Experiment on radial inflow turbines and performance prediction using deep neural network for the organic Rankine cycle

Abstract

The organic Rankine cycle makes it possible to accomplish energy recovery from a low-temperature heat source, which is typically not recovered for economic reasons. As the expander for the organic Rankine cycle, the radial turbine is easy to manufacture and has advantages in terms of size and efficiency. The radial turbine design modeler (RTDM), which was developed from in-house code, is a preliminary design program for radial inflow turbines and is different from the commercially available program RITAL. In this study, an experiment on radial inflow turbines is performed using both RTDM and RITAL. As a result, the output and efficiency of the RTDM and RITAL turbines are 36.04 kW, 80.03% and 35.03 kW, 76.01%, respectively. Experimental results demonstrate that the performance of the RTDM turbine is almost similar to the RITAL turbine. We also perform analysis on performance prediction utilizing a deep neural network with two hidden layers based on the experimental data. As a result, the minimum root mean squared errors of the RTDM turbine and RITAL turbine are estimated to be approximately 1.81 and 1.65, respectively. The deep neural network is able to predict the trends of the experiment for the organic Rankine cycle.