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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Microinjection--a tool to study gravitropism

Despite extensive studies on plant gravitropism this phenomenon is still poorly understood. The separation of gravity sensing, signal transduction and response is a common concept but especially the mechanism of gravisensing remains unclear. This paper focuses on microinjection as powerful tool to investigate gravisensing in plants. We describe the microinjection of magnetic beads in rhizoids of the green alga Chara and related subsequent manipulation of the gravisensing system. After injection, an external magnet can control the movement of the magnetic beads. We demonstrate successful injection of magnetic beads into rhizoids and describe a multitude of experiments that can be carried out to investigate gravitropism in Chara rhizoids. In addition to examining mechanical properties, bead microinjection is also useful for probing the function of the cytoskeleton by coating beads with drugs that interfere with the cytoskeleton. The injection of fluorescently labeled beads or probes may reveal the involvement of the cytoskeleton during gravistimulation and response in living cells. c2003 COSPAR. Published by Elsevier Ltd. All rights reserved.

NASA Program Fundamental Space Biology↗
NASA NTRS2013

Characterization of Heat Melt Compactor (HMC) Product Water

The Heat Melt Compactor (HMC) is designed to sterilize and process wastes produced during space missions. Benefits of the HMC include reduction of biohazards to the crew, reduction in volume of wastes that would otherwise require storage, production of radiation shielding tiles, and recovery of water and other resources. Water reuse is critical onboard spacecrafts; it reduces the need for resupply missions and saves valuable storage space. The main sources of water in HMC batches are food, beverages, shampoo, disinfecting wipes, toothpaste, and diapers. Water reclaimed by the HMC was analyzed for concentrations of Na+, NH4+, K+, Mg2+, Ca2+, Cl-­‐, NO2-­‐, Br-­‐, NO3-­‐, PO43-­‐, SO42-­‐, total organic carbon (TOC), total inorganic carbon (TIC), % total solids, and pH. The data are discussed in relation to the current water input characteristics established for the International Space Station Water Processor Assembly system. Batches with higher than average amounts of food produced HMC product water with higher sulfate content, and batches with higher proportions of disinfectant wipes and food yielded HMC product water with higher ammonium concentration. We also compared theoretical chemical composition of HMC product water based on food labels and literature values to experimental results.

Heat Melt Compactor↗
DOE OSTI2023

Deconvoluting Structures of Component Plant Biopolymers Using Deuterium Labeled Brassica oleracea Stems

Several Brassica species are cultivated globally for the production of seed oils as food, lubricants, and increasingly biofuels. The stem and leaf residues of these herbaceous dicotyledonous crops constitute another feedstock for biofuels and other products. Plant cell walls are complex, multipolymeric structures that consist primarily of polysaccharides and lignin. Cellulose chains coalesce to form crystalline microfibrils while the amorphous biopolymers, hemicellulose, and lignin form a network structure and fill the interstitial space. Neutron scattering has been used for the structural study of the assembly and deconstruction of the plant cell walls. However, similar neutron sensitivity of the different amorphous biopolymers has made structural association to individual component biopolymers nontrivial and ambiguous. To improve the association of structural features to specific biopolymer components, partial deuteration of the plant cell wall can be employed to increase the difference in the neutron scattering length density between amorphous carbohydrate and lignin plant polymers. Vegetative stems from partially deuterated Brassica oleracea acephala (kale) plants were obtained commercially, and the plant cell wall structures were studied by contrast-variation small-angle neutron scattering (CV-SANS) and Fourier-transform infrared spectroscopy (FTIR). FTIR results indicated that deuterium substitution for hydrogen in the carbohydrates was higher than that in lignin. By combining CV-SANS and FTIR results, the neutron scattering length density (nSLD) of the polysaccharides and lignin was determined to match nSLD of 65%:35% and 48%:52% D 2 O/H 2 O solvent mixtures, respectively. These nSLD values were higher than the nSLD values of polysaccharides and lignin for H 2 O grown biopolymers. The nSLD increase correlates to replacing about 42.5% of hydrogens present as both C–H and O–H groups in cellulose with deuterium atoms, while only 21% for lignin. In conclusion, this study lays the foundation to use partially deuterated plants to deconvolute structural features of the different component biopolymers, especially cellulose, hemicellulose, and lignin, of the plant cell wall without introducing unintended structural modifications due to the pretreatment extraction processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2020

Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

Abstract Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.

Johnson, Christopher W.↗
NASA NTRS2004

Living with a Star (LWS) Space Environment Testbeds (SET), Mission Carrier Overview and Capabilities

NASA has initiated the Living With a Star (LWS) Program to develop the scientific understanding to address the aspects of the Connected Sun-Earth system that affect life and society. A goal of the program is to bridge the gap between science, engineering, and user application communities. This will enable future science, operational, and commercial objectives in space and atmospheric environments by improving engineering approaches to the accommodation and/or mitigation of the effects of solar variability on technological systems. The three program elements of the LWS Program are Science Missions; Targeted Research and Technology; and Space Environment Testbeds (SETS). SET is an ideal platform for small experiments performing research on space environment effects on technologies and on the mitigation of space weather effects. A short description of the LWS Program will be given, and the SET will be described in detail, giving the mission objectives, available carrier services, and upcoming flight opportunities.

Patschke, Robert↗
NASA NTRS2007

High Performance Processors for Space Environments: A Subproject of the NASA Exploration Missions Systems Directorate "Radiation Hardened Electronics for Space Environments" Technology Development Program

Implementation of challenging Exploration Systems Missions Directorate objectives and strategies can be constrained by onboard computing capabilities and power efficiencies. The Radiation Hardened Electronics for Space Environments (RHESE) High Performance Processors for Space Environments project will address this challenge by significantly advancing the sustained throughput and processing efficiency of high-per$ormance radiation-hardened processors, targeting delivery of products by the end of FY12.

Johnson, M.↗
DOE OSTI2023

Modular machine learning-based elastoplasticity: Generalization in the context of limited data

The development of highly accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions and from the viewpoint of data availability, verification, and validation. Recently, data-driven modeling approaches have been proposed that aim to establish stress-evolution laws that avoid user-chosen functional forms by relying on machine learning representations and algorithms. However, these approaches not only require a significant amount of data but also need data that probes the full stress space with a variety of complex loading paths. Furthermore, they rarely enforce all necessary thermodynamic principles as hard constraints. Hence, they are in particular not suitable for low-data or limited-data regimes, where the first arises from the cost of obtaining the data and the latter from the experimental limitations of obtaining labeled data, which is commonly the case in engineering applications. In this work, we discuss a hybrid framework that can work on a variable amount of data by relying on the modularity of the elastoplasticity formulation where each component of the model can be chosen to be either a classical phenomenological or a data-driven model depending on the amount of available information and the complexity of the response. The method is tested on synthetic uniaxial data coming from simulations as well as cyclic experimental data for structural materials. The discovered material models are found to not only interpolate well but also allow for accurate extrapolation in a thermodynamically consistent manner far outside the domain of the training data. This ability to extrapolate from limited data was the main reason for the early and continued success of phenomenological models and the main shortcoming in machine learning-enabled constitutive modeling approaches. Training aspects and details of the implementation of these models into Finite Element simulations are discussed and analyzed.

42 ENGINEERING↗
NASA NTRS2009

Panel Discussion: Life in the Cosmos

Water appears to be essential to all life on Earth. For this reason, "Follow the Water" has been adopted as a mantra for the search for Life in the Cosmos. Expeditions have helped to establish the limits and biodiversity of life in the most extreme environments on Earth. Microbial extremophiles inhabit acidic streams; hypersaline and hyperalkaline lakes and pools; the cold deep sea floor, permafrost, rocks, glaciers, and perennially ice-covered lakes of the polar environments; geysers, volcanic fumaroles, hydrothermal vents and hot rocks deep within the Earth's crust. The ESA Venus Express Spacecraft entered Venusian Orbit in 2006 and continues to produce exciting results. The Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) instrument made the first detection of hydroxyl in the atmosphere of Venus, indicating it is much more similar to Earth and Mars than previously thought. Huge hurricane-like vortices have been found above the poles of the planet and as yet unidentified UV absorbers that form mysterious dark bands in the upper atmosphere. At 70 km and below, water vapor and sulfur dioxide combine to form sulfuric acid droplets that create a haze above the cloud tops. Thermophilic acidophiles, such as have recently been discovered on Earth, could possibly survive in the hot sulfuric acid droplets that exist in the upper atmosphere of Venus. In order to understand how to search for life elsewhere in the Solar System, over 40 VIRTIS images of Earth from Venus have been obtained to search for evidence of life on Earth. The signatures of water and molecular Oxygen were detected in the Earth s atmosphere, but the atmosphere of Venus also exhibits these signatures. The water and water ice are far more abundant on comet, the polar caps and permafrost of Mars and the icy moons of Jupiter and Saturn. These "frozen worlds" of our Solar System, are much more promising regimes where extant or extinct microbial life may exist. The ESA Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS) probe has found that both the North and South Polar Caps of Mars are approximately 3.5 km thick and are composed almost entirely of water ice. In winter, a thin dry ice layer covers the caps, but it sublimates directly to CO2 in the spring. The ESA Mars Express Orbiter images reveal Rupes Tenuis to be a vast snow-laden region on the southern edge of the Martian North Polar Cap. The Mars Exploration Rover Spirit found alkaline volcanic rocks in the Gusev Crater and the Phoenix Mars Lander has shown that the soil of Mars is much more alkaline than previously expected. The Phoenix Mars Lander has also made direct observations of frozen and liquid water on Mars. It is known that microorganisms from Alaska, Siberia and Antarctica can remain alive frozen in permafrost or ice for long periods of time. These discoveries increase the possibility that the Labeled Release Experiment may have discovered life on Mars during the Viking Mission and provide strong impetus for the return of life detection experiments to Mars. Changes in the spin rate of Saturn's moon Titan indicate that it may also harbor a 300 km thick liquid water ocean beneath its icy crust. The NASA/ESA/Italian Space Agency Cassini Spacecraft has imaged geysers containing water vapor, methane, carbon dioxide and organics erupting from the "tiger stripe" regions near the South Pole of Saturn's moon Enceladus. The high temperatures observed, the water vapor and large number of ice particles expelled suggest that a liquid water lake may exist beneath the "tiger stripe" ice cracks of Enceladus. The NASA Deep Impact probe found the surface temperature of comet 9P/Temple 1 at 1.5 AY was slightly above the ice/water phase change temperature (273 K). This suggests melting of water ice near the comet surface. A spectrometer the spacecraft detected a mixture of clay and carbonate minerals (that form in the presence of liquid water) streaming off the comet after the collision with the pactor. The study of chemical and mineral biomarkers, chiral amino acids and possible indigenous microfossils in SNC and carbonaceous meteorites continues. These results suggest that comets should be considered prime targets in the search for Life in the Cosmos. The ESA Rosetta mission is on track to rendezvous with comet 67P/Churyumov-Gerasimenko. The recent space observations combined with new information about the ability of microbial extremophiles to thrive in polar environments suggest that life may be far more widely distributed in the Cosmos than previously thought possible. The Panelists will review recent discoveries and provide their own insights about Life in the Cosmos -- followed by a question and answer session with the audience.

Hoover, Richard B.↗
NASA NTRSDate not supplied

LLMs and GenAI Tools to Depict Contributions of Human Systems to Spaceflight Tasks Execution

Recent advancements in Artificial Intelligence and Machine Learning (AI/ML) technologies, particularly Large Language Models (LLMs) capable of sophisticated syntax analysis, offer substantial potential in automating complex processes, thereby saving time and human resources. This study explores the development of an LLM-driven model designed to analyze and categorize a diverse set of Mars mission tasks into 18 predefined Human System Task Categories (HSTCs) based on their textual descriptions. As part of developing the Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA projects Performance Risk Model (PRisM) proof-of-concept, we established a framework to project performance scores from small-scale tests onto a preliminary list of Mars tasks. The foundation of our model was a comprehensive spreadsheet populated by NASA experts and clinicians, which detailed each Mars task alongside binary indicators of HSTC involvement. This dataset enabled the initial application of supervised ML, training and testing on existing HSTC labels. The HSTCs were originally defined from a medical system perspective, focusing on task impairments due to deteriorated human health. To expand our model's scope to include categories impacting performance, we face the challenge of generating binary labels (0 or 1) for new categories without pre-existing data. We address this by employing Generative AI (GenAI) software to determine whether a given task involved a new category by asking, "Does task A involve using category B?" We validate our approach by comparing the GenAI's binary classifications with the expert-provided labels for existing HSTCs. Notably, we utilize Ollama [4], a locally hosted GenAI tool that does not require cloud access, thus safeguarding NASA's proprietary data from unauthorized exposure. This study demonstrates the feasibility of leveraging cutting-edge AI tools to advance research, paving the way for automation and rapid decision-making in space exploration.

Mona Matar↗
NASA NTRS2003

Single-Event Effects Ground Testing and On-Orbit Rate Prediction Methods: The Past, Present and Future

Over the past 27 years, or so, increased concern over single event effects in spacecraft systems has resulted in research, development and engineering activities centered around a better understanding of the space radiation environment, single event effects predictive methods, ground test protocols, and test facility developments. This research has led to fairly well developed methods for assessing the impact of the space radiation environment on systems that contain SEE sensitive devices and the development of mitigation strategies either at the system or device level.

Reed, Robert A.↗
NASA NTRS2018

Earth Science Deep Learning: Applications and Lessons Learned

Deep Learning: A subfield of machine learning; Algorithms inspired by function of the brain; Scales with amount of training data; Powerful tool without the need for feature engineering; Suitable for Earth Science applications. Deep Learning for Earth science at MSFC (Marshall Space Flight Center): Phenomena identification; Hurricane intensity (wind speed) estimation; Severe storm (hailstorm) detection; Transverse bands detection; Entity extraction for knowledge graph creation; Ephemeral water detection.

Labeled Data↗
NASA NTRS1977

Status of the DOE /STOR/-sponsored national program on hydrogen production from water via thermochemical cycles

A pure thermochemical cycle is a system of linked regenerative chemical reactions which accepts only water and heat and produces hydrogen. Thermochemical cycles are potentially a more efficient and cheaper means of producing hydrogen from water than is the generation of electricity followed by electrolysis. The Energy Storage Systems Division of the Department of Energy is currently funding a national program on thermochemical hydrogen production. The National Aeronautics and Space Administration is responsible for the technical management of this program. The goal is to develop a cycle which can potentially operate with an efficiency greater than 40% using a heat source providing a maximum available temperature of 1150 K. A closed bench-scale demonstration of such a cycle would follow. This cycle would be labeled a 'reference cycle' and would serve as a baseline against which future cycles would be compared.

Baker, C. E.↗
NASA NTRS1984

Use of satellite data in agricultural surveys

The state-of-the-art of crop surveying by satellite is reviewed with an emphasis on the signature extension problem. Registration and preprocessing procedures are discussed with refereence to: normalization of the radiometric values of each scene for scene-to-scene differences; registration techniques, implemented at the NASA Johnson Space Center, capable of 0.5 pixel root-mean-square error; and current research in this direction. Data transformation and modeling techniques applied to the Landsat MSS images and a solution for the field-to-field variations of the greenness and brightness temporal trajectories are included. Finally, a review of the mixture decomposition method of labeling and estimating the areal proportions is given.

Hall, F. G.↗
DOE OSTI2023

Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

The Exa.TrkX project presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the Large Hadron Collider (LHC). Graphs describing particle interactions are formed by treating each detector hit as a node, with edges describing the relationships between hits. We utilise a multi-head attention message passing network which performs graph convolutions in order to label each node with a particle type.We present an updated variant of our GNN architecture, with several improvements. After testing the model on more realistic simulation with regions of unresponsive wires, the target was modified from edge classification to node classification in order to increase robustness. Removing edges as a classification target opens up a broader possibility space for edge-forming techniques; we explore the model’s performance across a variety of approaches, such as Delaunay triangulation, kNN, and radius-based methods. We also extend this model to the 3D context, sharing information between detector views. By using reconstructed 3D spacepoints to map detector hits from each wire plane, the model naively constructs 2D representations that are independent yet fully consistent.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery

To date, careful data treatment workflows and statistical detectors are used to perform hyperspectral image (HSI) detection of any gas contained in a spectral library, which is often expanded with physics models to incorporate different spectral characteristics. In general, surrounding evidence or known gas-release parameters are used to provide confidence in or confirm detection capability, respectively. This makes quantifying detection performance difficult as it is nearly impossible to develop an absolute ground truth for gas target pixel presence in collected HSI. Consequently, developing and comparing new detection methods, especially machine learning (ML)-based methods, is susceptible to subjectivity in derived detection map quality. Here, in this work, we demonstrate the first use of transformer-based paired neural networks (PNNs) for one-shot gas target detection for multiple gases while providing quantitative classification and detection metrics for their use on labeled data. Terabytes of training data are generated from a database of long-wave infrared HSI obtained from historical Mako sensor campaigns over Los Angeles. By incorporating labels, singular signature representations, and a model development pipeline, we can tune and select PNNs to detect multiple gas targets that are not seen in training on a quantitative basis. We additionally assess our test set detections using interpretability techniques widely employed with ML-based predictors, but less common with detection methods relying on learned latent spaces.

Hyperspectral imaging↗