The Solar Forecast Arbiter: An Open Source Evaluation Framework for Solar Forecasting.
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This study explores the “tug of war” between the effects of Arctic amplification (“AA”) and upper-troposphere tropical warming (“UTW”) on the response of the future North Atlantic atmospheric circulation. The late 21st century AA and UTW temperature anomalies are imposed in a high-top atmospheric model by nudging the temperature. Two sets of experiments are performed, with and without feedback of the polar stratosphere to highlight its role in the response to UTW, AA, and both combined. With interactive polar stratosphere, UTW forces an equatorward shift of the eddy-driven jet that reinforces the response to AA. However, when the polar stratosphere feedback is suppressed, the response to UTW is opposite and reflects the previously identified tug of war between the effects of UTW and AA in midlatitudes. This study highlights that the polar stratosphere is a key component for future changes in the North Atlantic atmospheric circulation and that it must be accurately represented in climate change scenarios.
Systems and methods are described for arbitrating access of a communication bus. In one embodiment, a method includes performing steps on one or more processors. The steps include: receiving an access request from a device of the communication bus; evaluating a bus schedule to determine an importance of the device based on the access request; and selectively granting access of the communication bus to the device based on the importance of the device.
Candidates for selection in a weighted arbitration system are assigned priority weights and random weights. The winning candidate is determined using a tree of selectors such as a comparators. At each stage of the tree, the candidate having the greatest priority weight is selected to pass to the next stage. If multiple candidates are tied for the greatest priority weight, the candidate having the greatest random weight is selected to pass to the next stage.
An apparatus is provided that includes a plurality of modules, a plurality of memory banks, and a multiplexor. Each module includes at least one agent that interfaces between a module and a memory bank. Each memory bank includes an arbiter that interfaces between the at least one agent of each module and the memory bank. The multiplexor is configured to assign data paths between the at least one agent of each module and a corresponding arbiter of each memory bank based on the assigned data path. The at least one agent of each module is configured to read data from the corresponding arbiter of the memory bank or write modified data to the corresponding arbiter of the memory bank.
The Solar Forecast Arbiter is an open-source evaluation framework for solar forecasting. The framework enables evaluations of solar irradiance, solar power, and net-load forecasts that are impartial, repeatable and auditable. The Solar Forecast Arbiter addresses stakeholder-informed use cases including evaluation of forecast skill, comparisons to reference data sets, private forecast trials, and evaluation of probabilistic forecast skill. The framework includes a data validation toolkit, reference data sources, data privacy protocols, and benchmark forecast capabilities for intra-hour and day ahead forecast horizons. Reports and metrics communicate the relative merits of the test and benchmark forecasts. The reports are created from standardized templates and include graphics for qualitatively evaluating deterministic and probabilistic forecasts and standard metrics for quantitatively evaluating forecasts. The Solar Forecast Arbiter is designed to support all solar forecasting stakeholders, including Solar Forecasting 2 Topic Area 2 and Topic Area 3 teams.
Understanding the penetration from the midplane region towards the divertor plate of filamentary structures in the main scrape-off layer of spherical tokamaks is of interest for divertor design and plasma material interaction. Insight into this problem is gained by studying the closely related problem of the parallel structure of resistive ballooning modes, localized to a particular flux surface by a perturbed blob-like density profile. The resulting linearized equations are solved using the Arbitrary Topology Equation Reader (ArbiTER) code. A metric applied to the spatial structure of the dominant eigenmode quantifies mode penetration. Trends in this quantity are then measured by performing parameter scans over temperature and density. Furthermore, the results of this study are consistent with the dominant role of collisionality in limiting mode interaction with the divertor plate.
Understanding turbulence in the divertor leg of tokamaks is essential to predicting the heat deposition profile on the divertor plate. This in turn is important for evaluating advanced divertor configurations, such as the super-X divertor. Within the divertor region, the private flux region is of interest because it is relatively unaffected by turbulence extending from the outboard midplane, so instabilities in this region could have a particularly pronounced effect on transport. These instabilities are modeled using the Arbitrary Topology Equation Reader (ArbiTER) eigenvalue code. Eigenmodes are examined further by comparing physics models to determine the fundamental mechanisms behind their formation, and quantifying the effect of individual terms. This analysis is conducted on both conventional and super-X divertors to compare these effects. Here, the resulting analysis reveals the presence of a geodesic curvature driven instability that is significantly more pronounced in the super-X configuration.
This study presents the first observation of divertor localized turbulence in the NSTX-Upgrade spherical tokamak. Previous work in NSTX discharges described fluctuations on the divertor target due to blobs generated on the low field side midplane. In the region that shows disconnection from upstream turbulence (private flux region and proximity of the strike point), divertor-localized fluctuations are observed via imaging of C III and D-α emission in diverted L-mode discharges and are characterized in this paper. Field-aligned filaments connected to the divertor target plate are radially localized at the separatrix on the outer divertor leg and in the private flux region on the inner divertor leg. These are limited to the region below the X-point with fluctuation levels up to 10%–20%. The filaments have comparable poloidal and radial correlation lengths (10–100 ion gyroradii ρ i ) and parallel correlation lengths of several meters. Toroidal mode numbers are in the range of 10–30 and 2–10 for outer and inner leg filaments. The poloidal motion of outer leg filaments has the same direction and comparable magnitude to advection due to E x B drift calculated by the multi-fluid edge transport code UEDGE with inclusion of cross field drifts. Disconnection between inner and outer leg filaments as well as the absence of correlation with upstream turbulence support the hypothesis of X-point disconnection for divertor leg filaments. Simulations with the ArbiTER linear eigenvalue solver and a resistive-balooning model were performed with a simulation grid limited to the divertor legs. Instabilities localized to the bad curvature side of the divertor legs were observed, in qualitative agreement with the experiment.
A novel event driven readout architecture, EDWARD (Event Driven with Access and 8 Reset Decoder) architecture, for highly granular pixel detectors is presented. It incorporates, inter alia, an asynchronous arbitration tree based on Seitz' arbiters, removing the need for an imposed prioritization scheme. It also provides protection against glitches during readout. The system allows not only reading pixel activities, but also retrieving additional data, both analog and digital, from the pixels. A novel in-channel logic allows the entire readout process to be split into consecutive phases for additional flexibility. All operations are controlled by only one edge of the clock signal, seen as an acknowledge token, so there is no dead time between readouts.
Due to rapidly changing sky conditions, the available solar irradiance for energy generation is subject to wide swings in amplitude. As the market penetration of solar energy continues to increase rapidly, these variations in solar power generation are beginning to have an impact on grid stability and increased wear on power switches. Solar power forecasting plays a critical role in operations for Independent System Operators (ISOs) and utilities. Accurate forecasts help maintain grid reliability, optimize generation from renewables, and reduce operating costs. Of particular interest to the ISOs and utilities are sudden changes in solar irradiance, termed “ramp events,” due to the movement of clouds. One significant impact of ramp events on the grid is additional ancillary service requirements necessary to manage such variability. Ramp events can also cause voltage fluctuations in the distribution grids and trigger actions of automated line equipment (e.g., tap changers), leading to additional maintenance costs. In high penetration solar regions, forecasts must be made for both transmission-connected and distribution-connected resources – either behind the meter or in front of it. Particularly for distributed solar energy resources, forecasting can be a challenge due to the lack of visibility of the energy resource. Brookhaven National Laboratory (BNL) has been working towards nowcasting Global Horizontal Irradiance (GHI) using low-cost technologies for several years now. The Solar NowCasting technology being developed by BNL is a 0 – 30 min solar “nowcasting” technology applicable to a scale covering both large generating facilities and residential, distributed solar installations that relies on a network of ground-based high-definition (HD) cameras and surface pyranometers. GHI forecasts are being produced for both regions using 8 ground-based HD cameras and at least 2 surface pyranometers. When stitched together, the camera images can be used to forecast the impact of clouds on available solar irradiance over a domain of ~50 km 2 . Phase I of the project was the engineering scale up conceptual design stage. Phase II was the initial field test and demonstration, in which the technology was scaled up by a factor of 20 times and successfully demonstrated in eastern Long Island. In Phase III, an additional forecasting network was added in upstate NY and both networks are currently being operated until at least a full year of data is gathered by the Albany network to allow for the collection of sufficient data to evaluate performance against the persistence and smart persistence models using the Solar Arbiter.
This paper discusses means of assessing the accuracy of maximum likelihood parameter estimates obtained from dynamic flight data. The commonly used analytical predictors of accuracy are compared from both statistical and simplified geometric standpoints. Emphasizing practical considerations, such as modeling error, the accuracy predictions are evaluated with real and simulated data. Improved computations of the Cramer-Rao bound to correct large discrepancies caused by colored noise and modeling error are presented. This corrected Cramer-Rao bound is the best available analytical predictor of accuracy. Engineering judgement, aided by such analytical tools, is the final arbiter of accuracy estimation.
Means of assessing the accuracy of maximum likelihood parameter estimates obtained from dynamic flight data are discussed. The most commonly used analytical predictors of accuracy are derived and compared from both statistical and simplified geometrics standpoints. The accuracy predictions are evaluated with real and simulated data, with an emphasis on practical considerations, such as modeling error. Improved computations of the Cramer-Rao bound to correct large discrepancies due to colored noise and modeling error are presented. The corrected Cramer-Rao bound is shown to be the best available analytical predictor of accuracy, and several practical examples of the use of the Cramer-Rao bound are given. Engineering judgement, aided by such analytical tools, is the final arbiter of accuracy estimation.
Electric power system automation for Space Station Freedom is intended to operate in a loop. Data from the power system is used for diagnosis and security analysis to generate Operations Management System (OMS) requests, which are sent to an arbiter, which sends a plan to a commander generator connected to the electric power system. This viewgraph presentation profiles automation software for diagnosis, scheduling, and constraint interfaces, and simulation to support automation development. The automation development process is diagrammed, and the process of creating Ada and ART versions of the automation software is described.
This paper discusses our initial experience with introducing automated assume-guarantee verification based on learning in the SPIN tool. We believe that compositional verification techniques such as assume-guarantee reasoning could complement the state-reduction techniques that SPIN already supports, thus increasing the size of systems that SPIN can handle. We present a "light-weight" approach to evaluating the benefits of learning-based assume-guarantee reasoning in the context of SPIN: we turn our previous implementation of learning for the LTSA tool into a main program that externally invokes SPIN to provide the model checking-related answers. Despite its performance overheads (which mandate a future implementation within SPIN itself), this approach provides accurate information about the savings in memory. We have experimented with several versions of learning-based assume guarantee reasoning, including a novel heuristic introduced here for generating component assumptions when their environment is unavailable. We illustrate the benefits of learning-based assume-guarantee reasoning in SPIN through the example of a resource arbiter for a spacecraft. Keywords: assume-guarantee reasoning, model checking, learning.
Apparatus and method for providing downlink frames to be transmitted from a spacecraft to a ground station. Each downlink frame includes a synchronization pattern and a transfer frame. The apparatus may comprise a monolithic Reed-Solomon downlink (RSDL) encoding chip coupled to data buffers for storing transfer frames. The RSKL chip includes a timing device, a bus interface, a timing and control unit, a synchronization pattern unit, and a Reed-Solomon encoding unit, and a bus arbiter.
This paper introduces an uncertainty model being developed for the National Transonic Facility (NTF). The model uses a Monte Carlo technique to propagate standard uncertainties of measured values through the NTF data reduction equations to calculate the combined uncertainties of the key aerodynamic force and moment coefficients and freestream properties. The uncertainty propagation approach to assessing data variability is compared with ongoing data quality assessment activities at the NTF, notably check standard testing using statistical process control (SPC) techniques. It is shown that the two approaches are complementary and both are necessary tools for data quality assessment and improvement activities. The SPC approach is the final arbiter of variability in a facility. Its result encompasses variation due to people, processes, test equipment, and test article. The uncertainty propagation approach is limited mainly to the data reduction process. However, it is useful because it helps to assess the causes of variability seen in the data and consequently provides a basis for improvement. For example, it is shown that Mach number random uncertainty is dominated by static pressure variation over most of the dynamic pressure range tested. However, the random uncertainty in the drag coefficient is generally dominated by axial and normal force uncertainty with much less contribution from freestream conditions.