cost.Therefore, it becomes challenging to create new approaches that let the correlation developers use smaller experimental datasets and provide correlations with sufficient accuracies. To surmount this challenge, the present work develops a new approach that benefits from the computational fluid dynamics method as a reliable and cheap tool and adequately enriches the original, insufficient dataset. Then, suitable enhanced correlations are developed using the new enriched experimental-numerical-based dataset. In parallel, the artificial neural network (ANN) is used to enrich the original insufficient dataset separately. Using this experimental-ANN-based dataset, it provides a totally ANN-based correlation. It is shown that the results of enhanced correlations are as accurate as those of the ANN-based correlation. However, the point is that the use of the present approach is about 100 times faster than using the ANN. The typical forced convection heat transfer through a pipe is examined here to show the capabilities of the current approach.