Modeling the near-wake of a vertical-axis cross-flow turbine with 2-D and 3-D RANS

TL;DR

This study compares 2-D and 3-D blade-resolved RANS simulations of a vertical-axis cross-flow turbine's near-wake, highlighting the importance of 3-D modeling for accurate prediction of turbine performance and flow characteristics.

Contribution

It demonstrates that 3-D blade-resolved RANS models, especially with the Spalart-Allmaras turbulence model, better predict turbine performance and flow features than 2-D models.

Findings

3-D simulations outperform 2-D in predicting turbine performance.

Spalart-Allmaras model closely matches experimental momentum transport.

2-D models overpredict turbine loading and miss vertical momentum transport.

Abstract

The near-wake of a vertical-axis cross-flow turbine (CFT) was modeled numerically via blade-resolved $k$-$\omega$ SST and Spalart-Allmaras RANS models in two and three dimensions. Results for each case are compared with experimental measurements of the turbine shaft power, overall drag, mean velocity, turbulence kinetic energy, and momentum transport terms in the near-wake at one diameter downstream. It was shown that 2-D simulations overpredict turbine loading and do not resolve mean vertical momentum transport, which plays an important role in the near-wake's momentum balance. The 3-D simulations fared better at predicting performance, with the Spalart-Allmaras model predictions being closest to the experiments. The SST model more accurately predicted the turbulence kinetic energy while the Spalart-Allmaras model more closely matched the momentum transport terms in the near-wake. These results show the potential of blade-resolved RANS as a design tool and a way to "extrapolate" experimental flow field measurements.