Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid.

This study predicts the parameters such as viscosity and thermal conductivity in silica-alumina-MWCN/water nanofluid using the artificial intelligence method and using design variables such as solid volume fraction and temperature. In this study, 6 optimization algorithms were used to predict and numerically model the μ and TC of silica-alumina-MWCNT/water-NF. In this study, six measurement criteria were used to evaluate the estimates obtained from the coupling process of GMDH ANN with each of these 6 optimization algorithms.

The effect of canard's optimum geometric design on wake control behind the car using Artificial Neural Network and Genetic Algorithm.

Canard is a cutting-edge aerodynamic attachment for lowering the vehicle's drag coefficient by efficiently directing the airflow as well as reducing the lift coefficient by enhancing down-force. This paper aims to simulate the airflow crossing over the car to investigate the effect of canards' geometric design on the rear-body wake using the application of CFD-based optimization. Hence, 7 design variables based on the geometry of the canard are considered, and the objective functions are set to be drag and lift coefficients that are aimed to be minimized.