Thermal performance analysis of a heat exchanger with a novel turbulator insert by using machine learning method - Jiangbo Wang; Chenzhe Li; Liangcai Zeng; Ting Fu; Kai Liu (Case Studies in Thermal Engineering)
A novel turbulence promoter comprising a V-shaped perforated rectangular winglet vortex generator integrated with a circular ring is introduced. The investigation delves into the heat transfer enhancement and pressure loss associated with its insertion within a tube, utilizing the artificial neural network approach, particularly the Multi-Layer Perceptron (MLP), for regression analysis. The outcomes are embodied in the Nusselt number ratio (Nu/Nu0), friction coefficient ratio (f/f0), and thermal enhancement factor (TEF). The findings underscore that the proposed turbulence promoter adeptly generates mixing vortices in the fluid flow, thereby enhancing heat transfer, with a maximum heat transfer enhancement of 3.9 times, and the optimal TEF value is 1.4. To evaluate the performance of the predictions, various metrics are employed, including the Regression Coefficient (R2), Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error, for each output variable. Furthermore, the MLP model emerges as a highly efficient tool for estimating the Nu/Nu0, f/f0, and TEF of the targeted heat exchanger, where the R2 value of Nu/Nu0 is 0.9821, f/f0 is 0.9967, and TEF is 0.9370.