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DOE OSTI2018

Laning and clustering transitions in driven binary active matter systems

It is well known that a binary system of nonactive disks that experience driving in opposite directions exhibits jammed, phase separated, disordered, and laning states. In active matter systems, such as a crowd of pedestrians, driving in opposite directions is common and relevant, especially in conditions which are characterized by high pedestrian density and emergency. In such cases, the transition from laning to disordered states may be associated with the onset of a panic state. In this study, we simulate a laning system containing active disks that obey run-and-tumble dynamics, and we measure the drift mobility and structure as a function of run length, disk density, and drift force. The activity of each disk can be quantified based on the correlation timescale of the velocity vector. We find that in some cases, increasing the activity can increase the system mobility by breaking up jammed configurations; however, an activity level that is too high can reduce the mobility by increasing the probability of disk-disk collisions. In the laning state, the increase of activity induces a sharp transition to a disordered strongly fluctuating state with reduced mobility. We identify a novel drive-induced clustered laning state that remains stable even at densities below the activity-induced clustering transition of the undriven system. Lastly, we map out the dynamic phase diagrams highlighting transitions between the different phases as a function of activity, drive, and density.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Raman spectroscopic characterization of SiO 2 phase transformation and Si substrate stress relevant to EBC performance

To accurately model the long-term durability of environmental barrier coatings (EBCs), a more complete understanding of the phase composition and transformations of the thermally grown oxide SiO 2 (TGO) is desired. For the TGO formed during thermal cycling in steam, cristobalite formation and the subsequent β- to α-cristobalite transformation has been identified as a potentially life-limiting mechanism. In this study, Raman micro-spectroscopy was used to quantify the cristobalite transformation on a polycrystalline Si coupon that was exposed to steam at 1350°C for 100 h. The phase transformation was mapped at 200–260°C on the TGO surface at different ramp rates using a heating stage and a micro-positioning stage. The stress in the Si substrate was also determined using Raman spectroscopy by measuring the stress induced peak shift. The α→β phase transformation produced a 300–500 MPa tensile stress in the Si substrate, which compared well to the stress predicted from the volumetric expansion of the cristobalite. In conclusion, quantifying the phase transformation and residual stress are critical tools in developing the next generation of high performance EBCs.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗

DOE OSTI2022

CityBES v2021

City Buildings, Energy, and Sustainability (CityBES) is a web-based data and computing platform, focusing on energy modeling and analysis of a city's building stock to support district or city-scale building energy efficiency programs. CityBES uses an international open data standard, CityGML, to represent and exchange 3D city models. CityBES employs EnergyPlus to simulate building energy use and savings from energy efficient retrofits. Other CityBES features include energy benchmarking, district heating and cooling system modeling, rooftop PV analysis, building performance visualization, heat resilience modeling, as well as urban scale mapping of microclimate and heat vulnerability at census tract level. Different from other tools, CityBES uses integrated open and standard 3D city building data and models each individual building using EnergyPlus. CityBES can be used by urban planners, city energy managers, building owners, utilities, energy consultants and researchers.

Hong, Tianzhen↗

DOE OSTI2018

Model Authorized Product Realization (MAP-R) Analysis

The Model Authorized Product Realization (MAP-R) project was a collaboration between Sandia National Laboratories (SNL) and Kansas City National Security Campus (KCNSC) to advance the Nuclear Security Enterprise's (NSE) ability to quantify the differences between the current drawing centric/drawing-based process and a part centric/model-based process. In short, MAP-R identified the key business benefits of the part centric model-based process using quantifiable data. MAP-R builds upon the past, but leverages current advances in technology, processes/standards, and the motivation of our current workforce to use a part-centric/model- based design and manufacturing method.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2019

Remote sensing and geophysical characterization of a floodplain-hillslope system in the East River watershed, Colorado

This package is part of the Watershed Function SFA Data Collection and contains remote sensing data and geophysical measurements acquired at the Pumphouse hillslope-floodplain site, in the East River, Colorado. The remote sensing dataset includes: a) a WorldView-2 (WV-2) pan-sharpened multi-spectral remote sensing image, and b) LiDAR derived products. The WV-2 image was acquired on September 24th, 2015, and it is composed of eight spectral bands (coastal, blue, green, yellow, red, red edge, NIR1, and NIR2) with a spatial resolution of 0.5 m, covering an area of 750x750 m. The LiDAR products are a digital surface model (DSM), representing the top-of-canopy elevation, and a digital terrain model (DTM), representing the bare-ground elevation, at the spatial resolution of 0.5 m. Such data are extracted from the LiDAR point-cloud dataset acquired over the entire watershed on August 2015 (http://dx.doi.org/10.21952/WTR/1412542). The geophysical dataset is composed of the electrical resistivity tomography (ERT) data acquired along a 158.75 m long transect, spanning the hillslope topographical gradient with 1.25 m electrode spacing. The dataset also includes soil water content measured with a TDR system. The package provides also polygons of different vegetation communities, which were used as reference data for mapping plant community distributions. The spatial polygons are based on plant locations acquired during the field campaign of June-July 2017 using an RTK DGPS system. The reference dataset is composed by 11 vegetation classes, namely, deciduous forest, evergreen forest, riparian shrubland, sagebrush, shrubland, potentilla, frasera, lupine meadow, veratrum, bunchgrass meadow, and forb, as well as four non-vegetation classes, such as water-river, water-lake, man-made, bare area, and shadow. The vegetation classes are determined based on distinct spectral and/or structural signatures of dominant species. The package includes products derived by these datasets, such as: a) a plant height map, computed by subtracting the DTM from the DSM, and b) a plant community distribution map, obtained by using a data-fusion machine-learning-based framework that exploit the WV-2 image and plant height information.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Importance of Multiplicity Fluctuations in Entropy Scaling

One of the greatest uncertainties in heavy-ion collisions is the description of the initial state. Different models predict a wide range of initial energy density distributions based on their underlying assumptions. Final flow harmonics are sensitive to these differences in the initial state due to the nearly linear mapping between eccentricities and anisotropic flow harmonics. The Trento code uses a model-agnostic approach by phenomenologically parameterizing the initial state and constraining those parameters from a Bayesian analysis. There the multiplicity fluctuations were determined by a one parameter Γ distribution. However, initial-state models arising from the Color-Glass Condensate (CGC) framework lead to an initial energy density which is outside the functional form considered in Trento and its later Bayesian analyses because they rely on log-normal multiplicity fluctuations. We compare T A T B scaling (CGC-like) to √T A T B scaling (preferred from a Trento Bayesian analysis) and find that the T A T B form together with log-normal fluctuations is a reasonable candidate to describe the multiplicity fluctuations but leads to larger eccentricities, which would affect the extraction of viscosity in small systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Vegetation Detection Using Deep Learning and Conventional Methods

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer vision, and machine learning (ML) techniques, is also proposed. The vegetation detection methods were applied to high-resolution airborne color images which consist of RGB and near-infrared (NIR) bands. RGB color images alone were also used with the two deep learning methods to examine their detection performances without the NIR band. The detection performances of the deep learning methods with respect to the object-based detection approach are discussed and sample images from the datasets are used for demonstrations.

58 GEOSCIENCES↗

DOE OSTI2021

SolarSMART 2020 Georgia Survey

The SolarSMART 2020 Georgia Survey dataset provides results from a survey of 1,544 residents of the State of Georgia above the age of 18 on their perceptions of photovoltaic solar energy. The survey includes the following: demographic information for each respondent, their location in the state (by ZIP Code); the type of house they reside within; whether they have or have not adopted solar energy (and if so, how did they adopt solar energy); their personal views and perceptions of solar energy both for themselves and within their community; and their perceptions of who within Georgia and across the United States would or would not be inclined to adopt solar. Respondents were asked to provide their responses to questions addressing the following issues as they pertained to both themselves and their perceptions of others (Georgians and Americans writ large): (a) perception of how home type shape solar adoption; (b) perceptions of personal values (interpersonal, political, environmental); (c) the design of solar and the marketing of it; (d) financial dimensions of adopting solar energy; (d) and favorability towards new technologies. This data was collected via an online survey through a third-party distributor between June and September of 2020. Respondents were from 151 of the 159 counties of the State of Georgia, with representation of both urban and rural residents. In total, the dataset includes 171 variables, with one being an open-ended question coded by the research team. Variables include demographic characteristics (personal, housing, location), multiple-choice single answer, ranked-choice responses to different statements about solar, and a series of two digital map exercises where respondents highlighted geographic regions (states or parts of Georgia) based on whether the believe residents there do, do not, or they do not know if the respondents do or do not adopt solar.

14 SOLAR ENERGY↗

NASA NTRS1972

Inventory of forest and rangeland and detection of forest stress

There are no author-identified significant results in this report. Some ERTS-1 imagery has been received for each of the test sites: Black Hills, Atlanta, and Manitou. Only small portions of each site are covered and clouds have precluded capturing good imagery over the center of each site. Discoloration infestations of ponderosa pine are being located and sized on CIR transparencies. A computer program was completed from microdensitometer scans of CIR photos which maps areas of an image which are spectrally similar. Decided differences between forest types are present as well as differences between forest and other vegetative and nonvegetative land classes.

Heller, R. C.↗

NASA NTRS1972

Geology of Utah and Nevada by ERTS imagery

The author has identified the following significant results. Imagery for a portion of the study area was received, catalogued for in-house availability, and examined for relevant data. Topographic, geologic, aeromagnetic, and gravity maps were gathered to form a basis of comparison. Overlay drawings were prepared indicating significant features for further study and eventual field checking. The number of significant observations appears to be very great, and it is apparent that methods of handling the acquired information for easy access, for planning field checks, and for final presentation, must be improved. The tracing of lithologic variations and contacts is readily noted on many of the images. Some of the features vary from what is shown on geological maps but the correlation, or lack of same, is still to be studied in the field and by comparison of ground truth geological maps with the specific images. Numerous lineaments, based on tonal differences, are noted in the districts, some of which extend for tens of kilometers. Several positive and negative magnetic anomalies are noted with the suggestion that the sources of the mineralizing fluids were derived from hidden intrusives.

Jensen, M. L.↗

NASA NTRS1972

Trinity Bay Study: Dye tracing experiments

An analysis of the heat balance and temperature distribution within Trinity Bay near Galveston, Texas is presented. The effects of tidal currents, wind driven circulations, and large volume inflows are examined. Emphasis is placed on the effects of turbulent diffusion and local shears in currents. The technique of dye tracing to determine the parameters characterizing dispersion is described. Aerial photographs and maps are provided to show the flow conditions existing at different times and seasons.

Ward, G. H., Jr.↗

NASA NTRS1973

Discrimination techniques employing both reflective and thermal multispectral signals

Recent improvements in remote sensor technology carry implications for data processing. Multispectral line scanners now exist that can collect data simultaneously and in registration in multiple channels at both reflective and thermal (emissive) wavelengths. Progress in dealing with two resultant recognition processing problems is discussed: (1) More channels mean higher processing costs; to combat these costs, a new and faster procedure for selecting subsets of channels has been developed. (2) Differences between thermal and reflective characteristics influence recognition processing; to illustrate the magnitude of these differences, some explanatory calculations are presented. Also introduced, is a different way to process multispectral scanner data, namely, radiation balance mapping and related procedures. Techniques and potentials are discussed and examples presented.

Malila, W. A.↗

NASA NTRS1974

The use of remote sensing and natural indicators to delineate floodplains - Preliminary findings

Feasibility studies to delineate floodplains using aerial photography have been conducted by the Army Corps of Engineers. The objective of this project is to establish floodplain boundaries using natural indicators such as vegetation types, soil types, moisture differences, and geologic variations. Using aircraft multispectral data, an automated mapping routine should enable the Corps of Engineers to reduce costly field surveys and improve the accuracy of floodplain delineation in pristine areas. The purpose of this paper is to document the methodology utilized in this investigation while using one of a series of available data sets and test sites and to report preliminary findings.

Sollers, S. C.↗

NASA NTRS1976

Processing of LANDSAT imagery for dissemination purposes

The author has identified the following significant results. The differentiation between bands 4 and 5 was achieved through enhancement. Depiction of dormant vegetation on the upper Chesapeake Bay image map in tones of green is an example of significant difference between bands 4 and 5 which can be achieved through proper enhancement.

Colvocoresses, A. P.↗

NASA NTRS1978

Pilot Tests of Satellite Snowcover/Runoff Forecasting Systems

Major snow zones of the western U.S. were selected to test the capability of satellite systems for mapping snowcover in various snow, cloud, climatic, and vegetation regimes. Different satellite snowcover analysis methods used in each area are described along with results.

Rango, A.↗

NASA NTRS1981

Surface mapping of selected regions in the Orion Nebula

Low dispersion, large aperture, ultraviolet spectra of selected regions in the Orion Nebula were obtained with the International Ultraviolet Explorer (IUE) scientific instrument. Spectra obtained at 35 contiguous locations defining a mosaic within the nebula were used to generate monochromatic images of high spatial resolution at the wavelengths of the ultraviolet emission lines. Image processing techniques were utilized to generate and analyze these ultraviolet surface maps. The imagery at the three wavelengths studied shows definite differences in the spatial distribution of emission from the CII CIII and OII ions. Ways of using the imagery to determine ionization structure and C/O abundance ratios throughout the regions observed are developed, in addition to means of analyzing the extensive continuum measurements in terms of dust scattering characteristics.

Perry, P. M.↗

NASA NTRS1981

Elevation of a cane-growing area of the state of Sao Paulo using LANDSAT data

Images at a scale of 1:250.000 were visually interpreted for identification and area estimates of sugar cane plantations in Sao Paulo. The basic criteria for crop identification were the spectral characteristics of channels 5 and 7 and their temporal variations observed from different LANDSAT passes. Using this technique, it was possible to map the sugar cane areas as well as the sugar cane already harvested. An area of 801,950 hectares was estimated within the study area. The confidence interval of correct classification ranged from 87.11% to 94.71%.

Dejesusparada, N.↗