Multiphysics Numerical Investigations of Hot Components in a sCO2 Power Plant Turbine

                                
==Abstract==

To achieve a higher energy conversion efficiency, the use of supercritical CO2 (sCO2) in closed-loop Brayton and Rankine cycles has become relevant in the last decades due to an increased interest in its properties. sCO2 allows a more efficient heat transfer, chemical stability, non-flammability, and greater system efficiency. The necessity of a sealing system, which creates a barrier between the high-pressure fluid in the turbine and compressor and low-pressure regions, became essential for high-efficiency preservation and plant emissions reduction. In this regard, Dry Gas Seals (DGS) become one of the substantial components for sCO2 turbomachinery design due to lower leakage and higher efficiency than a traditional labyrinth radial seal. The high fluid pressure and density, connected to a small size sealing clearance and a high rotational speed, results in a significant friction heat, which characterizes the domain temperature distribution. The necessity for a thermal analysis of the domain becomes compelling to respect the maximum temperatures allowed in the turbomachine. When drawing up a thermal analysis, the high computational costs of a 3D simulation of the fluid domain (CFD) could be unfavourable due to the different orders of magnitude of secondary flows cavity sizes and DGS seals gaps, and the necessity to run a high number of simulations to define a geometrical sensitivity and optimization of crucial zones. A segregated conjugate heat transfer (CHT) iterative procedure has been implemented, relating a commercial 1D fluid modeller (Altair Flow Simulator) and a commercial finite element solver (Ansys Mechanical). To assess the procedure developed, 3D CFD simulations and CHT analysis of specific critical areas of the domain have been carried out. The segregated approach, implemented within the European project CO2OLHEAT, showed results in line with 3D CFD and CHT analysis, reducing computational time and cost.