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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Wind Lidars for Aero-Assisted Entry, Descent, and Landing on Mars

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.

Mars↗
NASA NTRSDate not supplied

Numerical Predictor-Corrector Based Guidance Scheme For Aero-Gravity Assist at Titan For Enceladus Missions

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.

Daniel Louis Engel↗
NASA NTRSDate not supplied

Numerical Predictor-Corrector Based Guidance Scheme For Aero-Gravity Assist at Titan For Enceladus Missions

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.

Daniel L. Engel↗
NASA NTRSDate not supplied

Aero-Propulsive Modeling for eVTOL Aircraft Using Wind Tunnel Testing with Multisine Inputs

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.

system identification↗
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 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 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↗
DOE OSTI2023

Aero‐servo‐elastic co‐optimization of large wind turbine blades with distributed aerodynamic control devices

Abstract This work introduces automated wind turbine optimization techniques based on full aero‐servo‐elastic models and investigates the potential of trailing edge flaps to reduce the levelized cost of energy (LCOE) of wind turbines. The Wind Energy with Integrated Servo‐control (WEIS) framework is improved to conduct the presented research. Novel methods for the generic implementation and tuning of trailing edge flap devices and their controller are also introduced. Primary flap and controller parameters are optimized to demonstrate potential maximum blade tip deflection reductions of 21 % . Concurrent design optimization (i.e., co‐design) of a novel segmented wind turbine blade with trailing edge flaps and its controller is then conducted to demonstrate blade cost savings of 5 % . Additionally, rotor diameter co‐design optimization is demonstrated to reduce the LCOE by 1.3 % without significant load increases to the tower. These results demonstrate the efficacy of control co‐design optimization using trailing edge flaps, and the entirety of this work provides a foundation for numerous control co‐design‐oriented studies for distributed aerodynamic control devices.

17 WIND ENERGY↗
DOE OSTI2021

A semi-coupled aero-servo-hydro numerical model for floating vertical axis wind turbines operating on TLPs

Floating vertical axis wind turbines (VAWTs) have many advantages over floating horizontal axis wind turbines (HAWTs) at large scales in deep water; however, there are several key challenges to overcome as well. One of the challenges is accurate prediction of the dynamic motion and loads performance of a floating VAWT. Here, a new semi-coupled aero-servo-hydro method is developed to assess dynamic responses of a floating VAWT by modeling the system as a 7-degree-of-freedom (7-DOF) model: the supporting platform is considered as a 6-DOF rigid body; the rotation of the rotor is considered as the 7th DOF. Aerodynamic, hydrodynamic, and mooring loads and control of the rotor speed are fully considered. This model can predict performance of floating VAWTs with reasonable fidelity according to validation with OrcaFlex through static and dynamic responses of a floating VAWT with Darrieus rotor operating on a new tension-leg platform (TLP). Being a reduced complexity model, the 7-DOF model can be efficiently applied to assess performance of the newly designed floating VAWT. This model is used to examine the relative contributions of aerodynamic and wave loads imparted to the floating system and the benefits of a three-bladed VAWT over a two-bladed VAWT through dynamic and fatigue analysis.

17 WIND ENERGY↗
DOE OSTI2020

Servo-aero-gravo-elastic (SAGE) scaling and its application to a 13-MW downwind turbine

Reduced scale wind turbines can be extremely cost-effective to test new rotor concepts since prototype costs are heavily dependent on the rotor diameter. Ideally, the scaled model would have the same non-dimensional deflections, dynamics, and control behavior as the full-scale model. This would provide a high-fidelity demonstration of the full-scale performance, which is ideal if the full-scale turbine has significant aeroelastic interactions. To this end, servo-aero-gravo-elastic (SAGE) scaling is developed and applied to a 13-MW turbine that is scaled to a 20% scale model. The scaling preserves the tip-speed ratio, the rotor speed normalized by the flapping frequency, and the tip deflections normalized by the blade length. In addition, the controller employs the same control structure (gain-scheduled pitch control and variable speed torque control) and is scaled dynamically (e.g., matching non-dimensional time constant of the pitch angle, etc.). Furthermore, the thrust, gravity, and centrifugal moments are scaled such that the load angles are preserved as a function of a non-dimensional wind speed. However, the environmental scaling must consider differences in Reynolds number (since this parameter cannot be held constant) and subsequent changes in the axial induction factor. While the presented results showcase these differences during operational conditions, the non-dimensional tip deflections remain comparable through all wind speed ranges, indicating the viability of the SAGE scaling method in matching full-scale aeroelastic responses.

Kaminski, Meghan (ORCID:0000000322334784)↗
DOE OSTI2024

Density of molten oxides measured in an aero-acoustic levitator

Knowing the thermophysical properties of high-temperature melts can aid the design of melt processes and validate atomic structural models, such as those used in studying glass formation. Property measurements on such melts are challenging, however, due to container-related contamination and heterogeneous nucleation. Containerless processing techniques that employ levitation can be used to avoid these obstacles. In that context, we demonstrate here the application of silhouette imaging to measure the density of molten oxides in an aero-acoustic levitation instrument (AAL). The AAL combines gas jet levitation with actively controlled acoustic positioning to enable full optical access to samples ca. 2–4 mm in diameter, which are laser beam heated and melted. The cross sections of molten drops are imaged using a monochromatic light source and narrowband-filtered camera. Melt volume is calculated from the cross sections and used to find density at several temperatures ranging 1530–1920 K, including up to 350 K of supercooling. We report densities for CaAl 2 O 4 , Ca 12 Al 14 O 33 , CaSiO 3 , their Fe 2 O 3 -bearing analogs, and 83TiO 2 -17RE 2 O 3 (RE = La or Nd). These provide important benchmarks of the capabilities, measurement uncertainties, and future outlook for this technique.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Numerical Investigation of Wall-Cooling Effect on Aero-Optical Distortions for Hypersonic Boundary Layer

Compressible wall-modeled large-eddy simulations of Mach 8 turbulent boundary-layer flows over a flat plate were carried out for the conditions of the hypersonic wind tunnel at Sandia National Laboratories. The simulations provide new insight into the effect of wall cooling on the aero-optical path distortions for hypersonic turbulent boundary-layer flows. Four different wall-to-recovery temperature ratios, 0.3, 0.48, 0.71, and 0.89, are considered. Despite the much lower grid resolution, the mean velocity, temperature, and resolved Reynolds stress profiles from the simulation for a temperature ratio of 0.48 are in good agreement with those from a reference direct numerical simulation. The normalized root-mean-square optical path difference obtained from the present simulations is compared with that from reference direct numerical simulations, Sandia experiments, as well as predictions obtained with a semi-analytical model by Notre Dame University. Here the present analysis focuses on the effect of wall cooling on the wall-normal density correlations, on key underlying assumptions of the aforementioned model such as the strong Reynolds analogy, and on the elevation angle effect on the optical path difference. Wall cooling is found to increase the velocity fluctuations and decrease the density fluctuations, resulting in an overall reduction of the normalized optical path distortion. Compared to the simulations, the basic strong Reynolds analogy overpredicts the temperature fluctuations for cooled walls. Also different from the strong Reynolds analogy, the velocity and temperature fluctuations are not perfectly anticorrelated. Finally, as the wall temperature is raised, the density correlation length, away from the wall but inside the boundary layer, increases significantly for beam paths tilted in the downstream direction.

42 ENGINEERING↗
DOE OSTI2021

Measurements of Density of Liquid Oxides with an Aero-Acoustic Levitator

Densities of liquid oxide melts with melting temperatures above 2000 °C are required to establish mixing models in the liquid state for thermodynamic modeling and advanced additive manufacturing and laser welding of ceramics. Accurate measurements of molten rare earth oxide density were recently reported from experiments with an electrostatic levitator on board the International Space Station. In this work, we present an approach to terrestrial measurements of density and thermal expansion of liquid oxides from high-speed videography using an aero-acoustic levitator with laser heating and machine vision algorithms. The following density values for liquid oxides at melting temperature were obtained: Y 2 O 3 4.6 ± 0.15; Yb 2 O 3 8.4 ± 0.2; Zr 0.9 Y 0.1 O 1.95 4.7 ± 0.2; Zr 0.95 Y 0.05 O 1.975 4.9 ± 0.2; HfO 2 8.2 ± 0.3 g/cm 3 . The accuracy of density and thermal expansion measurements can be improved by employing backlight illumination, spectropyrometry and a multi-emitter acoustic levitator.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Exploratory tests of a simple aero-mechanical ride comfort system for lightly loaded aircraft

Some exploratory wind tunnel and radio-controlled free-flight tests were made with a small high-wing airplane model (1.23m wing span) to study the concept of a simple aero mechanical system intended to alleviate gust loads and improve ride comfort of lightly loaded aircraft. The system consisted essentially of the outer portions of each wing being hinged in the chordwise direction and connected directly to the wing flaps using internal counter weights to provide neutral mass balance. When the wing experienced a change in velocity or angle of attack, the movable wing panels, acting as sensors and flap actuators, deflected in response to the changes in lift on the wing. The corresponding movements of the interconnected flaps tended to reduce the changes in the wing lift.

Hewes, D. E.↗
NASA NTRS1974

The AEROS mission

The principal objective of the two AEROS satellites is the study of the physical and chemical processes in the earth's upper atmosphere and ionosphere by means of simultaneous measurements of various parameters. The parameters include the neutral atmosphere composition and density, the ion composition and density, electron density and flux of suprathermal electrons, and the solar EUV ionizing radiation. Temperature measurements of neutral particles, ions, and electrons are also conducted. The scientific payload is discussed along with details regarding the satellite orbits.

Laemmerzahl, P.↗
NASA NTRS1975

Exospheric temperature inferred from the Aeros-A neutral composition measurement

The derivation of exospheric temperature from satellite drag measurements is based on an assumption of invariant conditions of the neutral atmosphere at 120 km. Since it has been established that atomic oxygen, which is usually the major neutral constituent in the region of drag measurements, is subject to considerable variability with season, latitude, and solar and geomagnetic activity in the altitude region of 120 km, its value as an indicator of exospheric temperature is questionable. Ogo 6 neutral mass spectrometer measurements revealed that molecular nitrogen is a better indicator of exospheric temperature, since it is not subject to changes caused by eddy mixing and is therefore relatively less variable near the turbopause. However, theoretical arguments show that argon, even though it is a minor constituent, is relatively less variable with respect to changes in eddy diffusion coefficient and hence a better indicator of exospheric temperature than O and N2. In this paper the relative merits of these gases for deriving exospheric temperature are investigated by using observational data from the Aeros-A Nate experiment.

Chandra, S.↗