Engineering Commons^™

Artificial Neural Network For Predicting Heat Transfer Rates In Supercritical Carbon Dioxide, Vinusha Dasarla Giri Babu

Doctoral Dissertations and Master's Theses

Supercritical carbon dioxide as a working fluid in a closed Brayton cycle is proving to be more efficient than a conventional steam-based Rankine engine. Understanding the heat transfer properties of supercritical fluids is important for the design of a working engine cycle. The thermophysical properties of supercritical fluids tend to vary non-linearly near the pseudo-critical region. Traditionally, empirical correlations are used to calculate the heat transfer coefficient. It has been shown in the literature and within our own studies that these correlations provide inaccurate predictions near the pseudo-critical line, where heat transfer may be deteriorated or enhanced, resulting from strong …