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These notes are intended to furnish practical and general data on the effect of altitude on engine power. The effective horsepower of an engine is a function of the mean pressure of the fluid acting on the pistons, of the R.P.M. of the engine and of the mechanical efficiency.
One of the biggest challenges of manned spaceflight beyond low earth orbit and the moon is harmful radiation that astronauts would be exposed to on their long journey to Mars and further destinations. Using nuclear energy has the potential to be a more effective means of propulsion compared to traditional chemical engines (higher specific impulse). An upper stage nuclear engine would allow astronauts to reach their destination faster and more fuel efficiently. Testing these engines poses engineering challenges due to the need to totally capture the engine exhaust. The Thermal Propulsion Capture System is a concept for cost effectively and safely testing Nuclear Thermal Engines. Nominally, hydrogen exhausted from the engine is not radioactive, but is treated as such in case of fuel element failure. The Thermal Propulsion Capture System involves injecting liquid oxygen to convert the hydrogen exhaust into steam. The steam is then cooled and condensed into liquid water to allow for storage. The Thermal Propulsion Capture System concept for ground testing of a nuclear powered engine involves capturing the engine exhaust to be cooled and condensed before being stored. The hydrogen exhaust is injected with liquid oxygen and burned to form steam. That steam must be cooled to saturation temperatures before being condensed into liquid water. A crossflow heat exchanger using water as a working fluid will be designed to accomplish this goal. Design a cross flow heat exchanger for the Thermal Propulsion Capture System testing which: Eliminates the need for water injection cooling, Cools steam from 5800 F to saturation temperature, and Is efficient and minimizes water requirement.
The SUbsonic Single Aft eNgine (SUSAN) program is being developed as a sustainable subsonic regional aircraft. NASA is conducting trade studies for this future hybrid single aisle passenger aircraft. The goal of reducing emission levels by 50% will be enabled through an advanced design. An aft mounted single turbine engine provides thrust and electric power to drive 16 wing mounted electric engines. Using the 16 counter-rotating electric fans will create an efficient, very high bypass ratio, engine. Electric engines allow for distributed thrust, reducing control surfaces and provide a greater dynamic response. Batteries provide boost for takeoff and climb, allowing a smaller, more efficient, turbine engine. As part of the SUSAN effort, two pilot-in-the-loop studies were conducted at NASA Langley Research Center in Virginia to explore flight deck design issues. In particular: how many throttles are required/desired for a 17-engine aircraft, does the hybrid electric design change the answer, what are the requirements for electric engine displays, what system pages are required, and what information is required to monitor the batteries. Objective and subjective data were collected after each scenario and at the end of the day with a guided set of questionnaires from sixteen crews paired by airline. Data along with pilot comments were used to improve the throttle and engine displays between the studies and used to make the final recommendations on flight deck configurations. This paper provides final design recommendations for the SUSAN flight deck and justification for each recommendation.
Future civil transport designs may incorporate engine inlets integrated into the body of the aircraft to take advantage of efficiency increases due to weight and drag reduction. Additional increases in engine efficiency are predicted if the inlet ingests the lower momentum boundary layer flow. Previous studies have shown, however, that efficiency benefits of Boundary Layer Ingesting (BLI) ingestion are very sensitive to the magnitude of fan and duct losses, and blade structural response to the non-uniform flow field that results from a BLI inlet has not been studied in-depth. This paper presents an effort to extend the modeling capabilities of an existing rotating turbomachinery unsteady analysis code to include the ability to solve the external and internal flow fields of a BLI inlet. The TURBO code has been a successful tool in evaluating fan response to flow distortions for traditional engine/inlet integrations, such as the development of rotating stall and inlet distortion through compressor stages. This paper describes the first phase of an effort to extend the TURBO model to calculate the external and inlet flowfield upstream of fan so that accurate pressure distortions that result from BLI configurations can be computed and used to analyze fan aerodynamics and structural response. To validate the TURBO program modifications for the BLI flowfield, experimental test data obtained by NASA for a flushmounted S-duct with large amounts of boundary layer ingestion was modeled. Results for the flow upstream and in the inlet are presented and compared to experimental data for several high Reynolds number flows to validate the modifications to the solver. Quantitative data is presented that indicates good predictive capability of the model in the upstream flow. A representative fan is attached to the inlet and results are presented for the coupled inlet/fan model. The impact on the total pressure distortion at the AIP after the fan is attached is examined.
Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.
Effective deployment of trained machine-learning models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a Windows app that has been created to deploy trained machine-learning models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of machine-learning application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). Current version of the app focuses on the performance prediction of conventional turbofans. The app gets user input for a turbofan design, preprocesses the input data, and deploys trained machine-learning models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The machine-learning predictive models were built by employing supervised deep-learning algorithm to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these machine-learning models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.
Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.
The control system designed under the Multivariable Control Synthesis (MVCS) program for the F100 turbofan engine is described. The MVCS program, applied the linear quadratic regulator (LQR) synthesis methods in the design of a multivariable engine control system to obtain enhanced performance from cross-coupled controls, maximum use of engine variable geometry, and a systematic design procedure that can be applied efficiently to new engine systems. Basic components of the control system, a reference value generator for deriving a desired equilibrium state and an approximate control vector, a transition model to produce compatible reference point trajectories during gross transients, gain schedules for producing feedback terms appropriate to the flight condition, and integral switching logic to produce acceptable steady-state performance without engine operating limit exceedance are described and the details of the F100 implementation presented. The engine altitude test phase of the MVCS program, and engine responses in a variety of test operating points and power transitions are presented.
Engine performance and mission studies were carried out for turbofan engines equipped with supersonic through-flow fans. The mission was for a commercial supersonic transport with a Mach 2.32 capability. The advantages of the supersonic fan engines are discussed in terms of mission range comparisons with other engine types. The effects of fan efficiency, inlet losses, and engine weight on engine performance and mission range are shown. The range of a supersonic transport with supersonic fan engines could be 10 to 20 percent better than with other types having the same technology core.
Exhaust energy used for supercharging and extra shaft power. Flow of exhaust apportioned by waste gate to meet demand of turbocharger, and portion not fed to turbocharger sent to power-recovery turbine. Expected to increase fuel efficiency.
Ceramic thermal and environmental barrier coatings (TEBCs) are used in gas turbine engines to protect engine hot-section components in the harsh combustion environments, and extend component lifetimes. For future high performance engines, the development of advanced ceramic barrier coating systems will allow these coatings to be used to simultaneously increase engine operating temperature and reduce cooling requirements, thereby leading to significant improvements in engine power density and efficiency. In order to meet future engine performance and reliability requirements, the coating systems must be designed with increased high temperature stability, lower thermal conductivity, and improved thermal stress and erosion resistance. In this paper, ceramic coating design and testing considerations will be described for high temperature and high-heat-flux engine applications in hot corrosion and oxidation, erosion, and combustion water vapor environments. Further coating performance and life improvements will be expected by utilizing advanced coating architecture design, composition optimization, and improved processing techniques, in conjunction with modeling and design tools.
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The F100 Multivariable control synthesis (MVCS) program, was aimed at demonstrating the benefits of LGR synthesis theory in the design of a multivariable engine control system for operation throughout the flight envelope. The advantages of such procedures include: (1) enhanced performance from cross-coupled controls, (2) maximum use of engine variable geometry, and (3) a systematic design procedure that can be applied efficiently to new engine systems. The control system designed, under the MVCS program, for the Pratt & Whitney F100 turbofan engine is described. Basic components of the control include: (1) a reference value generator for deriving a desired equilibrium state and an approximate control vector, (2) a transition model to produce compatible reference point trajectories during gross transients, (3) gain schedules for producing feedback terms appropriate to the flight condition, and (4) integral switching logic to produce acceptable steady-state performance without engine operating limit exceedance.
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NASA and the Army are currently exploring the possibility of using magnetic bearings in gas turbine engines. The use of magnetic bearings in gas turbine engines could increase the reliability by eliminating the lubrication system. The use of magnetic bearings could also increase the speed and the size of the shafts in the engine, thus reducing vibrations and possibly eliminating third bearings. Magnetic bearings can apply forces to the shafts and move them so that blade tips and seals do not rub. This could be part of an active vibration cancellation system. Also, whirling (displacing the shaft center line) may delay rotating stall and increase the stall margin of the engine. Magnetic bearings coupled with an integral starter generator could result in a more efficient 'more electric' engine. The IHPTET program, a joint DOD-industry program, has identified a need for a high temperature, (as high as 1200 F), magnetic bearing that could be demonstrated in a phase m engine. A magnetic bearing is similar to an electric motor. The magnetic bearing has a laminated rotor and stator made out of cobalt steel. The stator has a series of coils of wire wound around it. These coils f u. a series of electromagnets around the circumference. These magnets exert a force on the rotor to keep the rotor in the center of the cavity. The centering force is commanded by a controller based on shaft position, (measured by displacement probes). The magnetic bearing can only pull and is basically unstable before active control is applied The engine shafts, bearings, and case form a flexible structure which contain a large number of modes. A controller is necessary to stabilize these modes. A power amplifier is also necessary to provide the current prescribed by the controller to the magnetic bearings. In case of very high loads, a conventional back up bearing will engage and stop the rotor and stator from rubbing.
NASA Marshall Space Flight Center s Propulsion Systems Department (PSD) is four months into a fifteen month Knowledge Management (KM) initiative to support enhanced engineering decision making and analyses, faster resolution of anomalies (near-term) and effective, efficient knowledge infused engineering processes, reduced knowledge attrition, and reduced anomaly occurrences (long-term). The near-term objective of this initiative is developing a KM Pilot project, within the context of a 3-5 year KM strategy, to introduce and evaluate the use of KM within PSD. An internal NASA/MSFC PSD KM team was established early in project formulation to maintain a practitioner, user-centric focus throughout the conceptual development, planning and deployment of KM technologies and capabilities with in the PSD. The PSD internal team is supported by the University of Alabama's Aging Infrastructure Systems Center Of Excellence (AISCE), Intergraph Corporation, and The Knowledge Institute. The principle product of the initial four month effort has been strategic planning of PSD KM implementation by first determining the "as is" state of KM capabilities and developing, planning and documenting the roadmap to achieve the desired "to be" state. Activities undertaken to support the planning phase have included data gathering; cultural surveys, group work-sessions, interviews, documentation review, and independent research. Assessments and analyses have been performed including industry benchmarking, related local and Agency initiatives, specific tools and techniques used and strategies for leveraging existing resources, people and technology to achieve common KM goals. Key findings captured in the PSD KM Strategic Plan include the system vision, purpose, stakeholders, prioritized strategic objectives mapped to the top ten practitioner needs and analysis of current resource usage. Opportunities identified from research, analyses, cultural/KM surveys and practitioner interviews include: executive and senior management sponsorship, KM awareness, promotion and training, cultural change management, process improvement, leveraging existing resources and new innovative technologies to align with other NASA KM initiatives (convergence: the big picture). To enable results based incremental implementation and future growth of the KM initiative, key performance measures have been identified including stakeholder value, system utility, learning and growth (knowledge capture, sharing, reduced anomaly recurrence), cultural change, process improvement and return-on-investment. The next steps for the initial implementation spiral (focused on SSME Turbomachinery) have been identified, largely based on the organization and compilation of summary level engineering process models, data capture matrices, functional models and conceptual-level systems architecture. Key elements include detailed KM requirements definition, KM technology architecture assessment, evaluation and selection, deployable KM Pilot design, development, implementation and evaluation, and justifying full implementation (estimated Return-on-Investment). Features identified for the notional system architecture include the knowledge presentation layer (and its components), knowledge network layer (and its components), knowledge storage layer (and its components), User Interface and capabilities. This paper provides a snapshot of the progress to date, the near term planning for deploying the KM pilot project and a forward look at results based growth of KM capabilities with-in the MSFC PSD.
NASA Marshall Space Flight Center's Propulsion Systems Department (PSD) is four months into a fifteen month Knowledge Management (KM) initiative to support enhanced engineering decision making and analyses, faster resolution of anomalies (near-term) and effective, efficient knowledge infused engineering processes, reduced knowledge attrition, and reduced anomaly occurrences (long-term). The near-term objective of this initiative is developing a KM Pilot project, within the context of a 3-5 year KM strategy, to introduce and evaluate the use of KM within PSD. An internal NASA/MSFC PSD KM team was established early in project formulation to maintain a practitioner, user-centric focus throughout the conceptual development, planning and deployment of KM technologies and capabilities within the PSD. The PSD internal team is supported by the University of Alabama's Aging Infrastructure Systems Center of Excellence (AISCE), lntergraph Corporation, and The Knowledge Institute. The principle product of the initial four month effort has been strategic planning of PSD KNI implementation by first determining the "as is" state of KM capabilities and developing, planning and documenting the roadmap to achieve the desired "to be" state. Activities undertaken to suppo~th e planning phase have included data gathering; cultural surveys, group work-sessions, interviews, documentation review, and independent research. Assessments and analyses have beon pedormed including industry benchmarking, related local and Agency initiatives, specific tools and techniques used and strategies for leveraging existing resources, people and technology to achieve common KM goals. Key findings captured in the PSD KM Strategic Plan include the system vision, purpose, stakeholders, prioritized strategic objectives mapped to the top ten practitioner needs and analysis of current resource usage. Opportunities identified from research, analyses, cultural1KM surveys and practitioner interviews include: executive and senior management sponsorship, KM awareness, promotion and training, cultural change management, process improvement, leveraging existing resources and new innovative technologies to align with other NASA KM initiatives (convergence: the big picture). To enable results based incremental implementation and future growth of the KM initiative, key performance measures have been identified including stakeholder value, system utility, learning and growth (knowledge capture, sharing, reduced anomaly recurrence), cultural change, process improvement and return-on-investment. The next steps for the initial implementation spiral (focused on SSME Turbomachinery) have been identified, largely based on the organization and compilation of summary level engineering process models, data capture matrices, functional models and conceptual-level svstems architecture. Key elements include detailed KM requirements definition, KM technology architecture assessment, - evaluation and selection, deployable KM Pilot design, development, implementation and evaluation, and justifying full implementation (estimated Return-on-Investment). Features identified for the notional system architecture include the knowledge presentation layer (and its components), knowledge network layer (and its components), knowledge storage layer (and its components), User Interface and capabilities. This paper provides a snapshot of the progress to date, the near term planning for deploying the KM pilot project and a forward look at results based growth of KM capabilities with-in the MSFC PSD.
An integrated team of NASA personnel, Government contractors, industry partners, and university staff have developed an innovative new technology for commercial fan cases that will substantially influence the safety and efficiency of future turbine engines. This effective team, under the direction of the NASA Glenn Research Center and with the support of the Federal Aviation Administration, has matured a new class of carbon/polymer composites and demonstrated a 30- to 50-percent improvement in specific containment capacity (blade fragment kinetic energy/containment system weight). As the heaviest engine component, the engine case/containment system greatly affects both the safety and efficiency of aircraft engines. The ballistic impact research team has developed unique test facilities and methods for screening numerous candidate material systems to replace the traditional heavy, metallic engine cases. This research has culminated in the selection of a polymer matrix composite reinforced with triaxially braided carbon fibers and technology demonstration through the fabrication of prototype engine cases for three major commercial engine manufacturing companies.