Computational fluid dynamics for non-conventional power cycles

Abstract

The global temperature rise, which directly results from greenhouse gases emitted by burning fossil fuels, requires humanity to harness renewable energy sources at an increased rate. However, renewable energy sources are either highly intermittent, such as wind and solar radiation, or available at low temperatures leading to low efficiencies of current thermal conversion systems.
Two exciting technologies that can alleviate the low thermal conversion efficiencies of power plants with low-temperature heat sources are supercritical carbon dioxide (s-CO2) and organic Rankine cycles (ORCs). Compared to conventional power cycles, ORCs and s-CO2 power cycles have different workingmedia (e.g., CO2 and hydrocarbons), such that the working fluid provides an additional degree of freedom to better adapt to low-grade heat sources. As a consequence, the power cycles have higher thermal efficiency and a more compact design.
However, the main difficulty in designing highly efficient components of these nonconventional power plants lies in the fact that the heat exchangers and the turbines operate either with fluids of high molecular complexity or with fluids in highly non-ideal thermodynamic conditions. These complexitiesmake it challenging to accurately design efficient components with computational fluid dynamic (CFD) software that can reliably predict heat transfer and pressure losses in heat exchangers, and aerodynamic performance parameters in turbomachinery equipment.