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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Colloidal State Machines as Smart Tracers for Chemical Reactor Analysis

A widely utilized tool in reactor analysis is passive tracers that report the residence time distribution, allowing estimation of the conversion and other properties of the system. Recently, advances in microrobotics have introduced powered and functional entities with sizes comparable to some traditional tracers. This has motivated the concept of Smart Tracers that could record the local chemical concentrations, temperature, or other conditions as they progress through reactors. Herein, the design constraints and advantages of Smart Tracers by simulating their operation in a laminar flow reactor model conducting chemical reactions of various orders are analyzed. It is noted that far fewer particles are necessary to completely map even the most complex concentration gradients compared with their conventional counterparts. Design criteria explored herein include sampling frequency, memory storage capacity, and ensemble number necessary to achieve the required accuracy to inform a reactor model. Cases of severe particle diffusion and sensor noise appear to bind the functional upper limit of such probes and require consideration for future design. The results of the study provide a starting framework for applying the new technology of microrobotics to the broad and impactful set of problems classified as chemical reactor analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

High precision control and deep learning-based corn stand counting algorithms for agricultural robot

This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm designed to enable an ultra-compact ground robot to count corn stands by driving through the fields autonomously. The algorithm leverages a deep network to detect corn plants in images, and a visual tracking model to re-identify detected objects at different time steps. We collected data from 53 corn plots in various fields for corn plants around 14 days after emergence (stage V3 - V4). The robot predictions have agreed well with the ground truth with C robot =1.02×C human -0.86 and a correlation coefficient R=0.96. The mean relative error given by the algorithm is -3.78%, and the standard deviation is 6.76%. These results indicate a first and significant step towards autonomous robot-based real-time phenotyping using low-cost, ultra-compact ground robots for corn and potentially other crops.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Optimization of the number and locations of the calibration stations needed to monitor soil moisture using distributed temperature sensing systems: A proof-of-concept study

The single-probe heat-pulse (SPHP) technique combined with the Fiber-optic Distributed Temperature Sensing (DTS) technology can offer novel high-resolution measurements of soil moisture (θ) over spatial scales ranging from several centimeters to several kilometers. However, the key limitation of this method is in obtaining the calibration relationship between θ and soil thermal conductivity (λ) across a specific field. In a previous study, a new methodology using a Gaussian processes model was presented to account for the spatial variability in the λ-θ relationship. The model aggregated θ measurements from soil moisture sensors scattered over the SPHP transect with the corresponding DTS λ measurements at their locations. In this study, a novel methodology is tested to optimize the number and locations of soil moisture sensors required to account for the spatial variability of the λ - θ relationship to achieve higher accuracy from the SPHP technique. The proposed methodology utilizes hierarchical clustering to analyze the information contained in the spatial structure of the SPHP measurements as the soil dries from a nearly-saturated condition. The proposed methodology was tested using data from a field in Oklahoma. Monte-Carlo simulation was performed to validate the performance of the proposed methodology. The predictions obtained from the proposed methodology resulted in θ measurements accuracy comparable to those obtained from the 10% best Monte-Carlo iterations of randomly assigned soil moisture locations. Further, this study demonstrates that the proposed methodology is more efficient than the traditional practice of randomly spreading calibration soil moisture sensors along the SPHP transect.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Solvent–Solvent Correlations across Graphene: The Effect of Image Charges

Wetting experiments show pure graphene to be weakly hydrophilic, but its contact angle (CA) also reflects the character of the supporting material. Measurements and Molecular Dynamics simulations on suspended and supported graphene often reveal a CA reduction due to the presence of the supporting substrate. A similar reduction is consistently observed when graphene is wetted from both sides. The effect has been attributed to transparency to molecular interactions across the graphene sheet, however, the possibility of substrate-induced graphene polarization has also been considered. Computer simulations of CA on graphene have so far been determined by ignoring the material’s conducting properties. We improve the graphene model by incorporating its conductivity according to the Constant Applied Potential Molecular Dynamics. Using this method, we compare the wettabilities of suspended graphene and graphene supported by water by measuring the CA of cylindrical water drops on the sheets. The inclusion of graphene conductivity and concomitant polarization effects lead to a lower CA on suspended graphene but the CA reduction is significantly bigger when the sheets are also wetted from the opposite side. The stronger adhesion is accompanied by a profound change in the correlations among water molecules across the sheet. While partial charges on water molecules interacting across an insulator sheet attract charges of the opposite sign, apparent attraction among like charges is manifested across the conducting graphene. The change is associated with graphene polarization, as the image charges inside the conductor attract equally signed partial charges of water molecules on both sides of the sheet. Additionally, by using a non-polar liquid (diiodomethane), we affirm a detectable wetting translucency when liquid-liquid forces are dominated by dispersive interactions. Furthermore, our findings are important for predictive modeling toward a variety of applications including sensors, fuel cell membranes, water filtration, and graphene-based electrode materials in high-performance supercapacitors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

SCOPE: Spectral cloud optical property estimation using real-time GOES-R longwave imagery

The output of ground-based, solar power generation systems is strongly dependent on cloud cover, which is the main contributor to solar power variability and uncertainty. Cloud optical properties are typically over-simplified in forecasting applications due to the lack of realtime, accurate estimates. In this work, we introduce a method, the Spectral Cloud Optical Property Estimation (SCOPE), for estimating cloud optical properties directly from high-resolution (5-min, 2 km) imagery from Geostationary Operational Environmental Satellite (GOES)-R, which is the newest generation of the GOES system. The SCOPE method couples a two-stream, spectrally resolved radiative model with the longwave GOES-R sensor output to simultaneously estimate the cloud optical depth, cloud top height, and cloud thickness during both day and night at 5-min intervals. The accuracy of SCOPE is evaluated using one year (2018) of downwelling longwave (DLW) radiation measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. During daytime clear-sky periods, SCOPE predicts DLW within instrument uncertainty (10 W m -2 ) for four of the seven locations, with the remaining locations yielding errors of the order of 11.2, 17.7, and 20.2 W m -2 . For daytime cloudy-sky, daytime all-sky (clear or cloudy), and nighttime all-sky periods, SCOPE achieves root mean square error values of 23.0–34.5 W m -2 for all seven locations. These results, together with the low-latency of the method (1 s per sample), show that SCOPE provides a viable solution to real-time, accurate estimation of cloud optical properties for both day and night.

42 ENGINEERING↗
DOE OSTI2015

Structures of regulatory machinery reveal novel molecular mechanisms controlling B. subtilis nitrogen homeostasis

All cells must sense and adapt to changing nutrient availability. However, detailed molecular mechanisms coordinating such regulatory pathways remain poorly understood. In Bacillus subtilis, nitrogen homeostasis is controlled by a unique circuitry composed of the regulator TnrA, which is deactivated by feedback-inhibited glutamine synthetase (GS) during nitrogen excess and stabilized by GlnK upon nitrogen depletion, and the repressor GlnR. Here we describe a complete molecular dissection of this network. TnrA and GlnR, the global nitrogen homeostatic transcription regulators, are revealed as founders of a new structural family of dimeric DNA binding proteins with C-terminal, flexible, effector-binding sensors that modulate their dimerization. Remarkably, the TnrA sensor domains insert into GS intersubunit catalytic pores, destabilizing the TnrA dimer and causing an unprecedented GS dodecamer-to-tetradecamer conversion, which concomitantly deactivates GS. In contrast, each subunit of the GlnK trimer ‘‘templates’’ active TnrA dimers. Unlike TnrA, GlnR sensors mediate an autoinhibitory dimer-destabilizing interaction alleviated by GS, which acts as a GlnR chaperone. Thus, these studies unveil heretofore unseen mechanisms by which inducible sensor domains drive metabolic reprograming in the model Gram-positive bacterium B. subtilis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF). We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulation-based optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real- time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

Vaccination with Recombinant Mycobacterium tuberculosis PknD Attenuates Bacterial Dissemination to the Brain in Guinea Pigs

We have previously identified Mycobacterium tuberculosis PknD to be an important virulence factor required for the pathogenesis of central nervous system (CNS) tuberculosis (TB). Specifically, PknD mediates bacillary invasion of the blood-brain barrier, which can be neutralized by specific antisera, suggesting its potential role as a therapeutic target against TB meningitis. We utilized an aerosol challenge guinea pig model of CNS TB and compared the protective efficacy of recombinant M. tuberculosis PknD subunit protein with that of M. bovis BCG against bacillary dissemination to the brain. BCG vaccination limited the pulmonary bacillary burden after aerosol challenge with virulent M. tuberculosis in guinea pigs and also reduced bacillary dissemination to the brain (P = 0.01). PknD vaccination also offered significant protection against bacterial dissemination to the brain, which was no different from BCG (P>0.24), even though PknD vaccinated animals had almost 100-fold higher pulmonary bacterial burdens. Higher levels of PknD-specific IgG were noted in animals immunized with PknD, but not in BCG-vaccinated or control animals. Furthermore, pre-incubation of M. tuberculosis with sera from PknD-vaccinated animals, but not with sera from BCG-vaccinated or control animals, significantly reduced bacterial invasion in a human blood-brain barrier model (P<0.01). Current recommendations for administering BCG at birth are based on protection gained against severe disease, such as TB meningitis, during infancy. We demonstrate that vaccination with recombinant M. tuberculosis PknD subunit offers a novel strategy to protect against TB meningitis, which is equivalent to BCG in a guinea pig model. Moreover, since BCG lacks the PknD sensor, BCG could also be boosted to develop a more effective vaccine against TB meningitis, a devastating disease that disproportionately affects young children.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data for "Depth of nutrient uptake by deep-rooted plants is regulated by water availability"

The data set consists of strontium (Sr) isotope ratios (87Sr/86Sr), water isotopes, soil cation concentrations, soil water potential sensor data, and results of 87Sr/86Sr mixing model. The plant canopy size files include the dataset of canopy dimension of sagebrush, lupine, and sunflower. The soil and plant ICPMS (Inductively Coupled Plasma Mass Spectrometry) data file includes both of 87Sr/86Sr, and cation concentration dataset from soil exchangeable pool, apatite pool, silicate extract, atmospheric rain deposition, and plant leaf and stem tissues. The plant dendrochronology file includes the dendrochronogical ring width of several sagebrush, and dendrochemical sample data includes the 87Sr/86Sr for each separated growth ring. The modeling result gives the proportion of nutrient sources of each plants (based on their 87Sr/86Sr in leaf tissues and growth rings) from atmospheric deposition and mineral weathering. Soil water potential data includes continuous collection of soil water potential dataset at 2 depths (30 cm and 60 cm, from Nov 24 - Jun 25) of the sampling site. All the samples were collected from 2 sampling campaign June and July 2023, and rain water is a separate sampling from Aug - Sept 2023, at north-facing hillslope near pumphouse site. The data showed that the depth of cation nutrient acquisition is thus tightly coupled with, and likely determined by, water availability in soil, saprolite and bedrock. The enhanced uptake of cations and water from regions of mineral weathering could confer plant and ecosystem resilience during low water years and may impact the rate of bedrock weathering and watershed chemistry during drought. This dataset includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type; a location metadata file (locations.csv); and a samples metadata file (samples.csv). All files are provided as comma-separated values (CSV) files (.csv). 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↗
DOE OSTI2016

DIORAMA Model of Satellite Body Orientation

The DIORAMA GPS satellite platform orientation model is described. Satellites need to keep sensors pointed towards the earth and solar panels oriented to face the sun (when not in the earth’s shadow) while they orbit the earth.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Automated, Unmanned Detection and Quantification of Fugitive Methane Emissions

Compressor stations used to move natural gas are one of the largest sources of fugitive methane emissions in the midstream sector, accounting for approximately 50% of all fugitive emissions (Zimmerle et al., 2015). This problem is most widespread at reciprocating compressors (Subramanian et al., 2015) where faulty seals are a key contributor to methane emissions (Johnson et al., 2015). As such, there is a significant need for a robust technology that could provide an early indication of an unexpected emission. Equally important, the technology needs to be able to account for biogenic versus anthropogenic sources of methane. One means of indirectly making this determination, is to leverage optical technologies that can autonomously pinpoint the source of such leaks. This presentation discusses recent work funded by the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL), focused on the development of an innovative remote sensing technology that can reliably and autonomously detect fugitive methane emissions in near real-time, using computer vision and deep learning. The technology called the Smart Methane Leak Detection (SLED/M) system was initially developed to monitor facilities such as compressor stations in a stationary, pan-tilt-zoom configuration. The system has recently been adapted to monitor facilities from an unmanned aerial system (UAS). The speed and maneuverability of UAS platforms are attractive to leak detection and repair program operators, but introduce several challenges. Many existing methane detection algorithms rely on mostly static backgrounds becoming unusable with motion. In addition, top-down views of fugitive methane emissions present differently in Optical Gas Imagers (OGI) compared to looking across the plume. Our work has focused on overcoming these challenges, enhancing the operators ability to detect methane emissions, and pinpoint their sources. Another recent adaptation to SLED/M is the ability to quantify methane emissions using passive sensors (OGI, thermal camera), environmental conditions, plume modeling, and deep learning. SLED/M advances the state-of-the-art for methane emission detection and quantification by focusing on three key critical criteria for effective methane emission mitigation: (1) autonomy (no need for a human to be in the loop), (2) high reliability (low false alarm rates), and (3) real-time performance. Results from this work will be presented.

03 NATURAL GAS↗
NASA NTRS1970

Passive microwave sensors

Passive microwave sensors based on brightness temperature vs frequency model for various atmospheric conditions, presenting vegetation pictures

Hovis, W.↗
NASA NTRS1971

Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

Investigation of problems related to control of a mobile planetary vehicle according to a systematic plan for the exploration of Mars has been undertaken. Problem areas receiving attention include: (1) overall systems analysis; (2) vehicle configuration and dynamics; (3) toroidal wheel design and evaluation; (4) on-board navigation systems; (5) satellite-vehicle navigation systems; (6) obstacle detection systems; (7) terrain sensing, interpretation and modeling; (8) computer simulation of terrain sensor-path selection systems; and (9) chromatographic systems design concept studies. The specific tasks which have been undertaken are defined and the progress which has been achieved during the period July 1, 1971 to December 31, 1971 is summarized.

Frederick, D. K.↗
NASA NTRS1972

A detailed analysis of Mariner nine TV navigation data.

Mariner 9 data analysis showed a good performance of the Mars probe planetary approach navigation systems and a good agreement between measured and predicted probe-approach characteristics. It was found that all operations were completed on time and that the calibration of the instruments and the observation of a greater-than-planned number of stars resulted in a higher-than-expected measurement accuracy. The key instruments of the Mariner were TV cameras taking pictures of Phobos and Deimos against a background of stars, calibrated sun and star sensors, and a scan platform. The error models used for optical navigation system elements, the calibration of the scan platform and of the TV cameras, and the image detection and location techniques are discussed in detail.

Breckenridge, W. G.↗
NASA NTRS1974

TERSSE: Definition of the Total Earth Resources System for the Shuttle Era. Volume 2: An Assessment of the Current State-of-the-Art

Results of a state-of-the-art assessment of technology areas which affect the Earth Resources Program are presented along with a functional description of the basic earth resources system. Major areas discussed include: spacecraft flight hardware, remote sensors, data processing techniques and hardware, user models, user interfaces, and operations technology.

Source record↗
NASA NTRS1975

LANDSAT menhaden and thread herring resources investigation

The author has identified the following significant results. The most significant achievement realized thus far has been the successful completion of the data acquisition phase. This success must be attributed to the interest, support, and competency of the participants. The apparent consistency of water color and turbidity condition over time and between test sites at sites of menhaden capture is significant especially since color is readily measured with satellite and aircraft sensors and a LANDSAT MSS based computer model for inferring tubidity has been developed.

Kemmerer, A. J.↗
NASA NTRS1976

Computer simulation and evaluation of edge detection algorithms and their application to automatic path selection

The construction of an autonomous roving vehicle requires the development of complex data-acquisition and processing systems, which determine the path along which the vehicle travels. Thus, a vehicle must possess algorithms which can (1) reliably detect obstacles by processing sensor data, (2) maintain a constantly updated model of its surroundings, and (3) direct its immediate actions to further a long range plan. The first function consisted of obstacle recognition. Obstacles may be identified by the use of edge detection techniques. Therefore, the Kalman Filter was implemented as part of a large scale computer simulation of the Mars Rover. The second function consisted of modeling the environment. The obstacle must be reconstructed from its edges, and the vast amount of data must be organized in a readily retrievable form. Therefore, a Terrain Modeller was developed which assembled and maintained a rectangular grid map of the planet. The third function consisted of directing the vehicle's actions.

Longendorfer, B. A.↗
NASA NTRS1977

Corps of Engineers applications for remote sensing of the environment

An objective overview is presented of the application of remote sensing technology in the Corps of Engineers. Examples are given of attempts to use the current state of the art to achieve particular disciplinary or mission oriented goals. The Corps, presently engaged in both research and development and technology transfer, has encountered some interesting situations. Practical operational utilization depends not only on technology, but also economic benefit/cost factors and some unprecedented legal, political, and social issues. Yet, at a time when increased agency commitment to operational usage is being sought, an assessment of the state of the art reveals that sensor technology, data processing and analysis, and models still require further development. There is a challenge in synchronizing technology push with the demand pull of dimly perceived user needs. They should complement each other rather than oppose. The goal is to use the combined push-pull effect to lead to increased productivity and responsiveness by the Corps.

Kurtz, M. K., Jr.↗