scholarly journals Modeling and Simulation of the Start-Up Operation of a Heavy-Duty Gas Turbine by Using NARX Models

2014 ◽  
Author(s):  
Hamid Asgari ◽  
XiaoQi Chen ◽  
Raazesh Sainudiin ◽  
Mirko Morini ◽  
Michele Pinelli ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Time Series Data ◽  
Model Development ◽  
Series Data ◽  
Data Sets ◽  
Heavy Duty ◽  
Start Up ◽  
Narx Models ◽  
Heavy Duty Gas Turbine ◽  
Nonlinear Autoregressive

In this study, nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine (GT) are developed and validated. The GT is a power plant gas turbine (General Electric PG 9351FA) located in Italy. The data used for model development are three time series data sets of two different maneuvers taken experimentally during the start-up procedure. The resulting NARX models are applied to three other experimental data sets and comparisons are made among four significant outputs of the models and the corresponding measured data. The results show that NARX models are capable of satisfactory prediction of the GT behavior and can capture system dynamics during start-up operation.

Development of Reliable NARX Models of Gas Turbine Cold, Warm and Hot Start-Up

2017 ◽  
Author(s):  
Hilal Bahlawan ◽  
Mirko Morini ◽  
Michele Pinelli ◽  
Pier Ruggero Spina ◽  
Mauro Venturini
Keyword(s):  
Gas Turbine ◽  
Training Data ◽  
Series Data ◽  
Data Sets ◽  
Control Logic ◽  
Start Up ◽  
Hot Start ◽  
Narx Models ◽  
Set Up ◽  
Rapid Transients

This paper documents the set-up and validation of nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine. The considered gas turbine is a General Electric PG 9351FA located in Italy. The data used for model training are time series data sets of several different maneuvers taken experimentally during the start-up procedure and refer to cold, warm and hot start-up. The trained NARX models are used to predict other experimental data sets and comparisons are made among the outputs of the models and the corresponding measured data. Therefore, this paper addresses the challenge of setting up robust and reliable NARX models, by means of a sound selection of training data sets and a sensitivity analysis on the number of neurons. Moreover, a new performance function for the training process is defined to weigh more the most rapid transients. The final aim of this paper is the set-up of a powerful, easy-to-build and very accurate simulation tool which can be used for both control logic tuning and gas turbine diagnostics, characterized by good generalization capability.

A Novel Modeling Method for Aircraft Engine Using Nonlinear Autoregressive Exogenous (NARX) Models Based on Wavelet Neural Networks

2018 ◽  
Vol 35 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Bing Yu ◽  
Wenjun Shu ◽  
Can Cao
Keyword(s):  
Neural Networks ◽  
Time Series Data ◽  
Aircraft Engine ◽  
Modeling Method ◽  
Series Data ◽  
Data Sets ◽  
Wavelet Neural Networks ◽  
Component Level ◽  
Narx Models ◽  
Nonlinear Autoregressive

Abstract A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine’s dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.

Development of Reliable NARX Models of Gas Turbine Cold, Warm, and Hot Start-Up

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Hilal Bahlawan ◽  
Mirko Morini ◽  
Michele Pinelli ◽  
Pier Ruggero Spina ◽  
Mauro Venturini
Keyword(s):  
Gas Turbine ◽  
Generalization Capability ◽  
Control Logic ◽  
Performance Function ◽  
Start Up ◽  
Hot Start ◽  
Narx Models ◽  
Model Training ◽  
Rapid Transients ◽  
Nonlinear Autoregressive

This paper documents the setup and validation of nonlinear autoregressive network with exogenous inputs (NARX) models of a heavy-duty single-shaft gas turbine (GT). The data used for model training are time series datasets of several different maneuvers taken experimentally on a GT General Electric PG 9351FA during the start-up procedure and refer to cold, warm, and hot start-up. The trained NARX models are used to predict other experimental datasets, and comparisons are made among the outputs of the models and the corresponding measured data. Therefore, this paper addresses the challenge of setting up robust and reliable NARX models, by means of a sound selection of training datasets and a sensitivity analysis on the number of neurons. Moreover, a new performance function for the training process is defined to weigh more the most rapid transients. The final aim of this paper is the setup of a powerful, easy-to-build and very accurate simulation tool, which can be used for both control logic tuning and GT diagnostics, characterized by good generalization capability.

A New Superalloy Enabling Heavy Duty Gas Turbine Wheels for Improved Combined Cycle Efficiency

2017 ◽  
Author(s):  
Andrew Detor ◽  
◽  
Richard DiDomizio ◽  
Don McAllister ◽  
Erica Sampson ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Combined Cycle ◽  
Heavy Duty ◽  
Heavy Duty Gas Turbine ◽  
Cycle Efficiency

Impact of Different Film-Cooling Modes at Trailing Edge on the Aerodynamic Performance of Heavy Duty Gas Turbine Cascade

2011 ◽  
Vol 84-85 ◽  
pp. 259-263
Author(s):  
Xun Liu ◽  
Song Tao Wang ◽  
Xun Zhou ◽  
Guo Tai Feng
Keyword(s):  
Gas Turbine ◽  
Film Cooling ◽  
Large Mass ◽  
Turbine Cascade ◽  
Heavy Duty ◽  
Trailing Edge ◽  
Central Difference ◽  
Adiabatic Effectiveness ◽  
Heavy Duty Gas Turbine ◽  
The Impact

In this paper, the trailing edge film cooling flow field of a heavy duty gas turbine cascade has been studied by central difference scheme and multi-block grid technique. The research is based on the three-dimensional N-S equation solver. By way of analysis of the temperature field, the distribution of profile pressure, and the distribution of film-cooling adiabatic effectiveness in the region of trailing edge with different cool air injection mass and different angles, it is found that the impact on the film-cooling adiabatic effectiveness is slightly by changing the injection mass. The distribution of profile pressure dropped intensely at the pressure side near the injection holes line with the large mass cooling air. The cooling effect is good in the region of trailing edge while the injection air is along the direction of stream.

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