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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Rapid Aero Modeling for Urban Air Mobility Aircraft in Computational Experiments

Rapid Aero Modeling (RAM) applied to computational testing, RAM-C, is an approach to efficiently and automatically obtain aerodynamic models during computational investigations. RAM-C is designed to estimate models appropriate for flight dynamics studies and simulations. The approach responds to a demand for experimental efficiency and model fidelity that has increased with growing aircraft complexity and aerodynamic nonlinearities associated with hybrid and electric vertical takeoff and landing (eVTOL) aircraft. In an Urban Air Mobility (UAM) transportation system, it is expected that aircraft will embrace many features from both airplanes and rotorcraft. These vehicles present many more factors than conventional aircraft which can lead to increased computational costs and missed key factor interactions when applying traditional testing and modeling methods. RAM-C provides feedback loops around computational codes to rapidly guide testing toward aerodynamic models meeting user-defined fidelity goals. It combines and extends concepts from design of experiment theory and aircraft system identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity in terms of prediction error. RAM-C only collects enough data required to meet the user-specified prediction error requirements thus saving computational time and resources. The overall achievable fidelity of the final model also depends on the accuracy of the test facility, or in this case, the computational modeling approach. Previous studies to support development of the RAM-T process were conducted in wind tunnel tests to assess potential metrics, algorithms, and procedures. This paper presents results from the next steps taken and tests conducted for the development of RAM-C technology and highlights some of the unique features of RAM applied eVTOL configurations in a computational study.

Aerodynamics↗
NASA NTRSDate not supplied

Rapid Aero Modeling for Urban Air Mobility Aircraft in Wind-Tunnel Tests

Rapid Aero Modeling (RAM) applied to wind tunnel testing, RAM-T, is an approach to efficiently and automatically obtain aerodynamic models during testing. The approach saves time and resources by responding to the demand for experimental efficiency and model fidelity. Motivation for this demand is more acute when investigating a class of vehicles categorized as Urban Air Mobility (UAM) aircraft where many features from both aircraft and rotorcraft are present. RAM-T provides a feedback loop around the test facility to guide the test toward high-fidelity, statistically rigorous aircraft models. The general RAM approach is applicable to computational or physical experiments. It combines concepts from design of experiment theory and aircraft system identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity in terms of prediction error. RAM only collects data required to meet the user-specified fidelity and fidelity is only limited by the facility and test article capabilities. This paper presents results from tests conducted for development of an automated RAM-T technology. The results highlight some of the unique features of RAM applied to eVTOL configurations.

Aerodynamics↗
NASA NTRSDate not supplied

Lessons Learned from Large-Scale Aerospace Structural Testing

Large-scale testing of aerospace structures is frequently the final step in a development project to validate the structural performance, and that typically involves a large cost and time investment. In order to ensure that the testing provides the required data, avoiding errors that can result in an unsuccessful test and failure to meet objectives is critical. Presented herein are five lessons learned to provide insight to those conducting tests in order to help them avoid known pitfalls that may result in an unsuccessful test. Five subject large-scale tests are described, and include two composite wing tests, a composite hybrid-wing body center section test, a full-scale 27.5-ft diameter metallic barrel test, and an 8-ft diameter metallic barrel test. Problems identified during the testing and mitigation approaches to solve the problems are presented, then the lessons learned are summarized.

Lessons Learned↗
NASA NTRSDate not supplied

Lessons Learned from Large-Scale Aerospace Structural Testing

Large-scale testing of aerospace structures is frequently the final step in a development project to validate the structural performance, and that typically involves a large cost and time investment. In order to ensure that the testing provides the required data, avoiding errors that can result in an unsuccessful test and failure to meet objectives is critical. Presented herein are five lessons learned to provide insight to those conducting tests in order to help them avoid known pitfalls that may result in an unsuccessful test. Five subject large-scale tests are described, and include two composite wing tests, a composite hybrid-wing body center section test, a full-scale 27.5-ft diameter metallic barrel test, and an 8-ft diameter metallic barrel test. Problems identified during the testing and mitigation approaches to solve the problems are presented, then the lessons learned are summarized.

Lessons Learned↗
NASA NTRS1994

(abstract) Measurement of Aerodynamic Roughness Using Radar Backscatter over Vegetated Surfaces

Gathering of local information from remote platforms is often a more cost-effective way of obtaining the required data type than are local measurements. A good example is the collection of wind regime data in order to infer the amount and type of aeolian transport at a given site. This paper describes a potential mechanism for remotely inferring the wind regime from synthetic aperture radar data and describes a project to estimate the practicality of using such a mechanism. Both radar backscatter and wind flow are functions of the roughness of the site under study. It is therefore reasonable to suspect that a fairly well-behaved relationship might exist between normalized radar backscatter coefficient and aerodynamic roughness length. Aeolian transport of small particles depends in turn on wind flow and is an important quantity to measure for several economic-related reasons, but the direct measurement of wind flow regime generally involves construction of wind towers and many days of data collection, making such data extremely expensive and prohibitive in areas that cannot be easily accessed. For both desert and vegetated sites where extensive wind profile data already exist, a NASA airborne multifrequency, multipolarization radar was used to acquire radar data at several incidence angles. At the vegetated sites, vegetation height varied from tens of centimeters to several meters, and surface roughness was a few centimeters.

radar↗
NASA NTRS2014

NASA 2014 The Hyperspectral Infrared Imager (HyspIRI) – science impact of deploying instruments on separate platforms

The Hyperspectral Infrared Imager (HyspIRI) mission was recommended for implementation by the 2007 report from the U.S. National Research Council Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, also known as the Earth Science Decadal Survey. The HyspIRI mission is science driven and will address a set of science questions identified by the Decadal Survey and broader science community. The mission includes a visible shortwave infrared (VSWIR) imaging spectrometer, a multispectral thermal infrared (TIR) imager and an intelligent payload module (IPM). The IPM enables on-board processing and direct broadcast for those applications with short latency requirements. The science questions are organized as VSWIR-only, TIR-only and Combined science questions, the latter requiring data from both instruments.

Quattrochi, Dale↗
NASA NTRS2016

Low Frequency 1/f Noise on QWIPs, nBn, and Superlattice Focal Plane Array

Noise Equivalent Difference Temperature (NE△T) is a standard performance metric for most infrared focal plane array (FPA) systems. The frequency bandwidth and range associated with NE△T is at high frequency and it does not describe the long time noise behavior or the very low frequency noise of the FPA. Very low frequency noise measurement requires data capture that takes longer time duration and sampling interval. This study investigates low frequency noise in QWIP, nBn and LWIR n-type Complementary Barrier Infrared Detector (CBIRD) FPAs. The corner frequencies are extracted from the power spectral density (PSD) as function of frequency. The peak wavelength, quantum efficiency of QWIP detector are 6.2 µm and 2 %, respectively. QWIP FPA has a mean NE△T ~ 25 mK at an operating temperature of 65 K and an integration time of 16 msec. The mean QWIP PSD plot shows a corner frequency of < 0.5 mHz. The nBn FPA with 4 µm cut off and quantum efficiency of 67 % has NE△T ~ 15.6 mK at an integration time ~ 7.52 msec and an operating temperature of 120 K. The nBn has corner frequency of > 50 mHz. Lastly the n-type CBIRD FPA with a 50% cutoff at 8.8 µm and quantum efficiency of ~ 50 % has an NE△T ~ 18.6 mK at an integration time of 1.86 msec and operating temperature 120 K. The superlattice FPA has a corner frequency ~ 10 mHz. The investigation of the tail on the NE△T histogram reveals that its origin is not accounted for entirely by the high noise current, but also it needs the inclusion of lower responsivity for some pixels.

Pepper, B.↗
NASA NTRS2019

UAVSAR real-time embedded GPU processor

Synthetic aperture radar (SAR) can provide high-resolution imagery regardless of cloud cover or lighting conditions. These qualities make SAR potentially well-suited for informing response efforts to natural and man-made disasters, but such applications require data products with minimal latency. To meet this challenge, we implemented a real-time SAR processor capable of producing 10 m imagery using an NVIDIA Jetson TX2 embedded GPU module. With its low mass (87 g module) and power consumption under 8 W, the system also holds promise for spaceborne applications.

Tung, Wayne↗
NASA NTRS2019

Highly Charged Ions in a New Era of High Resolution X-Ray Astrophysics

X‐ray astronomy and ground‐based atomic physics have a long history of fruitful collaboration: Sound understanding of the underlying atomic physics is the key to reliable interpretation of the spectra from celestial sources; conversely, astronomical spectra have been used to benchmark and advance atomic physics. This interplay is about to become even more important as we enter a new era of high‐resolution X‐ray astrophysics with large effective collection area. Although high‐resolution observations with the gratings on the Chandra and XMM‐Newton observatories continue to drive new science, upcoming planned and proposed missions will open up new discovery space in the near future that is currently challenging to access: high‐resolution spectroscopy on extended sources, in the Fe K band, and on short time scales. This review summarizes open questions in these areas and the design parameters for the Hitomi, XRISM, Athena, and Arcus observatories. The expected high quality of spectra taken with these observatories puts new constraints on the accuracy of atomic reference data required to take full advantage of the diagnostic potential of these spectra.

Natalie Hell↗
NASA NTRS2019

Enabling Real-time Multi-messenger Astrophysics Discoveries with Deep Learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

E A Huerta↗
NASA NTRSDate not supplied

Nancy Grace Roman Space Telescope Observatory Implementation and Challenges

NASA’s Nancy Grace Roman Space Telescope (Roman), previously referred to as Wide Field Infrared Survey Telescope (WFIRST), was named after Dr. Nancy Grace Roman, an astronomer and NASA pioneer of modern space-based astronomy who is known as the “mother of the Hubble Space Telescope”. Roman is a deep space infrared observatory with a Hubble-sized telescope and wide field of view instrument (greater than 100 times that of Hubble’s) that will conduct a high latitude time-domain survey, a high latitude imaging and spectroscopic survey, and a galactic bulge time-domain survey to characterize dark energy and expand the census of exoplanets in our galaxy while allowing a broad range of astrophysics research. Roman will also demonstrate exoplanet coronagraphy with active wave front control technology and provide general investigator programs for the science community. Roman is finishing the critical design phase and is planning for launch in 2026. It will operate in a quasi-halo orbit about Sun-Earth L2, 1.5 million kilometers from Earth, for a five-year primary mission life. The Observatory features an Optical Telescope Assembly with an existing, repurposed 2.4m primary mirror, a Wide Field Instrument with a focal plane array comprised of 18 HgCdTe near-infrared detectors and a grism, prism and filter elements for imaging and spectroscopy in support of the primary surveys, as well as a Coronagraph instrument technology demonstration with starlight suppression technology for direct imaging and spectroscopy of exoplanets. The telescope is mounted to the Instrument Carrier composite truss structure which also optically meters each instrument, includes a Launch Load and Vibration Isolation System to provide passive isolation of spacecraft jitter sources while also supporting the payload during launch and is attached to the Spacecraft Bus. The Spacecraft also includes a Solar Array Sunshield ,Deployable Aperture Cover, Lower Instrument Sunshade, High Gain Antenna System, and Outer Barrel Assembly. Figure 1 shows an overview of the Roman Observatory. When fully integrated, Roman will be the largest Observatory assembled and tested at NASA’s Goddard Space Flight Center. Figure 1. Roman Observatory Overview Development of scientific satellites is challenging by nature, as the pursuit to broaden scientific knowledge always pushes the boundary of what has come before. The implementation of the Roman mission is a prime example and expected challenges have been augmented by the foundational decision to use the existing telescope components, developed in the early 2000s by another Government agency for a different application. Other unique aspects of the Roman mission, such as its survey nature, the vast amount of data required to meet science objectives, and packaging of the Observatory elements around the existing telescope components, create constrained design spaces that drive competing requirements across Observatory subsystems. Given these challenges, systems engineering has been a critical discipline in balancing implementation decisions for the Roman mission and will continue to play a key role going forward. This paper will discuss details of the Roman Observatory configuration, as well as some of the systems engineering challenges and the decision-making process used to mature the Roman Space Telescope preliminary design to implementation.

Lisa Ml Bartusek↗
NASA NTRS2021

Comparison of soil dielectric mixing models for Soil Moisture Retrieval using SMAP Brightness Temperature over croplands in India

The accurate estimation of soil moisture (SM) using microwave remote sensing depends mostly on careful selection of retrieval parameters among which the soil dielectric mixing model is the important one. These models are often categorized into empirical, semi-empirical or volumetric based on their methodologies and input data requirements. To study in detail, the comparative performance of four dielectric mixing models -- Wang & Schmugge model, Hallikainen model, Dobson model and Mironov model were used with Soil Moisture Active Passive (SMAP) L-band brightness temperature and Single Channel Algorithm for SM retrieval over agricultural landscapes in India. The highest performance statistics combination in terms of Root Mean Square Error (RMSE), correlation coefficient (R^(2)) and percentage bias (PBIAS) against the concurrent in-situ SM measurements were calculated at the selected validation sites. The overall results indicate that the best performance was given by the Mironov model (RMSE = 0.07 cu. m/cu. m), followed by Wang & Schmugge model (RMSE = 0.08 cu. m3/cu. m), Hallikainen model (RMSE = 0.09 cu. m/cu. m), Dobson model (RMSE = 0.10 cu. m/cu. m) and original SMAP radiometer SM (RMSE = 0.12 cu. m/cu. m). Findings of this study provides important insights into application and performance of dielectric mixing models in mapping surface SM variations. This study also underlines the pivotal role of local conditions for SM retrieval which should be carefully included in the algorithms.

Swati Suman↗
NASA NTRSDate not supplied

Repurposing Drilling Control Diagnostics for Subsurface Edge Detection and Boundary Advisement During Planetary Drilling

Informed decision-making during lunar drilling and sampling missions will require data monitoring tools and specialized ground data systems. Accurate and updated situational awareness, with ongoing data monitoring, is critical for timely responses by to incoming science data. Traverse plans and scheduled activities may need to be flexibly changed in order to react to unexpected data or situations. Unlike (for example) Mars missions, the relative lightspeed closeness of the Moon allows for near-real-time ground processing of incoming mission and instrument data. An Apollo-class lunar regolith drill will in a sense “travel” a meter or two vertically at a given subsurface characterization site. As the drill penetrates into lunar regolith, it is likely to encounter a range of material densities, orientations, fracture toughness, and (perhaps) ice percentages. Lunar drill telemetry can provide science teams with a valuable first look into the subsurface structure, the regolith bulk properties, and constituents at each drilled site. Real-time AI-based recognition and reaction to downhole situations has been developed for automated deeper drilling on Mars and beyond. We can leverage the same knowledge bases and pattern-matching as areal-time interpreter of the subsurface, a situational awareness tool during drilling operations. We recently (Sept. 2019) demonstrated this AI drilling monitoring and analysis capability, in control of in-situ drilling and sampling operations, mounted on a KREX-2 rover in Chile’s Atacama Desert. Terrestrial automated drilling log analyses in oil exploration have used similar machine learning techniques in classifying and identifying features in drilling logs –but these typically are designed assuming a drilling fluid influencing downhole measurements and data (permeability, resistivity). Drilling models and existing AI software designed to detect and respond to drilling faults and hard materials can be repurposed, for near-real-time (ground-based) interpretation of drilling telemetry –a potentially valuable advisory tool for strata boundaries and changes in drilling parameters. On the Moon, this approach could be used to study the structure and to some extent the composition of lunar regolith vs. borehole depth, based on recognizable variations in fracture hardness, drilling energy and penetration rates while actively drilling. Since the early 2000s, a series of increasingly-capable real-time drilling telemetry interpretation and characterization software tools have been developed. These subsurface models and software tools have monitored the real-time drilling data received, and automatically identified changes in drill behavior (e.g., encountering a harder target layer, bit inclusions, drill choking due to infall downhole, and others) correlating these with subsurface structures and features. We discuss the mappings between drill borehole parameters, faults or events detected, and modeled changes in rock layer boundaries, in examples drawn from field testing at analog sites in an Arctic impact crater, Rio Tinto, and Chile’s Atacama Desert. These demonstrate how subsurface structural boundaries led to fault detections and responses by the software.

drilling advisor↗
NASA NTRSDate not supplied

Comparing Two Statistical Models for Low Boom Dose-response Relationships with Correlated Responses

This study compares two statistical modeling approaches to construct summary dose-response curves for low boom community noise surveys. NASA field survey data were used that consist of multiple responses from a survey participant. These data require an approach that accounts for the correlation among repeated annoyance observations from the same participant. A multilevel model accounts for the correlation by allowing estimated parameters to vary with each survey participant. On the other hand, a population average model utilizes generalized estimating equations and accounts for the correlation via a user-specified within-subject correlation structure. A visual comparison of the dose-response curves for these two methods reveals similar results. When comparing specific points along the summary curves, the multilevel model yields more precise confidence bounds than the population average model. The similarity between the summary curves derived from each model lends validity to both approaches for approximating a population representative summary curve.

X-59↗
NASA NTRS2022

Comparison of Two Statistical Models for Low Boom Dose-response Relationships with Correlated Responses

This study compares two statistical models to construct summary dose-response curves for low boom community noise surveys. Data from two NASA field surveys are used that consist of multiple responses per survey participant. These data require an approach that accounts for the correlation among repeated annoyance observations from the same participant. A multilevel model accounts for the correlation by allowing estimated parameters to vary with each survey participant. On the other hand, a population average model utilizes generalized estimating equations and accounts for the correlation via a userspecified within-subject correlation structure. A visual comparison of the dose-response curves for these two methods reveals similar results. When comparing specific points along the summary curves, the multilevel model yields more precise confidence bounds than the population average model. The similarity between the summary curves derived from each model lends validity to both approaches for approximating a population representative summary curve, though modeling assumptions may lend favor to the multilevel logistic modeling approach over the population average model.

dose-response↗