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At least 343 records · Page 19

A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure

In this work, cascading transmission line failures are studied through a dynamical model of the power system operating under fixed conditions. The power grid is modeled as a stochastic dynamical system where first-principles electromechanical dynamics are excited by small Gaussian disturbances in demand and generation around a specified operating point. In this context, a single line failure is interpreted in a large deviation context as a first escape event across a surface in phase space defined by line security constraints. The resulting system of stochastic differential equations admits a transverse decomposition of the drift, which leads to considerable simplification in evaluating the quasipotential (rate function) and, consequently, computation of exit rates. Tractable expressions for the rate of transmission line failure in a restricted network are derived from large deviation theory arguments and validated against numerical simulations. Extensions to realistic settings are considered, and individual line failure models are aggregated into a Markov model of cascading failure inspired by chemical kinetics. Cascades are generated by traversing a graph composed of weighted edges representing transitions to degraded network topologies. Numerical results indicate that the Markov model can produce cascades with qualitative power-law properties similar to those observed in empirical cascades.

Kinetic Monte Carlo, rare events, Cascading Failur↗

What can solve the strong CP problem?

Three possible strategies have been advocated to solve the strong CP problem. The first is the axion, a dynamical mechanism that relaxes any initial value of the CP violating angle $\overline{θ}$ to zero. The second is the imposition of new symmetries that are believed to set $\overline{θ}$ to zero in the UV. The third is the acceptance of the fine tuning of parameters. We argue that the latter two solutions do not solve the strong CP problem. The θ term of QCD is not a parameter — it does not exist in the Hamiltonian. Rather, it is a property of the quantum state that our universe finds itself in, arising from the fact that there are CP violating states of a CP preserving Hamiltonian. It is not eliminated by imposing parity as a symmetry since the underlying theory is already parity symmetric and that does not preclude the existence of CP violating states. Moreover, since the value of θ realized in our universe is a consequence of measurement, it is inherently random and cannot be fine tuned by choice of parameters. Rather any fine tuning would require a tuning between parameters in the theory and the random outcome of measurement. Our results considerably strengthen the case for the existence of the axion and axion dark matter. The confusion around θ arises from the fact that unlike classical mechanics, the Hamiltonian and Lagrangian are not equivalent in quantum mechanics. The Hamiltonian defines the differential time evolution, whereas the Lagrangian is a solution to this evolution. Consequently, initial conditions could in principle appear in the Lagrangian but not in the Hamiltonian. This results in aspects of the initial condition such as θ misleadingly appearing in the Lagrangian as parameters. We comment on the similarity between the θ vacua and the violations of the constraint equations of classical gauge theories in quantum mechanics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Using harmonic oscillators to determine the spot size of Hermite-Gaussian laser beams

The similarity of the functional forms of quantum mechanical harmonic oscillators and the modes of Hermite-Gaussian laser beams is illustrated. This functional similarity provides a direct correlation to investigate the spot size of large-order mode Hermite-Gaussian laser beams. The classical limits of a corresponding two-dimensional harmonic oscillator provide a definition of the spot size of Hermite-Gaussian laser beams. The classical limits of the harmonic oscillator provide integration limits for the photon probability densities of the laser beam modes to determine the fraction of photons detected therein. Mathematica is used to integrate the probability densities for large-order beam modes and to illustrate the functional similarities. The probabilities of detecting photons within the classical limits of Hermite-Gaussian laser beams asymptotically approach unity in the limit of large-order modes, in agreement with the Correspondence Principle. The classical limits for large-order modes include all of the nodes for Hermite Gaussian laser beams; Sturm's theorem provides a direct proof.

Steely, Sidney L.↗

Inertial Orientation Trackers with Drift Compensation

A class of inertial-sensor systems with drift compensation has been invented for use in measuring the orientations of human heads (and perhaps other, similarly sized objects). These systems can be designed to overcome some of the limitations of prior orientation-measuring systems that are based, variously, on magnetic, optical, mechanical-linkage, and acoustical principles. The orientation signals generated by the systems of this invention could be used for diverse purposes, including controlling head-orientation-dependent virtual reality visual displays or enabling persons whose limbs are paralyzed to control machinery by means of head motions. The inventive concept admits to variations too numerous to describe here, making it necessary to limit this description to a typical system, the selected aspects of which are illustrated in the figure. A set of sensors is mounted on a bracket on a band or a cap that gently but firmly grips the wearer s head to be tracked. Among the sensors are three drift-sensitive rotationrate sensors (e.g., integrated-circuit angular- rate-measuring gyroscopes), which put out DC voltages nominally proportional to the rates of rotation about their sensory axes. These sensors are mounted in mutually orthogonal orientations for measuring rates of rotation about the roll, pitch, and yaw axes of the wearer s head. The outputs of these rate sensors are conditioned and digitized, and the resulting data are fed to an integrator module implemented in software in a digital computer. In the integrator module, the angular-rate signals are jointly integrated by any of several established methods to obtain a set of angles that represent approximately the orientation of the head in an external, inertial coordinate system. Because some drift is always present as a component of an angular position computed by integrating the outputs of angular-rate sensors, the orientation signal is processed further in a drift-compensator software module.

Foxlin, Eric M.↗

Sparse distributed memory prototype: Principles of operation

Sparse distributed memory is a generalized random access memory (RAM) for long binary words. Such words can be written into and read from the memory, and they can be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original right address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech and scene analysis, in signal detection and verification, and in adaptive control of automated equipment. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. The research is aimed at resolving major design issues that have to be faced in building the memories. The design of a prototype memory with 256-bit addresses and from 8K to 128K locations for 256-bit words is described. A key aspect of the design is extensive use of dynamic RAM and other standard components.

Flynn, Michael J.↗

Molecular H 2 as the Reducing Agent in Low-Temperature Oxide Reduction Using Calcium Hydride

Low-temperature synthesis is crucial for advancing sustainable manufacturing and accessing novel metastable phases. Metal hydrides have shown great potential in facilitating the reduction of oxides at low temperatures, yet the underlying mechanism—whether driven by H - , H 2 , or atomic H—remains unclear. Here, in this study, we employ in situ electrical transport measurements and first- principles calculations to investigate the CaH 2 -driven reduction kinetics in epitaxial α-Fe 2 O 3 thin films. Intriguingly, samples in direct contact with or separated from CaH 2 powders exhibit similar apparent activation energies for H 2 reduction, although direct contact significantly increases the reduction rate. These findings indicate that molecular H 2 is the dominant reducing species in the low-temperature reduction of oxides using CaH 2 , with a key aspect of the hydrides' superior reducing power attributed to their ability to eliminate residual moisture. This work underscores the critical role of moisture control in enabling effective low-temperature oxide reduction for advanced material synthesis.

Wang, Jiayue [SLAC National Accelerator Laboratory↗

Generalizable Image Segmentation for Microstructure Characterization Through Integrated SEM and EBSD Analysis

We demonstrate generalizable semantic segmentation using minimal ground truth data. Correlated scanning electron microscopy (SEM) images and electron backscatter diffraction (EBSD) measurements of frictionstir processed 316L stainless steel plates were used to train deep learning models for grain boundary segmentation. Secondary electron (SE) imaging taken at an accelerating voltage of 10 keV correlated to EBSD-derived grain boundaries produced the best performing model. Notably, an ensemble of three models trained on a single SE image produced accurate segmentation over a series of BSE images of samples manufactured under different processing parameters, with a resultant mean absolute error in grain size of 0.34 µm. The striking generalizability of the models likely results from the similar escape depths of the SE training input and the EBSD training output and the reduced probability of dislocation artifacts appearing in the image. This finding highlights the importance of considering the physical principles behind imaging in the development of robust segmentation models for microstructure characterization.

Taufique, Mohammad Fuad Nur↗

Validation of FAST.Farm Against Full-Scale Turbine SCADA Data for a Small Wind Farm

FAST.Farm is a new midfidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on some of the principles of the Dynamic Wake Meandering model—including passive tracer modeling of wake meandering—but addresses many of the limitations of previous Dynamic Wake Meandering (DWM) implementations. Previous FAST.Farm verification studies show the similarities and differences between FAST.Farm and large-eddy simulations for rigid and flexible turbines. In this validation study, FAST.Farm turbine responses are compared to multiturbine measurements from a subset of a full-scale wind farm. FAST.Farm predictions of turbine generator power, rotor speed, and blade pitch for five-turbine simulations are compared to supervisory control and data acquisition results. Results reveal that FAST.Farm generator power mean and standard deviation results reasonably match measured data for upstream and downstream turbines, as well as the mean rotor speed and blade pitch above rated wind speeds. However, FAST.Farm generally underpredicts the mean rotor speed and overpredicts the mean blade pitch below rated operation. These errors are likely related to inaccuracies in the generic controller simulated. Despite controller differences, FAST.Farm predicts the same overall relative rotor power trends for all waked turbines at all wind speeds.

17 WIND ENERGY↗

Tuning the critical Li intercalation concentrations for MoX 2 bilayer phase transitions using classical and machine learning approaches

Transition metal dichalcogenides (TMDs) such as MoX 2 are known to undergo a structural phase transformation as well as a change in the electronic conductivity upon Li intercalation. These properties make them candidates for charge tunable ion-insertion materials that could be used in electro-chemical devices for neuromorphic computing applications. In this work we study the phase stability and electronic structure of Li-intercalated bilayer MoX 2 with X=S, Se or Te. Using first-principles calculations in combination with classical and machine learning modeling approaches we find that the energy needed to stabilize the conductive phase decreases with increasing atomic mass of the chalcogen atom X. A similar decreasing trend is found in the threshold Li concentration where the structural phase transition takes place. While the electronic conductivity increases with increasing ion concentration at low concentrations, we do not observe a conductivity jump at the phase transition point.

36 MATERIALS SCIENCE↗

Electrical properties of multi p-n junction devices

The electrical properties of multi p-n junction devices are analyzed. It is found that this type of device possesses bistable characteristics similar to that of a Shockley diode and thus provides an alternative realization of devices for switching applications. The inherently greater current gains involved in the operations of such a device yield in principle higher breakover voltages and higher holding currents. Furthermore, the incorporation of heterostructures in this device introduces a new degree of freedom in tailoring their switching characteristics. Multi p-n heterojunction devices operating as SCR lasers were fabricated, and the experimental results are presented.

Katz, J.↗

Human Systems Integration Relationships Between Deep Space and Deepwater Exploration Challenges

This presentation provides a summary discussion of the paper related to HSI used at NASA and the critical role human factors engineering will play in the HSI process for future missions along with the challenges. Industries such as oil and gas (O&G) could benefit from the content of this paper to help improve the safety and operations of deep water/oil and gas exploration activities. NASA engineering and human systems subject-matter expert personnel reviewed challenges reported in space-related environments for long space duration missions and identified similarities with the challenges encountered in deep water exploration. The researchers noted the space industry's current solutions and proposed a system development view to solve future deep water exploration challenges using HSI principles.

George Salazar↗

A New Family of Proton‐Conducting Electrolytes for Reversible Solid Oxide Cells: BaHf x Ce 0.8− x Y 0.1 Yb 0.1 O 3− δ

Abstract Reversible solid oxide cells based on ceramic proton conductors have potential to be the most efficient system for large‐scale energy storage. The performance and long‐term durability of these systems, however, are often limited by the ionic conductivity or stability of the proton‐conducting electrolyte. Here new family of solid oxide electrolytes, BaHf x Ce 0.8− x Y 0.1 Yb 0.1 O 3− δ (BHCYYb), which demonstrate a superior ionic conductivity to stability trade‐off than the state‐of‐the‐art proton conductors, BaZr x Ce 0.8− x Y 0.1 Yb 0.1 O 3− δ (BZCYYb), at similar Zr/Hf concentrations, as confirmed by thermogravimetric analysis, Raman, and X‐ray diffraction analysis of samples over 500 h of testing are reported. The increase in performance is revealed through thermodynamic arguments and first‐principle calculations. In addition, lab scale full cells are fabricated, demonstrating high peak power densities of 1.1, 1.4, and 1.6 W cm −2 at 600, 650, and 700 °C, respectively. Round‐trip efficiencies for steam electrolysis at 1 A cm −2 are 78%, 72%, and 62% at 700, 650, and 600 °C, respectively. Finally, CO 2 H 2 O electrolysis is carried out for over 700 h with no degradation.

Murphy, Ryan↗

Nonintuitive Surface Self-Assembly of Functionalized Molecules on Ag(111)

The fabrication of nanomaterials involves selfordering processes of functional molecules on inorganic surfaces. To obtain specific molecular arrangements, a common strategy is to equip molecules with functional groups. However, focusing on the functional groups alone does not provide a comprehensive picture. Especially at interfaces, processes that govern self-ordering are complex and involve various physical and chemical effects, often leading to unexpected structures, as we showcase here on the example of a homologous series of quinones on Ag(111). Naively, one could expect that such quinones, which all bear the same functionalization, form similar motifs. In salient contrast, our joint theoretical and experimental study shows that profoundly different structures are formed. Using a machine-learning-based structure search algorithm, we find that this is due to a shift of the balance of three antagonizing driving forces: adsorbate-substrate interactions governing adsorption sites, adsorbate-adsorbate interactions favoring close packing, and steric hindrance inhibiting certain otherwise energetically beneficial molecular arrangements. The theoretical structures show excellent agreement with our experimental characterizations of the organic/ inorganic interfaces, both for the unit cell sizes and the orientations of the molecules within. The nonintuitive interplay of similarly important interaction mechanisms will continue to be a challenging aspect for the design of functional interfaces. With a detailed examination of all driving forces, we are, however, still able to devise a design principle for self-assembly of functionalized molecules.

structure prediction↗

Neuromorphic learning with Mott insulator NiO

Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. In this work, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia, habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability–plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.

42 ENGINEERING↗

Quantifying the Solar Energy Resource for Puerto Rico

After Hurricane Maria, multiple U.S. Department of Energy laboratories studied the state of the electric grid in Puerto Rico and analyzed grid resilience and grid integration of renewable energy. As part of the work done at the National Renewable Energy Laboratory, researchers created new solar resource data, conducted a technical potential and supply curve analysis, and studied the interannual variability of the solar resource. A new methodology was developed to downscale solar resource data from the National Solar Radiation Data Base (NSRDB) from a 4-km x 4-km spatial and 30-minute temporal resolution to a 2 km x 2 km and 5-minute resolution. This methodology primarily used simple physical principles to develop high-resolution cloud properties which were then used to compute solar radiation. The high-resolution datasets were validated against ground measurements and the error metrics were found to be similar to the original lower resolution dataset. Using 20 years of downscaled data from the NSRDB multi-year capacity factors for photovoltaics (PV) were developed for both single-axis tracking and fixed latitude-tilt configurations. Use of the multi-year data provides the ability to understand variability in capacity factors due to variability in weather over a long period of time. For Puerto Rico the coastal regions were found to have significant higher capacity factors than inland. Using land-use and terrain information a technical potential analysis was conducted for Puerto Rico. This analysis restricted single PV plant development to a maximum of 100 MW nameplate capacity. The nameplate capacity for each municipality were then determined. Based on our assumptions, 56 of the 78 total municipalities of Puerto Rico contain some level of solar capacity. Most of the interior municipalities did not have any capacity because of the geographic exclusions used in this study. The lowest capacity for a PV plant observed in a municipality was 10 MW. The maximum capacity within a county was 2,000 MW. Further a supply curve analysis was conducted by taking the results of the technical potential and quantifying system and transmission costs. The levelized cost of energy (LCOE) was calculated for each theoretical PV plant site, and the levelized cost of transmission was added to the LCOE to produce a total cost estimate for each site. The results of the supply curve analysis allow for a relative comparison of the cost for integrating new PV capacity into the grid. This analysis indicates that cheaper total LCOE sites tend to be larger in capacity. The total capacity in this study was found to be far beyond the maximum peak load for the island. However, this study does not consider the economic and market potential for development. The cumulative capacity presented in this study assumes that the best locations are developed first and ignores the complex decision paths for new power plant development. Therefore, this analysis can only be treated as illustrative. Finally this study investigates the impact of inter-annual variability of resource using a variety of metrices including probability of exceedance and variation in capacity factor and LCOE. This study demonstrates that the capacity factor or LCOE could vary by over 10% year to year. This clearly indicates the risks involved in using any particular year of data and clearly points to the use of multi-year data to reduce some of the risks related to variability in weather.

14 SOLAR ENERGY↗

Color enhancement of highly correlated images. II - Channel ratio and 'chromaticity' transformation techniques

Two techniques for enhancing the color of multispectral images are described; both involve ratioing of data from different image channels. In the first technique, the ratioed data are assigned the primary color for display as color ratio pictures, and in the second method, image data are transformed to RGB chromaticity coordinates by ratioing the data acquired in three channels to the sum of their intensities. The two techniques are applied to a NASA Thermal-IR Multispectral Scanner (TIMS) image of Death Valley and to a Landsat MSS image of the Mojave Desert. The basic principles of ratioing are discussed, and the effects of atmospheric path radiances on the interpretation of ratioed images are investigated. It is observed that the color pictures produced using these two enhancement techniques are similar to the pictures enhanced by decorrelation and hue-saturation-intensity methods.

Gillespie, Alan R.↗

Linear Back-Drive Differentials

Linear back-drive differentials have been proposed as alternatives to conventional gear differentials for applications in which there is only limited rotational motion (e.g., oscillation). The finite nature of the rotation makes it possible to optimize a linear back-drive differential in ways that would not be possible for gear differentials or other differentials that are required to be capable of unlimited rotation. As a result, relative to gear differentials, linear back-drive differentials could be more compact and less massive, could contain fewer complex parts, and could be less sensitive to variations in the viscosities of lubricants. Linear back-drive differentials would operate according to established principles of power ball screws and linear-motion drives, but would utilize these principles in an innovative way. One major characteristic of such mechanisms that would be exploited in linear back-drive differentials is the possibility of designing them to drive or back-drive with similar efficiency and energy input: in other words, such a mechanism can be designed so that a rotating screw can drive a nut linearly or the linear motion of the nut can cause the screw to rotate. A linear back-drive differential (see figure) would include two collinear shafts connected to two parts that are intended to engage in limited opposing rotations. The linear back-drive differential would also include a nut that would be free to translate along its axis but not to rotate. The inner surface of the nut would be right-hand threaded at one end and left-hand threaded at the opposite end to engage corresponding right- and left-handed threads on the shafts. A rotation and torque introduced into the system via one shaft would drive the nut in linear motion. The nut, in turn, would back-drive the other shaft, creating a reaction torque. Balls would reduce friction, making it possible for the shaft/nut coupling on each side to operate with 90 percent efficiency.

Waydo, Peter↗