## 1 Introduction
The performance characteristics of the centrifugal fan are generally obtained through a set of experimentations conducted on a well-equipped test rig and a set of calibrated measurement devices. Hence, there is a dire need for a simplified process to investigate the fan performance characteristics in which a series of experimental performance tests can be avoided. The fan manufacturers and application engineers therefore also have a keen interest in obtaining the performance of fans for various geometrical interventions and multiple input conditions within the minimum amount of time. The requirement of the industry can be satisfied either by conducting the experimentation or by performing numerical analyses using built-in algorithms of fluid-flow analysis software. The task of numerical analysis is also found to be a challenging task due to the complexity of the geometrical configurations of the fan and additional computational requirements. Alternatively, the fan's performance characteristics may be predicted using an Artificial Neural Network (ANN). In this method, the pre-existing results procured from either experimentation or numerical work are used to obtain the desired fan characteristics. Nowadays, ANN is generally used in diversified domains such as solar heating, IC engines, control systems, biomedical analysis, forecasting, optimization, manufacturing, etc. [PERSON] et al. [8] performed experimentation on a centrifugal pump to study the severity of flow blockages and cavitation. The suction area restricted the flow of water into the impeller to achieve various levels of blockages. The rotational speed of the impeller was also varied to analyze the performance under various operating conditions. Pressure signals were captured using calibrated pressure transducers for various levels of blockages. The binary data classification method of deep learning was used to categorize the data obtained from pressure transducers. The authors found that the index of overall evaluation for the prediction accuracy is 98.32% for the combination of blockage and cavitation. [PERSON] et al. [9] predicted the adiabatic efficiency of the N-staged centrifugal pump based on an artificial intelligence technique and improved the prediction ability of the model using an S fold cross-validation algorithm and smoothing factor circulation screening. They achieved a relative error in the range of 0.85-3.99%. [PERSON] et al. [10] verified the performance of a radial pump using multilayer ANN. Around 200 data sets obtained from numerical simulation were considered as the sample size for the ANN prediction. Particle swarm analysis was also incorporated into their prediction analysis. They achieved a marginal difference in pump efficiency of about 0.454% between CFD simulation and predicted value. A CFD analysis was conducted by [PERSON] et al. [11] to obtain the relative and absolute flow structure near the impeller-volute region of a centrifugal fan. The study found that the significant interactions within the centrifugal fan were mostly localized in the impeller channels near the tongue area. [PERSON] et al. [12] created the requisite sound modulation by simulating tonal noise control with a spinning impediment placed on the fan bell mouth. They showed that shaping the fan hub prevents flow separation at the hub and increases fan performance. [PERSON] et al. [13] used a centrifugal compressor impeller in their case study, and the line shape of the hub was changed to investigate the effect of line curvature on compressor performance. The numerical research findings revealed that within the design value range, the curvature of the impeller hub profile increased as the flow range and surge margin of the compressor impeller increased. [PERSON] et al. [14] discovered that the hub inclination angle (HIA) has a substantial impact on the flow characteristics of a centrifugal pump impeller.