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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records

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NASA NTRS2005

Intelligent Life-Extending Controls for Aircraft Engines

Aircraft engine controllers are designed and operated to provide desired performance and stability margins. The purpose of life-extending-control (LEC) is to study the relationship between control action and engine component life usage, and to design an intelligent control algorithm to provide proper trade-offs between performance and engine life usage. The benefit of this approach is that it is expected to maintain safety while minimizing the overall operating costs. With the advances of computer technology, engine operation models, and damage physics, it is necessary to reevaluate the control strategy fro overall operating cost consideration. This paper uses the thermo-mechanical fatigue (TMF) of a critical component to demonstrate how an intelligent engine control algorithm can drastically reduce the engine life usage with minimum sacrifice in performance. A Monte Carlo simulation is also performed to evaluate the likely engine damage accumulation under various operating conditions. The simulation results show that an optimized acceleration schedule can provide a significant life saving in selected engine components.

Guo, Ten-Huei↗

NASA NTRS1980

Lightweight diesel aircraft engines for general aviation

A methodical design study was conducted to arrive at new diesel engine configurations and applicable advanced technologies. Two engines are discussed and the description of each engine includes concept drawings. A performance analysis, stress and weight prediction, and a cost study were also conducted. This information was then applied to two airplane concepts, a six-place twin and a four-place single engine aircraft. The aircraft study consisted of installation drawings, computer generated performance data, aircraft operating costs and drawings of the resulting airplanes. The performance data shows a vast improvement over current gasoline-powered aircraft. At the completion of this basic study, the program was expanded to evaluate a third engine configuration. This third engine incorporates the best features of the original two, and its design is currently in progress. Preliminary information on this engine is presented.

Berenyi, S. G.↗

NASA NTRS1982

Cooling Air Inlet and Exit Geometries on Aircraft Engine Installations

A semispan wing and nacelle of a typical general aviation twin-engine aircraft was tested to evaluate the cooling capability and drag or several nacelle shapes; the nacelle shapes included cooling air inlet and exit variations. The tests were conducted in the Ames Research Center 40 x 80-ft Wind Tunnel. It was found that the cooling air inlet geometry of opposed piston engine installations has a major effect on inlet pressure recovery, but only a minor effect on drag. Exit location showed large effect on drag, especially for those locations on the sides of the nacelle where the suction characteristics were based on interaction with the wing surface pressures.

Katz, Joseph↗

NASA NTRS1980

Study of cooling air inlet and exit geometries for horizontally opposed piston aircraft engines

A semispan wing and nacelle of a typical general aviation twin-engine aircraft was tested to evaluate the cooling capability and drag of several nacelle shapes; the nacelle shapes included cooling air inlet and exit variations. The tests were conducted in the Ames Research Center's 40- by 80-Foot Wind Tunnel. It was found that the cooling air inlet geometry of opposed piston engine installations has a major effect on inlet pressure recovery, but only a minor effect on drag. Exit location showed a large effect on drag, especially for those locations on the sides of the nacelle where the suction characteristics were based on interaction with the wing surface pressures.

Katz, J.↗

NASA NTRS1988

Investigation of the misfueling of reciprocating piston aircraft engines

The Aircraft Misfueling Detection Project was developed by the Goddard Space Flight Center/Wallops Flight Facility at Wallops Island, Virginia. Its purpose was to investigate the misfueling of reciprocating piston aircraft engines by the inadvertent introduction of jet fuel in lieu of or as a contaminant of aviation gasoline. The final objective was the development of a device(s) that will satisfactorily detect misfueling and provide pilots with sufficient warning to avoid injury, fatality, or equipment damage. Two devices have been developed and successfully tested: one, a small contamination detection kit, for use by the pilot, and a second, more sensitive, modified gas chromatograph for use by the fixed-base operator. The gas chromatograph, in addition to providing excellent quality control of the fixed-base operator's fuel handling, is a very good backup for the detection kit in the event it produces negative results. Design parameters were developed to the extent that they may be applied easily to commercial production by the aircraft industry.

Scott, J. Holland, Jr.↗

NASA NTRS1982

Optical tip clearance sensor for aircraft engine controls

Aircraft gas turbine performance and efficiency are related to airfoil tip clearance. The possibility has been considered to obtain optimum performance and efficiency by reducing clearance to a safe minimum with the aid of a closed-loop tip clearance control system, which utilizes a tip clearance sensor. The use of optical sensing methods appears to represent a potential solution to the tip clearance measurement problem. Principles of sensor operation are discussed along with dimensional considerations and diffraction limitations. A description is presented of the study of the feasibility of a certain sensor, taking into account the test rig system, the optical components, and the mounting tube. Attention is also given to the operation of the feasibility-study sensor, performance estimations, the optical fiber bundle, light beam refraction, and aspects of aircraft engine implementation.

Poppel, G. L.↗

NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

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.

machine learning↗

NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

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.

machine learning↗

NASA NTRS2005

Intelligent Life-Extending Controls for Aircraft Engines Studied

Current aircraft engine controllers are designed and operated to provide desired performance and stability margins. Except for the hard limits for extreme conditions, engine controllers do not usually take engine component life into consideration during the controller design and operation. The end result is that aircraft pilots regularly operate engines under unnecessarily harsh conditions to strive for optimum performance. The NASA Glenn Research Center and its industrial and academic partners have been working together toward an intelligent control concept that will include engine life as part of the controller design criteria. This research includes the study of the relationship between control action and engine component life as well as the design of an intelligent control algorithm to provide proper tradeoffs between performance and engine life. This approach is expected to maintain operating safety while minimizing overall operating costs. In this study, the thermomechanical fatigue (TMF) of a critical component was selected to demonstrate how an intelligent engine control algorithm can significantly extend engine life with only a very small sacrifice in performance. An intelligent engine control scheme based on modifying the high-pressure spool speed (NH) was proposed to reduce TMF damage from ground idle to takeoff. The NH acceleration schedule was optimized to minimize the TMF damage for a given rise-time constraint, which represents the performance requirement. The intelligent engine control scheme was used to simulate a commercial short-haul aircraft engine.

Guo, Ten-Huei↗

NASA NTRS1975

Preferential concentration of certain elements in smaller aerosols emitted from aircraft engines

Aerosols from aircraft engines were collected with an eight-stage cascade sampler for a period of 24 h. The aerosol samples from each stage were analyzed for their elemental composition using the proton-induced X-ray emission (PIXE) technique. Seventeen elements (Si, P, S, Cl, K, Ca, Ti, V, Fe, Ni, Cu, Zn, Br, Sr, Nb, Sn, and Pb) were positively identified and quantitated at each stage. Six elements (S, Ca, Fe, Zn, Sn, and Pb) showed a fractional concentration increase with decreasing aerosol size. Similar, but less well-defined, trends were also observed for V and Ni. Silicon and chlorine, on the other hand, showed an opposite trend. Neutron-activation analysis of bulk aerosol samples collected every 2 h over the same period showed a correlation between concentration of Si, Ca, V, Ti, Zn, Br, and Sn and the density of air traffic at the airport. Analysis of the aviation-fuel samples by PIXE indicates that major fractions of Pb, Sn, Br, Zn, Ni, Fe, V, Ca, and S observed in these aerosol studies come from the aircraft engine exhaust.

Jolly, R. K.↗

NASA NTRS2002

Life-Extending Control for Aircraft Engines Studied

Current aircraft engine controllers are designed and operated to provide both performance and stability margins. However, the standard method of operation results in significant wear and tear on the engine and negatively affects the on-wing life--the time between cycles when the engine must be physically removed from the aircraft for maintenance. The NASA Glenn Research Center and its industrial and academic partners have been working together toward a new control concept that will include engine life usage as part of the control function. The resulting controller will be able to significantly extend the engine's on-wing life with little or no impact on engine performance and operability. The new controller design will utilize damage models to estimate and mitigate the rate and overall accumulation of damage to critical engine parts. The control methods will also provide a means to assess tradeoffs between performance and structural durability on the basis of mission requirements and remaining engine life. Two life-extending control methodologies were studied to reduce the overall life-cycle cost of aircraft engines. The first methodology is to modify the baseline control logic to reduce the thermomechanical fatigue (TMF) damage of cooled stators during acceleration. To accomplish this, an innovative algorithm limits the low-speed rotor acceleration command when the engine has reached a threshold close to the requested thrust. This algorithm allows a significant reduction in TMF damage with only a very small increase in the rise time to reach the commanded rotor speed. The second methodology is to reduce stress rupture/creep damage to turbine blades and uncooled stators by incorporating an engine damage model into the flight mission. Overall operation cost is reduced by an optimization among the flight time, fuel consumption, and component damages. Recent efforts have focused on applying life-extending control technology to an existing commercial turbine engine, and doing so without modifying the hardware or adding sensors. This approach makes it possible to retrofit existing engines with life-extending control technology by changing only the control software in the full-authority digital engine controller (FADEC). The significant results include demonstrating a 20- to 30-percent reduction in TMF damage to the hot section by developing and implementing smart acceleration logic during takeoff. The tradeoff is an increase, from 5.0 to 5.2 sec, in the time required to reach maximum power from ground idle. On a typical flight profile of a cruise at Mach 0.8 at an altitude of 41,000 ft, and cruise time of 104 min, the optimized system showed that a reduction in cruise speed from Mach 0.8 to 0.79 can achieve an estimated 25-to 35-percent creep/rupture damage reduction in the engine's hot section and a fuel savings of 2.1 percent. The tradeoff is an increase in flight time of 1.3 percent (1.4 min).

Guo, Te-Huei↗

NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

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.

machine learning↗

NASA NTRS1985

Polymer, metal and ceramic matrix composites for advanced aircraft engine applications

Advanced aircraft engine research within NASA Lewis is being focused on propulsion systems for subsonic, supersonic, and hypersonic aircraft. Each of these flight regimes requires different types of engines, but all require advanced materials to meet their goals of performance, thrust-to-weight ratio, and fuel efficiency. The high strength/weight and stiffness/weight properties of resin, metal, and ceramic matrix composites will play an increasingly key role in meeting these performance requirements. At NASA Lewis, research is ongoing to apply graphite/polyimide composites to engine components and to develop polymer matrices with higher operating temperature capabilities. Metal matrix composites, using magnesium, aluminum, titanium, and superalloy matrices, are being developed for application to static and rotating engine components, as well as for space applications, over a broad temperature range. Ceramic matrix composites are also being examined to increase the toughness and reliability of ceramics for application to high-temperature engine structures and components.

Mcdanels, D. L.↗

NASA NTRS1986

Polymer, metal, and ceramic matrix composites for advanced aircraft engine applications

Mcdanels, D. L.↗

NASA NTRS1938

Engine Performance and Knock Rating of Fuels for High-output Aircraft Engines

Data are presented to show the effects of inlet-air pressure, inlet-air temperature, and compression ratio on the maximum permissible performance obtained on a single-cylinder test engine with aircraft-engine fuels varying from a fuel of 87 octane number to one 100 octane number plus 1 ml of tetraethyl lead per gallon. The data were obtained on a 5-inch by 5.75-inch liquid-cooled engine operating at 2,500 r.p.m. The compression ratio was varied from 6.50 to 8.75. The inlet-air temperature was varied from 120 to 280 F. and the inlet-air pressure from 30 inches of mercury absolute to the highest permissible. The limiting factors for the increase in compression ratio and in inlet-air pressure was the occurrence of either audible or incipient knock. The data are correlated to show that, for any one fuel,there is a definite relationship between the limiting conditions of inlet-air temperature and density at any compression ratio. This relationship is dependent on the combustion-gas temperature and density relationship that causes knock. The report presents a suggested method of rating aircraft-engine fuels based on this relationship. It is concluded that aircraft-engine fuels cannot be satisfactorily rated by any single factor, such as octane number, highest useful compression ratio, or allowable boost pressure. The fuels should be rated by a curve that expresses the limitations of the fuel over a variety of engine conditions.

Rothbrock, A M↗

NASA NTRS1929

Calculation of the pressures on aircraft engine bearings

For aircraft engines the three principal operating conditions are idling speed, cruising speed, and diving with the engine stopped. In what follows, we will discuss a method which affords a good idea of the course of pressure for the above mentioned operating conditions. The pressures produced in the driving gear are of three kinds; namely, the pressure due to gases, the pressure due to the inertia of the rotating masses, and the pressure due to the inertia of the reciprocating masses.

BEARINGS - PRESSURES - CALCULATION↗

NASA NTRSDate not supplied

Utilizing Radiocarbon and Thermo-optical Measurements to Quantify Source Contributions for Aircraft Engine Particulate Emissions

Recent technological advancements in aircraft engine technology and fuel sources have promising potential for reducing particulate emissions from aircraft engines. During the 2022 Boeing ecoDemonstrator ground-based field campaign, particle and trace gas emissions were sampled for four different fuels, including 100% sustainable aviation fuel (SAF), petroleum-based Jet A fuel, petroleum-based Jet A low sulfur fuel, and a SAF blend. Particulate emissions were collected on quartz microfiber filters and analysed for their radiocarbon content and their elemental carbon (EC) and organic carbon (OC) concentrations. In addition, the radiocarbon content of Jet A fuel, SAF, and aircraft engine oil was also measured. We used the radiocarbon content of the particulate emissions samples and source end members to quantify the relative contribution from engine oil and fuel to carbonaceous aerosol emissions. The results from this study can be used to understand how the implementation of technological advancements in aircraft engine technology and fuel sources will likely impact future aircraft particulate emissions.

Liz Wiggins↗

NASA NTRS1999

High Reliability Engine Control Demonstrated for Aircraft Engines

For a dual redundant-control system, which is typical for short-haul aircraft, if a failure is detected in a control sensor, the engine control is transferred to a safety mode and an advisory is issued for immediate maintenance action to replace the failed sensor. The safety mode typically results in severely degraded engine performance. The goal of the High Reliability Engine Control (HREC) program was to demonstrate that the neural-network-based sensor validation technology can safely operate an engine by using the nominal closed-loop control during and after sensor failures. With this technology, engine performance could be maintained, and the sensor could be replaced as a conveniently scheduled maintenance action.

Guo, Ten-Huei↗