Thermodynamic analysis of two-shaft radial gas turbine data using artificial neural network method

Document Type : English


Department of Mechanical Engineering, Islamic Azad University, Khomeinishahr Branch, Khomeinishahr, Esfahan, Iran


In the present research, an artificial neural network was designed and conducted to thermodynamically analyze performance variables of two-shaft radial flow gas turbine model GT185. To do this, firstly the needed tests were conducted at different operating conditions and the essential variables like temperature, pressure, rotational speed, mass flow rate which totaled 17 inputs were recorded. Then, using the relations regarding radial flow turbines and the laboratory results, performance variables including compressor, gas turbine and free turbine power and efficiency and finally the cycle heat efficiency were calculated. After calculation of these variables for all laboratory data, a neural network was designed and tested using Matlab software toolbox in order to facilitate the obtaining of performance variables in different operating conditions. In this network, highest errors absolute values of training, verification and testing data were 0.32, 0.86 and 1.39 respectively. Error value of the produced function of sample laboratory results and manual calculations was less than 0.1%