Axial-Flow Compressor Performance Prediction in Design and Off-Design Conditions through 1-D and 3-D Modeling and Experimental Study


School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran


In this study, the main objective is to develop a one dimensional model to predict design and off design performance of an operational axial flow compressor by considering the whole gas turbine assembly. The design and off-design performance of a single stage axial compressor are predicted through 1D and 3D modeling. In one dimensional model the mass, momentum and energy conservation equations and ideal gas equation of state are solved in mean line at three axial stations including rotor inlet, rotor outlet and stator outlet. The total to total efficiency and pressure ratio are forecasted using the compressor geometry, inlet stagnation temperature and stagnation pressure, the mass flow rate and the rotational speed of the rotor, and the available empirical correlation predicting the losses. By changing the mass flow rate while the rotational speed is fixed, characteristic curves of the compressor are obtained. The 3D modeling is accomplished with CFD method to verify one dimensional code at non-running line conditions. By defining the three-dimensional geometry of the compressor and the boundary conditions coinciding with one dimensional model for the numerical solver, axial compressor behavior is predicted for various mass flow rates in different rotational speeds. Experimental data are obtained from tests of the axial compressor of a gas turbine engine in Sharif University gas turbine laboratory and consequently the running line is attained. As a result, the two important extremities of compressor performance including surge and choking conditions are obtained through 1D and 3D modeling. Moreover, by comparing the results of one-dimensional and three-dimensional models with experimental results, good agreement is observed. The maximum differences of pressure ratio and isentropic efficiency of one dimensional modeling with experimental results are 2.1 and 3.4 percent, respectively.