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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

PV Inverter Systems Enabled by Monolithically Integrated SiC based Four Quadrant Power Switch (4-QPS) [BiDFET]

The purpose of this project was to develop a new breed of Power Conversion Systems (PCS) for PV integration that is enabled by the newly developed 4-Quadrant Single Die SiC Power Semiconductor Switches (4-QPS) or also referred to as “Bidirectional FET (BIDFET)”. This work includes semiconductor die development, advanced packaging, converter design, development, and testing of 4-QPS enabled hardware prototypes at 1 kW (for single phase residential application) and 10 kW (for three phase commercial application). The BiDirectional Field-Effect Transistor (BiDFET) can enable circuit topologies requiring four-quadrant switches, that were earlier designed using discrete combinations of MOSFETs, IGBTs, GaN HEMTs, and PiN diodes. The monolithic nature of the BiDFET allows lower device count, smaller switch volume, lower inductance, and simpler packaging, and hence more reliable and commercially viable implementation in power electronics converters. The matrix converter topologies, now feasible using BiDFETs, can eliminate the bulky and unreliable dc link capacitors or inductors required for conventional voltage-source or current-source converters in ac–ac and ac–dc applications. The 1.2 kV BiDFET has the potential to disrupt all the applications utilizing 1.2 kV switches, including electric vehicle (EV) drivetrain, bidirectional EV chargers, industrial motor drives, solid-state transformers, datacenter power supplies, elevator drives, dc microgrids, energy storage grid integration, solid-state breakers, etc.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Multi-Sensor Optimal Motion Planning for Radiological Contamination Surveys by Using Prediction-Difference Maps

Distributed and networked mobile sensor platforms using unmanned aerial and/or ground vehicles to survey areas of interest offer a safer and more efficient method for radiological contamination mapping; however, most applications rely on uniformly sweeping of the area in a raster-type motion without utilizing the information available in a dynamic sense. We have developed a fully autonomous optimal motion planning procedure for networks with two or more mobile sensors. The procedure utilizes well-established concepts of Gaussian processes in combination with control laws based on centroidal Voronoi tessellations to achieve optimal next-iteration sensor movements. A new method of informing optimal motion planning is proposed, whereby the absolute difference between the prior and current full-map prediction, referred to as the prediction-difference map, is used as the spatial density function within each Voronoi cell, providing immediate and iterative feedback for dynamic use of available information. The Gaussian process regression model used to estimate the contamination in unvisited locations also provides prediction uncertainties, and can be used as a quantitative metric to assess the confidence in the calculated contamination map; these estimates and prediction uncertainties are unavailable for standard uniform survey routines as they can only produce maps in the vicinity of observed locations. We present through simulation the achievable performance gains from using this new method by directly comparing to a uniform survey method. Results show that using the prediction-difference maps to inform motion planning procedures offers a faster rate of producing an accurate and convergent map relative to a uniform survey route.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Quantification of Off-Channel Inundated Habitat for Pacific Chinook Salmon ( Oncorhynchus tshawytscha ) along the Sacramento River, California, Using Remote Sensing Imagery

Off-channel areas are one of the most impacted aquatic habitats by humans globally, as extensive agricultural and urban development has limited them to roughly 10% of historical extent. This is also true for California’s Sacramento River Valley, where historically frequent widespread inundation has been reduced to a few off-channel water bodies along the mid-Sacramento River. This remaining shallow-water habitat provides crucial ecological benefits to multiple avian and fish species, but especially to floodplain-adapted species such as Chinook salmon (Oncorhynchus tshawytscha). Characterizing spatiotemporal off-channel dynamics, including inundation extent and residence time, is fundamental to better understanding the intrinsic value of such habitats and their potential to support recovery actions. Remote sensing techniques have been increasingly used to map surface water at regional and local scales, with improved resolutions. As such, this study maps off-channel inundation areas and describes their temporal dynamics by analyzing pixel-based time- series of multiple water indices, modified Normalized Difference Water Index (mNDWI) and the Automated Water Extraction Index (AWEI), generated from LandSat-8 and Sentinel-2 data between 2013–2021. Quantified off-channel area was similar with each water index and method used, but improved performance was associated with Sentinel-2 products and AWEI index to identify wetted areas under lower mainstem discharges. Results indicate an uneven distribution of off-channel habitat in the study area, with limited inundated areas in upstream reaches (<16% of total off-channel area for greater flows). In addition, much less habitat exists for flows under 400 m 3 /s, an important migration cue for endangered winter-run Chinook salmon, limiting juvenile access to areas with enhanced rearing conditions. Off-channel habitat residence times averaged between 7 and 16 days, primarily defined by the rate of receding flows, with rapid flow recession providing marginal off-channel habitat. This study shows reasonable performance of moderate resolution LandSat-8 and Sentinel-2 remote sensing imagery to characterize shallow-water inundated habitat in higher-order rivers, and as a method to inform restoration and native fish recovery efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment

Abstract The spatial dispersion of photoelements within a vegetation canopy, quantified by the clumping index (CI), directly regulates the within‐canopy light environment and photosynthesis rate, but is not commonly implemented in terrestrial biosphere models to estimate the ecosystem carbon cycle. A few global CI products have been developed recently with remote sensing measurements, making it possible to examine the global impacts of CI. This study deployed CI in the radiative transfer scheme of the Community Land Model version 5 (CLM5) and used the revised CLM5 to quantitatively evaluate the extent to which CI can affect canopy absorbed radiation and gross primary production (GPP), and for the first time, considering the uncertainty and seasonal variation of CI with multiple remote sensing products. Compared to the results without considering the CI impact, the revised CLM5 estimated that sunlit canopy absorbed up to 9%–15% and 23%–34% less direct and diffuse radiation, respectively, while shaded canopy absorbed 3%–18% more diffuse radiation across different biome types. The CI impacts on canopy light conditions included changes in canopy light absorption, and sunlit–shaded leaf area fraction related to nitrogen distribution and thus the maximum rate of Rubisco carboxylase activity ( V cmax ), which together decreased photosynthesis in sunlit canopy by 5.9–7.2 PgC year −1 while enhanced photosynthesis by 6.9–8.2 PgC year −1 in shaded canopy. With higher light use efficiency of shaded leaves, shaded canopy increased photosynthesis compensated and exceeded the lost photosynthesis in sunlit canopy, resulting in 1.0 ± 0.12 PgC year −1 net increase in GPP. The uncertainty of GPP due to the different input CI datasets was much larger than that caused by CI seasonal variations, and was up to 50% of the magnitude of GPP interannual variations in the tropical regions. This study highlights the necessity of considering the impacts of CI and its uncertainty in terrestrial biosphere models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes

Cells can sense changes in their extracellular environment and subsequently adapt their biomass composition. Nutrient abundance defines the capability of the cell to produce biomass components. Under nutrient-limited conditions, resource allocation dramatically shifts to carbon-rich molecules. Here, we used dynamic biomass composition data to predict changes in growth and reaction flux distributions using the available genome-scale metabolic models of five eukaryotic organisms (three heterotrophs and two phototrophs). We identified temporal profiles of metabolic fluxes that indicate long-term trends in pathway and organelle function in response to nitrogen depletion. Surprisingly, our calculations of model sensitivity and biosynthetic cost showed that free energy of biomass metabolites is the main driver of biosynthetic cost and not molecular weight, thus explaining the high costs of arginine and histidine. We demonstrated how metabolic models can accurately predict the complexity of interwoven mechanisms in response to stress over the course of growth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Data-driven Cyberattack Detection for Photovoltaic (PV) Systems through Analyzing Micro-PMU Data

With increasing exposure to software-based sensing and control, Photovoltaic (PV) systems are facing higher risks of cyber attacks. Here, to ensure the system stability and minimize potential economic losses, it is imperative to monitor operating states and detect attacks at the early stage. To meet this demand, Micro-Phasor Measurement Units (μPMU) are increasingly popular in monitoring distribution networks. However, due to the relatively low sampling rate, μPMU has not yet been used to detect and classify cyber-attacks in power electronics enabled smart grid. To our knowledge, this is one of the first attempts to use μPMU to detect cyber attacks that degrade the performance of power electronics systems. We propose to apply data-driven methods on micro-PMU data to implement attack detection. We have evaluated data-driven methods, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM) and convolutional neural network (CNN). The proposed CNN model achieves the required performances with the highest 99.23% accuracy and 0.9963 F 1 score.

14 SOLAR ENERGY↗
DOE OSTI2021

Parameterization of arctic hydrometeor physics using new precipitation measurement technologies: Final Report

Predictions of precipitation are highly sensitive to the accuracy of parameterized growth and sedimentation processes, especially in remote regions such as the Arctic where observations are scarce. The goal of this study was to combine measurements of meteorological conditions and retrievals from passive and active remote sensors to provide refined parameterizations of precipitation properties and processes with a particular focus on the Arctic. To accomplish the project goals, new instrumentation was used from the Oliktok Point Mobile Facility and the Utqiagvik (Barrow) North Slope of Alaska ARM site focusing in particular on Multi-Angle Snowflake Camera (MASC) data. The MASC is the first device able to automatically photograph precipitation particles in free-fall from multiple angles while simultaneously measuring their fall speed. The MASC installed at the ARM Oliktok Point and Utqiagvik facilities was used to examine the nature of Arctic precipitation, taking particular advantage of the unique suite of precipitation, meteorological, and remote sensing instrumentation that is available at the high latitude sites. Combined with radiometer data we were able to explore the relationship between precipitation particle characteristics and the clouds where the snow is created. Ground-based wind measurements provided detailed data on turbulence. Further theoretical work explored the physical mechanisms controlling precipitation size distributions, the physics determining hydrometeor fallspeed, and the existence of general solutions to the Navier-Stokes equations for falling particles. Outreach work introduced hydrometeor classification to classrooms across the United States.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Creating Accurate Methane Emission Inventories through Data-Driven Airborne Survey Strategies

Because natural gas emits less carbon than other fossil fuels, it holds promise as a green energy transition fuel. However, the overall carbon footprint of natural gas is significantly elevated by methane emissions that occur during its production and transmission (Cusworth et al. 2022). Methane “super-emitters,” while comprising only about 1% of sites, are responsible for the majority of oil- and gas-sourced methane emissions, making their detection and mitigation critical in reducing the climate impact of natural gas and in meeting national and global sustainability goals (Sherwin et al. 2024). Yet, despite advancements in detection, significant uncertainties remain regarding the size, frequency, and duration distributions of methane emissions (e.g., Frankenberg et al. 2016, Cusworth et al. 2022, Chen, Sherwin et al. 2022, Conrad et al. 2023, Johnson et al. 2023, Sherwin et al. 2024) underscoring the need for comprehensive emissions inventories segmented by basin across the US. Airborne surveys are well-suited for collecting data to build these comprehensive, basin-level inventories because they allow for extensive spatial coverage, and have the spatial resolution, and the sensitivity to pinpoint individual methane sources. As remote sensing technologies enable rapid basin-scale surveys, it is imperative to establish scientifically and statistically robust standards to generate reliable and actionable emissions inventories. Recent work has shown that differences in airborne sampling strategies, detection technologies, and analysis can lead to large differences between survey conclusions if not correctly accounted for (Chen et al. 2024). This elevates the importance of incorporating proper sampling and analysis techniques when designing a methane emissions monitoring campaign to produce accurate results and facilitate cross-study comparisons. In this paper, we describe a survey strategy designed using the latest conclusions from the literature to align results from different aerial surveys. We identify several sampling and analysis principles, including large sample sizes, balanced sampling across oil and gas production, careful survey area definition, and a unified protocol for analysis, to be vital to producing an unbiased estimate of basin-scale emissions. We present results from a Department of Energy-funded project that deployed this survey strategy in two understudied oil and gas- producing regions in the United States: the Haynesville Basin in Texas and Louisiana, and the Woodford Shale in the Anadarko Basin in Oklahoma.

03 NATURAL GAS↗
DOE OSTI2023

Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning

This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Cobalt-Doped Tungsten Oxide Nanoparticles for Electrochemical Sensing and Detection of a Penicillin Antibiotic: Amoxicillin

A sensitive electrochemical sensor for the determination of antibacterial amoxicillin (AMXL) in pharmaceutical dosage and environmental samples was developed, since its residues were found in water, food, and biological samples to pose adverse health risks such as immune–allergic reactions and antimicrobial resistance. WO 3 -based sensors are reported to provide higher sensitivity, selectivity, and stability during the electrochemical reaction. Considering this, Co-doped WO 3 nanostructures were synthesized by the hydrothermal method yielding a mixture of nanoparticle and nanorod structures and applied to the assay of AMXL. Extensive characterizations were carried out by SEM, XRD, TEM, and XPS analysis that confirm the monoclinic crystal structure with a uniform Co distribution in the WO 3 lattice. The optimum doping amount of Co in the lattice is found to be 1.80 at %. The electrochemical performance of the Co-WO 3 /carbon paste electrode (CPE) was evaluated for the determination of AMXL under optimum conditions using cyclic voltammetry, electrochemical impedance spectroscopy, and square wave voltammetry. The Co-WO 3 /CPE showed two linear detection ranges at 0.01–0.1 μM with the linear regression I pa = 3.73 [AMXL] + 0.77 with R 2 = 0.96 and at 0.1 to 30.0 μM with the linear regression I pa = 3.76 [AMXL] + 0.69 with R 2 = 0.95. The limit of detection was 9.1 nM (SNR = 3), and the limit of quantification was 30.6 nM (SNR = 10). The sensitivities for lower and higher concentration ranges were 63.22 and 63.73 μA μM –1 cm –2 , respectively. Furthermore, Co-WO 3 /CPE showed high selectivity and anti-interference capabilities. In conclusion, the sensing and disposable Co-WO 3 /CPE sensor showed a great potential for AMXL analysis in spiked soil, water, and Amoxil tablet samples with desirable percentage recovery.

Shanbhag, Mahesh M. [KLE Technological University,↗
DOE OSTI2022

Future energy infrastructure, energy platform and energy storage

Today fossil energy dominates energy consumption across the world. There has been an increasing momentum to reduce fossil energy consumption and increase renewable energy utilization to more than 70%. Such high penetrations of distributed renewable resources bring large uncertainty and complexity that cannot be easily handled by the current infrastructure. For the last two decades, telecommunication industry has gone through a similar transition from centralized to distributed systems, and more recently to platform approaches, which become the corner stone of today’s vibrant platform economy. Based on telecommunication history, we believe that a platform-based approach, called the energy platform, is a viable solution for addressing the renewable energy challenges. The energy platform consists of an array of computational algorithms, sensing and control technologies for key industry, energy generators and users to jointly manage and control the complex energy infrastructure. It includes the following key components: (1) the hardware and software to generate, store, control and transmit electricity/data (the energy cloud), (2) the digital platforms and tools for key energy producers, digital, service and communication industry to share the assets, participate in and manage the complex infrastructure, and for customers to participate in energy transactions, (3) the transaction platform for new service and business models. Additionally, the energy platform requires breakthroughs in large scale energy storage and many other areas including efficient power electronics, sensors and controls, new mathematical and computational tools, and deep integration of energy technologies and information sciences to control and stabilize such complex chaotic systems.

25 ENERGY STORAGE↗
DOE OSTI2022

Dual-band selective circular dichroism in mid-infrared chiral metasurfaces

Most chiral metamaterials and metasurfaces are designed to operate in a single wavelength band and with a certain circular dichroism (CD) value. Here, mid-infrared chiral metasurface absorbers with selective CD in dual-wavelength bands are designed and demonstrated. The dual-band CD selectivity and tunability in the chiral metasurface absorbers are enabled by the unique design of a unit cell with two coupled rectangular bars. It is shown that the sign of CD in each wavelength band can be independently controlled and flipped by simply adjusting the geometric parameters, the width and the length, of the vertical rectangular bars. The mechanism of the dual-band CD selection in the chiral metasurface absorber is further revealed by studying the electric field and magnetic field distributions of the antibonding and bonding modes supported in the coupled bars under circularly polarized incident light. Furthermore, the chiral resonance wavelength can be continuously increased by scaling up the geometric parameters of the metasurface unit cell. The demonstrated results will contribute to the advance of future mid-infrared applications such as chiral molecular sensing, thermophotovoltaics, and optical communication.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fast response High Altitude Laser Optical Sensor (HALOS) for atmospheric water and temperature measurements

Advanced instrumentation and sensing methods are the key factor in the determination of many climate science research problems. In this Phase I STTR a laser-based diagnostic system for atmospheric temperature and water concentrations measurements was developed, this system, HALOS – High Altitude Laser Optical Sensor, employs a mid-infrared (MIR) quantum cascade laser (QCL) of the distributed feedback variety (DFB) powered by a 100 kHz rep rate ramp modulated current profile which enables spectral profiling. Measurements made during Phase I showed that HALOS can surpass the available hygrometers refresh rates while offering superior accuracy (<4%) from the low Frost Point (Tfrost) of -110°C to dew points (Tdew) at 35°C while being able to encompass sub ppm concentration at altitude and supersaturated conditions at ground level. Readings from HALOS include temperature sensitivity on the order of 0.2°C and absolute humidity sensitivity (AH) on the order of 0.010 μmol/m 3 (0.18 mg/m 3 ) at a data output rate exceeding 1 kHz. HALOS can be easily integrable in existing Department of Energy (DOE) or National Oceanic and Atmospheric Administration (NOAA) conventional or remote operated aircraft having either a fixed or rotary wing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An Automated Method for Mapping Giant Kelp Canopy Dynamics from UAV

Satellite and aerial imagery have been used extensively for mapping the abundance and distribution of giant kelp ( Macrocystis pyrifera ) in southern California. There is now great potential for using unoccupied aerial vehicles (UAVs) to map kelp canopy at very high resolutions. However, tides and currents have been shown to affect the amount of floating kelp canopy on the water surface, and the impacts of these processes on remotely sensed kelp estimates in this region have not been fully quantified. UAVs were used to map fine-scale changes in canopy area due to tidal height and current speed at kelp forests off the coast of Palos Verdes, CA and Santa Barbara, CA. An automated method for detecting kelp canopy was developed that was 67% accurate using red-green-blue (RGB) UAV imagery and 93% accurate using multispectral UAV imagery across a range of weather, ocean, and illumination conditions. Increases in tidal height of 1 m reduced the amount of floating kelp canopy by 15% in Santa Barbara and by over 30% in Palos Verdes. The effect of current speed on visible kelp canopy was inconclusive, but there was a trend towards lower canopy area with increased current speed. Therefore, while tidal height and current speed can introduce significant variability to estimates of kelp abundance, the magnitude of this variability is site specific. Still, UAVs are a valuable tool for mapping of kelp canopy and can provide greater spatial resolution and temporal coverage than is possible from many satellite sensors. This data can provide insight into the patterns and drivers of high frequency fluctuations in kelp abundance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Protection schemes used in North American microgrids

This study reviewed existing conventional and nonconventional protection schemes for grid-connected and islanded mode operations in North American microgrid projects. The microgrid projects investigated in this study used different types of distributed energy resources (DERs) and integrated hydropower/diesel generators, gas/steam/wind turbines, and photovoltaic systems with energy storage. In this work, conventional protection schemes were defined as those within the IEEE Standard C37.2-2008, whereas nonconventional schemes were those not defined within this standard. The pros and cons of conventional and nonconventional protection schemes were discussed in detail. The overvoltage, undervoltage, and frequency elements were the most common conventional protection schemes applied in microgrid projects in North America. These protection elements were used to detect the islanded conditions and faults that could not be sensed by overcurrent relays because of small fault currents contributed by low-inertia DERs and power-electronic sources. Directional overcurrent elements were used to distinguish between external (grid) and internal (microgrid) faults. Adaptive protection was the most popular nonconventional protection scheme applied to the microgrid projects. In conclusion, different types of DERs and operational modes must be considered in order to address the protection and control challenges of each microgrid and to obtain the best technical and economical solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Advanced fuel fusion, phase space engineering, and structure-preserving geometric algorithms

Non-thermal advanced fuel fusion trades the requirement of a large amount of recirculating tritium in the system for that of large recirculating power. Phase space engineering technologies utilizing externally injected electromagnetic fields can be applied to meet the challenge of maintaining non-thermal particle distributions at a reasonable cost. The physical processes of the phase space engineering are studied from a theoretical and algorithmic perspective. It is emphasized that the operational space of phase space engineering is limited by the underpinning symplectic dynamics of charged particles. The phase space incompressibility according to the Liouville theorem is just one of many constraints, and Gromov's non-squeezing theorem determines the minimum footprint of the charged particles on every conjugate phase space plane. In this sense and level of sophistication, the mathematical abstraction of phase space engineering is symplectic topology. To simulate the processes of phase space engineering, such as the Maxwell demon and electromagnetic energy extraction, and to accurately calculate the minimum footprints of charged particles, recently developed structure-preserving geometric algorithms can be used. The family of algorithms conserves exactly, on discretized spacetime, symplecticity and thus incompressibility, non-squeezability, and symplectic capacities. The algorithms apply to the dynamics of charged particles under the influence of external electromagnetic fields as well as the charged particle–electromagnetic field system governed by the Vlasov–Maxwell equations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗