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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Global Assimilation of Multi-Sensor Snow Observations for Improved Characterization of Snow Processes

Snow conditions on the land surface are recognized to be key components of the global hydrological cycle as they play a critical role in the determination of local and regional climate. In many mid-latitude and high-latitude regions, the seasonal water storage and associated spring snowmelt dominate the local hydrology. The contribution to the runoff and moisture conditions from snow is vital in supporting agriculture and in determining water resources management practices. Consequently, accurate characterization of snow properties becomes important for both end-use applications and weather and climate research. Recently a joint effort between the u.S. Air Force and NASA has enabled a blended, multi-sensor snow product known as the AFWA NASA Snow Algorithm (ANSA). This global snow dataset has been generated by utilizing the Earth Observation System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) datasets. ANSA product includes estimates of snow cover extent, snow water equivalent (SWE) and SWE-derived snow depth fields. The MODIS-based products enable snow cover mappings under cloud-free conditions whereas the passive microwave data from AMSR-E provides measurements under cloudy conditions. These remotely-sensed snow observations are further augmented with the information from ground-based snow measurements through data fusion techniques. The resulting ANSA products are employed in the NASA Land Information System (LIS) data assimilation framework, which provides a comprehensive environment for integrating community land surface models, ground and satellite-based observations, and ensemble-based data assimilation tools. LIS incorporates the multisensor ANSA snow retrievals with the land surface model estimates to generate spatially and temporally continuous estimates of snow states, through data assimilation. A suite of experiments to assimilate ANSA snow cover, SWE and snow depth estimates with different land surface models in LIS are conducted and the resulting estimates of snow conditions are evaluated against a number of in-situ observational datasets, over several regions of the world. These evaluations are used to compare and contrast the advantages and disadvantages of these multi-sensor snow observations.

Kumar, Sujay↗
NASA NTRS2007

Advanced Image Processing for NASA Applications

The future of space exploration will involve cooperating fleets of spacecraft or sensor webs geared towards coordinated and optimal observation of Earth Science phenomena. The main advantage of such systems is to utilize multiple viewing angles as well as multiple spatial and spectral resolutions of sensors carried on multiple spacecraft but acting collaboratively as a single system. Within this framework, our research focuses on all areas related to sensing in collaborative environments, which means systems utilizing intracommunicating spatially distributed sensor pods or crafts being deployed to monitor or explore different environments. This talk will describe the general concept of sensing in collaborative environments, will give a brief overview of several technologies developed at NASA Goddard Space Flight Center in this area, and then will concentrate on specific image processing research related to that domain, specifically image registration and image fusion.

LeMoign, Jacqueline↗
DOE OSTI2024

Distributed strain sensing using Bi-metallic coated fiber optic sensors embedded in stainless steel

Silica fiber optic sensors are resistant to corrosive environments and high temperatures, making them attractive candidates for harsh conditions found in nuclear and aerospace industries. Moreover, fibers can be deployed remotely for continuous measuring of spatially distributed temperatures and strains. Here, this study investigated embedding a Ni/Cu bi-metallic coated fiber in a stainless-steel 316 (SS316) matrix using laser powder bed fusion towards functionalizing metal components for site-specific health monitoring. The embedded fiber was continuously interrogated during controlled heating to 1000°C. The measured fiber strains were similar to the expected differential thermal strains between the fiber and the SS316 matrix, until divergent behavior was observed at temperatures >500°C. No debonding at the matrix–coating–fiber interfaces was observed during microscopy, but significant interactions between the coatings and matrix resulted in diffusion-driven chemistry variations and Kirkendall void formation. Applying the strain-lag theory revealed plastic behavior in the Ni coating at temperatures >500°C, limiting the strain transfer to the fiber at higher temperatures. It was estimated that the elastic modulus in the Ni coating had decreased from ~200 GPa at room temperature to below 40 GPa, starting at 600°C. The low elastic modulus above 600°C is within the margin of what the tangent modulus would be in the case of bilinear isotropic hardening. Regardless of the divergent strain transfer at higher temperatures, the fiber was exposed to the equivalent of 1.9 % engineering strain at 1000°C, but measured only a 0.7 % engineering strain due to the poor strain transfer. Although compensating for the plastic behavior of Ni proved challenging, the bonding of a brittle silica fiber to a metal matrix surviving to 1000°C invites potential iterations on coating material for future application. For example, the embedded fiber is sufficient for acoustic energy transfer, realizing high temperature distributed acoustic sensing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optical and x-ray characterization of the Daedalus ultrafast x-ray imager

The Daedalus ultrafast x-ray imager is the latest generation in Sandia’s hybrid CMOS detector family. With three frames along an identical line of sight, 1 ns minimum integration time, a higher full well than Icarus, and added features, Daedalus brings exciting new capabilities to diagnostic applications in inertial confinement fusion and high energy density science. In this work, we present measurements of time response, dynamic range, spatial uniformity, pixel cross-talk, and absolute x-ray sensitivity using pulsed optical and x-ray sources. Here, we report a measured 1.5 Me — full well, pixel sensitivity at 9.58 × 10 —7 V/e — , and an estimate of spatial uniformity at ~5% across the sensor array.

47 OTHER INSTRUMENTATION↗
NASA NTRSDate not supplied

Pushing the Application Limit of Hyperspectral IR Sounder Remote Sensing for Frontiers of Weather and Climate Studies

Since the launch of AQUA Atmospheric Infrared Sounder (AIRS) in 2002, space-borne hyperspectral infrared (IR) sounders have provided information-rich, climate quality, and time-continuous spectral radiance measurements at the top of the atmosphere for more than two decades. Satellite based hyperspectral IR sensors, including AIRS, Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI), have demonstrated their critical roles for various weather applications and climate related studies. New use of hyperspectral IR sounder data will focus on underexplored areas. Current use of those data is generally limited by the capability of fully exploring and exploiting high spectral resolution information content, achieving radiometric accuracy and stability defined by the instruments, and merging data from different sensors to build a consistent, long-term climate data record. We will present the on-going research efforts at NASA Langley Research Center (LaRC) to develop novel hyperspectral IR sounder data products to address those potentials yet to be fully realized. Some applications of these products, including the study of Planetary Boundary Layer (PBL), the construction of climate data record over polar region, and the fusion of AIRS and CrIS data via the radiometric consistent climate fingerprinting method will be discussed.

Wan Wu↗
DOE OSTI2023

Fully Distributed Acoustic and Magnetic Field Monitoring Via a Single Fiber Line for Optimized Production of Unconventional Resource Plays

This is the final technical report for the Virginia Tech Center for Photonics Technology (VT-CPT) research project entitled “Fully Distributed Acoustic and Magnetic Field Monitoring Via a Single Fiber Line for Optimized Production of Unconventional Resource Plays”. In the the four-year effort, VT-CPT collaborated with the Departments of Mathmatics at VT and Sentek Instrument, LLC (Sentek) to develop a fiber-optic sensing system capable of real-time simultaneous distributed measurement of multiple subsurface, drilling, and production parameters. The ultra-sensitive fiber optic distributed acoustic sensing technology, picoDAS, developed by Sentek was integrated with a novel multi-material optical sensing fiber fabricated by CPT-VT to obtain distributed acoustic and magnetic field measurements with ultrahigh sensitivity and high spatial resolution. The sensing technology successfully developed and demonstrated under this research program is truly unique has application beyond subsurface imaging to include carbon storage and electrical grid monitoring, as well as healthcare and nuclear fusion. The project, sponsored by the Advanced Technology Program, began in 2019 and had the original goal of developing the next generation of harsh environment sensing systems. Exhaustive theoretical modeling was performed to optimize the multi-material optical fiber design. VT developed the processing techniques employed to successfully fabricate single mode fibers with metal cladding wires. Sensing fibers with nickel and Metglas cladding wires were fully characterized, inscribed with sensors, and fully integrated with the picoDAS system. Laboratory scale testing was performed to demonstrate performance upon exposure to lateral and transverse magnetic fields. Preliminary field trials were performed for prototype magnetic and acoustic sensing systems upon near surface deployment at VT. The distributed acoustic and magnetic field sensing system was advanced from a (Technology Readiness Level) TRL=2 to a TRL=5 via laboratory scale system validation in relevant environments. The truly one-of-its kind distributed magnetic and acoustic sensing system is expected to find immediate applications that require subsurface monitoring, to include carbon storage site monitoring, and has the potential to be a distributive technology in healthcare and electric grid markets. The technologies developed by Virginia Tech’s Center for Photonics Technology will support the mission of the National Energy Technology Laboratory (NETL) to drive innovation and deliver solutions for an environmentally sustainable and prosperous energy future by ensuring affordable, abundant, and reliable energy that drives a robust economy and national security. Technical accomplishments during the program are summarized briefly here and described in detail in the remainder of the report.

02 PETROLEUM↗
NASA NTRS1982

Adaptive materials processing

New sensors for both arc welding and resistance spot welding processes were studied. For arc welding, the arc itself is used as the sensor. Minor changes in both composition and geometry of the plasma result in fluctuations in the arc voltage. While the process as currently employed is an off-line system, development of an on-line process controller would be possible once the optimum analysis technique is developed. It has been shown that for resistance spot welding, the dynamic resistance of the weld joint can be correlated to growth of the fusion zone. It is indicated that this technique is not as useful for projection welds as for spot welds.

Eager, T. W.↗
NASA NTRSDate not supplied

Design of a GRCop-42 Regeneratively Cooled Thrust Chamber Assembly and Feed System

An additively manufactured thrust chamber assembly was designed and printed using laser powder bed fusion. The NASA-developed material GRCop-42 was used for its high strength and high temperature characteristics. The combustion chamber and nozzle utilize regenerative coolant channels to achieve long duration hot fire tests. Design for additive manufacturing was used to create geometries not easily attainable with traditional machining. The thrust chamber assembly was post processed to ensure print quality and verify for testing. A feed system – encompassing all propellant lines, valves, sensors, and tanks – was designed and built to enable steady state, pressure regulated testing. Pressure transducers, thermocouples, and load cells were placed to enable measurements of the propellant properties and engine performance. Venturis and orifices were used throughout the system to control the flow rates to the thrust chamber. Aside from providing propellants, the feed system provides pressure using nitrogen, and purges the lines during shutdown. The design process for this testing platform is described. A testing campaign on the hardware will be conducted by the Akronauts Rocket Design Team at the University of Akron in Spring 2023.

Dillon M. Petty↗
NASA NTRSDate not supplied

Design of a GRCop-42 Regeneratively Cooled Thrust Chamber Assembly and Feed System

An additively manufactured thrust chamber assembly was designed and printed using laser powder bed fusion. The NASA-developed material GRCop-42 was used for its high strength and high temperature characteristics. The combustion chamber and nozzle utilize regenerative coolant channels to achieve long duration hot fire tests. Design for additive manufacturing was used to create geometries not easily attainable with traditional machining. The thrust chamber assembly was post processed to ensure print quality and verify for testing. A feed system – encompassing all propellant lines, valves, sensors, and tanks – was designed and built to enable steady state, pressure regulated testing. Pressure transducers, thermocouples, and load cells were placed to enable measurements of the propellant properties and engine performance. Venturis and orifices were used throughout the system to control the flow rates to the thrust chamber. Aside from providing propellants, the feed system provides pressure using nitrogen, and purges the lines during shutdown. The design process for this testing platform is described. A testing campaign on the hardware will be conducted by the Akronauts Rocket Design Team at the University of Akron in Spring 2023.

Dillon M. Petty↗
DOE OSTI2020

Report on Progress of correlation of in-situ and ex-situ data and the use of artificial intelligence to predict defects

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear components which will be assembled into a fully functional microreactor core. Compared with traditional manufacturing technologies, AM technologies allow (1) real-time observation of the manufacturing process at a much higher resolution using in-situ monitoring technologies to capture the sensor signatures that scientifically describe each event occurring over time and space and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on in-situ and ex-situ data correlation and associated data analytics results. Examples are provided to illustrate progress with respect to laser powder bed fusion (L-PBF), binder jetting, computed tomography (CT) reconstruction, and mechanical testing. Elements of the Digital Thread and data management infrastructure are discussed in the main document, and an extensive supplemental appendix is provided detailing the Digital Platform, as well as its implementation and subcomponents. In conclusion, the path forward for the next fiscal year is also discussed.

42 ENGINEERING↗
DOE OSTI2023

Spin-squeezing for improving optical quantum sensors [Slides]

Spin-squeezing brief overview: Very high levels were achieved in various experiments; In the field of magnetometry, a very moderate spin squeezing was demonstrated (Romalis, Polzik, etc.); At some conditions, significant improvement is possible and this motivates our project; We proposed a significant spin squeezing demonstration for atomic magnetometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing

Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. Here, this study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg –1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R 2 = 0.96, RMSE = 30.81 g·kg –1 ), mineral soils (SOC ≤ 120 g·kg –1 , R 2 = 0.71, RMSE = 10.60 g·kg –1 ), and organic soils (SOC > 120 g·kg –1 , R 2 = 0.78, RMSE = 62.31 g·kg –1 ). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m 3 ·m –3 , green leaf area < 0.3 m 2 ·m –2 , plant residue <0.4 m 2 ·m –2 ) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Infrared sensors and systems for enhanced vision/autonomous landing applications

There exists a large body of data spanning more than two decades, regarding the ability of infrared imagers to 'see' through fog, i.e., in Category III weather conditions. Much of this data is anecdotal, highly specialized, and/or proprietary. In order to determine the efficacy and cost effectiveness of these sensors under a variety of climatic/weather conditions, there is a need for systematic data spanning a significant range of slant-path scenarios. These data should include simultaneous video recordings at visible, midwave (3-5 microns), and longwave (8-12 microns) wavelengths, with airborne weather pods that include the capability of determining the fog droplet size distributions. Existing data tend to show that infrared is more effective than would be expected from analysis and modeling. It is particularly more effective for inland (radiation) fog as compared to coastal (advection) fog, although both of these archetypes are oversimplifications. In addition, as would be expected from droplet size vs wavelength considerations, longwave outperforms midwave, in many cases by very substantial margins. Longwave also benefits from the higher level of available thermal energy at ambient temperatures. The principal attraction of midwave sensors is that staring focal plane technology is available at attractive cost-performance levels. However, longwave technology such as that developed at FLIR Systems, Inc. (FSI), has achieved high performance in small, economical, reliable imagers utilizing serial-parallel scanning techniques. In addition, FSI has developed dual-waveband systems particularly suited for enhanced vision flight testing. These systems include a substantial, embedded processing capability which can perform video-rate image enhancement and multisensor fusion. This is achieved with proprietary algorithms and includes such operations as real-time histograms, convolutions, and fast Fourier transforms.

Kerr, J. Richard↗
DOE OSTI2021

Data Fusion to Enhance Quality Control and Analysis with Instruments at the Marine and Coastal Research Laboratory

Deploying environmental monitoring instruments in the marine environment can be challenging, facing challenges around device survivability, biofouling and corrosion, and consistent data collection. This project explores the use of data fusion – the process of integrating multiple data sources to produce more consistent, accurate, and useful information – to build a consistent long-term monitoring system at the Marine and Coastal Research Laboratory (MCRL) in Sequim, Washington. Unused instruments that had been acquired from past projects were inventoried and deployments planned on the MCRL pier and floating dock. A total of 8 instruments were deployed including a tide gauge, hydrophone, acoustic Doppler current profiler (ADCP), photosynthetically active radiation (PAR) sensors, meteorological station, and three water quality sensors. Deployments were planned to be well-protected around the pier structure and a maintenance schedule was created for cleaning and recalibration. An automated data pipeline was created to aggregate data on edge computers that push data to Amazon Web Services (AWS) cloud storage every 15 minutes, performing automated quality control and data transformations using the Time Series Data Analytical Toolkit (TSDAT). Continued efforts are underway to maintain this system into the future, take a data-driven approach to maintenance scheduling, improve the reliability of the system, and share the data with a variety of end-users.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Comparing Low-Cost Sensors with Ground-Based and Airborne In-Situ and Column Observations of NO2 and HCHO during the FRAPPE Field Campaign in Colorado, USA

Even in the presence of more reliable air quality tools, low-cost sensors have the benefit of recording data on highly localized spatial and temporal scales, allowing for multiple measurements within a single satellite pixel and on pixel boundaries. However, they are less accurate than their regulatory-grade counterparts, requiring regular co-locations with accepted instruments to ensure their validity. Thus, the addition of low-cost sensors to a field campaign – where reference-grade air quality instruments are abundant – not only provides ample opportunities for low-cost sensor co-location and calibration, but also allows the low-cost instruments to be used for sub-pixel validation, covering more surface area than the regulatory instruments alone with a network of sensors. During the summer of 2014, our low-cost sensor network was deployed during the Front Range Air Pollution and Photochemistry Experiment (FRAPPÉ) campaign conducted to sample the composition of air at and above ground level in northeastern Colorado, USA. The low-cost sensor platform included a suite of gas-phase sensors, notably NO2 and two generalized volatile organic compound (VOC) sensors, which were leveraged together to quantify speciated hydrocarbons such as formaldehyde. These key pollutants were chosen for their impacts on human health and climate change, as well as their inclusion on the TEMPO satellite launching this year. Airborne campaign measurements included slant column optical observations of formaldehyde (HCHO), nitrogen dioxide (NO2), and ozone (O3). Myriad additional in-situ instruments described chemical composition up to approximately 5 km above surface level. Ground-based instrumentation included both stationary and mobile regulatory-grade instruments, which were used for sensor calibration. Machine learning techniques such as artificial neural networks (ANNs) were used to match the low-cost signals to that of the reference-grade instruments. Here, we compare calibrated low-cost sensor data collected at ground level in a variety of locations along Colorado’s Front Range to various data sources from the FRAPPÉ campaign to better understand how well airborne and regulatory ground-based measurements can be extrapolated to other locations. Further, as the slant column measurements act as satellite simulators, we explore how low-cost instruments can be used for satellite validation purposes. Comparisons among these different data types also have important implications in data fusion.

Kristen Okorn↗
DOE OSTI2025

In situ Visible Light and Thermal Imaging Data from a Laser Powder Bed Fusion Additive Manufacturing Process Co-Registered to X-ray Computed Tomography and Fatigue Data

This dataset is comprised of in situ sensing data collected during a laser-based powder bed fusion additive manufacturing process, as well as rasterized scan path information, post-build X-ray computed tomography (XCT), and fatigue test results. A total of 64 cylinders, approximately 15 mm in diameter and 102 mm tall, were printed out of stainless steel 316H on a Colibrium Additive Concept Laser M2 Series 5 machine. Parameters known to produce dense material were used to construct 56 of these cylinders, while the remaining 8 cylinders were printed with relatively high energy density parameters prone to producing keyhole pores. In addition, two spatter generation blocks were constructed upstream of the 64 cylinders such that ejecta produced during the melting of the spatter generators were stochastically seeded onto the 64 cylinders. Based on previous experiments, these spatter particles were theorized to produce stochastic lack-of-fusion pores. During the construction of the build, high-resolution images of reflected light in the visible spectrum were captured both before and after recoating for each print layer. Additionally, temporally integrated thermal imaging in the near infrared spectrum produced integrated sum and max images on a layerwise basis. The multimodal in situ data has been co-registered to the build plate coordinate system, allowing for identification of process anomalies (e.g., spatter particles) apparent in the two sensors. Following construction of the build, the cylinders were subjected to XCT to identify internal flaws, and the resulting data have also been registered to the build plate coordinate system. Finally, 60 of the 64 cylinders were machined into fatigue coupons conforming to ASTM E466 and subsequently subjected to either high- or -low-cycle fatigue testing. The results of the fatigue tests have also been included in the dataset, and the XCT data corresponded to the approximate location of the gauge sections of the machine fatigue specimen geometry.

42 ENGINEERING↗
DOE OSTI2024

Adamantine 1.0: A Thermomechanical Simulator for Additive Manufacturing

Adamantine is a thermomechanical simulation code that is written in C++ and built on top of deal.II (Arndt et al., 2023), p4est (Burstedde et al., 2011), ArborX (Lebrun-Grandié et al., 2020), Trilinos (The Trilinos Project Team, 2020), and Kokkos (Trott et al., 2022). Adamantine was developed with additive manufacturing in mind and it is particularly well adapted to simulate fused filament fabrication, directed energy deposition, and powder bed fusion. Adamantine employs the finite element method with adaptive mesh refinement to solve a nonlinear anisotropic heat equation, enabling support for various additive manufacturing processes. It can also perform elastoplastic and thermoelastoplastic simulations. It can handle materials in three distinct phases (solid, liquid, and powder) to accurately reflect the physical state during different stages of the manufacturing process. To enhance simulation accuracy, adamantine incorporates data assimilation techniques (Asch et al., 2016). This allows it to integrate experimental data from sensors like thermocouples and infrared (IR) cameras. This combined approach helps account for errors arising from input parameters, material properties, models, and numerical calculations, leading to more realistic simulations that reflect what occurs in a particular print.

36 MATERIALS SCIENCE↗