Off-design performance analysis of a radial fan using experimental, computational, and artificial intelligence approaches

Abstract

Radial fans play a critical role as indispensable turbomachines in various industrial sectors. However, the conventional manufacturing process for these fans is often characterized by its resource-intensive and time-consuming nature. Traditionally, computational fluid dynamics (CFD) has been the go-to method for predicting and analyzing the performance of radial fans at different geometrical and operational conditions. Yet, in recent years, the rapid advancements in machine learning (ML) and deep learning (DL) techniques, particularly the rise of artificial neural networks (ANNs), have propelled significant progress in the field of predicting and optimizing the performance of radial fans. The present study aims to analyze the performance of a radial fan through a comprehensive experimental investigation and a meticulous three-dimensional numerical simulation. Subsequently, in order to predict the off-design performance of the fan, an extensive set of numerical simulations is conducted at various volumetric flow rates and rotational speeds. These simulations are used to analyze the fan performance and identify the most efficient operating condition. Moreover, the simulations serve as inputs for a finely-tuned ANN architecture. The predictive accuracy of the ANN model for both interpolation and extrapolation cases is then compared against two alternative techniques, namely support vector machine (SVM) and random forest (RF). The results explicitly highlight the superiority of the ANN model in terms of its predictive accuracy, thereby solidifying its position as the most reliable method for predicting the performance of radial fans.