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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

NeMO=NET↗
NASA NTRS2018

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

satellite data↗
NASA NTRS2017

An Overview of Surface Heat Microbial Reduction as a Viable Microbial Reduction Modality for Spacecraft Surfaces

In accordance with NASA Planetary Protection (PP) policy requirements, flight project hardware may be required to undergo microbial reduction processes to prevent the forward contamination of target planetary bodies with Earth organisms. Heat microbial reduction (HMR) is the most commonly employed modality used at JPL for reducing the microbial bioburden on flight hardware. In 2013, longstanding HMR specifications were abandoned, and revised specifications were developed which integrated the latest findings on bacterial spore heat resistivity. Revised decimal reduction values (D-values) for time-temperature lethality curves (110 °C to 200 °C) were developed to account for “hardy” bacterial spores that exhibit greater heat resistance than previously understood. Presented here is a comparative analysis of the revised NASA HMR specifications against empirical data compiled from recent JPL studies, and peer-reviewed, published literature. Bacillus sp. strain ATCC 29669 displayed high heat resistance, and this strain’s 4-log heat lethality curve was comparable to the revised 4-log specification. Spores of Bacillus atrophaeus ATCC 9372 displayed less heat resistance, and exhibited D-values which were less than the revised 3-log microbial reduction specifications. Extrapolations indicate that the current 6-log reduction credit applied to 350 °C for 1 hr. and 500 °C for 0.5 sec. is highly conservative. Projections indicate that a 10- to 18-log reduction of both hardy and non-hardy spores may be achievable at bakeouts of 350 °C for 1 hr. The findings reported here indicate the revised NASA HMR specifications from 110 °C to 200 °C are appropriate for achieving 4-log and 6-log reductions with hardy spore populations; however, for non-hardy spore populations, or for temperatures above 200 °C, the specifications are exceedingly conservative.

Shirey, Brian T.↗
NASA NTRS2019

Modal Test of the Fluted Cylinder

The fluted cylinder test article was available from a past test, and was a representative shape and size for the USA program to test its acoustic foam treatment. Modal testing's purpose was to define the damping of the structure to be fed into the acoustic models.

Pederson, Kenneth↗
NASA NTRS2018

A 3D Citizen Science Video Game for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network aimed at accurately assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. We present here the active learning component of the project, which consists of an interactive video game prototype for tablet and mobile devices where players are able to intuitively label morphology classifications over mm-scale 3D coral reef imagery. Active learning applications present a novel methodology for engaging the public while efficiently providing large-scale training and test data for increasingly complex and data-intensive machine learning algorithms. NeMO-Net trains players on domain-specific knowledge through interactive tutorials and periodically checks players' input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide personalized classification training. Players can rate the classifications of other players, unlock rewards and join a global community as they explore and classify coral reefs and other shallow marine environments.

Citizen Science↗
NASA NTRS2020

Test-Analysis Modal Correlation of Rocket Engine Structures in Liquid Hydrogen - Phase II

Many structures in a launch vehicle operate in liquid hydrogen, from the hydrogen fuel tanks through the ducts and valves and into the turbopumps. Calculating the structural dynamic response of these structures is critical for successful qualification, but accurate knowledge of the natural frequencies is based entirely on numerical or analytical predictions since testing in operating conditions is problematic. A comprehensive test/analysis program has therefore been performed at NASA/MSFC to enable accurate prediction of the modal characteristics of the Space Launch System’s (SLS) RS-25 Low Pressure Fuel Turbopump Inducer including the effects of fluid-added mass, mechanical property change at cryogenic temperatures, operation within tight tip clearances, acoustic/structure interaction, and hydroelasticity. The process also has to account for complicated cyclic symmetry mode shapes which can be easily mistuned in test, and geometric, boundary condition, and material modifications between the sub-scale inducer used as a test article and the actual flight component. The first phase of the program, documented previously, focused on testing of a cantilever beam in a number of fluids and temperatures to isolate the effects of fluid-added mass and temperature, while the second phase reported here documents the additional issues associated with the more realistic inducer test article. Preliminary structural dynamic analysis of the flight hardware including variability for the above parameters indicated potential severe resonances, requiring implementation of undesired programmatic constraints, so the improved predictive capability may allow removal of these constraints.

Brown, Andrew M.↗
NASA NTRS2019

Modal Analysis of the Orion Capsule Two Parachute System

As discussed in Ref [1], it is apparent from flight tests that the system made up of two main parachutes and a capsule can undergo several distinct dynamical behaviors. The most significant and problematic of these is the pendulum mode in which the system develops a pronounced swinging motion with an amplitude of up to 24 deg. Large excursions away from vertical by the capsule could cause it to strike the ground at a large horizontal or vertical speed and jeopardize the safety of the astronauts during a crewed mission. In reference [1], Ali et al. summarized a series of efforts taken by the Capsule Parachute Assembly System (CPAS) Program to understand and mitigate the pendulum issue. The period of oscillation and location of the system's pivot point are determined from post-flight analysis. Other noticeable but benign modes include: 1) flyout (scissors) mode, where the parachutes move back and forth symmetrically with respect to the vertical axis similar to the motion of a pair of scissors; 2) maypole mode, where the two parachutes circle around the vertical axis at a nearly constant radius and period; and 3) breathing mode, in which deformation of the non-rigid canopies affects the axial acceleration of the system in an oscillatory manner. Because these modes are relatively harm- less, little effort has been devoted to analyzing them in comparison with the pendulum motion. Motions of the actual system made up of two parachutes and a capsule are extremely complicated due to nonlinearities and flexibility effects. Often it is difficult to obtain insight into the fundamental dynamics of the system by examining results from a multi-body simulation based on nonlinear equations of motion (EOMs). As a part of this study, the dynamics of each mode observed during flight is derived from first principles on an individual basis by making numerous simplifications along the way. The intent is to gain a better understanding into the behavior of the complex multi-body system by studying the reduced set of differential equations associated with each mode. This approach is analogous to the traditional modal analysis technique used to study airplane flight dynamics, in which the full nonlinear behavior of the airframe is decomposed into the phugoid and short period modes for the longitudinal dynamics and the spiral, roll-subsidence, and dutch-roll modes for the lateral dynamics. It is important to note that the study does not address the mechanisms that cause the system to transition from one mode to another, nor does it discuss motions during which two or more modes occur simultaneously.

Pei, J.↗
NASA NTRS2021

DGEN Aeropropulsion Research Turbofan Core/Combustor-Noise Measurements-Experiment and Modal Structure at Core-Nozzle Exit

Data from a recent core/combustor-noise source-diagnostic test utilizing a small turbo-fan engine are analyzed. The campaign continued the exploration begun in a baseline test, but with more extensive acoustic instrumentation. Both tests were aimed at developing a better understanding of propulsion-noise sources and their impact on the farfield noise signature, in order to enable improved turbofan noise-prediction methods and noise-mitigation techniques. Simultaneous high-data-rate acoustic measurements (93 channels in total) were obtained using a circumferential sensor array at the core-nozzle exit in conjunction with sideline and farfield microphone arrays for several relevant engine operational points. Measurements were repeated for different circumferential and sideline array configurations, as well as for redundancy. The unsteady pressure field at the core-nozzle exit is documented in detail. Previous work suggested that the±1azimuthal duct mode could be cut-on at this location, which would have implications for combustor-noise modeling and prediction. The modal decomposition of the combustor noise at the core-nozzle exit verifies this observation. Select farfield sound-pressure-level spectra are also presented.

Aeroacoustics↗
NASA NTRS2000

Real-time simulation of dynamically deformable finite element models using modal analysis and spectral Lanczos decomposition methods

We present two efficient methods for simulating real-time behavior of a dynamically deformable 3D object modeled by finite element equations. The first method is based on modal analysis, which uses the most significant vibration modes of the object to compute the deformations in real-time for applied forces. The second method uses the spectral Lanczos decomposition to obtain the explicit solutions of the finite element equations.

medical↗
NASA NTRS2020

A Multi-Modal Approach for Monitoring Changes in Agriculture in the Mekong River Delta

Smallholder farms in South East Asia are characterized by small irregular field patterns, dense cloud cover and haze which limits our ability to observe changes in agriculture land-use. Very-high resolution (VHR, < 5 m) Earth observing (EO) satellites have grown into constellations with global repeat coverage that can support multiple observations within a growing season. Here we present a multi-modal approach to map and monitor changes in agriculture at the field scale in the Mekong River Delta with VHR optical imagery from Worldview-1, 2 and 3, and synthetic aperture radar (SAR) from Sentinel-1. Our approach leverages the benefits of SAR and VHR imagery with geographic object-based image analysis (GEOBIA) in a high-end computing environment to rapidly map the distribution and size of agriculture fields that are not observable at moderate resolution (> 30 m) in regions with dense persistent cloud cover and haze from biomass burning.

Christopher S R Neigh↗
NASA NTRSDate not supplied

Simulation of the Aerosol Size Distribution Using a Neural Network Surrogate for the Modal Aerosol Module (MAM7)

One objective of atmospheric simulations is to quantify the distribution of aerosols and their properties. Accurate parameterizations of the processes governing aerosol mass, particle number, and particle size distribution are important for predicting the Earth’s net radiative balance and aerosol-cloud interactions. The Modal Aerosol Module (MAM7) is a two-moment aerosol model that simulates mass, number, and size distribution of seven modes comprised of internally mixed aerosol species. The two-moment scheme adds significant computational expense but allows for the prediction of varying particle size distribution relative to the bulk method which predicts only total mass. In this work, we developed a neural network surrogate model for MAM7 (MAMnet) to predict the aerosol number concentration in NASA’s Global Earth Observing System (GEOS) without adding prohibitive computational expense. MAMnet, can be driven by output from a single moment, mass-based, aerosol scheme (Goddard Chemistry Aerosol and Radiation model (GOCART)) or from reanalysis products (Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)). MAMnet was trained using number concentrations from a 5-year GEOS/MAM7 simulation at 1-degree horizontal resolution and using the total mass calculated across modes as inputs, as well as temperature and air density. The model architecture for MAMnet was based on AlexNet, the 2012 winner of the ImageNet Large Scale Visual Recognition Challenge. While some modifications were necessary to accommodate our problem, important aspects of the network were preserved. MAMnet was able to reproduce zonal dynamics and spatial distributions of the aerosol number concentration however predictability in the upper troposphere was poor.

Katherine H Breen↗
NASA NTRS2021

Computational Inference of Vibratory System with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains

Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.

K Zhou↗
NASA NTRS2023

High Intensity Modal Impedance Tube Development at NASA Langley

A High Intensity Modal Impedance Tube (HIMIT) was developed for evaluation of acoustic liners in a normal incidence, high sound pressure level (SPL), and high frequency environment. Capabilities of the HIMIT are demonstrated here by testing three classes of acoustic liners: a narrow chamber liner, conventional single degree of freedom liners, and over-the-rotor liners. The experimental results are compared to results in NASA Langley's Normal Incidence Tube (NIT) for validation. The Zwikker and Kosten Transmission Line (ZKTL) model is also compared to the HIMIT results for selected configurations to study its applicability at high SPLs and frequencies. The HIMIT and NIT impedance spectra compare favorably up to 3.0 kHz, which is the upper frequency limit of the NIT analysis. The ZKTL model was used at frequencies up to 6.0 kHz and showed great comparison for the entire frequency range tested, indicating that the model may be used at high frequencies when the plane wave mode is dominant.

Chelsea Solano↗
NASA NTRSDate not supplied

Exploring the Climatic Impact of Bi-Modal Dust Particle Size Distributions During the MY34/2018 Global Dust Storm with the NASA Ames Mars Global Climate Model.

We have implemented a bi-modal dust lifting scheme in the NASA Ames Mars Global Climate Model (GCM) and investigated the climatic impact on the MY34/2018 Global Dust Storm (GDS). We found that the climate is measurably responsive to the inclusion of a second, smaller dust mode. Dust tends to be lifted through a deeper column in the atmosphere, which is reflected in the climate as changes to atmospheric temperature, tide amplitudes, and surface temperatures. Adding a modest fraction of a smaller dust mode improves simulations during the GDS compared to Curiosity semi-diurnal tide observations as well as Mars Climate Sounder (MCS) T15 brightness temperatures

R A Urata↗
DOE OSTI2024

Multi-Modal Characterization of Interfacial Corrosion of Ni-based Alloys in Chloride-based Molten Salts

The United States Department of Energy (DOE) is committed to the advancement of nuclear reactor technology through initiatives such as the Advanced Reactor Development Program (ARDP), to diversify the United States energy portfolio towards more sustainable energy options. The ARDP includes demonstration by industry partners of molten salt fast reactors (MSRs). Construction of molten salt reactor technology requires qualified nuclear structural materials. Unfortunately, there are no current materials that meet current qualification requirements dictated by the Nuclear Regulatory Commission for construction of MSRs. Adapting current structural material qualifications requires expansion of our current knowledgebase on corrosion performance. In this investigation, we assess microstructural changes in a Ni-based superalloy after exposure to a chloride-containing salt system through a correlated multi-modal approach combining several advanced characterization techniques. Namely we will highlight the impact of grain boundary phenomena at the onset of corrosion attack, including the role of intergranular crack propagation and development of internal corrosion products. The findings from this investigation will further expand our assessment of the corrosion performance of structural materials being investigated for construction of MSR components.

36 - MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Effect of Micrometeoroid and Orbital Debris Impact on Modal Response of Persistent Assets

Protecting the infrastructure and payload components of persistent assets (PAs) from damage caused by MicroMeteoroid and/or Orbital Debris (MMOD) impacts is a significant design consideration. The impact resistance to MMOD will need to be provided by the robustness of the structural configuration such that the asset does not suffer loss of function or catastrophic failure. Finite element modelling (FEM) can be used to investigate the effects of MMOD impact on the overall structural behavior of the PA. The models are being used here to investigate the structural response to ‘missing’ truss members, and parametric studies are being performed to identify the worst-case scenario of an impact catastrophically damaging a truss element. These models will be used to determine which truss members are the most critical to the structural performance, and thus, which may require a higher degree of protection or robustness to ensure they can withstand an MMOD impact without causing catastrophic effects on the entire structure. Initial results have been generated indicating a reduction in the global frequency due to missing truss members as a result of MMOD impact.

in-space assembly (ISA)↗
DOE OSTI2024

3D Reconstruction of a High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning with High-Precision EBSD and Surface Profilometry

High-energy diffraction microscopy (HEDM) combined with in situ mechanical testing is a powerful nondestructive technique for tracking the evolving microstructure within polycrystalline materials during deformation. This technique relies on a sophisticated analysis of X-ray diffraction patterns to produce a three-dimensional reconstruction of grains and other microstructural features within the interrogated volume. However, it is known that HEDM can fail to identify certain microstructural features, particularly smaller grains or twinned regions. Characterization of the identical sample volume using high-resolution surface-specific techniques, particularly electron backscatter diffraction (EBSD), can not only provide additional microstructure information about the interrogated volume but also highlight opportunities for improvement of the HEDM reconstruction algorithms. In this study, a sample fabricated from undeformed “low solvus, high refractory” nickel-based superalloy was scanned using HEDM. The volume interrogated by HEDM was then carefully characterized using a combination of surface-specific techniques, including epi-illumination optical microscopy, zero-tilt secondary and backscattered electron imaging, scanning white light interferometry, and high-precision EBSD. Custom data fusion protocols were developed to integrate and align the microstructure maps captured by these surface-specific techniques and HEDM. The raw and processed data from HEDM and serial sectioning have been made available via the Materials Data Facility (MDF) at https://doi.org/10.18126/4y0p-v604 for further investigation.

36 MATERIALS SCIENCE↗