Axial Compressor Performance Map Prediction Using Artificial Neural Network

Ghorbanian, K.; Gholamrezaei, Mohammad

doi:10.1115/gt2007-27165

Cited by 19 publications

(12 citation statements)

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“…Moraal and Kolmanovsky [13] indicate that an ANN can produce better performance compared with other curve fitting techniques if the ANN is sufficiently trained. Feedforward neural networks (FFNNs) [6,14,15] with the back-propagation learning algorithm and the RBFN [3,16] are the most widely used ANNs to train performance maps for turbomachinery. An FFNN, which is one type of ANN, also called multi-layer perceptrons, consists of one input layer, one or several hidden layers, and one output layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Verstraete et al [2] propose to use the onlinetrained ANN, which uses the new calculated results to update the existing ANN, and increases the accuracy of the ANN gradually as the iteration proceeds. Ghorbanian and Gholamrezaei [3] use the rotated general regression neural network. Its basic theory is that rotation can reduce the non-linear characteristics of the relationship between geometry parameters and performance parameters in the new rotated coordinate system.…”

Section: Introductionmentioning

confidence: 99%

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Improved centrifugal compressor impeller optimization with a radial basis function network and principle component analysis

Engeda

Cave³

et al. 2010

Proceedings of the Institution of Mechanical Engineers, Part C:

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The development of a fast and reliable computer-aided design and optimization procedure for centrifugal compressors has attracted a great deal of attention both in the industry and in academia. Artificial neural networks (ANNs) have been widely used to create an approximate performance map to substitute the direct application of flow solvers in the optimization procedure. Although ANNs greatly decrease the computational time for the optimization, their accuracies still limit their applications. Furthermore, ANNs also bring errors to the final results. In this study, principal component analysis (PCA) or independent component analysis (ICA) is applied to transform the training database and make a radial basis function network (RBFN), a type of ANN, trained in a new coordinate system. The present study compares the accuracies of three different trained ANNs: RBFN, RBFN with PCA, and RBFN with ICA. Furthermore, the total performances of the centrifugal compressor impeller optimization procedures using these three different trained ANNs are compared. Genetic algorithm (GA) is used as an optimization method in the optimization procedure and influences of GA parameters on the optimization procedure performances are also studied. All results demonstrate that the application of PCA significantly increases the accuracy of trained ANN as well as the total performance of the centrifugal compressor impeller optimization procedure.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved centrifugal compressor impeller optimization with a radial basis function network and principle component analysis

Engeda

Cave³

et al. 2010

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…Neural networks can also be applied in extrapolation and multilayer perceptron network can improve the quality of prediction [12]. Tian et al [13] introduced the hybrid ANN-partial least squared (PLS) model into compressor performance prediction, producing better results than other structures of ANNs when enough data are provided [14]. However, reducing the total number of samples could rapidly drop the grade of prediction.…”

Section: Introductionmentioning

confidence: 99%

Performance Prediction of the Centrifugal Compressor Based on a Limited Number of Sample Data

Jiang

Dong

Liu

et al. 2019

Mathematical Problems in Engineering

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Centrifugal compressor is widely used in various engineering domains, and predicting the performance of a centrifugal compressor is an essential task for its conceptual design, optimization, and system simulation. For years, researchers seek to implement this mission through various kinds of methods, including interpolation, curve fitting, neural network, and other statistics-based algorithms. However, these methods usually need a large amount of data, and obtaining data may cost considerable computing or experimental resources. This paper focuses on constructing the performance maps of pressure ratio and isentropic efficiency using a limited number of sample data while maintaining accuracy. Firstly, sample data are generated from simulation using Vista CCD. Then, corrected flow rate and corrected rotational speed are used as independent variables, and the regression expressions with physical meaning of pressure ratio and isentropic efficiency are derived and simplified through thermodynamic analysis and loss analysis of centrifugal compressor, resulting in two loss-analysis-based models. Meanwhile, kriging models based on a second-order polynomial and neural network models are built. Results show that, when predicting inside data boundary, the loss-analysis-based model and the kriging model produce higher accuracy prediction even in a small data set, and the predicting result is stable, while the neural network model provides better results only in a more extensive data set with more speed lines. For the prediction outside the data boundary, the loss-analysis-based model can provide relatively accurate results. Besides, it takes less time to train and utilize a loss-analysis-based model than other models.

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“…Experimental data as well as results of the first training are used to train the neural network. Ghorbanian and Gholamrezaei [20][21][22] presented a comparison across different ANNs in predicting axial compressor performance map.…”

Section: Introductionmentioning

confidence: 99%

Compressor map generation using a feed-forward neural network and rig data

Gholamrezaei

Ghorbanian

2009

Proceedings of the Institution of Mechanical Engineers, Part A:

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In this article, a feed-forward neural network is explored to reconstruct the performance map of an axial compressor through the utilization of a limited number of experimental data. The Levenberg—Marquardt algorithm with Bayesian regularization method is used to adjust the weights and biases of the network. The proposed technique is utilized to estimate the mass flowrate, the pressure ratio, the shaft speed, and the efficiency in regions where no experimental data are available. The surge line is predicted and the line of maximum efficiencies is determined. The results are compared with experimental data.

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Axial Compressor Performance Map Prediction Using Artificial Neural Network

Cited by 19 publications

References 0 publications

Improved centrifugal compressor impeller optimization with a radial basis function network and principle component analysis

Improved centrifugal compressor impeller optimization with a radial basis function network and principle component analysis

Performance Prediction of the Centrifugal Compressor Based on a Limited Number of Sample Data

Compressor map generation using a feed-forward neural network and rig data

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