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The hardware and software development of the A-10/TF34 turbine engine monitoring system (TEMS) is described. The operation and interfaces of the A-10/TF34 TEMS hardware are discussed with particular emphasis on function, capabilities, and limitations. The TEMS data types are defined and the various data acquisition modes are explained. Potential data products are also discussed.
The space shuttle utilizes several rocket engine systems, all of which must function with a high degree of reliability for successful mission completion. The space shuttle main engine (SSME) is by far the most complex of the rocket engine systems and is designed to be reusable. The reusability of spacecraft systems introduces many problems related to testing, reliability, and logistics. Components must be assembled from parts inventories in a manner which will most effectively utilize the available parts. Assembly must be scheduled to efficiently utilize available assembly benches while still maintaining flight schedules. Assembled components must be assigned to as many contiguous flights as possible, to minimize component changes. Each component must undergo a rigorous testing program prior to flight. In addition, testing and assembly of flight engines and components must be done in conjunction with the assembly and testing of developmental engines and components. The development, testing, manufacture, and flight assignments of the engine fleet involves the satisfaction of many logistical and operational requirements, subject to many constraints. The purpose of the SSME Component Assembly and Life Management Expert System (CALMES) is to assist the engine assembly and scheduling process, and to insure that these activities utilize available resources as efficiently as possible.
Liftoff debris is a top risk to the Space Shuttle Vehicle. To manage the Liftoff debris risk, the Space Shuttle Program created a team with in the Propulsion Systems Engineering & Integration Office. The Shutt le Liftoff Debris Team harnesses the Systems Engineering process to i dentify, assess, mitigate, and communicate the Liftoff debris risk. T he Liftoff Debris Team leverages off the technical knowledge and expe rtise of engineering groups across multiple NASA centers to integrate total system solutions. These solutions connect the hardware and ana lyses to identify and characterize debris sources and zones contribut ing to the Liftoff debris risk. The solutions incorporate analyses sp anning: the definition and modeling of natural and induced environmen ts; material characterizations; statistical trending analyses, imager y based trajectory analyses; debris transport analyses, and risk asse ssments. The verification and validation of these analyses are bound by conservative assumptions and anchored by testing and flight data. The Liftoff debris risk mitigation is managed through vigilant collab orative work between the Liftoff Debris Team and Launch Pad Operation s personnel and through the management of requirements, interfaces, r isk documentation, configurations, and technical data. Furthermore, o n day of launch, decision analysis is used to apply the wealth of ana lyses to case specific identified risks. This presentation describes how the Liftoff Debris Team applies Systems Engineering in their proce sses to mitigate risk and improve the safety of the Space Shuttle Veh icle.
Benefits provided by a full-authority digital engine control are related to improvements in engine efficiency, performance, and operations. An additional benefit is the capability of detecting and accommodating failures in real time and providing engine-health diagnostics. The digital electronic engine control (DEEC), is a full-authority digital engine control developed for the F100-PW-100 turbofan engine. The DEEC has been flight tested on an F-15 aircraft. The flight tests had the objective to evaluate the DEEC hardware and software over the F-15 flight envelope. A description is presented of the results of the flight tests, which consisted of nonaugmented and augmented throttle transients, airstarts, and backup control operations. The aircraft, engine, DEEC system, and data acquisition and reduction system are discussed.
An algorithm is derived for the real-time calibration of the engine fuel flowmeter and the engine mixture ratio during Space Shuttle Main Engine (SSME) ground testing. Because currently used calibration methods are post-test operations, there exists no fail-safe way of predicting at what mixture ratio a planned test will run. It is proposed that the algorithm developed here be used as part of an Automated Engine Calibration System (AECS) which could ensure that nearly all SSME tests are run at the proper mixture ratio. In this way, AECS has the potential of increasing the efficiency of the SSME ground test program. In addition to the derivation of the algorithm, an overview of this calibration system is presented along with the list of test stand facility instrumentation necessary for AECS implementation.
A key aspect of adopting model-based systems engineering as a practice in an organization is the design and development, and adoption of corresponding processes and tools that support the model-based paradigm. In an effort to enable the unified implementation of such processes and tools, this paper introduces a reference architecture model that serves as a specification for a model-based engineering environment. Current systems engineering practices, products, processes and technologies are used as input for continuously refining the architecture model. In the paper, we introduce and report on the current status of this reference architecture model, and present the methodology applied in developing the reference architecture. We conclude that while there are a very large number of domain- or application-specific processes requiring specialized behavior, these can be reduced through abstraction to a small set of core functions that need to be supported by a realization of a model-based engineering environment. Only very few organization-domain- or application-specific aspects require specialized consideration.
The third phase of a flight evaluation of a digital electronic engine control system in an F-15 has recently been completed. It was found that digital electronic engine control software logic changes and augmentor hardware improvements resulted in significant improvements in engine operation. For intermediate to maximum power throttle transients, an increase in altitude capability of up to 8000 ft was found, and for idle to maximum transients, an increase of up to 4000 ft was found. A nozzle instability noted in earlier flight testing was investigated on a test engine at NASA Lewis Research Center, a digital electronic engine control software logic change was developed and evaluated, and no instability occurred in the Phase 3 flight evaluation. The backup control airstart modification was evaluated, and gave an improvement of airstart capability by reducing the minimum airspeed for successful airstarts by 50 to 75 knots.
The third phase of a flight evaluation of a digital electronic engine control system in an F-15 has recently been completed. It was found that digital electronic engine control software logic changes and augmentor hardware improvements resulted in significant improvements in engine operation. For intermediate to maximum power throttle transients, an increase in altitude capability of up to 8000 ft was found, and for idle to maximum transients, an increase of up to 4000 ft was found. A nozzle instability noted in earlier flight testing was investigated on a test engine at NASA Lewis Research Center, a digital electronic engine control software logic change was developed and evaluated, and no instability occurred in the Phase 3 flight evaluation. The backup control airstart modification was evaluated, and gave an improvement of airstart capability by reducing the minimum airspeed for successful airstarts by 50 to 75 knots.
At Lawrence Livermore National Laboratory in the National Ignition Facility Optics Group, we use the systems engineering approach for project management and as a design tool. Systems engineering is used in a graded approach to design and project management that is based on risk, informing how much rigor to apply. The tools and techniques from systems engineering offer a framework to organize projects with everyone speaking the same language to provide consistent and repeatable project success that satisfies the stakeholders’ needs and meets the mission. The classes have provided a framework with tools for communicating system design, requirements, verification and validation, and an operational context.
Engineering studies for an International Energy Agency project for the design and construction of a 500 kW solar thermal electric power generation system of the distributed collector system (DCS) type are reviewed. The DCS system design consists of a mixed field of parabolic trough type solar collectors which are used to heat a thermal heat transfer oil. Heated oil is delivered to a thermocline storage tank from which heat is extracted and delivered to a boiler by a second heat transfer loop using the same heat transfer oil. Steam is generated in the boiler, expanded through a steam turbine, and recirculated through a condenser system cooled by a wet cooling tower.
The characteristics of the TF34 convertible engine, capable of producing shaft power, thrust, or a combination of both, is investigated with respect to the control system design, development, bench testing, and the anticipated transient response during engine testing at NASA. The modifications to the prototype standard TF34-GE-400 turbofan, made primarily in the fan section, consist of the variable inlet guide vanes and variable exit guide vanes. The control system was designed using classical frequency domain techniques and was based on the anticipated convertible/VTOL airframe requirements. The engine has been run in the fan mode and in the shaft mode, exhibiting a response of 0.14 second to a 5-percent thrust change.
As crewed missions move beyond Low-Earth Orbit, pre-mission planning cannot fully buy down the medical risks of exploration-class missions. Martian missions, where increased hazards exist, (such as long-duration spaceflight, surface-level EVA operations, and communications delays) will require a paradigm shift in the structure of a medical system. An Earth-Independent Medical Operations-based Medical System (EIMO-MS) will need to optimize four critical domains to help provide medical care: utilization of Pre-Mission Planning, augmentation of Acute and Prolonged Medical Decision Making, automated tracking of Resource Management, and assistance in Task Load Balance. The ideal EIMO-MS will be able to accomplish this goal by having an interactive, adaptable interface that will be able to provide real-time medical services. It must respond based on the level of crewmember training, medical situation, and available medical and non-medical resources. To showcase the capabilities and requirements of such a sophisticated automated MS, a series of clinical scenarios of escalating complexity were developed with clinical and systems engineering input. These scenarios describe in clinical detail what a theoretical future medical system, enhanced with multiple information streams (such as a medical database, an AI-based Decision Support System, real-time monitoring, enhanced in-situ laboratory imaging, etc.) can achieve in conjunction with a trained and experienced crew. Scenarios are comprised of: a context section including objectives and applicable spaceflight environment, a highlighted assumptions section, a clinical narrative section, and a systems engineering activity diagram demonstrating the integrated Medical System (MS). The “swim lanes” of the activity diagram act as the logistical core of each scenario and show how the MS will interact with the crew, ground support, and other in-flight systems. The Design Reference Mission that is used for the scenarios is based on existing reference mission profiles [1] with a projected 30-sol stay on the Martian surface. Scenarios span the spectrum from planned evaluations, minor medical care, urgent care, surgical guidance, critical and expectant management, and behavioral health care. Mission complexity will exponentially increase during deep space and Mars exploration-class missions, and medical support for these missions will likewise need to increase in autonomy and adaptability. The integrated system that will support these missions will need to provide assistance in a variety of anticipated and unforeseen scenarios. These medical scenarios, guided by clinician input, are initial steps in crafting the requirements for an EIMO-based medical system. By working in a systems engineering framework, requirements and capabilities can be extracted and mapped while maintaining a clinical core.
The state of systems engineering is such that a form of early and continued use of risk assessments is conducted (as evidenced by NASA's adoption and use of the 'Continuous Risk Management' paradigm developed by SEI). ... However, these practices fall short of theideal: (1) Integration between risk assessment techniques and other systems engineering tools is weak. (2) Risk assessment techniques and the insights they yield are only informally coupled to design decisions. (3) Individual riskassessment techniques lack the mix of breadth, fidelity and agility required to span the gamut of the design space. In this paper we present an approach that addresses these shortcomings. The hallmark of our approach is a simple representation comprising objectives (what the system is to do), risks (whose occurrence would detract from attainment of objectives) and activities (a.k.a. 'mitigations') that, if performed, will decrease those risks. These are linked to indicate by how much a risk would detract from attainment of an objective, and by how much an activity would reduce a risk. The simplicity of our representational framework gives it the breadth to encompass the gamut of the design space concerns, the agility to be utilized in even the earliest phases of designs, and the capability to connect to system engineering models and higher-fidelity risk tools. It is through this integration that we address the shortcomings listed above, and so achieve the intertwining between risk insights and design decisions needed to guide systems engineering towards superior final designs while avoiding costly rework to achieve them. The paper will use an example, constructed to be representative of space mission design, to illustrate our approach.
The space shuttle main engine (SSME) presented new requirements in the design of controls for large pump fed liquid rocket engine systems. These requirements were the need for built in full mission support capability, and complexity and flexibility of function not previously needed in this type of application. An engine mounted programmable digital control system was developed to meet these requirements. The engine system and controller and their function are described. Design challenges encountered during the course of development included accommodation for a very severe engine environment, the implementation of redundancy and redundancy management to provide fail operational/fail safe capability, removal of heat from the package, and significant constraints on computer memory size and processing time. The flexibility offered by programmable control reshaped the approach to engine design and development and set the pattern for future controls development in these types of applications.
Systems engineers worldwide have been working to implement Model-Based Systems Engineering (MBSE) environments, tools, and methodologies. A significant, expected benefit from MBSE is a reduction in the time commitment for all aspects of systems engineering. Although advances in MBSE have occurred, a gap has emerged between the traditional configuration management (CM) processes for documents and those needed in an MBSE environment.
This curriculum is intended to: Teach you about the Engineering Authorization (EA) System; Explain the course subject in detail; Provide references and guides that will help beyond this training.
Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a SimulinkR library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.
Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink(R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.