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Abstract: Modeling of turbine engines has been an important topic for a long time, because of its importance in simulation and controller design. This research work has concentrated on turbine engine model development, and control of turbine engines using MATLAB / SIMULINK. The model simulates the dynamics of the various turbine engine components. Mathematical equations derived from first principles are used for modeling the system. Algebraic equations model the thermodynamic processes, while differential equations are used to model the dynamic nature of the turbine engine. The dynamics of the turbine engine can be studied using this SIMULINK model. Engine health monitoring systems (including engine diagnostics and prognostics) can help reduce the total labor needed to maintain engines and allow planning of maintenance schedules to accomplish preventive maintenance more effectively. various failure detection techniques available in the literature are reviewed in order to perform Turbine Engine Diagnostics. The benefits of using a Bank of Kalman filters for sensor fault diagnostics were understood and the technique is applied for diagnostics of the Simulink model of the turbine engine developed. A literature review of turbine engine prognostic techniques reveals the potential of model-based prognostic methods to accurately predict the remaining useful life of various turbine engine components.
Overview of engine control systems -- Engine modeling and simulation -- Model reduction and dynamic analysis -- Design of set-point controllers -- Design of transient and limit controllers -- Control system integration -- Advanced control concepts -- Engine monitoring and health management -- Integrated control and health monitoring -- Appendix A. Fundamentals of automatic control systems -- Appendix B. Gas turbine engine performance and operability.
This paper provides an overview of the aircraft turbine engine control research at the NASA Glenn Research Center (GRC). A brief introduction to the engine control problem is first provided with a description of the state-of-the-art control law structure. A historical aspect of engine control development since the 1940s is then provided with a special emphasis on the contributions of GRC. With the increased emphasis on aircraft safety, enhanced performance, and affordability, as well as the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. The Controls and Dynamics Branch (CDB) at GRC is leading and participating in various projects to develop advanced propulsion controls and diagnostics technologies that will help meet the challenging goals of NASA Aeronautics Research Mission programs. The rest of the paper provides an overview of the various CDB technology development activities in aircraft engine control and diagnostics, both current and some accomplished in the recent past. The motivation for each of the research efforts, the research approach, technical challenges, and the key progress to date are summarized.
This paper provides an overview of the aircraft turbine engine control research at the NASA Glenn Research Center (GRC). A brief introduction to the engine control problem is first provided with a description of the state-of-the-art control law structure. A historical aspect of engine control development since the 1940s is then provided with a special emphasis on the contributions of GRC. With the increased emphasis on aircraft safety, enhanced performance, and affordability, as well as the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. The Controls and Dynamics Branch (CDB) at GRC is leading and participating in various projects to develop advanced propulsion controls and diagnostics technologies that will help meet the challenging goals of NASA Aeronautics Research Mission programs. The rest of the paper provides an overview of the various CDB technology development activities in aircraft engine control and diagnostics, both current and some accomplished in the recent past. The motivation for each of the research efforts, the research approach, technical challenges, and the key progress to date are summarized. Garg, Sanjay Glenn Research Center AIRCRAFT ENGINES; TURBINE ENGINES; ENGINE CONTROL; PROPULSION SYSTEM PERFORMANCE; PROPULSION SYSTEM CONFIGURATIONS; HISTORIES; CONTROL THEORY; TURBOFAN ENGINES; AIRCRAFT MODELS; SENSORS; FAULT DETECTION; ENGINE FAILURE
This book presents new studies in the area of turbomachine mathematical modeling with a focus on models applied to developing engine control and diagnostic systems. The book contains one introductory and four main chapters. The introductory chapter describes the area of modeling of gas and wind turbines and shows the demand for further improvement of the models. The first three main chapters offer particular improvements in gas turbine modeling. First, a novel methodology for the modeling of engine starting is presented. Second, a thorough theoretical comparative analysis is performed for the models of engine internal gas capacities, and practical recommendations are given on model applications, in particular for engine control purposes. Third, multiple algorithms for calculating important unmeasured parameters for engine diagnostics are proposed and compared. It is proven that the best algorithms allow accurate prognosis of engine remaining lifetime.The field of wind turbine modeling is presented in the last main chapter. It introduces a general-purpose model that describes both aerodynamic and electric parts of a wind power plant. Such a detailed physics-based model will help with the development of more accurate control and diagnostic systems.In this way, this book includes four new studies in the area of gas and wind turbine modeling. These studies will be interesting and useful for specialists in turbine engine control and diagnostics.
The propulsion system is arguably the most critical part of the aircraft; it certainly is the single most expensive component of the vehicle. Ensuring that engines operate reliably without major maintenance issues is an important goal for all operators, military or commercial. Engine health management (EHM) is a critical piece of this puzzle and has been a part of the engine maintenance for more than five decades. In fact, systematic condition monitoring was introduced for engines before it was applied to other systems on the aircraft. Diagnostics and Prognostics of Aerospace Engines is a collection of technical papers from the archives of SAE International, which introduces the reader to a brief history of EHM, presents some examples of EHM functions, and outlines important future trends. The goal of engine health maintenance is ultimately to reduce the cost of operations by catching problems before they become major issues, by helping reduce repair times through diagnostics, and by facilitating logistic optimization through prognostic estimates. Diagnostics and Prognostics of Aerospace Engines shows that the essence of these goals has not changed over time.
This paper presents a historical perspective of the advancement of control technologies for aircraft gas turbine engines. The paper primarily covers technology advances in the United States in the last 60 years (1940 to approximately 2002). The paper emphasizes the pioneering technologies that have been tested or implemented during this period, assimilating knowledge and experience from industry experts, including personal interviews with both current and retired experts. Since the first United States-built aircraft gas turbine engine was flown in 1942, engine control technology has evolved from a simple hydro-mechanical fuel metering valve to a full-authority digital electronic control system (FADEC) that is common to all modern aircraft propulsion systems. At the same time, control systems have provided engine diagnostic functions. Engine diagnostic capabilities have also evolved from pilot observation of engine gauges to the automated on-board diagnostic system that uses mathematical models to assess engine health and assist in post-flight troubleshooting and maintenance. Using system complexity and capability as a measure, we can break the historical development of control systems down to four phases: (1) the start-up phase (1942 to 1949), (2) the growth phase (1950 to 1969), (3) the electronic phase (1970 to 1989), and (4) the integration phase (1990 to 2002). In each phase, the state-of-the-art control technology is described and the engines that have become historical landmarks, from the control and diagnostic standpoint, are identified. Finally, a historical perspective of engine controls in the last 60 years is presented in terms of control system complexity, number of sensors, number of lines of software (or embedded code), and other factors.Jaw, Link C.a and Garg, SanjayGlenn Research CenterELECTRONIC CONTROL; ENGINE CONTROL; PROPULSION SYSTEM CONFIGURATIONS; GAS TURBINE ENGINES; PHASE CONTROL; MEASURING INSTRUMENTS; MATHEMATICAL MODELS; MAINTENANCE
Widely used for power generation, gas turbine engines are susceptible to faults due to the harsh working environment. Most engine problems are preceded by a sharp change in measurement deviations compared to a baseline engine, but the trend data of these deviations over time are contaminated with noise and non-Gaussian outliers. Gas Turbine Diagnostics: Signal Processing and Fault Isolation presents signal processing algorithms to improve fault diagnosis in gas turbine engines, particularly jet engines. The algorithms focus on removing noise and outliers while keeping the key signal features that may indicate a fault. The book brings together recent methods in data filtering, trend shift detection, and fault isolation, including several novel approaches proposed by the author. Each method is demonstrated through numerical simulations that can be easily performed by the reader. Coverage includes: Filters for gas turbines with slow data availability Hybrid filters for engines equipped with faster data monitoring systems Nonlinear myriad filters for cases where monitoring of transient data can lead to better fault detection Innovative nonlinear filters for data cleaning developed using optimization methods An edge detector based on gradient and Laplacian calculations A process of automating fault isolation using a bank of Kalman filters, fuzzy logic systems, neural networks, and genetic fuzzy systems when an engine model is available An example of vibration-based diagnostics for turbine blades to complement the performance-based methods Using simple examples, the book describes new research tools to more effectively isolate faults in gas turbine engines. These algorithms may also be useful for condition and health monitoring in other systems where sharp changes in measurement data indicate the onset of a fault.