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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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This webinar covers and introduction to customer rates and tariff design, drivers of utility costs, impacts of evolving grids on rate design, retail price signals, and methods for evaluating rates.
We present a new optical (400–950 nm) transmission spectrum of the hot Jupiter WASP-31b (M = 0.48 M {sub J}; R = 1.54 R {sub J}; P = 3.41 days), obtained by combining four transit observations. These transits were observed with IMACS on the Magellan Baade Telescope at Las Campanas Observatory as part of the ACCESS project. We investigate the presence of clouds/hazes in the upper atmosphere of this planet, as well as the contribution of stellar activity on the observed features. In addition, we search for absorption features of the alkali elements Na i and K i, with particular focus on K i, for which there have been two previously published disagreeing results. Observations with Hubble Space Telescope (HST)/STIS detected K i, whereas ground-based low- and high-resolution observations did not. We use equilibrium and nonequilibrium chemistry retrievals to explore the planetary and stellar parameter space of the system with our optical data combined with existing near-IR observations. Our best-fit model is that with a scattering slope consistent with a Rayleigh slope (α=5.3{sub −3.1}{sup +2.9}), high-altitude clouds at a log cloud top pressure of −3.6{sub −2.1}{sup +2.7} bars, and possible muted H{sub 2}O features. We find that our observations support other ground-based claims of no K i. Clouds are likely why signals like H{sub 2}O are extremely muted and Na or K cannot be detected. We then juxtapose our Magellan/IMACS transmission spectrum with existing VLT/FORS2, HST/WFC3, HST/STIS, and Spitzer observations to further constrain the optical-to-infrared atmospheric features of the planet. We find that a steeper scattering slope (α = 8.3 ± 1.5) is anchored by STIS wavelengths blueward of 400 nm and only the original STIS observations show significant potassium signal.
This presentation showcases a framework for oscillation analysis and mitigation based on small-signal analysis.
This paper proposes a TSO-DSO framework including DER set-point dispatch using sensitivity analysis. We address all three steps of TSO-DSO integration stating by estimating the capability curve of an unbalanced distribution system and then modeling of capability curve on the transmission side grid and estimating the reactive power needed from DSO during emergencies, and ending by dispatching the requested reactive power from TSO. The proposed interaction framework is tested on an IEEE 37-node distribution network connected to IEEE 9 bus transmission system. The simulation shows that using sensitivity-based dispatch would reduce the number of communication signals between DERs and DSO and that can decrease the risk of communication contingency.
Integration of renewable energy and energy storage based Distributed Energy Resource (DER) assets to the grid has seen an upsurge over the past few decades. Traditionally, energy would be generated at a thermal or nuclear power plant and then transmitted over long distance through the transmission grid and then supply to the customers at the distribution grid. Modern renewable energy based DER assets have brought energy production at the edge of distribution grid, which has made the grid vulnerable to cyber intrusion as the power flow controllers for such DER assets are now distributed across the grid from distribution level to transmission level. This paper discusses the vulnerability posed by such power flow controllers of DER assets and demonstrates a detection methodology for unauthorized access and manipulation of system configuration for Digital Signal Processor based Microcontrollers. Experimental results proving efficacy of the methodology have been shown in paper.
Aquatic organism tracking devices, systems and associated methods are described. According to one aspect, an aquatic organism tracking device includes a housing, a transducer coupled with the housing and configured to transmit a data transmission externally of the housing and an aquatic organism associated with the tracking device, a programmable oscillator coupled with the housing, and wherein the programmable oscillator is configured to generate a clock signal having a selected one of a plurality of different frequencies, processing circuitry coupled with the housing and configured to receive the clock signal from the programmable oscillator and to execute a plurality of executable instructions according to the clock signal, a power source coupled with the housing and configured to store electrical energy, and wherein the processing circuitry is configured to control the provision of the electrical energy from the power source to the transducer to generate the data transmission as a result of the execution of the instructions.
Tectonic faults fail in a continuum of modes from slow earthquakes to elastodynamic rupture. Precursory variations in elastic wavespeed and amplitude, interpreted as indicators of imminent failure, have been observed in limited natural settings and lab experiments where they are thought to arise from contact rejuvenation and microcracking within and around the fault zone. However, the physical mechanisms and connections to fault creep are poorly understood. Here we vary loading stiffness during frictional shear to generate a range of slip modes and measure fault zone properties using transmitted elastic waves. We find that elastic wave amplitudes show clear changes before fault failure. The temporal onset of amplitude reduction scales with lab earthquake magnitude and the magnitude of this reduction varies with fault slip. Our data provide clear evidence of precursors to lab earthquakes and suggest that continuous seismic monitoring could be useful for assessing fault state and seismic hazard potential.
The lack of sufficient labeled events and long training time limit the applicability of deep neural network-based power system event identification using synchrophasor data. In this paper, we propose to leverage transfer learning technique to boost the reliability and reduce the required training time of neural classifier for power system event identification. We use the weights of a neural classifier trained on one transmission system as the initial parameters of another neural classifier for a different transmission system. Numerical tests with real-world synchrophasor data from the Eastern and Western Interconnections of the United States show that the proposed transfer learning approach is very effective in not only improving the training reliability but also reducing the training time.
During long-distance sound propagation in planetary atmospheres, acoustic waves may reflect off the air/surface interface one or more times. For low sound frequencies and flat interfaces, the incident and reflected wave tend to be nearly identical. However, this may not be the case when the downgoing acoustic wave encounters topography. Here, we describe a set of direct and reflected acoustic signals recorded on free-flying balloons during the hypersonic entry of the OSIRIS-REx sample return capsule (SRC). In two of the three cases presented here, an impulsive reflected arrival similar in form to the direct sonic boom of the SRC is observed, followed by a diffuse coda. In contrast, one of the floating stations lacked an impulsive reflection entirely, with only the coda present. We use full waveform modeling to show how reflection in the presence of complex topography can explain coda seen in all three examples as well as the lack of impulsive arrival on the third. Our results indicate that the complex signals often observed in long range acoustic propagation could be due, in part, to interactions with topography during transmission.
Due to the rapid development of economies, large urban cities consume an increasing amount of energy and have a higher requirement for power quality. Voltage source converter based high voltage direct current (VSC-HVDC) is a promising device to transmit clean power from remote regions to urban power systems, while also providing wide area damping control (WADC) for frequency stabilization. However, the time-delay naturally existing in the VSC-HVDC system may degrade the performance of WADC and even result in instability. To address this issue, this paper develops a time-delay correction control strategy for VSC-HVDC damping control in urban power grids. First, a small signal model of WADC is built to analyze the negative impacts of time delay. Then, a data-driven approach is proposed to compensate for the inherent time delay in VSC-HVDC damping control. The extensive training data will be generated under various disturbances. After offline training, the long short-term memory network (LSTM) can be implemented online to predict the actual frequency deviation based on real-time measurements. Finally, the proposed method is validated through MATLAB-Simulink in a two-area four-machine system. The results indicate that the data-driven compensation has a strong generalization ability for random delay time constants and can improve the performance of WADC significantly.
We present a new ground-based visible transmission spectrum of the high-gravity, hot Jupiter HAT-P-23b, obtained as part of the ACCESS project. We derive the spectrum from five transits observed between 2016 and 2018, with combined wavelength coverage between 5200 Å and 9269 Å in 200 Å bins, and with a median precision of 247 ppm per bin. HAT-P-23b’s relatively high surface gravity (g ≈ 30 m s{sup −2}), combined with updated stellar and planetary parameters from Gaia DR2, gives a five-scale-height signal of 384 ppm for a hydrogen-dominated atmosphere. Bayesian models favor a clear atmosphere for the planet with the tentative presence of TiO, after simultaneously modeling stellar contamination, using spots parameter constraints from photometry. If confirmed, HAT-P-23b would be the first example of a high-gravity gas giant with a clear atmosphere observed in transmission at optical/near-IR wavelengths; therefore, we recommend expanding observations to the UV and IR to confirm our results and further characterize this planet. This result demonstrates how combining transmission spectroscopy of exoplanet atmospheres with long-term photometric monitoring of the host stars can help disentangle the exoplanet and stellar activity signals.
In this study, we report on the thermal conductivity of amorphous carbon generated in diamond via nitrogen ion implantation (N 3+ at 16.5 MeV). Here, transmission electron microscopy techniques demonstrate amorphous band formation about the longitudinal projected range, localized approximately 7 μm beneath the sample surface. While high-frequency time-domain thermoreflectance measurements provide insight into the thermal properties of the near-surface preceding the longitudinal projected range depth, a complimentary technique, steady-state thermoreflectance, is used to probe the thermal conductivity at depths which could not otherwise be resolved. Through measurements with a series of focusing objective lenses for the laser spot size, we find the thermal conductivity of the amorphous region to be approximately 1.4 W m –1 K –1 , which is comparable to that measured for amorphous carbon films fabricated through other techniques.
The ever-increasing use of Internet of Things (IoT) devices results in the implementation of multiple wireless technologies that would not only cater their data rate requirements but also support various applications. To optimize the energy efficiency and security of the wireless transmission, it is imperative to identify the wireless technologies in various IoT implementations. Many of the existing approaches are based on measuring only the receiving signal strength indicator (RSSI). However, such approaches may not work well because of transmit power control and complex channel variations among different wireless technologies. In this article, we propose an autonomous wireless detection scheme that considers multiple distinguishable physical (PHY)-layer settings for real-time identification of wireless technologies for real-time applications. Specifically, the proposed scheme relies on the PHY-layer measurements of the targeted spectrum. Transmission settings, such as bandwidth, carrier frequency, and RSSI are estimated from the raw in-phase and quadrature-phase (I/Q) measurements. In addition, a symbol-level extraction scheme is implemented to extract unique features of modulation settings. These aforementioned features are applied to a machine learning process to identify the received wireless technologies. Compared with raw I/Q measurements, the extracted features are much simplified and, thus, the machine learning classifier can be designed with a simple structure for fast processing on IoT nodes. Finally, the proposed schemes are primarily evaluated theoretically, followed by implementing them on a USRP software-defined radio (SDR)-based hardware testbed. The evaluation results demonstrate high accuracy in the real-time detection of different wireless technologies for seamless IoT applications.
As renewable energy penetration increases and the traditional generators retire in power grids, system inertia decreases and exhibits significant daily fluctuations. Furthermore, fast frequency responses (FFRs) provided by the inverter-based resources (IBRs) begin to playa very critical role and bring new challenges to real-time system inertia monitoring due to the difficulties of quantifying its artificial inertia contribution. Thus, necessitating an accurate real-time inertia estimation tool will not only benefit the secure power grid operations, but also provide insights on assessing the artificial inertia contribution from the IBRs. This paper presents a probing-based real-time inertia estimation tool that has been validated through a power-hardware-in-the-Ioop (PHIL) test system using identical hardware battery energy storage system (BESS) and control in an actual power grid. Preliminary results indicate high estimation accuracy of the developed tool and pave the way for the field test and deployment.
Liquid-phase resonant soft X-ray scattering (LP-RSoXS) is an emerging label-free technique to probe chemically resolved nanostructures of molecular or hybrid materials in liquid environments. Still, quantitative analysis is hindered by the pressure-induced deformation of thin silicon nitride (SiN) membranes used as windows in microfluidic flow cells, which attenuates the signal in nonlinear ways, making experimental optimization difficult. Here, in this work, we directly characterize this deformation under experimental conditions for a variety of cell configurations. We use this to develop a predictive model that combines transmission effects of SiN bowing, incident X-ray beam profiles, and material-dependent resonant scattering cross sections to simulate the effective scattering intensity at the detector across the carbon K-edge. Maps of the total signal across the flow cell window reveal that increasing the window width and polymer concentration shifts the anisotropic intensity distributions from the center toward the edges of the window. It was determined that an optimal SiN thickness of 50 nm, with a window aperture of 104 μm, maximizes the total signal for typical solute concentrations and energies across the carbon K-edge. Our results overturn the assumption that corner regions dominate the scattering signal, offering explicit design guidelines for maximizing LP-RSoXS signals and significantly advancing the quantitative application of this technique to the characterization of molecular and hybrid nanostructured materials in liquids.
Nanoparticle-based platforms are gaining strong interest in plant biology and bioenergy research to monitor and control biological processes in whole plants. However, in vivo monitoring of biomolecules using nanoparticles inside plant cells remains challenging due to the impenetrability of the plant cell wall to nanoparticles beyond the exclusion limits (5-20 nm). To overcome this physical barrier, we have designed unique bimetallic silver-coated gold nanorods (AuNR@Ag) capable of entering plant cells, while conserving key plasmonic properties in the near-infrared (NIR). To demonstrate cellular internalization and tracking of the nanorods inside plant tissue, we used a comprehensive multimodal imaging approach that included transmission electron microscopy (TEM), confocal fluorescence microscopy, two-photon luminescence (TPL), X-ray fluorescence microscopy (XRF), and photoacoustics imaging (PAI). We successfully acquired SERS signals of nanorods in vivo inside plant cells of tobacco leaves. On the same leaf samples, we applied orthogonal imaging methods, TPL and PAI techniques for in vivo imaging of the nanorods. This study first demonstrates the intracellular internalization of AuNR@Ag inside whole plant systems for in vivo SERS analysis in tobacco cells. This work demonstrates the potential of this nanoplatform as a new nanotool for intracellular in vivo biosensing for plant biology.
Understanding the atomic structure of quantum emitters, often originating from point defects or impuritie, is essential for designing and optimizing materials for quantum technologies such as quantum computing, communication, and sensing. Despite the availability of atomic-resolution scanning transmission electron microscopy and nanoscale cathodoluminescence microscopy, experimentally determining the atomic structure of individual emitters is challenging due to the conflicting needs for thick samples to generate strong cathodoluminescence signals and thin samples for structural analysis. To overcome this challenge, significantly enhanced cathodoluminescence at twisted interfaces is leveraged to achieve sub-nanometer localization precision for the first time in mapping individual quantum emitters in carbon-implanted hexagonal boron nitride. This unprecedent spatial sensitivity, together with correlative electron energy loss spectroscopy quantitative scanning transmission electron microscopy imaging, and first principles density functional theory calculations, enables the identification of the atomic structure of the 440 nm blue emitter in hexagonal boron nitride as a substituted vertical carbon dimer. Building on the atomic structure insights, nanoscale spatially precise creation of blue emitters is demonstrated by electron beam irradiation of carbon-coated hexagonal boron nitride. This advancement in correlating atomic structures with optical properties lays the foundation for a deeper understanding and precise engineering of quantum emitters, significantly advancing the development of cutting-edge quantum information technologies.
Here, we present a telecommunication-compatible bias-free photoconductive terahertz emitter composed of a bilayer InAs structure directly grown on a high-resistivity silicon substrate. The bilayer InAs structure includes p + -doped and undoped InAs layers, inducing a strong built-in electric field that enables terahertz generation without requiring any external bias voltage. A large-area plasmonic nanoantenna array is used to enhance and confine optical generation inside the photoconductive region with the highest built-in electric field, leading to the generation of a strong ultrafast photocurrent and broadband terahertz radiation. Thanks to a higher terahertz transmission through the silicon substrate and a shorter carrier lifetime in the InAs layers grown on silicon, higher signal-to-noise ratios are achieved at high terahertz frequencies compared with previously demonstrated bias-free terahertz emitters realized on GaAs. In addition to compatibility with silicon integrated optoelectronic platforms, the presented bias-free photoconductive emitter provides more than a 6 THz radiation bandwidth with more than 100 dB dynamic range when used in a terahertz time-domain spectroscopy system.