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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Hypergravity Loading the Cultured Osteoblasts: Modeling and Experimental Analysis of Cellular Morphology and the Cytoskeleton

Bone forming cells, osteoblasts, respond to various mechanical forces, including mechanical strain and fluid-induced shear stress. This study examined whether osteoblasts detect changes in gravity as a mechanical force, as assessed by cellular morphology and dimensions of the cytoskeletal network. We used modeling to evaluate how gravity influences cell morphology given theoretical differences in densities between the surrounding medium, cytoplasm, and nucleus. A mechanical model was built based on analysis of axisymmetric shell structures (Fast4 software) to study the effects of 10 times gravity (10G) on cell height. The model indicated 0.02% decrease in overall cell height when the medium was 10% denser than the nucleus or cytoplasm, 5.9 x 10(exp-5)% decrease when the nucleus was 10% denser than the cytoplasm or medium, and 1.3 x 10(exp-5)% decrease when the cell cytoplasm was 10% denser than the nucleus or medium. To experimentally evaluate the influence of gravity, cultured primary fetal rat osteoblasts were grown to near confluence and centrifuged at 10G for 3 hours. Actin, microtubules, and nuclei were fluorescently labeled and analyzed by confocal microscopy to determine overall microtubule and actin network height. Centrifugation led to an apparent reduction in height of both the microtubule (-16%) and the actin (-20%) networks relative to stationary controls. Thus, both modeling and experiments indicate that hypergravity reduces the height of the osteoblast cell layer and their microtubule and actin networks. This combination of modeling and experimental analyses will help us to better understand the mechanical loading of osteoblasts.

Searby, N. D.↗
NASA NTRS2003

Accelerating the Design of Space Vehicles

One of NASA's key goals is to increase the safety and reduce the cost of space transportation. Thus, a key element of NASA's new Integrated Space Transportation Plan is to develop new propulsion, structures, and operations for future generations of reusable launch vehicles (RLVs). As part of this effort to develop the next RLV, the ClCT Program's Computing, Networking, and Information Systems (CNIS) Project is developing and demonstrating collaborative software technologies that use the collective power of the NASA Grid to accelerate spacecraft design. One of these technologies, called AeroDB, automates the execution and monitoring of computational fluid dynamics (CFD) parameter studies on the NASA Grid. About the NASA Grid The NASA Grid, or Information Power Grid,. is being developed to leverage the distributed resources of NASA's many computers. instruments, simulators, and data storage systems. The goal is to use these combined resources to sdve difficult NASA challenges, such as iimulating the entire flight of a space vehicle from ascent to descent.To realize the vision of the NASA Grid, the CNIS Project is developing the software framework and protocols for building domain-specific environments and interfaces, new Grid services based on emerging industry standards, and advanced networking and computing testbeds to support new Grid-based applications such as AeroDB.

Laufenberg, Larry↗
DOE OSTI2025

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗
NASA NTRS2017

Fluid Transient Analysis during Priming of Evacuated Line

Water hammer analysis in pipe lines, in particularly during priming into evacuated lines is important for the design of spacecraft and other in-space application. In the current study, a finite volume network flow analysis code is used for modeling three different geometrical configurations: the first two being straight pipe, one with atmospheric air and other with evacuated line, and the third case is a representation of a complex flow network system. The numerical results show very good agreement qualitatively and quantitatively with measured data available in the literature. The peak pressure and impact time in case of straight pipe priming in evacuated line shows excellent agreement.

Bandyopadhyay, Alak↗
NASA NTRS1987

Lewis Information Network (LINK): Background and overview

The NASA Lewis Research Center supports many research facilities with many isolated buildings, including wind tunnels, test cells, and research laboratories. These facilities are all located on a 350 acre campus adjacent to the Cleveland Hopkins Airport. The function of NASA-Lewis is to do basic and applied research in all areas of aeronautics, fluid mechanics, materials and structures, space propulsion, and energy systems. These functions require a great variety of remote high speed, high volume data communications for computing and interactive graphic capabilities. In addition, new requirements for local distribution of intercenter video teleconferencing and data communications via satellite have developed. To address these and future communications requirements for the next 15 yrs, a project team was organized to design and implement a new high speed communication system that would handle both data and video information in a common lab-wide Local Area Network. The project team selected cable television broadband coaxial cable technology as the communications medium and first installation of in-ground cable began in the summer of 1980. The Lewis Information Network (LINK) became operational in August 1982 and has become the backbone of all data communications and video.

Schulte, Roger R.↗
NASA NTRS1992

Gravity related behavior of the acellular slime mold Physarum polycephalum (7-IML-1)

The objective of the experiment is to investigate the effect of near weightlessness on a single cell. The test object is the acellular slime mold Physarum polycephalum. This cell is composed of a network of protoplastic strands which perform rhythmic contractions in the minute range. These contractions of the strands' ectoplastic walls generate the force to drive the vigorous shuttle streaming of fluid protoplasm inside the strands (hydrostatic pressure flow). A net transport of protoplasm in one direction determines the direction of the cell's locomotion itself. In this way, gravity modifies the contraction rhythm of the strands, the streaming velocity of protoplasm in the strands, and the direction of locomotion of the whole slime mold (geotaxis). The other parts of this experiment will address the major question of how this cell, which does not possess any specialized gravireceptors, gets the information about the direction of the gravity vector. Details of the experimental setup are given.

Block, I.↗
NASA NTRS1992

Dynamic modeling of fluid transmission lines of the DSN 70-meter antennas by using a lumped parameter model

Fluid transmission lines and fittings were found to significantly affect the dynamic and steady state operation of the engineering system in which they are incorporated. Therefore, a better understanding of the operation of a system can be obtained by including the performance of the transmission lines and fittings within the system model. The most accurate model of a transmission line or fitting is obtained by using a distributed parameter model. However, a distributed parameter model tends to be very complex. This complexity can be avoided without significant loss of model accuracy by using a lumped parameter model when the length of the fluid path through the transmission line or fitting is short. This article develops a lumped parameter model for short fluid transmission lines and fittings, describes the conditions under which the model is valid, and presents the model parameters associated with the servo hydraulic system of the Deep Space Network (DSN) 70-meter antennas.

Bartos, R. D.↗
NASA NTRS2017

VESsel GENeration Analysis (VESGEN): Innovative Vascular Mappings for Astronaut Exploration Health Risks and Human Terrestrial Medicine

Currently, astronauts face significant health risks in future long-duration exploration missions such as colonizing the Moon and traveling to Mars. Numerous risks include greatly increased radiation exposures beyond the low earth orbit (LEO) of the ISS, and visual and ocular impairments in response to microgravity environments. The cardiovascular system is a key mediator in human physiological responses to radiation and microgravity. Moreover, blood vessels are necessarily involved in the progression and treatment of vascular-dependent terrestrial diseases such as cancer, coronary vessel disease, wound-healing, reproductive disorders, and diabetes. NASA developed an innovative, globally requested beta-level software, VESsel GENeration Analysis (VESGEN) to map and quantify vascular remodeling for application to astronaut and terrestrial health challenges. VESGEN mappings of branching vascular trees and networks are based on a weighted multi-parametric analysis derived from vascular physiological branching rules. Complex vascular branching patterns are determined by biological signaling mechanisms together with the fluid mechanics of multi-phase laminar blood flow.

astronaut↗
NASA NTRS1998

Prospect of Continuous VLBI Measurement of Earth Rotation in Monitoring Geophysical Fluids

Large-scale mass transports in the geophysical fluids of the Earth system excite Earth's rotational variations in both length-of-day and polar motion. The excitation process is via the conservation of angular momentum. Therefore Earth rotation observations contain information about the integrated angular momentum (consisting of both the mass term and the motion term) of the geophysical fluids, which include atmosphere, hydrosphere, mantle, and the outer and inner cores. Such global information is often important and otherwise unattainable depending on the nature of the mass transport, its magnitude and time scale. The last few years have seen great advances in VLBI measurement of Earth rotation in precision and temporal resolution. These advances have opened new. areas in geophysical fluid studies, such as oceanic tidal angular momentum, atmospheric tides, Earth librations, and rapid atmospheric angular momentum fluctuations. Precision of 10 microseconds in UTI and 200 microarcseconds in polar motion can now be achieved on hourly basis. Building upon this heritage, the multi-network geodetic VLBI project, Continuous Observation of the Rotation of the Earth (CORE), promises to further these studies and to make possible studies on elusive but tell-tale geophysical processes such as oscillatory modes in the core and in the atmosphere. Currently the early phase of CORE is underway. Within a few years into the new mellinnium, the upcoming space gravity missions (such as GRACE) will measure the temporal variations in Earth's gravitational field, thus providing complementary information to that from Earth rotation study for a better understanding of global geophysical fluid processes.

Chao, Benjamin F.↗
NASA NTRS2004

Transitioning a Fundamental Research Program to Align with the NASA Exploration Initiative-Perspectives from Microgravity Combustion Science and Fluid Physics

A new Vision for Space Exploration was announced earlier this year by U.S. President George W. Bush. NASA has evaluated on-going programs for strategic alignment with this vision. The evaluation proceeded at a rapid pace and is resulting in changes to the scope and focus of experimental research that will be conducted in support of the new vision. The existing network of researchers in the physical sciences - a highly capable, independent, and loosely knitted community - typically have shared conclusions derived from their work within appropriate discipline-specific peer reviewed journals and publications. The initial result of introducing this Vision for Space Exploration has been to shift research focus from a broad coverage of numerous, widely varying topics into a research program focused on a nearly-singular set of supporting research objectives to enable advances in space exploration. Two of these traditional physical science research disciplines, Combustion Science and Fluid Physics, are implementing a course adjustment from a portfolio dominated by "Fundamental Science Research" to one focused nearly exclusively on supporting the Exploration Vision. Underlying scientific and engineering competencies and infrastructure of the Microgravity Combustion Science and Fluid Physics disciplines do provide essential research capabilities to support the contemporary thrusts of human life support, radiation countermeasures, human health, low gravity research for propulsion and materials and, ultimately, research conducted on the Moon and Mars. A perspective on how these two research disciplines responded to the course change will be presented. The relevance to the new NASA direction is provided, while demonstrating through two examples how the prior investment in fundamental research is being brought to bear on solving the issues confronting the successful implementation of the exploration goals.

Sutliff, Thomas J.↗
DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗
NASA NTRS2017

Vascular Patterning Analysis by VESGEN 2D/3D with Bioinformatics: Updates for Rodent Tissues

Fractally branching vascular systems are a complex physiological requirement shared by humans with all higher terrestrial life forms, including other vertebrates, insects, and higher land plants. Vascular trees, networks, and tree-network composites are therefore mapped and quantified by the VESsel GENeration Analysis (VESGEN) software according to weighted physiological vascular rules that include vessel connectivity, tapering and bifurcational branching. According to fluid dynamics, successful vascular transport depends upon a complex distributed system of highly regulated laminar flow. VESGEN has elucidated changes in vascular patterning resulting from inflammatory, developmental and other signaling pathways within numerous tissues of major model organisms important for Space Biology, especially for rodents. Important early stage regenerative opportunities have been identified by VESGEN vascular analysis for visual impairments in the human retina, and is currently being used for research into astronaut visual and ocular disorders associated with long duration missions. The VESGEN 2D software is a mature, automated, widely published capability for which beta testing and public release by NASA is planned for the upcoming year. Early-stage capabilities for VESGEN 3D analysis are under development for the rodent retina and intestine as prototype tissues. A prototype VESGEN 2D Bioinformatics software capability has also been developed to associate phenotypic changes in molecular expression with vascular structure and function. By new VESGEN bioinformatic innovations, expression patterns of the genetic, transcriptional, protein and other markers for regulatory molecules such as vascular endothelial growth factor (VEGF) and their receptors, often indicators of tissue oxygenation status, are co-localized with alterations in vascular pattern. Biomarkers are therefore mapped and quantified as information dimensions directly correlated with the spatial dimensions of a vascular pattern. Further important technology innovations by NASA include substantial image segmentation advances for more automated binary extraction of the grayscale vascular patterns, together with informative associated image quality assessments. Vascular mapping and quantification capabilities for the rodent retina and intestine are illustrated for VESGEN 2D, along with technology status reports on VESGEN 3D and Bioinformatic capabilities. Research partially supported by Ames Center Innovation Awards.

Parsons-Wingerter, P.↗
NASA NTRS2015

The Effects of Long Duration Bed Rest as a Spaceflight Analogue on Resting State Sensorimotor Network Functional Connectivity and Neurocognitive Performance

Long duration spaceflight has been associated with detrimental alterations in human sensorimotor systems and neurocognitive performance. Prolonged exposure to a head-down tilt position during long duration bed rest can resemble several effects of the microgravity environment such as reduced sensory inputs, body unloading and increased cephalic fluid distribution. The question of whether microgravity affects other central nervous system functions such as brain functional connectivity and its relationship with neurocognitive performance is largely unknown, but of potential importance to the health and performance of astronauts both during and post-flight. The aims of the present study are 1) to identify changes in sensorimotor resting state functional connectivity that occur with extended bed rest exposure, and to characterize their recovery time course; 2) to evaluate how these neural changes correlate with neurocognitive performance. Resting-state functional magnetic resonance imaging (rsfMRI) data were collected from 17 male participants. The data were acquired through the NASA bed rest facility, located at the University of Texas Medical Branch (Galveston, TX). Participants remained in bed with their heads tilted down six degrees below their feet for 70 consecutive days. RsfMRI data were obtained at seven time points: 7 and 12 days before bed rest; 7, 50, and 65 days during bed rest; and 7 and 12 days after bed rest. Functional connectivity magnetic resonance imaging (fcMRI) analysis was performed to measure the connectivity of sensorimotor networks in the brain before, during, and post-bed rest. We found a decrease in left putamen connectivity with the pre- and post-central gyri from pre bed rest to the last day in bed rest. In addition, vestibular cortex connectivity with the posterior cingulate cortex decreased from pre to post bed rest. Furthermore, connectivity between cerebellar right superior posterior fissure and other cerebellar regions decreased from pre bed rest to the last day in bed rest. In contrast, connectivity within the default mode network remained stable over the course of bed rest. We also utilized a battery of behavioral measures including spatial working memory tasks and measures of functional mobility and balance. These behavioral measurements were collected before, during, and after bed rest. We will report the preliminary findings of correlations observed between brain functional connectivity and behavioral performance changes. Our results suggest that sensorimotor brain networks exhibit decoupling with extended periods of reduced usage. The findings from this study could aid in the understanding and future design of targeted countermeasures to alleviate the detrimental health and neurocognitive effects of long-duration spaceflight.

Cassady, K.↗
NASA NTRS2018

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. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗
NASA NTRS2008

NASA Tech Briefs, February 2008

Topics discussed include: Optical Measurement of Mass Flow of a Two-Phase Fluid; Selectable-Tip Corrosion-Testing Electrochemical Cell; Piezoelectric Bolt Breakers and Bolt Fatigue Testers; Improved Measurement of B(sub 22) of Macromolecules in a Flow Cell; Measurements by a Vector Network Analyzer at 325 to 508 GHz; Using Light to Treat Mucositis and Help Wounds Heal; Increasing Discharge Capacities of Li-(CF)(sub n) Cells; Dot-in-Well Quantum-Dot Infrared Photodetectors; Integrated Microbatteries for Implantable Medical Devices; Oxidation Behavior of Carbon Fiber-Reinforced Composites; GIDEP Batching Tool; Generic Spacecraft Model for Real-Time Simulation; Parallel-Processing Software for Creating Mosaic Images; Software for Verifying Image-Correlation Tie Points; Flexcam Image Capture Viewing and Spot Tracking; Low-Pt-Content Anode Catalyst for Direct Methanol Fuel Cells; Graphite/Cyanate Ester Face Sheets for Adaptive Optics; Atomized BaF2-CaF7 for Better-Flowing Plasma-Spray Feedstock; Nanophase Nickel-Zirconium Alloys for Fuel Cells; Vacuum Packaging of MEMS With Multiple Internal Seal Rings; Compact Two-Dimensional Spectrometer Optics; and Fault-Tolerant Coding for State Machines.

Source record↗
NASA NTRS2014

Training Knowledge Bots for Physics-Based Simulations Using Artificial Neural Networks

Millions of complex physics-based simulations are required for design of an aerospace vehicle. These simulations are usually performed by highly trained and skilled analysts, who execute, monitor, and steer each simulation. Analysts rely heavily on their broad experience that may have taken 20-30 years to accumulate. In addition, the simulation software is complex in nature, requiring significant computational resources. Simulations of system of systems become even more complex and are beyond human capacity to effectively learn their behavior. IBM has developed machines that can learn and compete successfully with a chess grandmaster and most successful jeopardy contestants. These machines are capable of learning some complex problems much faster than humans can learn. In this paper, we propose using artificial neural network to train knowledge bots to identify the idiosyncrasies of simulation software and recognize patterns that can lead to successful simulations. We examine the use of knowledge bots for applications of computational fluid dynamics (CFD), trajectory analysis, commercial finite-element analysis software, and slosh propellant dynamics. We will show that machine learning algorithms can be used to learn the idiosyncrasies of computational simulations and identify regions of instability without including any additional information about their mathematical form or applied discretization approaches.

Samareh, Jamshid A.↗
NASA NTRS1997

Distributed-Memory Computing With the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA)

The Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA), a Navier-Stokes solver, has been modified for use in a parallel, distributed-memory environment using the Message-Passing Interface (MPI) standard. A standard domain decomposition strategy is used in which the computational domain is divided into subdomains with each subdomain assigned to a processor. Performance is examined on dedicated parallel machines and a network of desktop workstations. The effect of domain decomposition and frequency of boundary updates on performance and convergence is also examined for several realistic configurations and conditions typical of large-scale computational fluid dynamic analysis.

Riley, Christopher J.↗
DOE OSTI2024

LINAC Electrical Cabinets - Structural Analysis

Along LINAC s downstream path is a floor containing various cabinets above the LINAC. Of the dozens of cabinets are 4 specific cabinet types: PFN (pulse-forming network) Module, PFN Power Supply, Water Skid, and Marx. Each of these cabinets may contain some of the various contents that include electronics, wiring, pipe lines, circuits, conduits. In addition, high voltage and flowing fluids are also present within the cabinets. Occasionally, Lab Technicians perform maintenance and inspections of the cabinets and need to access every part, including the top section that is subject to the weight of the technician standing on top. To ensure the cabinet is structurally safe, a CAD model will be developed for each cabinet and a Finite Element Analysis (FEA) will be performed on each cabinet to confirm it is safe to stand on.

Blas, Efren↗