Numerical Simulation and Aerodynamic Optimization of Two-Stage Axial High-Pressure Turbine Blades

TL;DR

This paper uses CFD and genetic algorithms to optimize the geometry of two-stage high-pressure turbine blades, significantly improving torque, power, and efficiency.

Contribution

It introduces a combined CFD and GA-based optimization method for turbine blade design, focusing on geometric angles to enhance performance.

Findings

Torque increased by 8.4%

Power increased by 0.69%

Polytropic efficiency increased by 1.2%

Abstract

Gas turbine engines are highly efficient and powerful because of their high-pressure turbines (HPTs). Furthermore, stationary blades shape and prepare high-pressure gas for efficient utilization by moving blades. Consequently, optimizing the geometric features of both stationary and moving blades during the first and second stages of HPT is necessary. By considering stagger, inlet, and outlet angles of the first and second stages of blades as design variables and polytropic efficiency as an objective, this study examines HPT performance. The performance characteristics of the turbine are examined using Computational Fluid Dynamics (CFD). To model the objective functions of the design variables, the Design of Experiments (DOE) method is employed. A Genetic Algorithm (GA) optimizes torque, power, and polytropic efficiency. Optimization provides valuable insights into optimal design principles. As shown by the simulation results, stagger, inlet, and outlet angles affect turbine performance. Through GA optimization, torque, power, and polytropic efficiency are improved by 8.4%, 0.69%, and 1.2%, respectively. As a result of these improvements, the optimization approach has been demonstrated to be effective in optimizing turbine performance. Upon examining the recommended design points, it becomes clear that stagger, inlet, and outlet angles of blades have a greater impact on performance than others.