In this paper, feed-forward recurrent neural networks (RNNs) with a single hidden layer and trained by using a back-propagation learning algorithm are studied and developed for the simulation of compressor behavior under unsteady conditions. The data used for training and testing the RNNs are both obtained by means of a nonlinear physics-based model for compressor dynamic simulation (simulated data) and measured on a multistage axial-centrifugal small-size compressor (field data). The analysis on simulated data deals with the evaluation of the influence of the number of training patterns and of each RNN input on model response, both for data not corrupted and corrupted with measurement errors, for different RNN configurations, and different values of the total delay time. For RNN models trained directly on experimental data, the analysis of the influence of RNN input combination on model response is repeated, as carried out for models trained on simulated data, in order to evaluate real system dynamic behavior. Then, predictor RNNs (i.e., those that do not include among the inputs the exogenous inputs evaluated at the same time step as the output vector) are developed and a discussion about their capabilities is carried out. The analysis on simulated data led to the conclusion that, to improve RNN performance, the adoption of a one-time delayed RNN is beneficial, with an as-low-as-possible total delay time (in this paper, ) and trained with an as-high-as possible number of training patterns (at least 500). The analysis of the influence of each input on RNN response, conducted for RNN models trained on field data, showed that the single-step-ahead predictor RNN allowed very good performance, comparable to that of RNN models with all inputs (overall error for each single calculation equal to 1.3% and 0.9% for the two test cases considered). Moreover, the analysis of multi-step-ahead predictor capabilities showed that the reduction of the number of RNN calculations is the key factor for improving its performance over a significant time horizon. In fact, when a high test data sampling time is chosen (in this paper, ), prediction errors were acceptable (lower than ).
Skip Nav Destination
Article navigation
July 2007
Technical Papers
Optimization of a Real-Time Simulator Based on Recurrent Neural Networks for Compressor Transient Behavior Prediction
M. Venturini
M. Venturini
Engineering Department in Ferrara (ENDIF),
University of Ferrara
, Via Saragat, 1, 44100 Ferrara, Italy
Search for other works by this author on:
M. Venturini
Engineering Department in Ferrara (ENDIF),
University of Ferrara
, Via Saragat, 1, 44100 Ferrara, ItalyJ. Turbomach. Jul 2007, 129(3): 468-478 (11 pages)
Published Online: May 31, 2006
Article history
Received:
May 26, 2006
Revised:
May 31, 2006
Citation
Venturini, M. (May 31, 2006). "Optimization of a Real-Time Simulator Based on Recurrent Neural Networks for Compressor Transient Behavior Prediction." ASME. J. Turbomach. July 2007; 129(3): 468–478. https://doi.org/10.1115/1.2437232
Download citation file:
Get Email Alerts
Cited By
Related Articles
Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
J. Turbomach (July,2006)
Multidisciplinary Optimization of a Radial Compressor for Microgas Turbine Applications
J. Turbomach (July,2010)
Direct Method for Optimization of a Centrifugal Compressor Vaneless Diffuser
J. Turbomach (January,2001)
Transient Optimization in Natural Gas Compressor Stations for Linepack Operation
J. Energy Resour. Technol (December,2007)
Related Proceedings Papers
Related Chapters
Constraints
Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments
Manipulability-Maximizing SMP Scheme
Robot Manipulator Redundancy Resolution
Optimization of Modular Neural Networks with Type-2 Fuzzy Integration Using General Evolutionary Method with Application in Multimodal Biometry
Intelligent Engineering Systems through Artificial Neural Networks