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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Adaptive Modeling of the International Space Station Electrical Power System

Software simulations provide NASA engineers the ability to experiment with spacecraft systems in a computer-imitated environment. Engineers currently develop software models that encapsulate spacecraft system behavior. These models can be inaccurate due to invalid assumptions, erroneous operation, or system evolution. Increasing accuracy requires manual calibration and domain-specific knowledge. This thesis presents a method for automatically learning system models without any assumptions regarding system behavior. Data stream mining techniques are applied to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). We also explore a knowledge fusion approach that uses traditional engineered EPS models to supplement the learned models. We observed that these engineered EPS models provide useful background knowledge to reduce predictive error spikes when confronted with making predictions in situations that are quite different from the training scenarios used when learning the model. Evaluations using ISS sensor data and existing EPS models demonstrate the success of the adaptive approach. Our experimental results show that adaptive modeling provides reductions in model error anywhere from 80% to 96% over these existing models. Final discussions include impending use of adaptive modeling technology for ISS mission operations and the need for adaptive modeling in future NASA lunar and Martian exploration.

Thomas, Justin Ray↗
NASA NTRS1993

Automated Welding System

Fully automated variable-polarity plasma arc VPPA welding system developed at Marshall Space Flight Center. System eliminates defects caused by human error. Integrates many sensors with mathematical model of the weld and computer-controlled welding equipment. Sensors provide real-time information on geometry of weld bead, location of weld joint, and wire-feed entry. Mathematical model relates geometry of weld to critical parameters of welding process.

Bayless, E. O.↗
NASA NTRS2020

Probabilistic Modeling of Heavy Machinery Using Machine Learning

NASA Glenn Research Center’s facility operations seeks to leverage its extensive instrumentation and historical data with machine learning to increase system efficiencies. The ultimate goal of this effort is to probabilistically model the behavior of Glenn’s central air service compressors for optimal decision making and planning. This project is a first step in that direction. We propose a multimodel approach that uses high-dimensional models to ask simple questions about complex dependent structures, and low-dimensional models to ask complex questions about simple dependent structures. While the low-dimensional models make strong assumptions, they can be visualized and they can be insightful. We show good fits for univariate models of compressor sensors, and preliminary work on high-dimensional multivariate models.

Machine learning↗
NASA NTRS1991

Opportunities for using the EOS Imaging Spectrometers and synthetic aperture radar in ecological models

Several promising approaches to assessing biochemical and architectural properties of landscapes are outlined. Strategies for using new EOS sensors in ecological models are examined. Ways in which ecological and remote sensing models can utilize information provided by the new sensors to characterize ecological properties at coarse scales and to estimate within-ecosystem properties are addressed.

Ustin, Susan L.↗
NASA NTRS1995

Earth horizon modeling and application to static Earth sensors on TRMM spacecraft

Data from Earth sensor assemblies (ESA's) often are used in the attitude determination (AD) for both spinning and Earth-pointing spacecraft. The ESA's on previous such spacecraft for which the ground-based AD operation was performed by the Flight Dynamics Division (FDD) used the Earth scanning method. AD on such spacecraft requires a model of the shape of the Earth disk as seen from the spacecraft. AD accuracy requirements often are too severe to permit Earth oblateness to be ignored when modeling disk shape. Section 2 of this paper reexamines and extends the methods for Earth disk shape modeling employed in AD work at FDD for the past decade. A new formulation, based on a more convenient Earth flatness parameter, is introduced, and the geometric concepts are examined in detail. It is shown that the Earth disk can be approximated as an ellipse in AD computations. Algorithms for introducing Earth oblateness into the AD process for spacecraft carrying scanning ESA's have been developed at FDD and implemented into the support systems. The Tropical Rainfall Measurement Mission (TRMM) will be the first spacecraft with AD operation performed at FDD that uses a different type of ESA - namely, a static one - containing four fixed detectors D(sub i) (i = 1 to 4). Section 3 of this paper considers the effect of Earth oblateness on AD accuracy for TRMM. This effect ideally will not induce AD errors on TRMM when data from all four D(sub i) are present. When data from only two or three D(sub i) are available, however, a spherical Earth approximation can introduce errors of 0.05 to 0.30 deg on TRMM. These oblateness-induced errors are eliminated by a new algorithm that uses the results of Section 2 to model the Earth disk as an ellipse.

Keat, J.↗
NASA NTRS1994

Highly transparent and rugged sensor for velocity determinations of cosmic dust particles

In order to understand the evolution of interplanetary dust, numerous dust particles have been collected and analyzed. An analysis of the composition often provides information on the particle's origin. So does its origin. Composition and orbit data complement each other and should be determined together. If the last orbit of a particle can be determined, its orbital history can often be calculated backward in time and associated with its parent body. To determine the last orbit, the velocity needs to be measured before the particle is collected. The precision required in determining the velocity components relative to the spacecraft should be 1 percent or better. A sensor for naturally charged cosmic dust particles is discussed. Two models of the sensor were tested, one with a free-falling steel ball and the other with particles accelerated to high speed. Analytic expressions of the sensor signals are presented and compared with the test results. The errors in speed and angle were estimated to be about 0.3 percent and 0.2 degrees respectively.

Auer, Siegfried↗
NASA NTRS2017

Narrowband Rejection of Reaction Wheel and Environmental Disturbances for the WFIRST OMC Testbed

The Wide Field Infrared Survey Telescope (WFIRST) is a NASA observatory with two scientific instruments. The first is the Wide Field Instrument (WFI) designed to perform wide field imaging and surveys of the near infrared (NIR) sky. The second is a coronagraph (CGI) that will enable astronomers to detect and measure properties of planets in other solar systems. The coronagraph requires 0.5 milli-arcsecond (mas) RMS pointing per axis of the line of sight (LOS) to achieve a contrast of 1x10-9. This paper discusses the approach used for achieving this level of pointing performance on the occulting mask coronagraph (OMC) testbed at JPL. This testbed uses a low-order wavefront sensing (LOWFS) camera and fast steering mirror (FSM) to suppress injected LOS jitter and environmental LOS jitter. The injected jitter includes representative broadband spacecraft attitude control system (ACS) LOS motion and tonal LOS jitter caused by the reaction wheel assemblies (RWA). The environmental jitter includes thermal LOS variations and harmonics of the line noise. The LOWFS camera uses high flux from the obscured science target to achieve high rate measurements of the LOS. These measurements are augmented with fictitious RWA tachometer information and an estimate of the line noise frequency. A LOS servo using a combination of feedback and feedforward control is used in the testbed to compensate for all of the disturbance sources and to mitigate jitter caused by in-band camera noise. The feedforward uses a novel robust least mean squares (RLMS) filter algorithm to reject the RWA and line frequency tones. High fidelity models of the sensors, disturbances and actuator are presented in this paper. These models were developed as part of a simulation of the LOS control system. Performance results from the hardware testbed at the Jet Propulsion Laboratory (JPL) are discussed in this paper.

Shields, Joel↗
NASA NTRS1972

Thin film oxygen partial pressure sensor

The development is described of a laboratory model oxygen partial pressure sensor using a sputtered zinc oxide thin film. The film is operated at about 400 C through the use of a miniature silicon bar. Because of the unique resistance versus temperature relation of the silicon bar, control of the operational temperature is achieved by controlling the resistance. A circuit for accomplishing this is described. The response of sputtered zinc oxide films of various thicknesses to oxygen, nitrogen, argon, carbon dioxide, and water vapor caused a change in the film resistance. Over a large range, film conductance varied approximately as the square root of the oxygen partial pressure. The presence of water vapor in the gas stream caused a shift in the film conductance at a given oxygen partial pressure. A theoretical model is presented to explain the characteristic features of the zinc oxide response to oxygen.

Wortman, J. J.↗
NASA NTRS2010

Final Plenary Session Transcript

Let s start with the report - as you know - when we are talking about flow control it is a multi-disciplinary type of work. So it involves many people and disciplines. This group first discussed the important issues associated with flow control. As you start doing flow control what are the issues to which you really have to start paying attention? That is the first part I am going to present. Then in the second part I will present some challenges - problems that we should really be looking at. So as far as the issues - if you want to control a flow, you really need to understand the flow physics, because anything that you do comes from the flow physics. The design of the controllers, your decisions on the actuators, sensors, reduced order modeling and all of that, would be helped if you understand flow physics. And you have to have a specific objective - what exactly are you controlling? Are you trying to reduce drag, eliminate separation, reduce noise, enhance mixing? So you have to have very specific control objectives. From all the talks we have seen here actuation is extremely important and it is very problem specific. It depends on what problem you are dealing with so you have to design and build actuators for that specific problem. Sensors obviously are very important, especially when you are dealing with feedback control. Consensus was that when you dealing with flow control, you must take an integrated approach; from the beginning you have to take into account every aspect of it and even maybe to modify your experiment, your geometry, to go along with the actuation, sensors and control models. Development of tools is very important in this multi-disciplinary problem. The tools include CFD, reduced order modeling, controller design, understanding and utilizing the instabilities of the flow, etc. So, in order to have success in flow control, we really need to develop these tools.

Gostelow, Paul↗
DOE OSTI2026

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Active Wave Propagation and Sensing in Plates

Health monitoring of aerospace structures can be done using an active interrogation approach with diagnostic Lamb waves. Piezoelectric patches are often used to generate the waves, and it is helpful to understand how these waves propagate through a structure. To give a basic understanding of the actual physical process of wave propagation, a model is developed to simulate asymmetric wave propagation in a panel and to produce a movie of the wave motion. The waves can be generated using piezoceramic patches of any size or shape. The propagation, reflection, and interference of the waves are represented in the model. Measuring the wave propagation is the second important aspect of damage detection. Continuous sensors are useful for measuring waves because of the distributed nature of the sensor and the wave. Two sensor designs are modeled, and their effectiveness in measuring acoustic waves is studied. The simulation model developed is useful to understand wave propagation and to optimize the type of sensors that might be used for health monitoring of plate-like structures.

Ghoshal, Anindya↗
DOE OSTI2024

Hydrogen Leak Modeling for Development of Smart Distributed Monitoring Under Unintended Releases

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower. Hydrogen has the potential to play a crucial role in decarbonizing industrial processes that are currently reliant on fossil fuels and provide long-duration and/or seasonal energy storage to enable electricity decarbonization. Hydrogen can also be used as a fuel for fuel cell vehicles, providing a zero-emission alternative to traditional internal combustion engines. DOE launched the Hydrogen Energy Earthshot (Hydrogen Shot) in June 2021 to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). While promising, Hydrogen is highly-flammable, and in the presence of oxygen, it can form explosive mixtures. . Therefore, understanding leak scenarios is essential to evaluate and mitigate the safety risks associated with potential hydrogen leaks. An increased understanding of leak behavior, and having tools to model leaks, can help assess how hydrogen would disperse in different environments, influencing emergency response plans and safety measures, and identify potential issues with materials and design systems that can withstand the challenges posed by hydrogen. Recently, researchers have attempted to study hydrogen leaks for development of risk management strategies. However, the focus has been on closed or semi-closed spaces like storage rooms, vehicles, garages, and fueling stations - all promising locations for future hydrogen infrastructure. In this presentation, the modeling environment extends the span of research further by modeling hydrogen leak in an outdoor, open space. We will present the key challenges with modeling hydrogen leaks in an uncontrollable environment, how they were handled, and how modeling results informed sensor selection and placement. A Hydrogen research facility at the National Renewable Energy Laboratory (NREL) was used as a case study to model hydrogen leaks. In the future, Hydrogen wide area detection methodologies will be developed and tested at this site to monitor for unintended and operational hydrogen releases. The data generated from modeling will be used to develop a predictive model to detect hydrogen leak location based on concentration measured by sensors in this open space. Furthermore, the facility was also chosen because controlled hydrogen releases can be performed. A computational fluid dynamics (CFD) based modeling approach was taken to model hydrogen leak. The full-scale hydrogen facility was modeled with a large ambient domain. The electrolyzer at the facility can produce a controlled release rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind direction, wind speed at various altitudes, and temperature were used as inputs to the model. To capture the variability of weather conditions, a subset of the weather conditions experienced during daytime hours without precipitation over the course of three months was generated; using established data clustering techniques, a total of 100 condition sets were chosen. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. These distributions are compared to experimental data from a constant mass flow, controlled hydrogen release at the facility. The stochastic wind conditions of the release make direct validation difficult, therefore, statistical comparison approaches were used. Wind conditions are found to significantly impact the release behavior, including direction and concentration. Sensor selection and placement is proposed for the facility and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions. Hydrogen holds great promise as a renewable energy fuel, but ensuring safety in its production, storage, and use is paramount. Studying potential leak scenarios in an open space will help develop sensors to detect hydrogen on a large spectrum of concentration and eventually build a smart distributed monitoring system.

CFD↗
NASA NTRS1974

Remote sensing of land use changes in US metropolitan regions: Techniques of analysis and opportunities for application

A graphic description is given of the Census Cities ERTS experiment in urban change detection using remote sensors. The relationship or model between land use data from sensors and socio-demographic data from the census is partly demonstrated. The example suggests how knowledge of land use changes acquired by sensors can be used to make estimates of population, and other attributes. The feasibility of nationwide mapping of land use, and land use changes, by direct computer classification of ERTS-1 multispectral digital data is also demonstrated. Potential applications in state and regional planning are many, and some are named. But the longer-range gains are likely to be improved understanding by legislators, managers and voters as to what it is that makes the country tick. One of the specific tasks could be the allocation of revenues to be shared.

Wray, J. R.↗
NASA NTRS2003

Satellite remote sensing of landscape freeze/thaw state dynamics for complex Topography and Fire Disturbance Areas Using multi-sensor radar and SRTM digital elevation models

We characterize differences in radar-derived freeze/thaw state, examining transitions over complex terrain and landscape disturbance regimes. In areas of complex terrain, we explore freezekhaw dynamics related to elevation, slope aspect and varying landcover. In the burned regions, we explore the timing of seasonal freeze/thaw transition as related to the recovering landscape, relative to that of a nearby control site. We apply in situ biophysical measurements, including flux tower measurements to validate and interpret the remotely sensed parameters. A multi-scale analysis is performed relating high-resolution SAR backscatter and moderate resolution scatterometer measurements to assess trade-offs in spatial and temporal resolution in the remotely sensed fields.

synthetic aperture radars (SARs)↗