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Transcript 
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Scheme of a recurrent neural network (RNN) model
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Implementation of the physics-based model for compressor dynamic simulation through the Simulink tool
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Scheme of the recurrent neural network models adopted in the paper
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Influence of the choice of the compressor maneuver used for training (one-time delayed RNNs, delay time=0.5s, nHLN=15)
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Influence of the number of neurons in the hidden layer (one-time delayed RNNs with total delay time=0.5s)
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Influence of the number of outputs for testing transients TR8 and TR11 (one-time delayed RNNs; total delay time=0.5s; nHLN=15)
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Influence of the delay time (nHLN=15)
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Influence of the delay time with respect to the number of neurons in the hidden layer normalized with the number of inputs
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Influence of the delay time (0.1, 0.5, 1.0s) for one-time delayed RNNs in the presence of measurement uncertainty
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Rotational speed profile versus time for the two test cases (measured data)
Date of download: 5/12/2017
Copyright © ASME. All rights reserved.
From: Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach. 2005;128(3):444-454. doi:10.1115/1.2183315
Figure Legend:
Comparison between predictions (calculated through both the physics-based model and through the RNN) and measured values
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