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Dynamic Modelling of Gas Turbines : Identification, Simulation, Condition Monitoring and Optimal Control - Gennady G. Kulikov

Dynamic Modelling of Gas Turbines

Identification, Simulation, Condition Monitoring and Optimal Control

By: Gennady G. Kulikov (Editor), Haydn A. Thompson (Editor)

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Published: 1st April 2004
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Gas turbines play an important role in power generation and aeroengines. An extended survey of methods associated with the control and systems identification in these engines, Dynamic Modelling of Gas Turbines reviews current methods and presents a number of new perspectives.- Describes a total modelling and identification program for various classes of aeroengine, allowing you to deal with the engine's behaviour over its complete life cycle- Shows how the above regime can be applied to a real engine balancing the theory with practical use- Follows a comparative approach to the study of existing and newly derived techniques thus offering an informed choice of controllers and models from the tied-and trusted to the most up-to-date evolutionary optimisation models- Presents entirely novel work in modelling, optimal control and systems identification to help you get the most from your engine designsDynamic Modelling of Gas Turbines represents the latest research of three groups of internationally recognised experts in gas turbine studies. It will be of interest to academics working in aeroengine control and to industrial practitioners in companies concerned with their design. The work presented here is easily extendible to be relevant in other areas in which gas turbines play a role such as power engineering.

Series Editors' Forewordp. vii
Prefacep. ix
Introductionp. xi
Notationp. xxiii
Introduction to Gas Turbine Engine Controlp. 1
Introductionp. 1
Calculation of Effective Thrustp. 2
Military Engine Examplep. 3
Engine Control Programsp. 5
On-Line Optimizationp. 8
Example of Maximum Distance Calculation at Cruise Flightp. 9
Power Plant Efficiencyp. 10
Limitations of Current Controllersp. 11
Design of Optimizing Controlp. 12
Concluding Remarksp. 13
Gas Turbine Models
Models and the Control System Design Cyclep. 17
Introductionp. 17
Mathematical Models and Controller Life Cyclep. 17
Design Stage Modelsp. 19
Experimental Demonstration and Production Stage Modelsp. 19
In-Service Use Modelsp. 19
Dynamic Modelling Techniquesp. 20
Dynamic Modelling for Control Systems Developmentp. 21
Dynamic Characteristicp. 22
Adaptation of Controller Structure and Parameters at the Design Stagep. 23
Linear Dynamic Modelsp. 24
Real-time Piecewise Linear Dynamic Modelsp. 24
Control System Testingp. 25
Concluding Remarksp. 26
Off-line Modelsp. 27
Introductionp. 27
Detailed Nonlinear Static Modellingp. 28
Detailed Nonlinear Dynamic Modellingp. 30
Relationship Between Static and Dynamic Modelsp. 31
Dynamic Characteristic Modelsp. 33
Deriving the Dynamic Characteristic Modelp. 33
Graphical Representationp. 35
Dynamic Characteristic in Reduced Parametersp. 35
Interpolation Between Operating Pointsp. 37
Linear Dynamic Modelsp. 39
Obtaining Linear Dynamic Modelsp. 40
Estimation of LDM Parameters Using Detailed Static Modelp. 40
Accounting for Additional Gas Flow Dynamicsp. 43
Twin-shaft Turbo Jet Examplep. 44
Concluding Remarksp. 46
On-line Modelsp. 47
Introductionp. 47
Conflicting Requirements of Simplicity, Speed and Accuracyp. 47
Stages of Building RPLDMp. 49
Combination of Static Line and Set of Linear Modelsp. 49
LDM at the Nearest Static Pointp. 50
Piecewise Linear Approximation of the Static Linep. 51
Operating Parameter and Perpendicular to Static Linep. 53
Piecewise Linear Approximation of LDM Parametersp. 55
Example of Turbo Jet Modellingp. 59
Accounting for Atmospheric Conditions of Engine Operationp. 60
Concluding Remarksp. 62
Gas Turbine System Identification
Linear System Identificationp. 65
Introductionp. 65
Linear Modelsp. 65
Model Estimationp. 68
Time-Domainp. 68
Frequency-Domainp. 71
Model Order Selection and Validationp. 76
Comparison of Models and Approachesp. 78
Experiment Designp. 79
Input Signalsp. 81
Analysing Periodic Datap. 85
Concluding Remarksp. 88
Linear Gas Turbine Modellingp. 89
Introductionp. 89
Gas Turbine Testingp. 89
Test Signal Designsp. 91
Nonparametric Analysisp. 91
Synchronisationp. 91
Drift and Repeatabilityp. 92
Noise and Nonlinearitiesp. 93
Frequency Response Functionsp. 96
Frequency-Domain Estimationp. 97
High-Pressure Shaftp. 100
Low-Pressure Shaftp. 101
Models at Different Operating Pointsp. 104
Low-Frequency Modep. 105
Influence of Engine Nonlinearityp. 105
Time-Domain Estimationp. 108
High-Pressure Shaftp. 108
Low-Pressure Shaftp. 109
Time- Versus Frequency-Domainp. 112
Comparison with Thermodynamic Modelsp. 115
Concluding Remarksp. 116
Closed-Loop Control and System Identificationp. 117
Introductionp. 117
Concept of Closed-Loop Identifiabilityp. 117
Existing Approaches to Closed-Loop Identifiabilityp. 119
Parametric Identifiabilityp. 120
Nonparametric Identifiabilityp. 121
Common Features of Parametric and Nonparametric Identifiabilityp. 122
Summary of Identifiability Conceptsp. 123
Example of Closed-Loop Identifiability Analysisp. 123
Relationship Between Identifiability and Distribution Functionp. 126
Identifiability Monitoring via Asymmetry and Excess Analysisp. 127
Identifiability Monitoring via Comparison of a priori Models with Estimatesp. 128
Identifiability and Instrumental Variable Methodp. 128
Concluding Remarksp. 130
Nonlinear Gas Turbine Modellingp. 131
Introductionp. 131
Nonlinear System Representationp. 131
Functional Representationsp. 132
Block-Structured Systemsp. 133
The Polynomial NARMAX Approachp. 134
Feedforward Neural Network Modelsp. 136
Local Approximationsp. 136
Nonlinear System Identificationp. 137
Nonlinear Gas Turbine Modeling Using NARMAX Structuresp. 139
Parameter Estimationp. 139
Structure Selectionp. 143
Model Validationp. 146
A Proposed Identification Schemep. 148
Gas Turbine Modellingp. 149
Nonlinear Gas Turbine Modeling Using Neural Network Modelsp. 151
Multilayer Perceptron Neural Networksp. 151
Gas Turbine Modelling Using Multilayer Perceptron Neural Networksp. 153
Concluding Remarksp. 155
New Perspectives in Modelling, Identification, Condition Monitoring and Control
Nonlinear Model Structure Selection Using Evolutionary Optimisation Methodsp. 159
Introductionp. 159
Genetic Algorithms and Genetic Programmingp. 159
Genetic Algorithmsp. 160
Genetic Algorithm Operatorsp. 161
Genetic Programmingp. 162
Genetic Programming Operatorsp. 163
Multiobjective Optimisationp. 165
Pareto-ranking Methodp. 166
Example of Engine Model Structure Selection Using Genetic Programmingp. 167
Description of the Engine Systemp. 168
Result Analysisp. 168
Concluding Remarksp. 176
System Identification Using Frequency Response Techniques with Optimal Spectral Resolutionp. 177
Introductionp. 177
Problem Formulationp. 178
Spectral Estimationp. 179
Spectral Analysis Calculationsp. 181
Mean Valuep. 181
Correlation Functionsp. 182
Spectral Densityp. 183
Spectral Windowingp. 183
Frequency Response and Coherencep. 186
Errors of FRF Estimation by Spectral Methodsp. 186
Spectral Estimation Biasp. 187
Spectral Estimation Variancep. 188
Optimal Spectral Resolutionp. 192
Graphical Interpretationp. 193
Optimum Spectral Resolution And Waveletsp. 194
Concluding Remarksp. 195
Turbo Prop Fan Engine Identification: Practical Issuesp. 197
Introductionp. 197
Turbo Prop Fan Identificationp. 197
Description of Experimentp. 197
Plant Decompositionp. 199
Identification of Fuel Metering Systemp. 199
Identification of Shaft Speed Dynamicsp. 203
Identification of Compressor Pressure Dynamicsp. 204
Identification of Turbine Temperature Dynamic Modelp. 206
Identification of VSV Actuators in LP And HP Compressorsp. 207
Identification of Step Drives in Prop Fan Controllerp. 211
Identification of Prop Fan Dynamicsp. 212
Closed-Loop Identifiability Analysisp. 214
Concluding Remarksp. 215
Stochastic Gas Turbine Engine Modelsp. 217
Introductionp. 217
Disturbances Affecting Control Systems of Aero Enginesp. 217
Applications of Stochastic Modelling in Aero Engine Controlp. 218
Signal Filteringp. 218
Identification for Control Systems Design and Condition Monitoringp. 219
Optimal Control of Power Plantp. 220
Stochastic Simulation in Control Systems Design and Testingp. 221
Markov Modelling of Dynamic Systemsp. 222
Basic Definitionsp. 222
Markov Chain Representation of Dynamic Systemsp. 223
Basic Descriptive Propertiesp. 226
Probability Density Functionp. 226
Mean Valuep. 227
Correlation Functionp. 227
Parameters of Autoregression Modelp. 227
Identification of Markov Chainsp. 228
Fuzzy Markov Chainsp. 229
Concluding Remarksp. 232
Markov Modelling of Turbo Prop Fanp. 233
Introductionp. 233
Experimentation with Turbo Prop Fanp. 233
Identification of Gas Turbine Dynamicsp. 234
Simulation of Random Environment in HIL Tests of Digital Controllersp. 238
Markov Simulation Techniquep. 239
Modelling of Inlet Pressure and Temperature with Markov Chainsp. 240
Hardware-in-the-Loop Simulation Using Stochastic Modelsp. 241
Markov Modelling in Condition Monitoring of Aero Enginesp. 244
Model-Based Approach to Condition Monitoringp. 245
Condition Monitoring in Dual-Lane Controlp. 245
Simulation Resultsp. 249
Concluding Remarksp. 249
Optimal Control of Gas Turbine Engines Using Mathematical Programmingp. 251
Introductionp. 251
Optimization of Engine Performance Through Optimal Controlp. 251
Problem Formulationp. 253
Algorithm for Real-Time Resolution of a Quadratic Programming Problemp. 256
Example of Turbo Jet Control, Optimal by Speedp. 259
Example of Turbo Jet Control, Optimal by Specific Fuel Consumptionp. 261
Concluding Remarksp. 270
Dynamic Model Identification of a Turbo Jet Enginep. 271
Introductionp. 271
Static Modelling Techniquesp. 271
Parametric Dynamic Modelling Techniquesp. 273
Static Model Identificationp. 275
Mean Valuesp. 275
"Three-Level" Testp. 275
Comparison with Previous Series of Testsp. 277
Notes Regarding Fuel Feed Systemp. 278
Dynamic Model Identificationp. 279
Multisine Testp. 279
Open-Loop Identification with No Test Signalp. 281
Closed-Loop Identification with No Test Signalp. 286
Concluding Remarksp. 292
Referencesp. 293
Indexp. 305
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9781852337841
ISBN-10: 1852337842
Series: Advances in Industrial Control
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 310
Published: 1st April 2004
Publisher: Springer London Ltd
Country of Publication: GB
Dimensions (cm): 23.5 x 15.5  x 2.54
Weight (kg): 1.45