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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Use of Modal Acoustic Emission to Monitor Damage Progression in Carbon Fiber/Epoxy Tows and Implications for Composite Structures

ASTM-based ILH methods were found to give a reproducible, quantitative estimate of the stress threshold at which significant accumulated damage began to occur. a) FR events are low energy (<2 V(exp 20 microsec) b) FR events occur close to the observed failure locus. c) FR events consist of more than 30% fiber breakage (>300 kHz) d) FR events show a consistent hierarchy of cooperative damage for composite tow, and for the COPV tested, regardless of applied load. Application of ILH or related stress profiles could lead to robust pass/fail acceptance criteria based on the FR. Initial application of FR and FFT analysis of AE data acquired on COPVs is promising.

Waller, Jess M.↗
NASA NTRS2011

Imaging Modalities Relevant to Intracranial Pressure Assessment in Astronauts

Learning Objectives of this slide presentation are: 1: To review the morphological changes in orbit structures caused by elevated Intracranial Pressure (ICP), and their imaging representation. 2: To learn about the similarities and differences between MRI and sonographic imaging of the eye and orbit. 3: To learn about the role of MRI and sonography in the noninvasive assessment of intracranial pressure in aerospace medicine, and the added benefits from their combined interpretation.

Sargsyan, Ashot E.↗
NASA NTRS2011

Multi-Modal Image Registration and Matching for Localization of a Balloon on Titan

A solution was developed that matches visible/IR imagery aboard a balloon in Saturn's moon Titan's atmosphere to SAR (synthetic aperture radar) and visible/IR data acquired from orbit. A balloon in Titan's atmosphere must be able to localize itself autonomously both globally and with respect to local terrain. The orbital data is used to provide the balloon imagery with global context. The work is novel in applying mutual information (MI) to orbital vs. aerial data. There are unique challenges in this setting. Image offsets are much higher than in medical imaging, there is local distortion due to 3D terrain relief, and the fields of regard from orbit and from the air are quite different.

Ansar, Adnan I.↗
NASA NTRS2011

A Bi-Modal Distribution of ALHA77307 Matrix Olivine: Evidence for Fine-Grained Mixing from Multiple Reservoirs in the CO Formation Zone

The CO 3.0 chondrite ALHA77307 is thought to be the least metamorphosed of all the CO chondrites [1]. As such, the fine-grained (<30 m) olivine found in its matrix is a valuable resource for investigating the CO formation environment since its compositions should be primary. In the CO matrix, we indeed find a wide range of major element compositions (Fa(0.5-71)). However, more importantly, we find that the olivines make up two compositionally distinct populations (Fa(0.5-5) and Fa(21-71)). Grains from both populations are found within an extremely close proximity and we see no obvious evidence of two distinct lithologies within our samples. Therefore, we conclude that the olivine grains found in the ALHA77307 matrix must have crystallized within two unique formation conditions and were later mixed at a very fine scale during the accretion epoch. Here, we propose a possible explanation based on Cr and Mn concentrations in the olivine.

Frank, D.↗
NASA NTRS2012

CoNNeCT Antenna Positioning System Dynamic Simulator Modal Model Correlation

The National Aeronautics and Space Administration (NASA) developed an on-orbit, adaptable, Software Defined Radios (SDR)/Space Telecommunications Radio System (STRS)-based testbed facility to conduct a suite of experiments to advance technologies, reduce risk, and enable future mission capabilities on the International Space Station (ISS). The Communications, Navigation, and Networking reConfigurable Testbed (CoNNeCT) Project will provide NASA, industry, other Government agencies, and academic partners the opportunity to develop and field communications, navigation, and networking technologies in both the laboratory and space environment based on reconfigurable, software-defined radio platforms and the STRS Architecture. The CoNNeCT Payload Operations Nomenclature is SCAN Testbed, and this nomenclature will be used in all ISS integration, safety, verification, and operations documentation. The SCAN Testbed (payload) is a Flight Releasable Attachment Mechanism (FRAM) based payload that will launch aboard the Japanese H-II Transfer Vehicle (HTV) Multipurpose Exposed Pallet (EP-MP) to the International Space Station (ISS), and will be transferred to the Express Logistics Carrier 3 (ELC3) via Extravehicular Robotics (EVR). The SCAN Testbed will operate on-orbit for a minimum of two years.

Jones, Trevor M.↗
NASA NTRS2012

Comparative Modal Analysis of Sieve Hardware Designs

The CMTB Thwacker hardware operates as a testbed analogue for the Flight Thwacker and Sieve components of CHIMRA, a device on the Curiosity Rover. The sieve separates particles with a diameter smaller than 150 microns for delivery to onboard science instruments. The sieving behavior of the testbed hardware should be similar to the Flight hardware for the results to be meaningful. The elastodynamic behavior of both sieves was studied analytically using the Rayleigh Ritz method in conjunction with classical plate theory. Finite element models were used to determine the mode shapes of both designs, and comparisons between the natural frequencies and mode shapes were made. The analysis predicts that the performance of the CMTB Thwacker will closely resemble the performance of the Flight Thwacker within the expected steady state operating regime. Excitations of the testbed hardware that will mimic the flight hardware were recommended, as were those that will improve the efficiency of the sieving process.

flight hardware↗
NASA NTRS2012

CoNNeCT Antenna Positioning System Dynamic Simulator Modal Model Correlation

The National Aeronautics and Space Administration (NASA) developed an on-orbit, adaptable, Software Defined Radios (SDR)/Space Telecommunications Radio System (STRS)-based testbed facility to conduct a suite of experiments to advance technologies, reduce risk, and enable future mission capabilities on the International Space Station (ISS). The Communications, Navigation, and Networking reConfigurable Testbed (CoNNeCT) Project will provide NASA, industry, other Government agencies, and academic partners the opportunity to develop and field communications, navigation, and networking technologies in both the laboratory and space environment based on reconfigurable, software-defined radio platforms and the STRS Architecture. The CoNNeCT Payload Operations Nomenclature is "SCAN Testbed," and this nomenclature will be used in all ISS integration, safety, verification, and operations documentation. The SCAN Testbed (payload) is a Flight Releasable Attachment Mechanism (FRAM) based payload that will launch aboard the Japanese H-II Transfer Vehicle (HTV) Multipurpose Exposed Pallet (EP-MP) to the International Space Station (ISS), and will be transferred to the Express Logistics Carrier 3 (ELC3) via Extravehicular Robotics (EVR). The SCAN Testbed will operate on-orbit for a minimum of two years.

Jones, Tevor M.↗
NASA NTRS2016

Influence of Exercise Modality on Cerebral-Ocular Hemodynamics and Pressures

Background: Moderate and high intensity aerobic or resistance exercise has clearly identified benefits for cardiac, muscle, and bone health. However, the impact of such exercise - either as a mitigating or an exacerbating factor - on the development of the visual impairment and intracranial pressure syndrome (VIIP) is unknown. Accordingly, our aim was to characterize the effect of an acute bout of resistance (RE), moderate-intensity continuous (CE), and high-intensity interval exercise (IE) during a cephalad fluid shift on cerebral-ocular hemodynamics and pressures. Methods: 10 male subjects (36 plus or minus 9 years) completed 4 testing days in a 15 degree head-down tilt (HDT): (1) assessment of maximum volume of O (sub 2), (2) RE session (4 sets of 12 repetition maximum leg press exercise), (3) CE session (30 minutes of cycling at 60 percent maximum volume of O (sub 2)), and (4) IE session (4 by 4-minute intervals of exercise at 85 percent maximum volume of O (sub 2) with 3-minute active rest periods). During each session, blood flow (Vivid-e, GE Healthcare) in extracranial arteries (common carotid artery, CCA; internal carotid artery, ICA; external carotid artery, ECA and vertebral artery, VA), and mean blood flow velocity in middle cerebral artery (MCA), internal jugular pressure (IJP; VeinPress), and intraocular pressure (IOP; Icare PRO) were measured at rest, at the end of each resistance or interval set, and every 5 minutes during continuous exercise. Translaminar pressure gradient (TLPG) was estimated by subtracting IJP from IOP. Results: There were no differences across days in pre-exercise resting blood flows or pressures. IOP decreased slightly from HDT rest (20.2 plus or minus 2.3 millimeters of mercury) to exercise (RE: 19.2 plus or minus 2.8 millimeters of mercury; CE: 18.9 plus or minus 3.2 millimeters of mercury; IE: 20.1 plus or minus 2.8 millimeters of mercury), while IJP decreased during CE (31.6 plus or minus 9.5 millimeters of mercury) and RE (32.0 plus or minus 8.1 millimeters of mercury), and increased during IE (35.1 plus or minus 9.5 millimeters of mercury) from HDT rest (33.3 plus or minus 6.5 millimeters of mercury). Estimated TLPG was increased during IE only. Compared to RE and CE, IE resulted in the greatest increase in MCA blood flow velocity and extracranial artery blood flow. Conclusions: These preliminary results suggest that high-intensity IE acutely increases cerebral blood flow, IJP, and TLPG. Alterations in TLPG is one mechanism that may contribute to optic nerve sheath edema in astronauts. Accordingly, acutely raising IOP and/or orbital pressure during exercise could optimize cerebral-ocular pressures during spaceflight.

Scott, J.↗
NASA NTRS2017

Walking the Filament of Feasibility: Global Optimization of Highly-Constrained, Multi-Modal Interplanetary Trajectories Using a Novel Stochastic Search Technique

Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.

Englander, Arnold C.↗
NASA NTRS2017

Walking the Filament of Feasibility: Global Optimization of Highly-Constrained, Multi-Modal Interplanetary Trajectories Using a Novel Stochastic Search Technique

Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.

Englander, Arnold C.↗
NASA NTRS2017

Multi Modality Brain Mapping System (MBMS) Using Artificial Intelligence and Pattern Recognition

A Multimodality Brain Mapping System (MBMS), comprising one or more scopes (e.g., microscopes or endoscopes) coupled to one or more processors, wherein the one or more processors obtain training data from one or more first images and/or first data, wherein one or more abnormal regions and one or more normal regions are identified; receive a second image captured by one or more of the scopes at a later time than the one or more first images and/or first data and/or captured using a different imaging technique; and generate, using machine learning trained using the training data, one or more viewable indicators identifying one or abnormalities in the second image, wherein the one or more viewable indicators are generated in real time as the second image is formed. One or more of the scopes display the one or more viewable indicators on the second image.

Kateb, Babak↗
NASA NTRS2017

Multi-Modal Active Perception for Autonomously Selecting Landing Sites on Icy Moons

Selecting suitable landing sites is fundamental to achieving many mission objectives in planetary robotic lander missions. However, due to sensing limitations, landing sites which are both safe and scientifically valuable often cannot be determined reliably from orbit, particularly, in icy moon missions where orbital sensing data is noisy and incomplete. This paper presents an active perception approach to Entry Descent and Landing (EDL) which enables the lander to autonomously plan informative descent trajectories, acquire high quality sensing data during descent and exploit this additional information to select higher utility landing sites. Our approach consists of two components: probabilistic modeling of landing site features and approximate trajectory planning using a sampling based planner. The proposed framework allows the lander to plan long horizons paths and remain robust to noisy data. Results in simulated environments show large performance improvements over alternative approaches and show promise that our approach has strong potential to improve science return of not only icy moon missions but EDL systems in general.

autonomy↗
NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
NASA NTRS2018

Two-Dimensional Modal Beamforming in Wavenumber Space for Duct Acoustics

As turbofan bypass ratio continues to increase, civilian aircraft noise is increasingly dominated by fan noise. Fan noise propagating from its rotor and stator origins to the community passes through the inlet or aft flow duct, where its confined situation makes it susceptible to characterization by wall-mounted microphone arrays. Recently, the NASA-Glenn Research Center adapted its W-8 Single Stage Axial Compressor Facility to this type of measurement. OptiNav, Inc. took the opportunity to improve and simplify the duct mode processing in its Beamform Interactive computer program. A new approach to in-duct beamforming with a 2D wall-mounted array of microphones was developed. The purpose of this paper is to document the beamforming approach and provide some sample results from the W-8 facility.

Turbofan↗