This study aims to investigate the cooling performance of gas turbine blades using computational fluid dynamics (CFD), focusing on the effects of different air velocities and turbulence models to enhance cooling efficiency.
A gas turbine blade with internal cooling channels was modeled and analyzed under three airflow velocities (30 m/s, 60 m/s and 90 m/s) using three turbulence models: k-ω shear stress transport (SST), k-ε and Reynolds stress model (RSM). The simulations evaluated temperature distribution, heat transfer rates and pressure drop across the cooling channels.
The k-ω SST model provided the most balanced performance, achieving less than 3% deviation in temperature prediction compared to other models. The RSM model offered detailed turbulence insights but resulted in 5% higher computational costs. Increasing air velocity reduced blade temperature by up to 7% at 90 m/s but increased pressure drops by 15%.
The results provide insights into selecting optimal turbulence models and air velocities to improve turbine cooling performance while maintaining computational efficiency.
Efficient turbine cooling contributes to energy savings and reduced environmental impact in power generation.
This study provides a comparative analysis of turbulence models and air velocities, offering practical recommendations for optimizing turbine blade cooling using CFD.
