PREDICTION OF FLUID FLOW AROUND 2D SQUARE OBJECTS INSIDE A DOUBLE LID DRIVEN CAVITY USING CFD AND ARTIFICIAL NEURAL NETWORK

Abstract

Using computational fluid dynamics (CFD) to solve fluid flow problems can use a lot of computer processing power and simulation time. Artificial neural networks (ANN) can be regarded as universal learners that are capable of learning nonlinear patterns or relationships among many variables. A very well-known benchmark problem for viscous incompressible fluid flow in the lid-driven cavity problem. People have developed different numerical procedures to solve it. It is widely regarded as the first problem people usually try to solve when they come up with a new approach. This research aims to apply fully connected neural networks to learn and predict fluid flow inside a lid-driven cavity. A double lid-driven cavity with top and bottom moving walls having some internal square objects was selected as a training data to train, test and compare several fully connected neural networks having different parameters to predict fluid flow inside it. The results show that by training a neural network to recognize fluid velocity patterns around simple square objects inside the cavity, it is possible to predict fluid velocities around objects having relatively complex geometries with significant accuracy in a fraction of the time required by a CFD solver. The results also show the comparison between effects of using different mesh sizes in CFD and different learning rates in the neural network model.