Statistical regression and artificial neural network analyses of impinging jet experiments

Abstract

The purpose of this paper is to focus on the experimentally obtained results of impinging jet applications by the help of two different analysis methods. Circular round pipes (D = 7.9, 10.8, 13.8 and 23.1 mm) have been used as the impinging jets. The heat transfer is calculated with Nusselt number (Nu). The variable parameters are the dimensionless jet-to-impingement plate distance (z/D), Reynolds number (Re) and dimensionless temperature measurement points on the heated surface (x/L, y/L). Some important analysis methods such as artificial neural network (ANN), statistical regression, and uncertainty analysis are applied to the obtained data. It is shown that the ANN application is not simply a classification analysis; it is actually an application of the convergence of functions. As a result, by considering random data, 4.57% convergence level is obtained regarding the pipe diameter. The software STATISTICA 5.0 is used to estimate new empirical correlations nonlinearly. The smallest regression coefficient for the correlations is 0.87, while the highest value is 0.99. The result of the uncertainty analyses showed that the total uncertainties are in the agreeable range; 8% for Nu, and 2.89% for Re.