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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
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Computer program for prediction of fuel consumption statistical data for an upper stage three-axes stabilized on-off control system

A FORTRAN coded computer program and method to predict the reaction control fuel consumption statistics for a three axis stabilized rocket vehicle upper stage is described. A Monte Carlo approach is used which is more efficient by using closed form estimates of impulses. The effects of rocket motor thrust misalignment, static unbalance, aerodynamic disturbances, and deviations in trajectory, mass properties and control system characteristics are included. This routine can be applied to many types of on-off reaction controlled vehicles. The pseudorandom number generation and statistical analyses subroutines including the output histograms can be used for other Monte Carlo analyses problems.

Source record↗
NASA NTRS2022

Updated Assessment of Turboelectric Boundary Layer Ingestion Propulsion Applied to a Single-Aisle Commercial Transport

Recent advances in technology and a push for more environmentally friendly air transportation has led to interest in electrified aircraft propulsion (EAP). EAP encompasses many different propulsion system architectures which can also enable new, synergistic propulsion-airframe integration approaches. This report evaluates the combination of turboelectric EAP and fuselage boundary layer ingestion (BLI) propulsion in a concept called “STARC-ABL.” The predicted fuel consumption benefits of STARC-ABL are updated from a prior 2016 study with new mission requirements and analysis methodologies. Additionally, certification noise is added to the concept evaluation. To properly assess the impact of EAP, a new concept, “ST-ABL,” is developed for comparison which includes the fuselage BLI propulsor driven mechanically instead of by a turboelectric system. STARC-ABL is predicted to provide a 3.4% reduction in fuel consumption for a single-aisle class, 3500 nmi design mission and a 2.7% reduction for a 900 nmi mission, both relative to an advanced technology conventional aircraft. STARC-ABL is also predicted to have a Chapter 14 cumulative noise margin of 7 EPNdB. The non-EAP ST-ABL concept results in slightly higher fuel consumption and noise than STARC-ABL, indicating that the turboelectric system provides benefits over the mechanical drive approach to fuselage BLI. Areas for future research and evolution of the concept include assessing its applicability to other aircraft sizes, increasing the fidelity of BLI and electric component modeling, and investigating other EAP architecture options.

BLI↗
NASA NTRS2007

Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

In a previous study, multiple regression techniques were applied to Flight Operations Quality Assurance-derived data to develop parsimonious model(s) for fuel consumption on the Boeing 757 airplane. The present study examined several data mining algorithms, including neural networks, on the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree methods reported correlation coefficients of .91 to .92, and General Linear Models and Multilayer Perceptron neural networks reported correlation coefficients of about .99. These data mining models show great promise for use in further examining large FOQA databases for operational and safety improvements.

Stolzer, Alan J.↗
NASA NTRS1992

Analysis of a hydrocarbon scramjet with augmented preburning

This paper presents the results of a feasibility study of a hydrocarbon scramjet design utilizing an augmented preburner upstream of the main fuel injector locations. The combustor design evaluated here is for a small hypersonic research vehicle. It consists of a preburner into which a small amount of fuel is burned with on-board liquid oxygen and injected into the main airflow, upstream of the main fuel injector locations, thus ensuring that combustion is present and uninterrupted. Two degrees of analysis are presented including a one-dimensional cycle analysis and a complete computational fluid dynamic analysis with finite-rate chemistry and a two-equation turbulence model. Comparison of these analyses show good agreement when the CFD-predicted fuel consumption schedule is used in the cycle analysis.

Molvik, Gregory A.↗
NASA NTRS2013

Peak-Seeking Optimization of Trim for Reduced Fuel Consumption: Architecture and Performance Predictions

A peak-seeking control approach for real-time trim configuration optimization for reduced fuel consumption has been developed by researchers at the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center to address the goals of the NASA Environmentally Responsible Aviation project to reduce fuel burn and emissions. The peak-seeking control approach is based on a steepest-descent algorithm using a time-varying Kalman filter to estimate the gradient of a performance function of fuel flow versus control surface positions. In real-time operation, deflections of symmetric ailerons, trailing-edge flaps, and leading-edge flaps of an F/A-18 airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) are controlled for optimization of fuel flow. This paper presents the design and integration of this peak-seeking controller on a modified NASA F/A-18 airplane with research flight control computers. A research flight was performed to collect data to build a realistic model of the performance function and characterize measurement noise. This model was then implemented into a nonlinear six-degree-of-freedom F/A-18 simulation along with the peak-seeking control algorithm. With the goal of eventual flight tests, the algorithm was first evaluated in the improved simulation environment. Results from the simulation predict good convergence on minimum fuel flow with a 2.5-percent reduction in fuel flow relative to the baseline trim of the aircraft.

performance optimization↗
NASA NTRS2013

Peak-Seeking Optimization of Trim for Reduced Fuel Consumption: Architecture and Performance Predictions

A peak-seeking control approach for real-time trim configuration optimization for reduced fuel consumption has been developed by researchers at the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center to address the goals of the NASA Environmentally Responsible Aviation project to reduce fuel burn and emissions. The peak-seeking control approach is based on a steepest-descent algorithm using a time-varying Kalman filter to estimate the gradient of a performance function of fuel flow versus control surface positions. In real-time operation, deflections of symmetric ailerons, trailing-edge flaps, and leading-edge flaps of an FA-18 airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) are controlled for optimization of fuel flow. This presentation presents the design and integration of this peak-seeking controller on a modified NASA FA-18 airplane with research flight control computers. A research flight was performed to collect data to build a realistic model of the performance function and characterize measurement noise. This model was then implemented into a nonlinear six-degree-of-freedom FA-18 simulation along with the peak-seeking control algorithm. With the goal of eventual flight tests, the algorithm was first evaluated in the improved simulation environment. Results from the simulation predict good convergence on minimum fuel flow with a 2.5-percent reduction in fuel flow relative to the baseline trim of the aircraft.

peak-seeking↗
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 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 NTRS2020

Machine Learning-Based Predictive Analytics for Aircraft Engine Conceptual Design

Big data and artificial intelligence/machine learning are transforming the global business environment. Data is now the most valuable asset for enterprises in every industry. Companies are using data-driven insights for competitive advantage. With that, the adoption of machine learning-based data analytics is rapidly taking hold across various industries, producing autonomous systems that support human decision-making. This work explored the application of machine learning to aircraft engine conceptual design. Supervised machine-learning algorithms for regression and classification were employed to study patterns in an existing, open-source database of production and research turbofan engines, and resulting in predictive analytics for use in predicting performance of new turbofan designs. Specifically, the author developed machine learning-based analytics to predict cruise thrust specific fuel consumption (TSFC) and core sizes of high-efficiency turbofan engines, using engine design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks application program interface (API) written in Python, with Google’s TensorFlow (an open source library for numerical computation) serving as the backend engine. The promising results of the predictive analytics show that machine-learning techniques merit further exploration for application in aircraft engine conceptual design.

deep-learning↗
NASA NTRSDate not supplied

A Machine-Learning Approach to Assess Aircraft Engine System Performance

Artificial intelligence (AI)/machine learning, and big data are transforming the global business environment. They have become the most disruptive technologies for organizations to improve workplace efficiency and productivity. This work explored the application of machine learning-based predictive analytics that would enable aircraft engine designers to estimate engine system performance quickly during the conceptual design stage. Supervised machine-learning algorithm was employed to study patterns in an existing database of production and research turbofan engines, and built predictive analytics for use in predicting system performance of new turbofan designs. Specifically, the author developed deep-learning analytics to predict turbofan system weight, using turbofan design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks API (application program interface) written in Python, with TensorFlow (an open-source artificial AI library developed by Google) serving as the backend engine. The current engine-weight prediction results, together with those for the TSFC (thrust specific fuel consumption) and core-size predictions that were studied previously by the author, show that machine learning-based predictive analytics can be an effective, time-saving tool for aircraft engine design-space exploration during the conceptual design stage. It would enable expeditious identification of the best engine design amongst several candidates.

Michael T Tong↗
NASA NTRS2017

Results of the Apogee-Raising Campaign of the Magnetospheric Multiscale Mission

This paper describes the apogee-raising campaign of the Magnetospheric Multiscale mission, where the spacecraft increased their apogee radii from 12 to 25 Earth radii in a total of 98 maneuvers. These maneuvers included an initial formation resize set to spread the spacecraft apart for safety, 32 apogee-raise delta-v maneuvers, their associated slews, four perigee-raise maneuvers and the associated slews, and finally a set of maneuvers to get back into formation. These activities were all accomplished successfully and on schedule with no anomalies, and at a fuel consumption somewhat less than predicted. As a result, MMS was set up ready to carry out in situ studies of magnetic reconnection in the magnetotail, with sufficient fuel remaining for a significant extended mission.

Mission Design↗
NASA NTRS1975

Hydrogen-enrichment-concept preliminary evaluation

A hydrogen-enriched fuels concept for automobiles is described and evaluated in terms of fuel consumption and engine exhaust emissions through multicylinder (V-8) automotive engine/hydrogen generator tests, single cylinder research engine (CFR) tests, and hydrogen-generator characterization tests. Analytical predictions are made of the fuel consumption and NO/sub x/ emissions which would result from anticipated engine improvements. The hydrogen-gas generator, which was tested to quantify its thermodynamic input-output relationships was used for integrated testing of the V-8 engine and generator.

Ecklund, E. E.↗
NASA NTRS2003

Radiative Forcing of the Lower Stratosphere over the Arctic by Light Absorbing Particles

Light absorbing particles (LAP), such as soot and dust, change the thermodynamic structure of the atmosphere and contribute to regional and global climate change. The lower stratosphere (LS) is particularly sensitive to the presence of LAP since the lifetime of particles in the LS may extend from months to years, in contrast to tropospheric lifetimes of at most a few days. The source of particles in the LS may be aircraft, meteorites or emissions from tropospheric sources. There has been a lack, however, of accurate, quantitative measurements made with sufficiently sensitive instruments. This limits our understanding of the origin and lifetime of aerosols in this region of the atmosphere. Here we present recent measurements in the Arctic UT/LS with a new, highly sensitive instrument that has detected black carbon (BC) mass concentrations of 20-1000 ng m(exp -3) that are 10-1000 times larger than those reported in previous studies and are at least 30 times larger than predicted masses based on fuel consumption by commercial aircraft that fly in these regions. Scattering and absorption of solar and terrestrial radiation by the particles in a layer from 8- 12 Km leads to a negative net forcing of -0.5 W sq m at the top of the atmosphere and 9C of heating in this layer during the average aerosol lifetime at these altitudes. The new measurements suggest that the influence of aircraft emissions have been underestimated or that aircraft may not be the only significant source of light absorbing particles in the UT/LS. The presence of these aerosols can cause local changes in the thermal structure of the lower stratosphere and a subsequent modification of stratosphere/tropopause exchange of gases and particles.

Baumgardner, D.↗
NASA NTRS1981

The design and development of a mixer compound exhaust system for a quiet clean general aviation turbofan

Lower engine fuel consumption has become a dominant factor in turbofan engine design due to rapidly increasing fuel costs. One engine component with a large impact on engine performance is the exhaust system. Previous exhaust system studies have demonstrated the significant exhaust system efficiency gains available through mixing of the core and bypass flows. Typically, a large, costly rig and engine program are required to develop and optimize these gains. The purpose of this paper is to present the results of the low-cost design system used for the quiet, clean general aviation turbofan mixer nozzle design and development. The scale model and full-scale engine test results confirm the predicted 3 to 5% reduction in cruise fuel consumption. This unique design system, which is based on integrating advanced three-dimensional viscous numerical methods with empirical optimization techniques, is summarized and detailed comparisons with test data are presented. The ability to accurately predict relative performance of mixer systems with substantially reduced development time and cost savings is demonstrated.

Blackmore, W. L.↗
NASA NTRS1980

Engine component improvement: Performance improvement, JT9D-7 3.8 AR fan

A redesigned, fuel efficient fan for the JT9D-7 engine was tested. Tests were conducted to determine the effect of the 3.8 AR fan on performance, stability, operational characteristics, and noise of the JT9D-7 engine relative to the current 4.6 AR Bill-of-Material fan. The 3.8 AR fan provides increased fan efficiency due to a more advanced blade airfoil with increased chord, eliminating one part span shroud and reducing the number of fan blades and fan exit guide vanes. Engine testing at simulated cruise conditions demonstrated the predicted 1.3 percent improvement in specific fuel consumption with the redesigned 3.8 AR fan. Flight testing and sea level stand engine testing demonstrated exhaust gas temperature margins, fan and low pressure compressor stability, operational suitability, and noise levels comparable to the Bill-of-Material fan.

Gaffin, W. O.↗
NASA NTRS1982

Rotorcraft convertible engines for the 1980s

Two rotorcraft studies were executed. The goal was to identify attractive techniques for implementing convertible powerplants for the ABC, Folded Tilt Rotor, and X-wing type high speed, high-L/D rotorcraft; to determine the DOC and fuel savings benefits achieved thereby; and to define research required to bring these powerplants into existence by the 1990's. These studies are reviewed herein and the different methods of approach are pointed out as well as the key findings. Fan shaft engines using variable inlet guide vanes or torque converters, and turboprop powerplants appear attractive. Savings in DOC and fuel consumption of over 15 percent are predicted in some cases as a result of convertible engine use rather than using separate engines for the thrust and the shaft functions. Areas of required research are fan performance (including noise), integrated engine/rotorcraft control, torque converters, turbine design, airflow for rotorcraft torque control, bleed for lift flow, and transmissions and clutches.

Eisenberg, J. D.↗
NASA NTRS2017

Point-Mass Aircraft Trajectory Prediction Using a Hierarchical, Highly-Adaptable Software Design

A highly adaptable and extensible method for predicting four-dimensional trajectories of civil aircraft has been developed. This method, Behavior-Based Trajectory Prediction, is based on taxonomic concepts developed for the description and comparison of trajectory prediction software. A hierarchical approach to the "behavioral" layer of a point-mass model of aircraft flight, a clear separation between the "behavioral" and "mathematical" layers of the model, and an abstraction of the methods of integrating differential equations in the "mathematical" layer have been demonstrated to support aircraft models of different types (in particular, turbojet vs. turboprop aircraft) using performance models at different levels of detail and in different formats, and promise to be easily extensible to other aircraft types and sources of data. The resulting trajectories predict location, altitude, lateral and vertical speeds, and fuel consumption along the flight path of the subject aircraft accurately and quickly, accounting for local conditions of wind and outside air temperature. The Behavior-Based Trajectory Prediction concept was implemented in NASA's Traffic Aware Planner (TAP) flight-optimizing cockpit software application.

Karr, David A.↗