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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

NASA Capabilities that Could Impact Terrestrial Smart Grids of the Future

Incremental steps to steadily build, test, refine, and qualify capabilities that lead to affordable flight elements and a deep space capability. Potential Deep Space Vehicle Power system characteristics: power 10 kilowatts average; two independent power channels with multi-level cross-strapping; solar array power 24 plus kilowatts; multi-junction arrays; lithium Ion battery storage 200 plus ampere-hours; sized for deep space or low lunar orbit operation; distribution120 volts secondary (SAE AS 5698); 2 kilowatt power transfer between vehicles.

Power systems↗
NASA NTRS2015

60NiTi Intermetallic Material Evaluation for Lightweight and Corrosion Resistant Spherical Sliding Bearings for Aerospace Applications, Report on NASA-Kamatics SAA3-1288

Under NASA Space Act Agreement (SAA3-1288), NASA Glenn Research Center and the Kamatics subsidiary of the Kaman Corporation conducted the experimental evaluation of spherical sliding bearings made with 60NiTi inner races. The goal of the project was to assess the feasibility of manufacturing lightweight, corrosion resistant bearings utilizing 60NiTi for aerospace and industrial applications. NASA produced the bearings in collaboration with Abbott Ball Corporation and Kamatics fabricated bearing assemblies utilizing their standard reinforced polymer liner material. The assembled bearings were tested in oscillatory motion at a load of 4.54 kN (10,000 lb), according to the requirements of the plain bearing specification SAE AS81820. Several test bearings were exposed to hydraulic fluid or aircraft deicing fluid prior to and during testing. The results show that the 60NiTi bearings exhibit tribological performance comparable to conventional stainless steel (440C) bearings. Further, exposure of 60NiTi bearings to the contaminant fluids had no apparent performance effect. It is concluded that 60NiTi is a feasible bearing material for aerospace and industrial spherical bearing applications.

friction↗
NASA NTRS2015

60NiTi Intermetallic Material Evaluation for Lightweight and Corrosion Resistant Spherical Sliding Bearings for Aerospace Applications

NASA Glenn Research Center and the Kamatics subsidiary of the Kaman Corporation conducted the experimental evaluation of spherical sliding bearings made with 60NiTi inner races. The goal of the project was to assess the feasibility of manufacturing lightweight, corrosion resistant bearings utilizing 60NiTi for aerospace and industrial applications. NASA produced the bearings in collaboration with Abbott Ball Corporation and Kamatics fabricated bearing assemblies utilizing their standard reinforced polymer liner material. The assembled bearings were tested in oscillatory motion at a load of 4.54kN (10,000 lb), according to the requirements of the plain bearing specification SAE AS81820. Several test bearings were exposed to hydraulic fluid or aircraft deicing fluid prior to and during testing. The results show that the 60NiTi bearings exhibit tribological performance comparable to conventional stainless steel (440C) bearings. Further, exposure of 60NiTi bearings to the contaminant fluids had no apparent performance effect. It is concluded that 60NiTi is a feasible bearing material for aerospace and industrial spherical bearing applications.

materials↗
NASA NTRS2016

Evaluation of Transient Pin-Stress Requirements for Spacecraft Launching in Lightning Environments. Pain Free Analysis to Alleviate Those Pin Stress Headaches

Spacecraft are generally protected from direct lightning attachment by encapsulation within the payload fairing of a launch vehicle and the ground structures that exist at the launch site. Regardless of where lightning strikes, potentially damaging indirect effects prevail from the coupling of electromagnetic fields into a loop created by outer shield of the payload umbilical. The energy coupled into individual spacecraft circuits is dependent on the umbilical current drive, the cable transfer impedance and the source/ load circuitry, and the reference potential used. Lightning induced transient susceptibility of the spacecraft avionics needs to be fully understood in order to define realistic re-test criteria in the event of a lightning occurrence during the launch campaign. Use of standards such as RTCA/DO-160 & SAE 5412 has some applicability but do not represent the indirect environment adequately. This paper evaluates the launch pad environments, the measurement data available, and computer simulations to provide pain-free analysis to alleviate the transient pin-stress headaches for spacecraft launching in Lightning environments.

Induced Transient↗
NASA NTRS2017

IPHE Regulations Codes and Standards Working Group - Type IV COPV Round Robin Testing

This manuscript presents the results of a multi-lateral international activity intended to understand how to execute a cycle stress test as specified in a chosen standard (GTR, SAE, ISO, EIHP...). The purpose of this work was to establish a harmonized test method protocol to ensure that the same results would be achieved regardless of the testing facility. It was found that accurate temperature measurement of the working fluid is necessary to ensure the test conditions remain within the tolerances specified. Continuous operation is possible with adequate cooling of the working fluid but this becomes more demanding if the cycle frequency increases. Recommendations for future test system design and operation are presented.

Maes, M.↗
NASA NTRS2019

Jet Noise Prediction Comparisons with Scale Model Tests and Learjet Flyover Data

Recent interest in commercial supersonic flight has highlighted the need to accurately predict Effective Perceived Noise Levels (EPNL) for aircraft and, since the dominant noise source at takeoff will likely be jet noise, specifically jet noise contributions. The current study compares predictions from historical jet-noise models within NASA’s Aircraft Noise Prediction Program and scale-model data to measurements made in a Learjet 25 flight test. The noise levels from the predictions and scale-model data were below those for the flight data by 2.5 – 3.5, 1 – 2, and 3 – 5 EPNdB for the SAE model, the Stone Jet model, and the scale-model data, respectively. Tones and broadband haystacks were identified in the flight spectra that are not associated with jet noise which increased the flight EPNL by at least 0.5 EPNdB over that computed from spectra with the tones and haystacks removed. The study highlights the need for accurate exhaust temperature measurements, aircraft flight position information, and averaging data across a line of microphones in flight tests. For example, a 100° F to 200° F difference in jet exhaust temperature is enough to explain the differences between flight, model scale, and prediction comparisons.

Henderson, Brenda↗
NASA NTRS2019

Statistical Process Control and Capability Study on the Water Content Measurements in NASA Glenn's Icing Research Tunnel

The Icing Research Tunnel (IRT) at NASA Glenn Research Center follows the recommended practice for icing tunnel calibration outlined in SAE's ARP5905 document. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within in the specifications presented to customers that the variation is within +/- 10% of the calibrated, target measurement. With recent measurements and instrumentation errors, a more thorough assessment of error source was desired. By constructing statistical process control charts, the ability to determine how the instrument varies in the short term, mid term, and long term was gained. The control charts offer a view of instrument error, facility error, or installation changes. It was discovered that there was a shift from target to mean baseline thus leading to the study of the overall capability indices of the liquid water content measuring instrument to perform within specifications defined in the IRT. This presentation describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, Chauvenet's Criterion for rejection of data, distribution check of data, and mean, median and mode for construction of control charts. Further data is presented to describe the repeatability of the IRT with the Multi-Element Sensor and the ability to maintain a stable process for the defined calibration schedule.

Timko, Emily N.↗
NASA NTRS2015

Simple Tool for Aircraft Noise-Reduction Route Design

The design of arrival and departure routes from an airport has to balance the conflicting requirements of fuel efficiency, airport capacity utilization and community emission and noise considerations. The commonly used tools for aircraft noise assessment are the FAA’s Integrated Noise Model (INM) and Aviation Environmental Design Tool (AEDT). These tools are suitable to generate precise noise contours. However, they are harder to use with other tools for route design optimization involving evaluation of a large number of aircraft trajectories. A simplified aircraft noise computation tool, named AIRNOISE, is developed for preliminary aircraft noise-reduction route design in this paper. AIRNOISE computes aircraft noise based on the same SAE-AIR-1845 procedures used by INM and AEDT. AIRNOISE does not consider components related to terrain and atmosphere adjustments. As a result, it is not only computationally efficient but also flexible to use for customized aircraft profiles. The aircraft noise results are compared with the FAA’s AEDT2b and show that the level of accuracy achieved by AIRNOISE can be used to reduce the number of route design options to a small number from a large pool for subsequent accurate analysis by INM.

Li, Jinhua↗
NASA NTRS2014

MARGInS: Model-Based Analysis of Realizable Goals in Systems

Under NASAs Constellation effort, the Exploration Technology Development Program funded research toward a system validation capability that applied machine learning and test-case generation techniques to the analysis of black-box system behavior. The behavior analysis capability scaled to spaces of hundreds of input parameters and tens of thousands of test cases. Aerospace systems at the vehicle level, especially those systems which contain some level of autonomy, are best described by hybrid and non-linear mathematics. Even simplified models of such systems need parameter dimensionalities in the hundreds or thousands of parameters in order to capture sufficient fidelity. The System Safety Assessments (such as those described in the SAE ARP 4761A Safety Assessment Process guidelines) for these systems are prone to errorinteractions between the vehicles subsystems are complex, and can display emergent behaviors. NASA captured this new analysis in the Model-based Analysis of Realizable Goals in Systems (MARGInS) tool and applied it to the Pad Abort 1 (PA-1) simulation as part of the independent validation and verification cycle before the PA-1 flight test in May of 2010. MARGInS evaluated the adherence of the high-fidelity simulation to its requirements, and deter- mined the margins to failure from the expected nominal input conditions. Following the PA-1 test, the capabilities within the MARGInS framework have been extended with sophisticated statistical and white-box test case generation techniques and applied to other NASA missions. The frame- work now includes a critical factors analysis that was applied to NASAs Orion simulation and design. NASAs Aeronautics Research Mission Directorate (ARMD) leveraged the existing MARGInS framework for work on aviation safety for civil transport vehicles and for research on autonomy issues. The NASA ARMD effort created a time series output prediction capability that has been used to characterize trajectories for a plane with an adaptive control system, and a safety boundary detection capability that has been applied to an air traffic control concept of operation for the Federal Aviation Administration. The statistical and machine- learning based techniques within MARGInS have been successfully combined with concolic execution to improve the coverage of a critical unit by driving system-level inputs. The use case driving the concolic execution and MARGInS integration was inspired by the Air France 447 disaster in which the loss of a critical functionality (the airspeed calculation from the pitot tubes) led to loss of the entire plane with the people aboard. To illustrate capabilities and limitations, we will highlight the analyses for the applications listed above. We will then discuss the future plans for MARGInS and its interfaces with other tools.

Validation↗
NASA NTRS2020

Jet Noise Prediction Comparisons with Scale Model Tests and Learjet Flyover Data

Recent interest in commercial supersonic flight has highlighted the need to accurately predict Effective Perceived Noise Levels (EPNL) for aircraft and, since the dominant noise source at takeoff will likely be jet noise, specifically jet noise contributions. The current study compares predictions from historical jet-noise models within NASA’s Aircraft Noise Prediction Program and scale-model data to measurements made in a Learjet 25 flight test. The noise levels from the predictions and scale-model data were below those for the flight data by 2.5 – 3.5, 1 – 2, and 3 – 5 EPNdB for the SAE model, the Stone Jet model, and the scale-model data, respectively. Tones and broadband haystacks were identified in the flight spectra that are not associated with jet noise which increased the flight EPNL by at least 0.5 EPNdB over that computed from spectra with the tones and haystacks removed. The study highlights the need for accurate exhaust temperature measurements, aircraft flight position information, and averaging data across a line of microphones in flight tests. For example, a 100° F to 200° F difference in jet exhaust temperature is enough to explain the differences between flight, model scale, and prediction comparisons.

Henderson, Brenda↗
NASA NTRS2017

One Step Plasma Passivation and Precision Cleaning

Over the past several years, Kennedy Space Center (KSC) researchers developed two solvent-free techniques for precision cleaning of ground support equipment (GSE) that possess low commodity requirements and produce small or no waste streams. During testing of low-pressure air plasma as one of these cleaning techniques, physical changes in the surface of stainless steel test pieces were observed; cursory testing revealed that the test part had developed a thick oxide layer on the surface compared to untreated parts. This result indicated that the plasma process might not only serve as a green technology for precision cleaning of parts, but it may also passivate the surface at the same time. Current methodology in use at KSC’s Components Refurbishment & Chemical Analysis (CRCA) Facility for passivation of corrosion-resistant steels for aerospace applications follows an SAE International Standard, AMS2700, where parts are submerged in either a nitric acid or nitric acid/sodium dichromate bath at predetermined temperatures and contact times. These baths require constant testing to ensure effective treatment, use corrosive and carcinogenic chemicals, and produce hazardous waste. The passivation bath is one of the multiple steps employed at the facility, while cleaning with multiple environmentally-unfriendly solvents comprise several more steps before parts are recertified to a specific cleanliness level in their clean room. Based on the behavior of the stainless steel surface during cleaning optimization studies, it was believed that the plasma technology could be extended to passivation, combining what is currently many steps into a single, solvent- and waste-free system.

Paul Hintze↗
NASA NTRS2022

Run Time Assurance for Electric Vertical Takeoff and Landing Aircraft

NASA is conducting research to demonstrate and evaluate the application of Run Time Assurance (RTA) as a means to assure safety in Electric Vertical Takeoff and Landing (eVTOL) aircraft with highly automated or autonomous flight capability supervised by a single onboard pilot. The work described in this report demonstrates an application of RTA and examines the implications for design and analysis of aircraft functions and systems; aircraft safety hazards; safety assurance; development assurance; and pilot tasks and performance. This research effort also seeks to assess the efficacy of the combined application of traditional Functional Hazard Analysis (FHA) and the more modern System Theoretic Process Analysis (STPA) techniques to perform hazard analyses on aircraft with complex automated and autonomous systems and an onboard pilot. During the research effort we developed architectural designs of two alternate eVTOL aircraft, generally following the process characterized in the SAE standards ARP4754 and ARP4761. The design has focused on the control architectures of these aircraft, which are identical except that one incorporates RTA techniques to reduce the criticality of some key software components. Artifacts of this process include a taxonomy of aircraft-level functions, aircraft-level architecture diagrams, aircraft-level functional hazard assessments (AFHA), function allocations onto aircraft systems and subsystems, functional block diagrams for a select set of control-related functions, and system-level functional hazard assessments (SFHA) for those functions. This project has highlighted the notion that DAL D is something of a sweet spot for low-confidence controllers in an RTA-based design. Among the many activities described in DO-178C, the activities related to requirement verifiability, algorithmic accuracy, and test coverage can be the most challenging for the kinds of advanced control techniques that may be desirable in novel UAM designs, such as adaptive control, machine-learning, artificial intelligence, numerical search, and Monte Carlo based algorithms. Moreover, the standard requires that development teams demonstrate that errors leading to unacceptable failure conditions have been removed from the software. The RTA architecture, which cordons off the low-confidence function, makes it much easier to show this for these kinds of algorithms. With regard to the use of STPA and FHA as complementary hazard analysis techniques, our research effort led us to the conclusion that STPA should be used to derive requirements for hardware and software systems and/or components. Also, STPA is a natural complement to other processes in ARP4754A involving design studies and iteration.

Run-time assurance↗
NASA NTRS2021

Extensive Characterization of Aerosol Optical Properties and Chemical Component Concentrations: Application of the GRASP/Component Approach to Long-term AERONET Measurements

A recently developed GRASP/Component approach (GRASP: Generalized Retrieval of Atmosphere and Surface Properties) was applied to AERONET (Aeronet Robotic Network) sun photometer measurements in this study. Unlike traditional aerosol component retrieval, this approach allows the inference of some information about aerosol composition directly from measured radiance, rather than indirectly through the inversion of optical parameters, and has been integrated into the GRASP algorithm. The newly developed GRASP/Component approach was applied to 13 AERONET sites for different aerosol types under the assumption of aerosol internal mixing rules to analyze the characteristics of aerosol components and their distribution patterns. The results indicate that the retrievals can characterize well the spatial and temporal variability of the component concentration for different aerosol types. A reasonable agreement between GRASP BC retrievals and MERRA-2 BC products is found for all different aerosol types. In addition, the relationships between aerosol component content and aerosol optical parameters such as aerosol optical depth (AOD), fine-mode fraction (FMF), absorption Ångström exponent (AAE), scattering Ångström exponent (SAE), and single scattering albedo (SSA) are also analyzed for indirect verifying the reliability of the component retrieval. It was demonstrated the GRASP/Component optical retrievals are in good agreement with AERONET standard products [e.g., correlation coefficient (R) of 0.93–1.0 for AOD, fine-mode AOD (AODF), coarse-mode AOD (AODC) and Ångström exponent (AE); R = ~ 0.8 for absorption AOD (AAOD) and SSA; RMSE (root mean square error) < 0.03 for AOD, AODF, AODC, AAOD and SSA]. Thus, it is demonstrated the GRASP/Component approach can provide aerosol optical products with comparable accuracy as the AERONET standard products from the ground-based sun photometer measurements as well as some additional important inside on aerosol composition.

Xindan Zhang↗
NASA NTRSDate not supplied

Data-Centric Operational Design Domain Characterization for Machine Learning-Based Aeronautical Products

We give for Machine Learning (ML)-based aeronautical products, a first rigorous characterization of Operational Design Domains (ODDs). Unlike in other application sectors (such as self-driving road vehicles) where ODD development is scenario-based, our approach is data-centric: we propose the dimensions along which the parameters that define an ODD can be explicitly captured, using a top-down approach starting from system specifications, and a bottom-up approach starting from detailed ML Model (MLM) designs. Then we give a categorization of the data that ML-based applications can encounter in operation, identifying their system-level relevance and impact. Specifically, we discuss how those data categories are useful to determine: (1) the requirements necessary to drive the design of MLMs; (2) the potential effects on the MLM and higher levels of the system hierarchy; (3) the learning assurance processes that may be needed, and (4) system architectural considerations. We illustrate the underlying concepts with an example of an aircraft flight envelope. The approach in this paper is one of the cornerstones of a future process guidance for development and certification/approval of safety-related aeronautical products implementing Artificial Intelligence (AI), currently being developed through aviation industry-based consensus, jointly by the SAE G-34 Committee for AI in aviation, and EUROCAE WG-114 for AI.

Aeronautical products↗
NASA NTRSDate not supplied

NASA Icing Overview 2023

This presentation summarizes NASA icing research for an invited talk to be presented on June 22, 2023, at the SAE International Conference on Icing of Aircraft, Engines, and Structures. The presentation describes the NASA icing portfolio including facilities and computational tools. In addition, high-level summaries of NASA’s current icing research activities are provided. This includes: (1) Transonic Truss Brace Wing, (2) High Lift Common Research Model, (3) Advanced Air Mobility, (4) Characterization of Low Ice Adhesion Materials, (5) High Ice Water Content Flight Research, (6) Simulated Inter-compressor Duct Research Model, (7) Efficient Quiet Integrated Propulsor. Finally, the presentation summarizes collaboration and partnership opportunities with NASA.

Icing↗
NASA NTRSDate not supplied

Jet Noise Flyover and Scale Model Tests

Renewed interest in commercial supersonic flight has rekindled the need for accurate jet-noise predictions as this source is believed to dominate at aircraft takeoff conditions. The current study compares scale-model data acquired in the NASA Aero-Acoustic Propulsion Laboratory with data obtained using a well-instrumented Learjet 25 in a flyover test completed in September 2022. The flight test included 73 flyovers with engine conditions ranging from 1.5 to 2.0 engine pressure ratios and flight Mach numbers between 0.24 and 0.27. Acoustic data were acquired with an 800-ft linear ground plate microphone array. Wind speed data were acquired up to 1000-ft altitude with a ground-based LiDAR system. Layered ambient temperature, pressure, and humidity were acquired with a weather drone. A 6% increase in the physical scale factor for the scale-model data was found to reasonably align the peak frequencies of the scale-model and flight data and resulted in peak levels for the scale model being roughly 0.7 dB above those for the flight data at NPR = 1.56 and roughly 1 dB below those for the flight data at NPR = 1.91 in the peak jet-noise direction. Comparisons with the SAE ARP876 model were poor especially at emission angles greater than, or equal to, 110° and at high frequencies.

jet noise, supersonic transport↗
NASA NTRSDate not supplied

Jet Noise Flyover and Scale Model Tests

Renewed interest in commercial supersonic flight has rekindled the need for accurate jet-noise predictions as this source is believed to dominate at aircraft takeoff conditions. The current study compares scale-model data acquired in the NASA Aero-Acoustic Propulsion Laboratory with data obtained using a well-instrumented Learjet 25D in a flyover test completed in September 2022. The flight test included 73 flyovers with engine conditions ranging from 1.5 to 2.0 engine pressure ratios and flight Mach numbers between 0.24 and 0.27. Acoustic data were acquired with an 800-ft linear ground plate microphone array. Wind speed data were acquired up to 1000-sft altitude with a ground-based LiDAR system. Layered ambient temperature, pressure, and humidity were acquired with a weather drone. A 6% increase in the physical scale factor for the scale-model data was found to reasonably align the peak frequencies of the scale-model and flight data and resulted in peak levels for the scale model being roughly 0.7 dB above those for the flight data at NPR = 1.56 and roughly 1 dB below those for the flight data at NPR = 1.91 in the peak jet-noise direction. Comparisons with the SAE ARP876 model were poor especially at emission angles greater than, or equal to, 110° and at high frequencies.

Acoustics, jet noise, supersonic transport↗