Pub Date : 2020-01-16DOI: 10.5772/intechopen.90803
Sergiy Yepifanov, F. Sirenko
The process of engine control development requires the models that describe engine operation and its response on a control action. The development flow required numerous models to be engaged, like component-level non-linear model, engine-level non-linear model, linear dynamic model, etc. Models made a great progress during the recent years and became reliable tools for control engineers. However, most models are derivatives from the component-level non-linear model, which in its turn consumes the component performances. Things turn different when one addresses the starting range of engine operation. The problem here is all about the missing performances of the engine components, as it is quite hard to harvest these performances in this region as the processes that happen in the engine are transient by nature. Different scientists offered different approaches to the problem of building the component level non-linear model of the sub-idle region, but the general idea is to somehow extrapolate the known performances to the sub-idle region. However, there are no known reports about a model that considers all aspects of this approach and simulates the engine starting. In this chapter, you can find an alternative view on a problem of simulation of a sub-idle operation. The proposed model belongs to a group of linear dynamic models including the static model as well as simplified static model to support the dynamic model. Instead of trying to extrapolate component performances and get the full-scale component-level model, you will see that the canonical component performances are replaced by the direct relations between parameters that are used in the control algorithms, like gas-path parameters against the RPM. As well in this chapter, you will find the exact instructions on how to create the model and an example of the one with the real test data.
{"title":"Turbine Engine Starting Simulation","authors":"Sergiy Yepifanov, F. Sirenko","doi":"10.5772/intechopen.90803","DOIUrl":"https://doi.org/10.5772/intechopen.90803","url":null,"abstract":"The process of engine control development requires the models that describe engine operation and its response on a control action. The development flow required numerous models to be engaged, like component-level non-linear model, engine-level non-linear model, linear dynamic model, etc. Models made a great progress during the recent years and became reliable tools for control engineers. However, most models are derivatives from the component-level non-linear model, which in its turn consumes the component performances. Things turn different when one addresses the starting range of engine operation. The problem here is all about the missing performances of the engine components, as it is quite hard to harvest these performances in this region as the processes that happen in the engine are transient by nature. Different scientists offered different approaches to the problem of building the component level non-linear model of the sub-idle region, but the general idea is to somehow extrapolate the known performances to the sub-idle region. However, there are no known reports about a model that considers all aspects of this approach and simulates the engine starting. In this chapter, you can find an alternative view on a problem of simulation of a sub-idle operation. The proposed model belongs to a group of linear dynamic models including the static model as well as simplified static model to support the dynamic model. Instead of trying to extrapolate component performances and get the full-scale component-level model, you will see that the canonical component performances are replaced by the direct relations between parameters that are used in the control algorithms, like gas-path parameters against the RPM. As well in this chapter, you will find the exact instructions on how to create the model and an example of the one with the real test data.","PeriodicalId":179085,"journal":{"name":"Gas Turbines - Control, Diagnostics, Simulation, and Measurements [Working Title]","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123745119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-28DOI: 10.5772/intechopen.90490
Sergiy Yepifanov, R. Zelenskyi
The chapter considers one of the main dynamic factors of the turbine engine — the dynamics of gas capacities. Typically, the most influencing capacities in the turbine engine are combustion chamber, afterburner, mixing chamber, secondary duct of turbofan, and jet nozzle. Simulation of high-frequency transients in turbine engines needs taking into account this factor. For the needs of automatic control and parametric diagnostics, the equations of capacities must be combined with the equations of rotor dynamics and, sometimes, with the equations of a measurement system and actuators. The model complexity consists in two features. The first feature is in how many segments are used to simulate the capacity. The second feature is in what of three basic laws are taken into account at the gas motion description: the mass conservation law, the energy conservation law, and the momentum conservation law. This chapter includes the analysis of models of different complexity followed by the conclusions about their applicability. In the last part of the chapter, the real case of the engine dynamics analysis is considered when the designer does not need the simulation of the capacities ’ dynamics in time, but needs estimating of the capacities ’ ability to oscillate and in their natural oscillation frequencies.
{"title":"Gas Turbine Simulation Taking into Account Dynamics of Gas Capacities","authors":"Sergiy Yepifanov, R. Zelenskyi","doi":"10.5772/intechopen.90490","DOIUrl":"https://doi.org/10.5772/intechopen.90490","url":null,"abstract":"The chapter considers one of the main dynamic factors of the turbine engine — the dynamics of gas capacities. Typically, the most influencing capacities in the turbine engine are combustion chamber, afterburner, mixing chamber, secondary duct of turbofan, and jet nozzle. Simulation of high-frequency transients in turbine engines needs taking into account this factor. For the needs of automatic control and parametric diagnostics, the equations of capacities must be combined with the equations of rotor dynamics and, sometimes, with the equations of a measurement system and actuators. The model complexity consists in two features. The first feature is in how many segments are used to simulate the capacity. The second feature is in what of three basic laws are taken into account at the gas motion description: the mass conservation law, the energy conservation law, and the momentum conservation law. This chapter includes the analysis of models of different complexity followed by the conclusions about their applicability. In the last part of the chapter, the real case of the engine dynamics analysis is considered when the designer does not need the simulation of the capacities ’ dynamics in time, but needs estimating of the capacities ’ ability to oscillate and in their natural oscillation frequencies.","PeriodicalId":179085,"journal":{"name":"Gas Turbines - Control, Diagnostics, Simulation, and Measurements [Working Title]","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130056185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-28DOI: 10.5772/intechopen.83690
I. Idrissi, H. Chafouk, R. E. Bachtiri, M. Khanfara
This chapter presents the modeling and simulation results of variable speed wind turbine driven by doubly fed induction generator (DFIG). The feeding of the generator is ensured through its stator directly connected to the electrical grid and by its rotor connected to the grid through two power converters, which are controlled by the pulse width modulation (PWM) technique. This configuration is the most used in the wind power generation systems. For the variable speed operation of the studied system, the maximum power point tracking strategy is applied for the turbine, and the stator flux-oriented vector control is used for the generator. The MATLAB/Simulink software is used for the system modeling and simulation. For the wind velocity model, a random wind profile is simulated, and the turbine and the generator parameters are extracted from an existing wind turbine system in the literature. The obtained results are addressed in this chapter.
{"title":"Modeling and Simulation of the Variable Speed Wind Turbine Based on a Doubly Fed Induction Generator","authors":"I. Idrissi, H. Chafouk, R. E. Bachtiri, M. Khanfara","doi":"10.5772/intechopen.83690","DOIUrl":"https://doi.org/10.5772/intechopen.83690","url":null,"abstract":"This chapter presents the modeling and simulation results of variable speed wind turbine driven by doubly fed induction generator (DFIG). The feeding of the generator is ensured through its stator directly connected to the electrical grid and by its rotor connected to the grid through two power converters, which are controlled by the pulse width modulation (PWM) technique. This configuration is the most used in the wind power generation systems. For the variable speed operation of the studied system, the maximum power point tracking strategy is applied for the turbine, and the stator flux-oriented vector control is used for the generator. The MATLAB/Simulink software is used for the system modeling and simulation. For the wind velocity model, a random wind profile is simulated, and the turbine and the generator parameters are extracted from an existing wind turbine system in the literature. The obtained results are addressed in this chapter.","PeriodicalId":179085,"journal":{"name":"Gas Turbines - Control, Diagnostics, Simulation, and Measurements [Working Title]","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122684210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-29DOI: 10.5772/intechopen.90307
Cristhian Maravilla, Sergiy Yepifanov
Monitoring systems to predict the remaining lifetime of gas turbine engines are a major field of investigation, in particular, the monitoring systems that allow an on-line prediction. This chapter introduces and analyzes a new approach to develop mathematical models to estimate unmeasured parameters in an engine lifetime monitoring system; these models in contrast to previously developed models allow an on-line monitoring of unmeasured parameters, which are necessary to perform an on-line lifetime prediction. The blade of a high-pressure turbine (HPT) of a two-spool free turbine power plant is the test case. Several candidate models are developed for each unmeasured parameter; the best models are selected by their accuracy and robustness using the instrumental and truncation error as criteria. Ten faulty engine conditions are considered to analyze the model robustness. Two methods for model developing are compared; the first method uses physics-based models (pro-posed in this chapter), and the second method develops the models using the similarity concept (reference methodology). The results of the comparison show that the physics-based models are more robust to engine faults and overall they deliver a significantly more accurate prediction of the engine lifetime.
{"title":"A New Approach for Model Developing to Estimate Unmeasured Parameters in an Engine Lifetime Monitoring System","authors":"Cristhian Maravilla, Sergiy Yepifanov","doi":"10.5772/intechopen.90307","DOIUrl":"https://doi.org/10.5772/intechopen.90307","url":null,"abstract":"Monitoring systems to predict the remaining lifetime of gas turbine engines are a major field of investigation, in particular, the monitoring systems that allow an on-line prediction. This chapter introduces and analyzes a new approach to develop mathematical models to estimate unmeasured parameters in an engine lifetime monitoring system; these models in contrast to previously developed models allow an on-line monitoring of unmeasured parameters, which are necessary to perform an on-line lifetime prediction. The blade of a high-pressure turbine (HPT) of a two-spool free turbine power plant is the test case. Several candidate models are developed for each unmeasured parameter; the best models are selected by their accuracy and robustness using the instrumental and truncation error as criteria. Ten faulty engine conditions are considered to analyze the model robustness. Two methods for model developing are compared; the first method uses physics-based models (pro-posed in this chapter), and the second method develops the models using the similarity concept (reference methodology). The results of the comparison show that the physics-based models are more robust to engine faults and overall they deliver a significantly more accurate prediction of the engine lifetime.","PeriodicalId":179085,"journal":{"name":"Gas Turbines - Control, Diagnostics, Simulation, and Measurements [Working Title]","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131963859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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