Abstract
Current research concerned with the aerodynamic instability of compressors aims at an extension of the operating range of the compressor towards decreased massflow. In practice, a safety margin is maintained between operating point and stability limit to prevent the compressor from going into stall and surge. In this article, we analyze the behavior of a 4-stage transonic axial compressor before entering the unstable range and present an approach to identifying incipient surge and stall using artificial neural networks. This method is based on measurements of the unsteady static wall pressure in front of the first rotor.Analyzing the static pressure signals by using the Fast Fourier Transform shows that peripheral disturbances (modal waves) can only be identified in a small range close to nominal speed (at 95%). At lower speeds (60 to 80% of nominal speed), the investigated compressor flow enters instability by spike-type stall.Monitoring stability over the entire speed range of the compressor relies on artificial neural networks using the unsteady wall pressure signal. In the present case, artificial neural networks show to be the most useful tool to indicate approaching instability. The method works reliably for both types of instabilities, spike-type stall as well as modal waves.