Performance Prediction Modeling of Axial-Flow Compressor by Flow Equations
Subject Areas : Smart & Advanced Materials
محمد افطاری
1
(M. Sc. Student, Dept. of Mechanical Engineering, South Tehran Branch, Islamic Azad University, Iran, Tehran)
حمید جوادیان جویباری
2
(M. Sc. Student, Dept. of Mechanical Engineering, South Tehran Branch, Islamic Azad University, Iran, Tehran)
مجید رضا شاه حسینی
3
(Assistant Professor, Dept. of Mechanical Engineering, Science and Research Tehran Branch, Islamic Azad University, Iran, Tehran)
فرهاد قدک
4
(Assistant Professor, Dept. of Mechanical Engineering, Emam Hosein University, Iran, Tehran)
منوچهر راد
5
(Professor, Dept. of Mechanical Engineering, Sharif University of Technology, Iran, Tehran.)
Keywords: Loss Coefficient, Axial flow Compressor, One-Dimensional Modeling, Performance Characteristics,
Abstract :
Design models of multi- stage, axial flow compressor are developed for gas turbine engines. Axial flow compressor is one of the most important parts of gas turbine units. Therefore, its design and performance prediction are very important. One-dimensional modeling is a simple, fast and accurate method for performance prediction of any type of compressors with different geometries. In this approach, inlet flow conditions and compressor geometry are identified and by considering various compressor losses, velocity triangles at rotor, and stator inlets and outlets are determined, and then compressor performance characteristics are predicted.Numerous models have been developed theoretically and experimentally for estimating various types of compressor losses. In the present work, performance characteristics of the axial-flow compressor are predicted based on one-dimensional modeling approach. Firstly, the proposed algorithm for modeling and then the losses model for calculation of pressure loss coefficient in the blades cascade have been represented. In this study, models of Lieblein, Koch-Smith, Aungier, Hawell are implemented to consider the compressor losses. Finally, the model results are compared with experimental data to validate the model.