An application and modelling on the artificial neural network to the RHV Tube

Abstract

Artificial neural networks represent highly advanced and adaptable tools for addressing challenges, attributed to their capacity for learning through examples and forming generalisations. The present investigation involved the development of an Artificial Neural Network (ANN) utilising a multilayer neural network model. An instance of the artificial neural network's application was demonstrated through the utilisation of the Ranque-Hilsch Vortex (RHV) tube. Through this study, the impact of transfer utilising function, data selection method, and data quantity on the artificial neural network's efficacy was examined. This approach is particularly favoured for resolving intricate non-linear issues that are challenging to model mathematically.

Keywords

• ranque-hilsch vortex (rhv)
• artificial neural networks (ann)
• multi-layer perceptron (mlp)
• modelling