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This paper describes an approach to creating simulations of the electric components for a hybrid electric propulsionsystem. The proposed modeling technique is based on power/load flow modeling and is designed to provide a modularframework that includes buses, lines, and other electrical components that can be connected together to form the electrical distribution system. The purpose of this paper is to detail an electric distribution system modeling technique and to demonstrate how these models may be integrated with turbomachinery simulations. These general modeling techniques were created to be utilized for system and control design studies. Additionally, steady-state and dynamic performance for a proposed model example is compared with data from a hardware in the loop simulation.

The Intelligent Control and Autonomy Branch at NASA Glenn Research Center hosted the Sixth Propulsion Control and Diagnostics Workshop on August 22-24, 2017. The objectives of this workshop were to disseminate information about research being performed in support of NASA Aeronautics programs; get feedback from peers on the research; and identify opportunities for collaboration. There were presentations and posters by NASA researchers, Department of Defense representatives, and engine manufacturers on aspects of turbine engine modeling, control, and diagnostics.

NASA Glenn Research Center hosted a Users' Workshop on the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) on August 21, 2017. The objective of this workshop was to update the user community on the latest features of T-MATS, and to provide a forum to present work performed using T-MATS. Presentations highlighted creative applications and the development of new features and libraries, and emphasized the flexibility and simulation power of T-MATS.

This paper describes an intelligent propulsion control architecture that coordinates with the flight control to reduce the amount of pilot intervention required to operate the vehicle. Objectives of the architecture include the ability to: automatically recognize the aircraft operating state and flight phase; configure engine control to optimize performance with knowledge of engine condition and capability; enhance aircraft performance by coordinating propulsion control with flight control; and recognize off-nominal propulsion situations and to respond to them autonomously. The hierarchical intelligent propulsion system control can be decomposed into a propulsion system level and an individual engine level. The architecture is designed to be flexible to accommodate evolving requirements, adapt to technology improvements, and maintain safety.

A preliminary simulation of a generic T-tail transport airplane configuration has been developed at the National Aeronautics and Space Administration Langley Research Center. The primary purpose of this piloted simulation is to assess aerodynamic model fidelity requirements for training airline pilots to recognize and recover from full-stall flight conditions in a T-tail airplane. As a result, significant flexibility has been designed into the flight dynamics model. The flight dynamics model is based on newly acquired static and dynamic stability and control data from sources that include: wind tunnel, water tunnel, and computational fluid dynamics. Preliminary results for initial stall show an unstable stall pitch break (if the stick pusher is inhibited), un-commanded motions due to stall asymmetries, significantly reduced dynamic roll stability, and decreased control effectiveness. Preliminary studies indicated an insensitivity to the fidelity of the pitch damping model.

This paper describes the design process for the control system of an advanced geared turbofan engine. This process is applied to a simulation that is representative of a 30,000 lbf thrust class concept engine with two main spools, ultra-high bypass ratio, and a variable area fan nozzle. Control system requirements constrain the non-linear engine model as it operates throughout its flight envelope of sea level to 40,000 ft and from 0 to 0.8 Mach. The control architecture selected for this project was developed from literature and reflects a configuration that utilizes a proportional integral controller integrated with sets of limiters that enable the engine to operate safely throughout its flight envelope. Simulation results show the overall system meets performance requirements without exceeding system operational limits.

This paper describes an intelligent propulsion control architecture that coordinates with the flight control to reduce the amount of pilot intervention required to operate the vehicle. Objectives of the architecture include the ability to: automatically recognize the aircraft operating state and flight phase; configure engine control to optimize performance with knowledge of engine condition and capability; enhance aircraft performance by coordinating propulsion control with flight control; and recognize off-nominal propulsion situations and to respond to them autonomously. The hierarchical intelligent propulsion system control can be decomposed into a propulsion system level and an individual engine level. The architecture is designed to be flexible to accommodate evolving requirements, adapt to technology improvements, and maintain safety.

The cost and risk associated with the design and operation of gas turbine engine systems has led to an increasing dependence on mathematical models. In this paper, the fundamentals of engine simulation will be reviewed, an example performance analysis will be performed, and relationships useful for engine control system development will be highlighted. The focus will be on thermodynamic modeling utilizing techniques common in industry, such as: the Brayton cycle, component performance maps, map scaling, and design point criteria generation. In general, these topics will be viewed from the standpoint of an example turbojet engine model; however, demonstrated concepts may be adapted to other gas turbine systems, such as gas generators, marine engines, or high bypass aircraft engines. The purpose of this paper is to provide an example of gas turbine model generation and system performance analysis for educational uses, such as curriculum creation or student reference.

In many non-linear gas turbine simulations, operation in the sub-idle region can lead to model instability. This paper lays out a method for extending the operational envelope of a map based gas turbine simulation to include the sub-idle region. This method develops a multi-simulation solution where the baseline component maps are extrapolated below the idle level and an alternate model is developed to serve as a safety net when the baseline model becomes unstable or unreliable. Sub-idle model development takes place in two distinct operational areas, windmilling/shutdown and purge/cranking/startup. These models are based on derived steady state operating points with transient values extrapolated between initial (known) and final (assumed) states. Model transitioning logic is developed to predict baseline model sub-idle instability, and transition smoothly and stably to the backup sub-idle model. Results from the simulation show a realistic approximation of sub-idle behavior as compared to generic sub-idle engine performance that allows the engine to operate continuously and stably from shutdown to full power.

In many non-linear gas turbine simulations, operation in the sub-idle region can lead to model instability. This paper lays out a method for extending the operational envelope of a map based gas turbine simulation to include the sub-idle region. This method develops a multi-simulation solution where the baseline component maps are extrapolated below the idle level and an alternate model is developed to serve as a safety net when the baseline model becomes unstable or unreliable. Sub-idle model development takes place in two distinct operational areas, windmilling/shutdown and purge/cranking/ startup. These models are based on derived steady state operating points with transient values extrapolated between initial (known) and final (assumed) states. Model transitioning logic is developed to predict baseline model sub-idle instability, and transition smoothly and stably to the backup sub-idle model. Results from the simulation show a realistic approximation of sub-idle behavior as compared to generic sub-idle engine performance that allows the engine to operate continuously and stably from shutdown to full power.

The National Aeronautics and Space Administration (NASA) has developed independent airframe and engine models that have been integrated into a single real-time aircraft simulation for piloted evaluation of propulsion control algorithms. In order to have confidence in the results of these evaluations, the integrated simulation must be validated to demonstrate that its behavior is realistic and that it meets the appropriate Federal Aviation Administration (FAA) certification requirements for aircraft. The paper describes the test procedures and results, demonstrating that the integrated simulation generally meets the FAA requirements and is thus a valid testbed for evaluation of propulsion control modes.