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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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The use of aero-gravity assist during hypersonic interplanetary flights is highlighted. Specifically, the use of large versus small planet for gravity asssist maneuvers, aero-gravity assist trajectories, launch opportunities and planetary waverider performance are addressed.
A full aero-thermal calibration of the NASA Glenn Icing Research Tunnel was completed in 2004 following the replacement of the inlet guide vanes upstream of the tunnel drive system and improvement to the facility total temperature instrumentation. This calibration test provided data used to fully document the aero-thermal flow quality in the IRT test section and to construct calibration curves for the operation of the IRT. The 2004 test was also the first to use the 2-D RTD array, an improved total temperature calibration measurement platform.
A full aero-thermal calibration of the NASA Glenn Icing Research Tunnel (IRT) was completed in 2012 following the major modifications to the facility that included replacement of the refrigeration plant and heat exchanger. The calibration test provided data used to fully document the aero-thermal flow quality in the IRT test section and to construct calibration curves for the operation of the IRT.
This paper covers the development of an integrated nonlinear dynamic model for a variable cycle turbofan engine, supersonic inlet, and convergent-divergent nozzle that can be integrated with an aeroelastic vehicle model to create an overall Aero-Propulso-Servo-Elastic (APSE) modeling tool. The primary focus of this study is to provide a means to capture relevant thrust dynamics of a full supersonic propulsion system by using relatively simple quasi-one dimensional computational fluid dynamics (CFD) methods that will allow for accurate control algorithm development and capture the key aspects of the thrust to feed into an APSE model. Previously, propulsion system component models have been developed and are used for this study of the fully integrated propulsion system. An overview of the methodology is presented for the modeling of each propulsion component, with a focus on its associated coupling for the overall model. To conduct APSE studies the de- scribed dynamic propulsion system model is integrated into a high fidelity CFD model of the full vehicle capable of conducting aero-elastic studies. Dynamic thrust analysis for the quasi-one dimensional dynamic propulsion system model is presented along with an initial three dimensional flow field model of the engine integrated into a supersonic commercial transport.
This paper covers the development of an integrated nonlinear dynamic model for a variable cycle turbofan engine, supersonic inlet, and convergent-divergent nozzle that can be integrated with an aeroelastic vehicle model to create an overall Aero-Propulso-Servo-Elastic (APSE) modeling tool. The primary focus of this study is to provide a means to capture relevant thrust dynamics of a full supersonic propulsion system by using relatively simple quasi-one dimensional computational fluid dynamics (CFD) methods that will allow for accurate control algorithm development and capture the key aspects of the thrust to feed into an APSE model. Previously, propulsion system component models have been developed and are used for this study of the fully integrated propulsion system. An overview of the methodology is presented for the modeling of each propulsion component, with a focus on its associated coupling for the overall model. To conduct APSE studies the described dynamic propulsion system model is integrated into a high fidelity CFD model of the full vehicle capable of conducting aero-elastic studies. Dynamic thrust analysis for the quasi-one dimensional dynamic propulsion system model is presented along with an initial three dimensional flow field model of the engine integrated into a supersonic commercial transport.
There are many trade-offs in aircraft design that ultimately impact the overall performance and characteristics of the final design. One well recognized and well understood trade-off is that of wing weight and aerodynamic efficiency. Higher aerodynamic efficiency can be obtained by increasing wing span, usually at the expense of higher wing weight. The proper balance of these two competing factors depends on the objectives of the design. For example, aerodynamic efficiency is preeminent for sailplanes and long slender wings result. Although the wing weight-drag trade is universally recognized, aerodynamic efficiency and structural efficiency are not usually considered in combination. This paper discusses the concept of "aero-structural efficiency," which combines weight and drag characteristics. A metric to quantify aero-structural efficiency, termed effective L/D, is then derived and tested with various scenarios. Effective L/D is found to be a practical and robust means to simultaneously characterize aerodynamic and structural efficiency in the context of aircraft design. The primary value of the effective L/D metric is as a means to better communicate the combined system level impacts of drag and structural weight.
The NASA Glenn Research Center's DGEN Aero-propulsion Research Turbofan (DART) is based on the Price Induction DGEN380 - a small, ~500-lbf thrust class, high-bypass, geared-turbofan engine with a separate flow nozzle. The general characteristics of the DART make it an ideal candidate for utilization as a test bed for engine aeroacoustic research in a relevant performance environment. To provide a baseline acoustic profile for the DART, the system was tested in the NASA Glenn Research Center's Aero-Acoustic Propulsion Laboratory. Acoustic measurements from multiple external arrays locations were acquired over the nominal fan operating range of 50-95%. The acoustic data are evaluated and reported in terms of overall, broadband, and tonal components. The existence of interaction tones (a result of the physics of the dual-spool interactions) are noted and presented. The symmetry of the acoustic directivity was measured in preparation for potential testing in other facilities.
The NASA Glenn Research Center's DGEN Aero-propulsion Research Turbofan (DART) is based on the Price Induction DGEN380 a small, ~500-lbf thrust class, high-bypass, geared-turbofan engine with a separate flow nozzle. The general characteristics of the DART make it an ideal candidate for utilization as a test bed for engine aeroacoustic research in a relevant performance environment. To provide a baseline acoustic profile for the DART, the system was tested in the NASA Glenn Research Center's Aero-Acoustic Propulsion Laboratory. Acoustic measurements from multiple external arrays locations were acquired over the nominal fan operating range of 50-95%. The acoustic data are evaluated and reported in terms of overall, broadband, and tonal components. The existence of interaction tones (a result of the physics of the dual-spool interactions) are noted and presented. The symmetry of the acoustic directivity was measured in preparation for potential testing in other facilities.
Precision landing of large payloads on Mars presents a challenge to the Entry, Descent, and Landing (EDL) community. Previous studies indicated that by incorporating the capability for a Hypersonic Inflatable Aerodynamic Decelerator (HIAD) to morph during reentry would result in a more accurate landing footprint by allowing modulation of the lift- to-drag (L/D) vector directly instead of through bank angle control. However, morphing the HIAD shape for trajectory control may expose the HIAD to potential structural loads or aero heating concerns. In this study, the application of an optimal control allocation (OCA) technique was investigated that would to enable the morphing HIAD to maximize trajectory control capabilities while simultaneously keeping the structural loads and aero heating below some thresholds. This concept was demonstrated in a 3 degree-of-freedom (DOF) EDL simulation and provides basis for future research.
This paper describes an aero-structural modeling method for the Transonic Truss-Braced Wing (TTBW) aircraft using VSPAERO. A vortex-lattice model of the TTBW aircraft is developed, and a transonic and viscous flow correction method is implemented in the VSPAERO models to account for transonic and viscous flow effects. A correction method for the wing-strut interference aerodynamics is developed and applied to the VSPAERO solver. Also, a structural dynamic finite-element model of the TTBW aircraft is developed. This finite-element model includes the geometric nonlinear effect due to the tension in the struts which cause a deflection dependent nonlinear stiffness. The VSPAERO models are coupled to the finite-element model to provide a rapid capability for aero-structural modeling and flutter analysis. A flight-optimized jig twist model is being developed and will be applied for the purpose of generating a full flight dynamic model of the TTBW aircraft.
NASA seeks to safely and consistently deliver 20 metric tons of payload to within 50m of the intended location on Mars. A study has been conducted to evaluate the utility of wind lidars to aid in aero-assisted entry, descent, and precision landing of vehicles carrying these payloads. Numerical simulation found that coherent-Doppler, infrared, aerosol-backscatter wind lidars on the Martian surface can measure winds nominally over a 15km radius hemisphere using eye-safe laser energies and optical apertures similar to those commercially available for terrestrial airport support. Such ground-based wind measurements around the target delivery site are useful for determining when to initiate atmospheric entry. Initial analyses of direct-Doppler, ultraviolet, molecular-backscatter wind lidars demonstrateforward-looking measurement of wind speed, atmospheric temperature, atmospheric density, and vehicle flight attitude beyond the vehicle boundary layer. These measurements would enable controlled flight of an aero-assisted cargo vehicle to the designated landing site.
Aero-gravity assist is a spacecraft maneuver than can enable a vehicle to insert into an orbit of one planetary body using the atmosphere and gravity field of a secondary body, reducing the required propellant mass relative to a fully-propulsive orbit insertion. The Fully Numerical Predictor-Corrector Aerocapture Guidance algorithm was modified to work with a direct force control blunt body vehicle, executing an aero-gravity assist at Titan to enter a Saturnian orbit and conduct fly-by at Enceladus. Additionally, a proportional-integral-derivative controller was implemented to command sideslip angles for control of the orbital inclination. Numerical simulation showed the developed guidance scheme was capable of minimizing the energy and inclination error at atmospheric exit, allowing Saturnian moon tour trajectories and Enceladus exploration at a small delta-V cost, on the order of 100 m/s, in the nominal case.
Aero-gravity assist is a spacecraft maneuver than can enable a vehicle to insert into an orbit of one planetary body using the atmosphere and gravity field of a secondary body, reducing the required propellant mass relative to a fully-propulsive orbit insertion. The Fully Numerical Predictor-Corrector Aerocapture Guidance algorithm was modified to work with a direct force control blunt body vehicle, executing an aero-gravity assist at Titan to enter a Saturnian orbit and conduct fly-by at Enceladus. Additionally, a proportional-integral-derivative controller was implemented to command sideslip angles for control of the orbital inclination. Numerical simulation showed the developed guidance scheme was capable of minimizing the energy and inclination error at atmospheric exit, allowing Saturnian moon tour trajectories and Enceladus exploration at a small delta-V cost, on the order of 100 m/s, in the nominal case.
A novel approach for modeling the aero-propulsive characteristics of an electric vertical takeoff and landing (eVTOL) aircraft was developed and demonstrated in wind tunnel testing. The approach was applied to the NASA LA-8 tandem tilt-wing eVTOL aircraft, using an efficient, hybrid experiment design composed of a static I-optimal response surface design for slowly-varying test variables, and dynamic orthogonal phase-optimized multisine excitations for the control surfaces and electric propulsors. Both the static and dynamic experiment designs were executed simultaneously to collect informative data for model identification. Statistically-weighted multivariate orthogonal function modeling was used to combine local modeling results computed in the frequency domain using data collected with dynamic excitations operating on the control effectors to form an aggregate aero-propulsive model. The final identified model exhibited good predictive capability when compared to validation data acquired separately from the data used to develop the model. The required test time using these new techniques was reduced by at least a factor of five compared to previous static wind tunnel testing for the LA-8 aircraft, while providing more informative data, greater parameterization flexibility, and high-quality models.
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.
Aero-astrodynamics - Newtonian aerodynamics, turbulent flux in hot and multicomponent flows, slender body theory, automatic data processing, and hot-wire turbulence measuring apparatus