Investigation of the thermal performance of a spiral fin-and-tube heat exchanger by numerical methods

Abstract

Spiral fin-and-tube heat exchangers (SPF HEX) are broadly employed heat transfer (HT) equipment for heating and cooling applications. Therefore, developing a model that predicts the HT characteristics could provide an advantage when selecting the required device considering operation conditions. The efficacy of the HT characteristics of a three-dimensional SPF HEX was examined under various boundary conditions with the help of a commercial computational fluid dynamics (CFD) solver and predicted by employing machine learning (ML) approaches as a rarely studied novel work. Three-dimensional, steady, and incompressible Reynolds-Averaged Navier-Stokes (RANS) formulas, including continuity, momentum and energy were solved to obtain numerical data. k-epsilon Realizable turbulence model, which has two additional transport equations, was employed. The outlet temperature of water was calculated at various water inlet velocity, air inlet velocity, and air inlet temperature with a constant water inlet temperature. First, the methods of CFD were utilized to obtain sixty cases, then the outlet temperatures of water were obtained at various conditions. The current CFD technique was verified with a work in literature. After collecting a labeled dataset, this data is fed into four recognized ML algorithms: Support Vector Machine (SVM), Linear Regression (LR), Decision Tree Regression (DT), and Random Forest (RF). In the presented results, the SVM algorithm achieved the highest accuracy rate with values of 0.1765 mean squared error (MSE), 0.3294 mean absolute error (MAE), and 0.9940 R-squared (R-2). Furthermore, these results indicate that ML algorithms can deliver high prediction performance, potentially reducing the need for many experimental and numerical studies.