Abstract
In this article, a novel artificial neural network integrating feed-forward back-propagation neural network with Gaussian kernel function is proposed for the prediction of compressor performance map. To demonstrate the potential capability of the proposed approach for the typical interpolated and extrapolated predictions, other two classical data-driven modeling methods including feed-forward back-propagation neural network and support vector machine are compared. An assessment is performed and discussed on the sensitivity of different models to the number of training samples (48 training samples, 32 training samples, and 18 training samples). All the results indicate that the proposed neural network in this article has superior prediction performance to the existing feed-forward back-propagation neural network and support vector machine, especially for the extrapolation with small samples. Furthermore, this study can be utilized in refining the existing performance-based modeling for improved simulation analysis, condition monitoring, and fault diagnosis of gas turbine compressor.
Highlights
Compressor behavior which can be represented by performance maps is the main concern for researchers to further understand and improve the design performance of any gas turbine
The two input variables of Gaussian kernel function back-propagation neural network (GBPNN) are corrected speed n and corrected flow G, and ratio pressure p is selected as the output variable of GBPNN, so it can match the model of compressor performance map which is shown in equation (1)
GBPNN proposed in this article will be applied to predict the performance map of a multistage axial flow compressor
Summary
Compressor behavior which can be represented by performance maps is the main concern for researchers to further understand and improve the design performance of any gas turbine. A novel network combined BPNN with Gaussian kernel function is proposed to improve the predicting accuracy of compressor performance map. The two input variables of GBPNN are corrected speed n and corrected flow G, and ratio pressure p is selected as the output variable of GBPNN, so it can match the model of compressor performance map which is shown in equation (1). The calculation steps of GBPNN are the same as most of the other forward neural networks, just as it is shown in following: Step 1: select training and testing samples; Step 2: determine parameters of GBPNN; Step 3: train GBPNN with training samples; Step 4: stop training if the error is acceptable; Step 5: predict compressor performance map; Step 6: analyze prediction accuracy with testing samples. 2(s À smin) smax À smin ð21Þ where s is the original sample, sk is the normalized sample with range [21, 1], smax and smin are the maximum and minimum of original sample, respectively
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