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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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Design of variable temperature gas transfer cell for use in solid state spectroscopy
Red, photographic infrared, near infrared spectral data of corn, cotton, soybeans, sugar beets, sorghum, sunflowers and tobacco were collected throughout the entire growing season by using a three band handheld radiometer. Different radiance patterns were found among these crops based on their morphology, green biomass duration and leaf size. Results show near infrared radiance is a good indicator of water content in plant tissue under small scale experimental conditions.
Variability in micrometeorological conditions and their influence on estimated reference evapotranspiration (RET) rates were evaluated across a heterogeneous urban environment. Micrometeorological data sets (incoming solar radiation, air temperature, relative humidity and wind speed) were collected over a one-year period at six weather stations in New York City, NY (USA). Weather stations are located at four new urban green space monitoring sites and two airports. Reference evapotranspiration (RET) rates were estimated from the micrometeorological data sets for a short reference surface at a daily time-step using the ASCE Standardized Reference Evapotranspiration Equation, a Penman-Monteith based combination equation. Nonparametric comparative statistical analyses (Kruskal-Wallis) revealed statistically significant differences (at significance level α = 0.05) in micrometeorological conditions and estimated RET rates between the six sites. On a cumulative annual basis, estimated RET varied by up to 40 percent between the sites. A new technique for adjusting weather data collected at one location (e.g. regional airports) for use at another location (e.g. interior engineered urban green spaces) was evaluated. The study highlights the importance, for accurate estimation of ET, of onsite micrometeorological data sets, but concludes that additional research is needed to more thoroughly characterize micrometeorological variability across heterogeneous urban environments, and also to evaluate the influence of non-meteorological determinants, e.g. vegetation type, soil/media type, media moisture conditions and anthropogenic heat fluxes, on urban ET.
Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.
Incorporating detailed chemical kinetic models is critical for accurate simulations of reacting flows. However, detailed models involve a large number of thermochemical (TC) state variables. Solving the governing equations to evolve these TC variables becomes impractical for real-world applications. In this work, we propose an autoencoder (AE) neural network (NN)-based reduced model to accelerate such simulations. The AE NN is first trained to find a low-dimensional latent representation of the TC states. Then, the evolving state of a chemical system can be tracked by solving the equations of the latent variables instead of the original TC equations. We demonstrate the reduced model in a syngas CO/H 2 combustion system, using training data collected from canonical perfectly stirred reactors (PSRs). It is found that the AE model can reduce the dimension of the combustion system from 12 to 2 while maintaining low reconstruction error and excellent elemental mass conservation for the test dataset. In the a posteriori test, the combustion states obtained from solving the two latent equations are compared to those from solving the 12 equations of the full model. The AE reduced method is found to be able to capture the diverse combustion states on the top two branches of the S-curve well including the extinction turning point, but with higher prediction errors for states near the ignition turning point.
Compressional seismic (P-wave) velocities were measured experimentally at different water potentials and confining pressure states using a variety of sediments collected from the US Department of Energy Hanford Site in southeastern Washington State. P-wave velocity was measured in variably saturated sediments using ultrasonic piezoelectric transducers and water potential was measured with a heat dissipation sensor. We propose a model to relate changes in P-wave velocity to soil stress conditions using an effective stress formulation that includes capillary stress as well as adsorptive stress. We show that compressional wave velocities can be related to water potential and confining stress using a model with five fitted parameters. A single set of parameters provides an excellent fit to data acquired from all of the sediment samples. Under very dry conditions, corresponding to low water potentials, water flow occurs in adsorptive thin films associated with very low unsaturated hydraulic conductivities. The results presented here suggest the utility of seismic methods for establishing low water potential and flow conditions within variably saturated Hanford formation sediments and providing feedback on the performance of mitigation efforts such as soil desiccation and surface infiltration barriers. Seismic methods can provide minimally invasive, three-dimensional information on relevant flow conditions in vadose zone sediments as well as changes that occur over time.
AbstractThe parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric $ \left({\hat{d}}_{\mathrm{t}}\right) $ and chromomagnetic $ \left({\hat{\mu}}_{\mathrm{t}}\right) $ moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb$^{−1}$. The linearized variable $ {A}_{\mathrm{FB}}^{(1)} $ is used to approximate the asymmetry. Candidate $ \mathrm{t}\overline{\mathrm{t}} $ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for $ \mathrm{t}\overline{\mathrm{t}} $ final states. The values found for the parameters are $ {A}_{\mathrm{FB}}^{(1)}={0.048}_{-0.087}^{+0.095}{\left(\mathrm{stat}\right)}_{-0.029}^{+0.020}\left(\mathrm{syst}\right),{\hat{\mu}}_{\mathrm{t}}=-{0.024}_{-0.009}^{+0.013}{\left(\mathrm{stat}\right)}_{-0.011}^{+0.016}\left(\mathrm{syst}\right), $ and a limit is placed on the magnitude of $ \left|{\hat{d}}_{\mathrm{t}}\right| $< 0.03 at 95% confidence level.[graphic not available: see fulltext]
The impacts of haze on visibility, air quality, and climate are not well quantified due to a lack of understanding of the evolution of the mixing state and phase state of atmospheric particles during haze processing. The variability of the mixing state of atmospheric particles contributes significantly to uncertainties associated with the estimated aerosol radiative forcing. We collected particle samples in January 2018 from the highly polluted Indo-Gangetic plain during hazy conditions to study haze-processed particles. Single particle analysis using multi-modal micro-spectroscopy techniques revealed an abundance (40–70% by number) of potassium-rich sulfate particles from biomass-burning influenced smoke. Tilted view imaging showed that most of the organic particles that had inorganic potassium and sulfate inclusions were liquid-like while those without inclusions were more semi-solid. High-resolution mass spectrometry analysis revealed a significant presence of organosulfates and nitroxy-organosulfates in the morning samples (24%) compared to the afternoon samples (9%), despite higher relative humidity in the afternoon. Overall, our results highlight the significant contribution of both organic and inorganic sulfate to the total particulate sulfur budget during haze processing in winter, when anthropogenic emissions such as household burning, agricultural burning, and vehicular emissions are major contributors to particle mass.
Energy systems generating power from ocean waves, currents, tides, and thermal and salinity gradients are being developed to power oceanographic instrumentation and autonomous vehicles in Earth’s oceans. Exploration missions enabled or extended with in-situ energy harvesting on Earth may be similar to those on extra-terrestrial ocean worlds including mapping, biogeochemical sampling, acoustic monitoring, search for building blocks of life, and physical oceanographic characterization. With the addition of ocean energy available at the point of observation, data can be collected on larger spatial and temporal scales, enabling more comprehensive missions on other worlds. Power resource characteristics (e.g. current speed, variability) will similarly need to be assessed. Earth and extra-terrestrial ocean missions share vast technical challenges including high expense, need for advanced autonomy, ability to survive in the harshest environments, and limited communications or opportunities for intervention. Marine energy systems may be the only option for in-situ generation in certain scenarios (e.g., under a thick layer of ice) or advantageous over onboard energy storage options that may interfere with the natural environment (e.g., radioisotope thermoelectric generators). This work provides an overview of existing marine energy technology pertaining to ocean observations, highlights emerging research and development and new funding and program initiatives, explores similarities in mission requirements between terrestrial and extra-terrestrial applications, and suggests innovation pathways towards enabling high-value ocean world exploration powered by ocean energy.
A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.
This dataset represents results from a field study aiming to understand the variability in post-fire responses of dissolved organic matter and determine drivers of post-fire responses. Samples were collected at 58 sites within the McKenzie River Watershed (Oregon, USA) that were upstream, within, and downstream of the Holiday Farm Fire burn perimeter. The samples were collected in June 2023 and September 2023 during storm events, approximately 3 years post-fire. Samples were characterized for benezenepolycarboxylic acids (BPCA) and ultra-high resolution mass spectrometry. Dissolved organic carbon and optics (absorbance and fluorescence) data can be found in a separate data packages (https://ir.library.oregonstate.edu/concern/datasets/zc77sz60m, https://ir.library.oregonstate.edu/concern/datasets/mc87q034m). Related data from a subset of sites from 2020-2022 can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1869708 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2478546. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset contains (1) file-level metadata; (2) data dictionary; (3) data package readme; (4) metadata; (5) methods information; (6) benzene polycarboxylic acid (BPCA) concentration data; (7) Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) methods; (8) folder of high resolution characterization of organic matter via 12 Tesla FTICR-MS data generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). This package contains the following file types: csv, xml, pdf.
This project supported the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), which aimed to characterize the extent, radiative properties, aerosol interactions, and precipitation of stratocumulus clouds in the Eastern Pacific coastal region of La Jolla, California. Given the frequent anthropogenic aerosols emitted into this region, characterization of this important coastal cloud region and the aerosol impact on the cloud characteristics will improve the understanding of aerosol indirect effects and its representation in global climate models. This specific project deployed the fog droplet monitor (FM-120, Droplet Measurement Technologies) at the Scripps Institution of Oceanography’s Mt. Soledad site from 16 February 2023 to 15 February 2024. This instrument measured droplet number size spectra between 2 and 50 microns, from which droplet number concentration, liquid water content, and effective diameter were calculated. These measurements will be used to characterize the seasonal variability of clouds at Mt. Soledad and investigate differences in cloud properties under regional polluted and clean marine conditions. The collected data will soon be posted as part of the digital collection for EPCAPE data hosted by the University of California, San Diego (Russell et al. 2023).
Early detection of outbreaks caused by emergent pathogens, using epidemiological surveillance data i.e., daily case counts, is difficult. This is because the data tend to be noisy during the early epoch of the outbreak. In contrast, the spread-rate of the disease tends to be well-behaved, as it depends only on the mixing patterns of the population and the characteristics of the pathogen, neither of which behave erratically in space-time. In this report, we explore whether the spread-rate can be used for epidemiological surveillance, conditional on case count data. Estimating the spread-rate from case count data allows us to exploit exogenous information, e.g., incubation period distributions etc., which can considerably smooth out any erratic temporal behavior. Further, epidemiological dynamics are spatially correlated, and if case counts are available for multiple areal units e.g., counties, these correlations could potentially be used to suppress noise in the early epoch data. These exogenous information and structure are not exploited by conventional syndromic surveillance detectors to extract a well-behaved latent variable for monitoring purposes. The technical challenge lies in the estimation of the spread-rate field jointly over a collection of areal units; further, the spread-rate varies over time. We develop a method based on mean-field variational inference to approximately estimate the spread-rate field, using a Gaussian Random Field Model for spatial regularization. The method is tested on the estimation of spread-rate in the thirty-three counties of New Mexico and detect the arrival of the Fall 2020 COVID-19 wave in September 2020. We find that the method is scalable, but underestimates the uncertainty in the estimated spread-rate field. We detect the arrival of the Fall 2020 wave a week ahead of conventional syndromic surveillance algorithms, but our simplistic detection algorithm, based on simple anomaly detection, suffers from a high false positive rate, similar to conventional detectors.
Vector is divided into three variable sections, and each section is processed by unary-to-binary decoder or adder. Resulting network performs on iterative collection process; all outputs of same kind are collected in same manner. In combination with simple comparator gates, weighting network can also be used as majority network.
In order to achieve and maintain required flatness of the antenna array, a rotating laser beam used for leveling applications on earth was utilized as a reference system. A photoconductive sensor with a reflective collecting surface determines the displacement and polarity of any misalignment and automatically engages a stepping motor to drive a variable-length mechanism to make the necessary corrections. Once aligned, little power is dissipated since a nulling bridge circuit that centers on the beam is used. A three-point subarray alignment arrangement is described which independently adjusts, in the three orthogonal directions, the height and tilt of subarrays within the MPTS array and readily adapts to any physical distortions of the secondary structure (such as that resulting from severe temperature extremes caused by an eclipse of the Sun). It is shown that only one rotating laser system is required since optical blockage is minimal on the array surface and that it is possible to incorporate a number of redundant laser systems for reliability without affecting the overall performance.
In an attempt to evaluate the relationship between hand-held infrared thermometers and aircraft thermal scanners in near-level terrain and to quantify the variability of surface temperatures within individual fields, ground-based and aircraft thermal sensor measurements were made along a 50-km transect on 3 May 1979 and a 20-km transect on 7 August 1980. These comparisons were made on fields near Davis, California. Agreement was within 1 C for fields covered with vegetation and 3.6 C for bare, dry fields. The variability within fields was larger for bare, dry fields than for vegetatively covered fields. In 1980, with improvements in the collection of ground truth data, the agreement was within 1 C for a variety of fields.
Airborne measurements of particle size distributions obtained during the spring and summer of 1975-1976 at various locations in the high plains of the U.S. are presented. The instrumentation and techniques used to collect the data are described. Particle number, surface area, and volume distribution are measured; differences in particle concentrations are investigated. Variables in particle size are related to changes in strength of ground sources of Aitken nuclei, age of the particle population, and presence or absence of rain. An accumulation mode between particles diameters of 0.1-1 micron, a coarse particle mode between particle diameters between 10-20 microns, and a nucleation mode at particle diameters less than 0.1 micron are detected. The control of the accommodation and coarse particle modes and their influence on particle concentration are studied. The elemental composition of the particles is analyzed and the morphologies are examined with an electron scanner microscope.
Spermatozoa, protozoa, and algae form macroscopic patterns somewhat analogous to thermally driven convection cells. These bioconvective patterns have attracted interest in the fluid dynamics community, but whether in all cases these waves were gravity driven was unknown. There are two conflicting theories, one gravity dependent (fluid density model), the other gravity independent (wave reinforcement theory). The primary objectives of the summer faculty fellows were to: (1) assist in sample collection (spermatozoa) and preparation for the KC-135 research airplane experiment; and (2) to collaborate on ground testing of bioconvective variables such as motility, concentration, morphology, etc., in relation to their macroscopic patterns. Results are very briefly given.