As the overall pressure ratio (OPR) and turbine inlet temperature (TIT) of modern gas turbines are constantly being increased in the pursuit of increasing efficiency and specific power, the effect of bleed cooling air on the engine performance is increasingly becoming important. During the thermodynamic cycle analysis and optimization phase, the cooling bleed air requirement is either neglected or is modeled by simplified correlations, which can lead to erroneous results. In this current research, a physics-based turbine cooling prediction model, based on semi-empirical correlations for heat transfer and pressure drop, is developed and verified with turbine cooling data available in the open literature. Based on the validated model, a parametric analysis is performed to understand the variation of turbine cooling requirement with variation in TIT and OPR of future advanced engine cycles. It is found that the existing method of calculating turbine cooling air mass flow with simplified correlation underpredicts the amount of turbine cooling air for higher OPR and TIT, thus overpredicting the estimated engine efficiency.

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