ANSYS Hall of Fame 2019 – Best in Show Stressfield Oy
Commercial: Using ANSYS CFX to simulate supercritical CO2 condensation near the critical point inside a centrifugal compressor has enabled engineers to improve the compressor efficiency and considerably decrease the annual use of primary energy sources.
Recently, the supercritical Brayton cycle using CO2 as the working fluid has attracted more attention, chiefly due to higher thermal efficiency. Even a seemingly marginal improvement in the compressor efficiency can considerably decrease the annual use of primary energy sources.
Since the inlet boundary conditions of the compressor in the supercritical CO2 power cycle are placed in the vicinity of the critical point, there is a possibility of condensation inside the compressor where the fluid properties cross the saturation curves. Liquid droplet formation affects the compressor performance and flow field, and consequently generates extra losses. Moreover, near the critical point, the thermophysical properties of the fluid change rapidly. Routine real gas equation models are not able to capture the property fluctuations in the vicinity of the critical point and saturation curves.
Non-equilibrium condensation was performed employing nucleation and droplet growth models using some CFX Expression Language (CEL) files. It was assumed that the nucleation produces the initial droplets of the liquid phase, while the phase transition was covered by the supercritical droplet growth. The external real gas properties (RGP) tables, including the main thermophysical properties of the fluid, were coupled with the solver (ANSYS CFX). The fluid properties inside the metastable region (between the spinodal and saturation curves) was generated using bilinear cubic extrapolation of the saturation properties to the spinodal curve.
- Accurate turbomachinery performance and flow field predictions and precise estimation of the total loss.
- Ability to predict the location of possible erosion/corrosion and find the optimum operating condition to avoid it. This saves resources and increases the working life of the machine.
- Higher accuracy and better agreement with the experimental data by modeling the condensation and consequently extra imposed losses.
- Turbomachinery design improvements.
- Lower computational time and stable simulation using the RGP table.
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