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DOE OSTI2020

Synchrophasor Measurements-based Events Detection Using Deep Learning

Deep learning algorithms have been developed for phasor measurement units (PMUs) analysis aiming at providing grid operators to observe and react to significant real-time changes in the grid associated with multiple factors (e.g., power generation and load variations, different type of faults, and equipment mailfunction), or for offline post-event system diagnostics. In this study, a Long Short-Term Memory (LSTM)-based deep neural network (DNN) is adopted and evaluated to identify the most appropriate model configurations for event detection and longer-term anomalous pattern extraction. The proposed DNN model shows the potential on long-term predictions with the ability to capture nonlinear and nonstationary mixture complex patterns in PMU datasets. Real-world PMU in the WECC system were used for model development and validation.

deep learning↗

DOE OSTI2023

Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

14 SOLAR ENERGY↗

NASA NTRS2000

The Potential of Phased Arrays for Planetary Exploration

Phased array antennas provide a set of operational capabilities which are very attractive for certain mission applications and not very attractive for others. Such antennas are by no means a panacea for telecommunications. In this paper the features of phased arrays are reviewed and their implications for space missions are considered in terms of benefits and costs. The primary capability provided by a phased array is electronic beam agility. The beam direction may be controlled at electronic speeds (vs. mechanical actuation) permitting time division multiplexing of multiple "users." Moreover, the beam direction can be varied over a full hemisphere (for a planar array). On the other hand, such antennas are typically much more complicated than the more commonly used reflectors and horns and this implies higher cost. In some applications, this increased cost must be accepted if the mission is to be carried out at all. The SIR-C radar is an example of such a case albeit not for deep space. Assuming for the sake of argument that the complexity and cost of a phased array can be significantly reduced, where can such antennas be of value in the future of planetary exploration? Potential applications to be discussed are planetary rovers, landers, and orbiters including both the areosynchronous and low orbit varieties. In addition, consideration is given to links from deep space to earth. As may be fairly obvious, the deep space link to earth would not benefit from the wide angle steering capability provided by a phase array whereas a rover could gain advantage from the capability to steer a beam anywhere in the sky. In the rover case, however, physical size of the aperture becomes a significant factor which, of course, has implications regarding the choice of frequency band. Recent research work concerning phased arrays has suggested that future phased arrays might be made less complex and, therefore, less costly. Successful realization of such phased arrays would enable many of the planetary missions discussed in this paper and significantly broaden the telecommunications capabilities available to the mission designers of the future.

Pogorzelski, Ronald J.↗

NASA NTRS2018

The Importance of Conducting Life Sciences Experiments on the Deep Space Gateway Platform

Over the last several decades important information has been gathered by conducting life science experiments on the Space Shuttle and on the International Space Station. It is now time to leverage that scientific knowledge, as well as aspects of the hardware that have been developed to support the biological model systems, to NASA's next frontier - the Deep Space Gateway. In order to facilitate long duration deep space exploration for humans, it is critical for NASA to understand the effects of long duration, low dose, deep space radiation on biological systems. While carefully controlled ground experiments on Earth-based radiation facilities have provided valuable preliminary information, we still have a significant knowledge gap on the biological responses of organisms to chronic low doses of the highly ionizing particles encountered beyond low Earth orbit. Furthermore, the combined effects of altered gravity and radiation have the potential to cause greater biological changes than either of these parameters alone. Therefore a thorough investigation of the biological effects of a cis-lunar environment will facilitate long term human exploration of deep space.

microgravity↗

DOE OSTI2022

A deep learning approach to identify missing is-a relations in SNOMED CT

Abstract Objective SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT. Materials and Methods Our focus is to identify missing is-a relations between concept-pairs exhibiting a containment pattern (ie, the set of words of one concept being a proper subset of that of the other concept). We use hierarchically related containment concept-pairs as positive instances and hierarchically unrelated containment concept-pairs as negative instances to train a model predicting whether an is-a relation exists between 2 concepts with containment pattern. The model is a binary classifier leveraging concept name features, hierarchical features, enriched lexical attribute features, and logical definition features. We introduce a cross-validation inspired approach to identify missing is-a relations among all hierarchically unrelated containment concept-pairs. Results We trained and applied our model on the Clinical finding subhierarchy of SNOMED CT (September 2019 US edition). Our model (based on the validation sets) achieved a precision of 0.8164, recall of 0.8397, and F1 score of 0.8279. Applying the model to predict actual missing is-a relations, we obtained a total of 1661 potential candidates. Domain experts performed evaluation on randomly selected 230 samples and verified that 192 (83.48%) are valid. Conclusions The results showed that our deep learning approach is effective in uncovering missing is-a relations between containment concept-pairs in SNOMED CT.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Speckle contrast diffuse correlation tomography of complex turbid medium flow

Purpose: Developed herein is a three-dimensional (3D) flow contrast imaging system leveraging advancements in the extension of laser speckle contrast imaging theories to deep tissues along with our recently developed finite-element diffuse correlation tomography (DCT) reconstruction scheme. This technique, termed speckle contrast diffuse correlation tomography (scDCT), enables incorporation of complex optical property heterogeneities and sample boundaries. When combined with a reflectance-based design, this system facilitates a rapid segue into flow contrast imaging of larger, in vivo applications such as humans. Methods: A highly sensitive CCD camera was integrated into a reflectance-based optical system. Four long-coherence laser source positions were coupled to an optical switch for sequencing of tomographic data acquisition providing multiple projections through the sample. This system was investigated through incorporation of liquid and solid tissue-like phantoms exhibiting optical properties and flow characteristics typical of human tissues. Computer simulations were also performed for comparisons. A uniquely encountered smear correction algorithm was employed to correct point-source illumination contributions during image capture with the frame-transfer CCD and reflectance setup. Results: Measurements with scDCT on a homogeneous liquid phantom showed that speckle contrast-based deep flow indices were within 12% of those from standard DCT. Inclusion of a solid phantom submerged below the liquid phantom surface allowed for heterogeneity detection and validation. The heterogeneity was identified successfully by reconstructed 3D flow contrast tomography with scDCT. The heterogeneity center and dimensions and averaged relative flow (within 3%) and localization were in agreement with actuality and computer simulations, respectively. Conclusions: A custom cost-effective CCD-based reflectance 3D flow imaging system demonstrated rapid acquisition of dense boundary data and, with further studies, a high potential for translatability to real tissues with arbitrary boundaries. A requisite correction was also found for measurements in the fashion of scDCT to recover accurate speckle contrast of deep tissues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

36 MATERIALS SCIENCE↗

DOE OSTI2022

EVs@Scale Deep Dive - SCM/VGI (Day 1: SCM/VGI Analysis)

The U.S. Department of Energy (DOE) Electric Vehicles at Scale Laboratory Consortium (EVs@Scale Lab Consortium) is accelerating research to support the establishment of a secure and scalable national network of charging infrastructure. Critical to this effort is an understanding of the potential grid impacts of EV charging and possible smart charge management (SCM) or vehicle-grid integration (VGI) capabilities that could mitigate these impacts. The EVs@Scale SCM/VGI Pillar is analyzing the impacts of EV charging and developing and demonstrating the capabilities of both SCM and VGI with many different vehicle use cases and grid scenarios. This Deep Dive Discussion from year 1 of the project encompasses the progress and future plans for the analysis components of the FUSE (Flexible charging to Unify the grid and transportation Sectors for Evs at scale) project.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

EVs@Scale Deep Dive - SCM/VGI (Day 2: SCM/VGI Demonstration)

The U.S. Department of Energy (DOE) Electric Vehicles at Scale Laboratory Consortium (EVs@Scale Lab Consortium) is accelerating research to support the establishment of a secure and scalable national network of charging infrastructure. Critical to this effort is an understanding of the potential grid impacts of EV charging and possible smart charge management (SCM) or vehicle-grid integration (VGI) capabilities that could mitigate these impacts. The EVs@Scale SCM/VGI Pillar is analyzing the impacts of EV charging and developing and demonstrating the capabilities of both SCM and VGI with many different vehicle use cases and grid scenarios. This Deep Dive Discussion from year 1 of the project encompasses the progress and future plans for the demonstration components of the FUSE (Flexible charging to Unify the grid and transportation Sectors for Evs at scale) project.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗

DOE OSTI2023

Electric Vehicles at Scale (EVs@Scale) Laboratory Consortium Deep-Dive Technical Meeting: May 18, 2023

The U.S. Department of Energy (DOE) Electric Vehicles at Scale Laboratory Consortium (EVs@Scale Lab Consortium) is accelerating research to support the establishment of a secure and scalable national network of charging infrastructure. Critical to this effort is an understanding of the potential grid impacts of EV charging and possible smart charge management (SCM) or vehicle-grid integration (VGI) capabilities that could mitigate these impacts. The EVs@Scale SCM/VGI Pillar is analyzing the impacts of EV charging and developing and demonstrating the capabilities of both SCM and VGI with many different vehicle use cases and grid scenarios. This Deep Dive Discussion from year 2 of the project encompasses the progress and future plans for the analysis components of the FUSE (Flexible charging to Unify the grid and transportation Sectors for Evs at scale) project.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2021

Conversion of marginal land into switchgrass conditionally accrues soil carbon but reduces methane consumption

Abstract Switchgrass is a deep-rooted perennial native to the US prairies and an attractive feedstock for bioenergy production; when cultivated on marginal soils it can provide a potential mechanism to sequester and accumulate soil carbon (C). However, the impacts of switchgrass establishment on soil biotic/abiotic properties are poorly understood. Additionally, few studies have reported the effects of switchgrass cultivation on marginal lands that have low soil nutrient quality (N/P) or in areas that have experienced high rates of soil erosion. Here, we report a comparative analyses of soil greenhouse gases (GHG), soil chemistry, and microbial communities in two contrasting soil types (with or without switchgrass) over 17 months (1428 soil samples). These soils are highly eroded, ‘Dust Bowl’ remnant field sites in southern Oklahoma, USA. Our results revealed that soil C significantly increased at the sandy-loam (SL) site, but not at the clay-loam (CL) site. Significantly higher CO2 flux was observed from the CL switchgrass site, along with reduced microbial diversity (both alpha and beta). Strikingly, methane (CH4) consumption was significantly reduced by an estimated 39 and 47% at the SL and CL switchgrass sites, respectively. Together, our results suggest that soil C stocks and GHG fluxes are distinctly different at highly degraded sites when switchgrass has been cultivated, implying that carbon balance considerations should be accounted for to fully evaluate the sustainability of deep-rooted perennial grass cultivation in marginal lands.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Using ‘heat tagging’ to understand the remote influence of atmospheric diabatic heating through long-range transport

In the circulating atmosphere, diabatic heating influences the potential temperature content of air masses far from where the heating occurs. Budgets that balance local diabatic sources with local heat divergence and storage do not retain information about this remote influence, which requires air-mass tracking. In this study, a process based, passive-tracer diagnostic, called heat tagging, is introduced. Heat tagging locally decomposes the potential temperature into contributions from the distinctive diabatic processes that generate them, wherever they occur. The distribution, variability and transport of atmospheric heat tags are studied in the relatively simple setting of an idealized aquaplanet model. Heat tags from latent heating are generated in the deep tropics and the midlatitude storm track and then transported throughout the troposphere. By contrast dry sensible heat tags are enhanced near the surface, and radiative tags are mainly confined to the stratosphere. As a result, local heat transport, variability of potential temperature and global poleward heat transport are dominated by heat tags related to latent heating, with heat tags from sensible and radiative heating only making contributions in the polar near surface and the stratosphere respectively. Heat tagging thus quantifies how water vapor and latent heating link the structural characteristics of the atmosphere and illustrates the importance of the hydrological cycle in poleward energy transport.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Exploring the Limits of Bottom-Up Gold Filling to Fabricate Diffraction Gratings

Gold deposition on rotating disk electrodes, Bi 3+ adsorption on planar Au films and superconformal Au filling of trenches up to 45 μm deep are examined in Bi 3+ -containing Na 3 Au(SO 3 ) 2 electrolytes with pH between 9.5 and 11.5. Higher pH is found to increase the potential-dependent rate of Bi 3+ adsorption on planar Au surfaces, shortening the incubation period that precedes active Au deposition on planar surfaces and bottom-up filling in patterned features. Decreased contact angles between the Au seeded sidewalls and bottom-up growth front also suggest improved wetting. The bottom-up filling dynamic in trenches is, however, lost at pH 11.5. The impact of Au concentration, 80 mmol/L versus 160 mmol/L Na 3 Au(SO 3 ) 2 , on bottom-up filling is examined in trenches up to ≈ 210 μm deep with aspect ratio of depth/width ≈ 30. The microstructures of void-free, bottom-up filled trench arrays used as X-ray diffraction gratings are characterized by scanning electron microscopy (SEM) and Electron Backscatter Diffraction (EBSD), revealing marked spatial variation of the grain size and orientation within the filled features.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2008

An ATP System for Deep-Space Optical Communication

An acquisition, tracking, and pointing (ATP) system is proposed for aiming an optical-communications downlink laser beam from deep space. In providing for a direction reference, the concept exploits the mature technology of star trackers to eliminate the need for a costly and potentially hazardous laser beacon. The system would include one optical and two inertial sensors, each contributing primarily to a different portion of the frequency spectrum of the pointing signal: a star tracker (<10 Hz), a gyroscope (<50 Hz), and a precise fluid-rotor inertial angular-displacement sensor (sometimes called, simply, "angle sensor") for the frequency range >50 Hz. The outputs of these sensors would be combined in an iterative averaging process to obtain high-bandwidth, high-accuracy pointing knowledge. The accuracy of pointing knowledge obtainable by use of the system was estimated on the basis of an 8-cm-diameter telescope and known parameters of commercially available star trackers and inertial sensors: The single-axis pointing-knowledge error was found to be characterized by a standard deviation of 150 nanoradians - below the maximum value (between 200 and 300 nanoradians) likely to be tolerable in deep-space optical communications.

Lee, Shinhak↗

DOE OSTI2020

An Observational Comparison of Level of Neutral Buoyancy and Level of Maximum Detrainment in Tropical Deep Convective Clouds

Tropical deep convective clouds are important drivers of large-scale atmospheric circulation representing the main vertical transport pathway through the depth of the troposphere for heat, momentum, water, and chemical species. The strength and depth of this transport are impacted by the convective updraft size and intensity that are driven by buoyancy, dynamical forcing, and mixing of environmental air, i.e., entrainment. In this paper, we identify tropical deep convective systems with well-defined forward anvils using Atmospheric Radiation Measurement (ARM) ground-based profiling radars, at three ARM fixed-sites in the Tropical Western Pacific (TWP; i.e., Manus, Nauru, Darwin) and three ARM Mobile Facility deployments in Niamey, Niger; Gan Island, Maldives; and Manacapuru, Brazil. We use the difference between the level of neutral buoyancy (LNB) and the level of maximum detrainment (LMD) as a proxy for the effective bulk convective entrainment (εproxy). The LNB, the theoretical height that a parcel raised above the level of free convection would reach with no mixing, is calculated based on pre-convection radiosonde measurements using parcel theory. The LMD is the height of the maximum reflectivity observed in forward anvil clouds by profiling radars. Deep convective systems over the TWP show higher LNBs that extend to 16.3 km on average and larger εproxy (median LNB minus LMD up to 6.5 km) compared to their continental counterparts in the Amazon and West Africa. Oceanic conditions show larger convective available potential energy (CAPE) coupled with higher moisture at low levels which favors larger εproxy. In contrast, continental cases initiate and develop under high convective inhibition, steeper environmental lapse rate, and high wind shear conditions, which show smaller offset between LNB and LMD. Deep convective cases that promote significant cold pools at the surface experience less εproxy. Using a Random Forest regression algorithm, CAPE is associated with the highest feature importance score for predicting convective εproxy, followed by low-level relative humidity. For continental cases, the low-level wind shear also indicates higher importance.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Thermal Equations of State of Magnesite: Implication for the Complex Mid‐Lower Mantle Seismic Scatterers

Abstract Magnesite (MgCO 3 ) entering the lower mantle together with the subducted oceanic crust is an important carbon carrier. The reaction between magnesite and mantle minerals has been documented, but its influence on the density and velocity profiles of lower mantle remains unexplored. To decipher the deep carbon transportation and its associated effect, here we determined the thermal equations of state of magnesite up to 120 GPa and 2600 K using X‐ray diffraction in laser‐heated diamond anvil cells. The obtained thermal elastic parameters of magnesite facilitated a comprehensive understanding on the influence of magnesite‐SiO 2 reaction, variation of carbon and SiO 2 content, and temperature on the origin of lower‐mantle scatterers at 1,000–1,800 km depth. Our modeling revealed that the depth of the lower‐mantleV S scatterers is mainly controlled by the Al 2 O 3 content in SiO 2 , while its magnitude depends on the SiO 2 content. Along normal geotherm, the magnesite‐SiO 2 reaction would occur before the post‐stishovite transition, consuming substantial SiO 2 in the subducted oceanic crust. Depending on the amount of residual SiO 2 , the post‐stishovite transition can produce a 2.5–5.2 (2)%V S reduction, compatible with the observed seismic scatterers in Izu‐Bonin and Mariana subduction zones. Along slab geotherm, this reaction occurs after the post‐stishovite transition, generating a greaterV S reduction of 4.4–6.4 (4)%. We thus propose that the reaction between sinking MgCO 3 and SiO 2 in the slab is one of the potential factors influencing the magnitude of the lower‐Vs scatterers at 1,000–1,900 km depth. Our results provide new insights into the deep‐mantle carbonate transportation influencing regional geophysics.

Geochemistry & Geophysics↗

DOE OSTI2018

3-D Modeling of Induced Seismicity Along Multiple Faults: Magnitude, Rate, and Location in a Poroelasticity System

Understanding of the potential to injection–induced seismicity along faults requires the response of fault zone system to spatiotemporal perturbations in pore pressure and stress. In this study, three–dimensional (3–D) model system consisting of the caprock, reservoir, and basement is intersected by vertical strike–slip faults. We examine the full poroelastic behavior of the formation and perform the mechanical analysis along each fault zone using the Coulomb stress change. The magnitude, rate, and location of potential earthquakes are predicted using the spatial distribution of stresses and pore pressure over time. Rapid diffusion of pore pressure into conductive faults initiates failure, but the majority of induced seismicity occurs at deep fault zones due to poroelastic stabilization near the injection interval. Less permeable faults can be destabilized by either delayed pore pressure diffusion or poroelastic stressing. A two–dimensional (2–D) horizontal model, representing the interface between the reservoir and the basement, limits diffusion of pore pressure and deformation of the formation in the vertical direction that may overestimate or underestimate the potential of earthquakes along the fault. Lastly, our numerical results suggest that the 3–D modeling of faulting system including poroelastic coupling can reduce the uncertainty in the seismic hazard prediction by considering the hydraulic and mechanical interaction between faults and bounding formations.

3‐D modeling↗

DOE OSTI2014

Phylogenetic and Functional Diversity of Microbial Communities Associated with Subsurface Sediments of the Sonora Margin, Guaymas Basin

Subsurface sediments of the Sonora Margin (Guaymas Basin), located in proximity of active cold seep sites were explored. The taxonomic and functional diversity of bacterial and archaeal communities were investigated from 1 to 10 meters below the seafloor. Microbial community structure and abundance and distribution of dominant populations were assessed using complementary molecular approaches (Ribosomal Intergenic Spacer Analysis, 16S rRNA libraries and quantitative PCR with an extensive primers set) and correlated to comprehensive geochemical data. Moreover the metabolic potentials and functional traits of the microbial community were also identified using the GeoChip functional gene microarray and metabolic rates. The active microbial community structure in the Sonora Margin sediments was related to deep subsurface ecosystems (Marine Benthic Groups B and D, Miscellaneous Crenarchaeotal Group, Chloroflexi and Candidate divisions) and remained relatively similar throughout the sediment section, despite defined biogeochemical gradients. However, relative abundances of bacterial and archaeal dominant lineages were significantly correlated with organic carbon quantity and origin. Consistently, metabolic pathways for the degradation and assimilation of this organic carbon as well as genetic potentials for the transformation of detrital organic matters, hydrocarbons and recalcitrant substrates were detected, suggesting that chemoorganotrophic microorganisms may dominate the microbial community of the Sonora Margin subsurface sediments.

59 BASIC BIOLOGICAL SCIENCES↗