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Compressor Map Prediction by Neural Networks



Author(s)

Thomas Palmé¹, Phillip Waniczek², Herwart Hönen², Mohsen Assadi¹ and Peter Jeschke²

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DOI:10.17265/1934-8975/2012.10.015

1. Department of Mechanical and Structural Engineering and Material Science, University of Stavanger, Stavanger N-4036, Norway
2. Institute of Jet Propulsion and Turbomachinery, RWTH Aachen University, Aachen 52062, Germany

This paper presents a study where artificial neural networks are used as a curve fitting method applying measured data from an axial compressor test rig to predict the compressor map. Emphasis is on models for prediction of pressure ratio, compressor mass flow and mechanical efficiency. Except for evaluation of interpolation and extrapolation capabilities, this study also investigates the effect of the design parameters such as number of neurons and size of training data. To reduce the effect of noise, the auto associative neural network has been applied for noise filtering of the data from the parameters used to calculate the efficiency. In summary, the results show that artificial neural network can be used for compressor map prediction, but it should be emphasized that the selection of data normalisation scale is crucial for the model where compressor mass flow is predicted. Furthermore, it is shown that the AANN (auto associative neural network) can be used to the reduce noise in measured data and thereby enhance the quality of the data.

Axial flow compressor, artificial neural networks, curve fitting, noise reduction.