Labeled Clearsky Period Data Set
Time series data set containing labeled clearsky periods.
14 SOLAR ENERGY↗
SEARCH · Engineering Papers
Results for “setting”
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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Optimization-Based Resiliency Verification in Microgrids via Maximal Adversarial Set Characterization
Critical energy infrastructures are increasingly relying on advanced sensing and control technologies for efficient and optimal utilization of flexible energy resources. Algorithmic procedures are needed to ensure that such systems are designed to be resilient to a wide range of cyber-physical adversarial events. This paper provides a robust optimization framework to quantify the largest adversarial perturbation that a system can accommodate without violating pre-specified resiliency metrics. We formulate the maximal adversarial set characterization as a bi-level optimization problem which is solved via Lagrangian relaxations.We illustrate the proposed algorithm on an islanded microgrid example: a modified IEEE 123-node feeder with distributed energy resources. Simulations are carried out to characterize the tolerable adversarial perturbations for varying levels of available flexibility (energy reserves).
Nazir, Mohammad Nawaf↗
Curated Modeled Fault Data Set
The curated fault model simulation data set consists of tagged and fully-described time series representing simulated faults for the AFDD test building (ORNLs Flexible Research Platform (FRP)), including baseline performance, faulty performance, and corresponding energy impact. A total of 26 fault models are considered for 99 simulation scenarios with various fault intensity levels. Additional Contacts: Principal investigator: Matt Leach Matt.Leach@nrel.gov Simulation performer: Janghyun Kim Janghyun.Kim@nrel.gov
AFDD↗
Curated Test Fault Data Set
The curated fault experiment data set consists of tagged and fully described time series representing measured faults from the AFDD test building (ORNLs Flexible Research Platform [FRP]), including baseline performance and faulty performance. A total of 10 different faults are tested for 49 different faulted and unfaulted scenarios with various fault intensity levels. Additional Contacts: Principal investigator: Matt Leach Matt.Leach@nrel.gov Experiments coordinator: Piljae Im imp1@ornl.gov Document preparation: Janghyun Kim Janghyun.Kim@nrel.gov
AFDD↗
Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets
This dataset contains turbine- and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in Steady State (FLORIS) model, version 2.3.0. The 252,500 cases include 500 unique wind plants generated randomly by a specialized Plant Layout Generator (PLayGen) that samples randomized realizations of wind plant layouts from one of four canonical configurations: (i) cluster, (ii) single string, (iii) multiple string, (iv) parallel string. Other wind plant layout parameters were also randomly sampled, including the number of turbines (25-200) and the mean turbine spacing (3D-10D, where D denotes the turbine rotor diameter). For each layout, 500 different sets of atmospheric conditions were randomly sampled. These include wind speed in 0-25 m/s, wind direction in 0 deg.-360 deg., and turbulence intensity chosen from low (6%), medium (8%), and high (10%). For each atmospheric inflow scenario, the individual turbine yaw angles were randomly sampled from a one-sided truncated Gaussian on the interval 0 deg.-30 deg. oriented relative to wind inflow direction. This random data is supplemented with a collection of yaw-optimized samples where FLORIS was used to determine turbine yaw angles that maximize power production for the entire plant. To generate this data, a subset of cases were selected (50 atmospheric conditions from 50 layouts each for a total of additional 2,500 cases) for which FLORIS was re-run with wake steering control optimization. The IEA onshore reference turbine, which has a 130 m rotor diameter, a 110 m hub height, and a rated power capacity of 3.4 MW was used as the turbine for all simulations. The simulations were performed using NREL's Eagle high performance computing system in February 2021 as part of the Spatial Analysis for Wind Technology Development project funded by the U.S. Department of Energy Wind Energy Technologies Office. The data was collected, reformatted, and preprocessed for this OEDI submission in May 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub repository under explore_wind_plant_data_h5.ipynb.
AI↗
Wind Resource Assessment for Alaska’s Offshore Regions: Validation of a 14-Year High-Resolution WRF Data Set
Offshore wind resource assessments for the conterminous U.S. and Hawai’i have been developed before, but Alaska’s offshore wind resource has never been rigorously assessed. Alaska, with its vast coastline, presents ample potential territory in which to build offshore wind farms, but significant challenges have thus far limited Alaska’s deployment of utility-scale wind energy capacity to a modest 62 MW (or approximately 2.7% of the state’s electric generation) as of this writing, all in land-based wind farms. This study provides an assessment of Alaska’s offshore wind resource, the first such assessment for Alaska, using a 14-year, high-resolution simulation from a numerical weather prediction and regional climate model. This is the longest-known high-resolution model data set to be used in a wind resource assessment. Widespread areas with relatively shallow ocean depth and high long-term average 100-m wind speeds and estimated net capacity factors over 50% were found, including a small area near Alaska’s population centers and the largest transmission grid that, if even partially developed, could provide the bulk of the state’s energy needs. The regional climate simulations were validated against available radiosonde and surface wind observations to provide the confidence of the model-based assessment. The model-simulated wind speed was found to be skillful and with near-zero average bias (−0.4–0.2 m s−1) when averaged over the domain. Small sample sizes made regional validation noisy, however.
17 WIND ENERGY↗
The NANOGrav nine-year data set: Astrometric measurements of 37 millisecond pulsars
Using the nine-year radio-pulsar timing data set from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav), collected at Arecibo Observatory and the Green Bank Telescope, we have measured the positions, proper motions, and parallaxes for 37 millisecond pulsars. We report twelve significant parallax measurements and distance measurements, and eighteen lower limits on distance. We compare these measurements to distances predicted by the NE2001 interstellar electron density model and find them to be in general agreement. We use measured orbital-decay rates and spin-down rates to confirm two of the parallax distances and to place distance upper limits on other sources; these distance limits agree with the parallax distances with one exception, PSR J1024–0719, which we discuss at length. Using the proper motions of the 37 NANOGrav pulsars in combination with other published measurements, we calculate the velocity dispersion of the millisecond pulsar population in Galactocentric coordinates. We find the radial, azimuthal, and perpendicular dispersions to be 46, 40, and 24 km s{sup −1}, respectively, in a model that allows for high-velocity outliers; or 81, 58, and 62 km s{sup −1} for the full population. These velocity dispersions are far smaller than those of the canonical pulsar population, and are similar to older Galactic disk populations. This suggests that millisecond pulsar velocities are largely attributable to their being an old population rather than being artifacts of their birth and evolution as neutron star binary systems. The components of these velocity dispersions follow similar proportions to other Galactic populations, suggesting that our results are not biased by selection effects.
79 ASTRONOMY AND ASTROPHYSICS↗
The NANOGrav nine-year data set: mass and geometric measurements of binary millisecond pulsars
We analyze 24 binary radio pulsars in the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) nine-year data set. We make 14 significant measurements of the Shapiro delay, including new detections in four pulsar-binary systems (PSRs J0613−0200, J2017+0603, J2302+4442, and J2317+1439), and derive estimates of the binary-component masses and orbital inclination for these MSP-binary systems. We find a wide range of binary pulsar masses, with values as low as m{sub p}=1.18{sub −0.09}{sup +0.10} M{sub ⊙} for PSR J1918−0642 and as high as m{sub p}=1.928{sub −0.017}{sup +0.017} M{sub ⊙} for PSR J1614−2230 (both 68.3% credibility). We make an improved measurement of the Shapiro timing delay in the PSR J1918−0642 and J2043+1711 systems, measuring the pulsar mass in the latter system to be m{sub p}=1.41{sub −0.18}{sup +0.21} M{sub ⊙} (68.3% credibility) for the first time. We measure secular variations of one or more orbital elements in many systems, and use these measurements to further constrain our estimates of the pulsar and companion masses whenever possible. In particular, we used the observed Shapiro delay and periastron advance due to relativistic gravity in the PSR J1903+0327 system to derive a pulsar mass of m{sub p}=1.65{sub −0.02}{sup +0.02} M{sub ⊙} (68.3% credibility). We discuss the implications that our mass measurements have on the overall neutron-star mass distribution, and on the “mass/orbital-period” correlation due to extended mass transfer.
79 ASTRONOMY AND ASTROPHYSICS↗
NuGrid stellar data set. I. Stellar yields from H to BI for stars with metallicities Z = 0.02 and Z = 0.01
We provide a set of stellar evolution and nucleosynthesis calculations that applies established physics assumptions simultaneously to low- and intermediate-mass and massive star models. Our goal is to provide an internally consistent and comprehensive nuclear production and yield database for applications in areas such as presolar grain studies. Our non-rotating models assume convective boundary mixing (CBM) where it has been adopted before. We include 8 (12) initial masses for Z = 0.01 (0.02). Models are followed either until the end of the asymptotic giant branch phase or the end of Si burning, complemented by simple analytic core-collapse supernova (SN) models with two options for fallback and shock velocities. The explosions show which pre-SN yields will most strongly be effected by the explosive nucleosynthesis. We discuss how these two explosion parameters impact the light elements and the s and p process. For low- and intermediate-mass models, our stellar yields from H to Bi include the effect of CBM at the He-intershell boundaries and the stellar evolution feedback of the mixing process that produces the {sup 13}C pocket. All post-processing nucleosynthesis calculations use the same nuclear reaction rate network and nuclear physics input. We provide a discussion of the nuclear production across the entire mass range organized by element group. The entirety of our stellar nucleosynthesis profile and time evolution output are available electronically, and tools to explore the data on the NuGrid VOspace hosted by the Canadian Astronomical Data Centre are introduced.
79 ASTRONOMY AND ASTROPHYSICS↗
Reconstructing the Universe: Testing the Mutual Consistency of the Pantheon and SDSS/eBOSS BAO Data Sets with Gaussian Processes
We test the mutual consistency between the baryon acoustic oscillation measurements from the eBOSS SDSS final release and the Pantheon supernova compilation in a model-independent fashion using Gaussian process regression. We also test their joint consistency with the ΛCDM model in a model-independent fashion. We also use Gaussian process regression to reconstruct the expansion history that is preferred by these two data sets. While this methodology finds no significant preference for model flexibility beyond ΛCDM, we are able to generate a number of reconstructed expansion histories that fit the data better than the best-fit ΛCDM model. These example expansion histories may point the way toward modifications to ΛCDM. We also constrain the parameters Ω{sub k} and H {sub 0} r {sub d} both with ΛCDM and with Gaussian process regression. We find that H {sub 0} r {sub d} = 10,030 ± 130 km s{sup −1} and Ω{sub k} = 0.05 ± 0.10 for ΛCDM and that H {sub 0} r {sub d} = 10,040 ± 140 km s{sup −1} and Ω{sub k} = 0.02 ± 0.20 for the Gaussian process case.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
What Sets the Radial Locations of Warm Debris Disks?
The architectures of debris disks encode the history of planet formation in these systems. Studies of debris disks via their spectral energy distributions (SEDs) have found infrared excesses arising from cold dust, warm dust, or a combination of the two. The cold outer belts of many systems have been imaged, facilitating their study in great detail. Far less is known about the warm components, including the origin of the dust. The regularity of the disk temperatures indicates an underlying structure that may be linked to the water snow line. If the dust is generated from collisions in an exo-asteroid belt, the dust will likely trace the location of the water snow line in the primordial protoplanetary disk where planetesimal growth was enhanced. If instead the warm dust arises from the inward transport from a reservoir of icy material farther out in the system, the dust location is expected to be set by the current snow line. We analyze the SEDs of a large sample of debris disks with warm components. We find that warm components in single-component systems (those without detectable cold components) follow the primordial snow line rather than the current snow line, so they likely arise from exo-asteroid belts. While the locations of many warm components in two-component systems are also consistent with the primordial snow line, there is more diversity among these systems, suggesting additional effects play a role.
79 ASTRONOMY AND ASTROPHYSICS↗
What Sets the Star Formation Rate of Molecular Clouds? The Density Distribution as a Fingerprint of Compression and Expansion Rates
We use a suite of 3D simulations of star-forming molecular clouds, with and without stellar feedback, magnetic fields, and driven turbulence, to study the compression and expansion rates of the gas as functions of density. We show that, around the mean density, supersonic turbulence promotes rough equilibrium between the amounts of compressing and expanding gas, consistent with continuous gas cycling between high- and low-density states. We find that the inclusion of protostellar jets produces rapidly expanding and compressing low-density gas. We find that the gas mass flux peaks at the transition between the lognormal and power-law forms of the density probability distribution function (PDF). This is consistent with the transition density tracking the post-shock density, which promotes an enhancement of mass at this density (i.e., shock compression and filament formation). At high densities, the gas dynamics are dominated by self-gravity: the compression rate in all of our runs matches the rate of the run with only gravity, suggesting that processes other than self-gravity have little effect at these densities. The net gas mass flux becomes constant at a density below the sink formation threshold, where it equals the star formation rate. The density at which the net gas mass flux equals the star formation rate is one order of magnitude lower than our sink threshold density, corresponds to the formation of the second power-law tail in the density PDF, and sets the overall star formation rates of these simulations.
79 ASTRONOMY AND ASTROPHYSICS↗
A Search for Pulsars around Sgr A* in the First Event Horizon Telescope Data Set
In 2017 the Event Horizon Telescope (EHT) observed the supermassive black hole at the center of the Milky Way, Sagittarius A* (Sgr A*), at a frequency of 228.1 GHz (λ = 1.3 mm). The fundamental physics tests that even a single pulsar orbiting Sgr A* would enable motivate searching for pulsars in EHT data sets. The high observing frequency means that pulsars—which typically exhibit steep emission spectra—are expected to be very faint. However, it also negates pulse scattering, an effect that could hinder pulsar detections in the Galactic center. Additionally, magnetars or a secondary inverse Compton emission could be stronger at millimeter wavelengths than at lower frequencies. We present a search for pulsars close to Sgr A* using the data from the three most sensitive stations in the EHT 2017 campaign: the Atacama Large Millimeter/submillimeter Array, the Large Millimeter Telescope, and the IRAM 30 m Telescope. We apply three detection methods based on Fourier-domain analysis, the fast folding algorithm, and single-pulse searches targeting both pulsars and burst-like transient emission. We use the simultaneity of the observations to confirm potential candidates. No new pulsars or significant bursts were found. Being the first pulsar search ever carried out at such high radio frequencies, we detail our analysis methods and give a detailed estimation of the sensitivity of the search. We conclude that the EHT 2017 observations are only sensitive to a small fraction (≲2.2%) of the pulsars that may exist close to Sgr A*, motivating further searches for fainter pulsars in the region.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
How Many Kilonovae Can Be Found in Past, Present, and Future Survey Data Sets?
The discovery of a kilonova (KN) associated with the Advanced LIGO (aLIGO)/Virgo event GW170817 opens up new avenues of multi-messenger astrophysics. Here, using realistic simulations, we provide estimates of the number of KNe that could be found in data from past, present, and future surveys without a gravitational-wave trigger. For the simulation, we construct a spectral time-series model based on the DES-GW multi-band light curve from the single known KN event, and we use an average of BNS rates from past studies of ${10}^{3}\,{\mathrm{Gpc}}^{-3}\,{\mathrm{yr}}^{-1}$, consistent with the one event found so far. Examining past and current data sets from transient surveys, the number of KNe we expect to find for ASAS-SN, SDSS, PS1, SNLS, DES, and SMT is between 0 and 0.3. We predict the number of detections per future survey to be 8.3 from ATLAS, 10.6 from ZTF, 5.5/69 from LSST (the Deep Drilling/Wide Fast Deep), and 16.0 from WFIRST. The maximum redshift of KNe discovered for each survey is $z=0.8$ for WFIRST, $z=0.25$ for LSST, and $z=0.04$ for ZTF and ATLAS. This maximum redshift for WFIRST is well beyond the sensitivity of aLIGO and some future GW missions. For the LSST survey, we also provide contamination estimates from Type Ia and core-collapse supernovae: after light curve and template-matching requirements, we estimate a background of just two events. Finally, more broadly, we stress that future transient surveys should consider how to optimize their search strategies to improve their detection efficiency and to consider similar analyses for GW follow-up programs.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
The NANOGrav 15 yr Data Set: Search for Signals from New Physics
The 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) shows positive evidence for the presence of a low-frequency gravitational-wave (GW) background. In this paper, we investigate potential cosmological interpretations of this signal, specifically cosmic inflation, scalar-induced GWs, first-order phase transitions, cosmic strings, and domain walls. We find that, with the exception of stable cosmic strings of field theory origin, all these models can reproduce the observed signal. When compared to the standard interpretation in terms of inspiraling supermassive black hole binaries (SMBHBs), many cosmological models seem to provide a better fit resulting in Bayes factors in the range from 10 to 100. However, these results strongly depend on modeling assumptions about the cosmic SMBHB population and, at this stage, should not be regarded as evidence for new physics. Furthermore, we identify excluded parameter regions where the predicted GW signal from cosmological sources significantly exceeds the NANOGrav signal. These parameter constraints are independent of the origin of the NANOGrav signal and illustrate how pulsar timing data provide a new way to constrain the parameter space of these models. Finally, we search for deterministic signals produced by models of ultralight dark matter (ULDM) and dark matter substructures in the Milky Way. We find no evidence for either of these signals and thus report updated constraints on these models. In the case of ULDM, these constraints outperform torsion balance and atomic clock constraints for ULDM coupled to electrons, muons, or gluons.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
The Dark Energy Survey: Cosmology Results with ∼1500 New High-redshift Type Ia Supernovae Using the Full 5 yr Data Set
Abstract We present cosmological constraints from the sample of Type Ia supernovae (SNe Ia) discovered and measured during the full 5 yr of the Dark Energy Survey (DES) SN program. In contrast to most previous cosmological samples, in which SNe are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being an SN Ia, we find 1635 DES SNe in the redshift range 0.10 < z < 1.13 that pass quality selection criteria sufficient to constrain cosmological parameters. This quintuples the number of high-quality z > 0.5 SNe compared to the previous leading compilation of Pantheon+ and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints, we combine the DES SN data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning 0.025 < z < 0.10. Using SN data alone and including systematic uncertainties, we find Ω M = 0.352 ± 0.017 in flat ΛCDM. SN data alone now require acceleration ( q 0 < 0 in ΛCDM) with over 5 σ confidence. We find ( Ω M , w ) = ( 0.264 − 0.096 + 0.074 , − 0.80 − 0.16 + 0.14 ) in flat w CDM. For flat w 0 w a CDM, we find ( Ω M , w 0 , w a ) = ( 0.495 − 0.043 + 0.033 , − 0.36 − 0.30 + 0.36 , − 8.8 − 4.5 + 3.7 ) , consistent with a constant equation of state to within ∼2 σ . Including Planck cosmic microwave background, Sloan Digital Sky Survey baryon acoustic oscillation, and DES 3 × 2pt data gives (Ω M , w ) = (0.321 ± 0.007, −0.941 ± 0.026). In all cases, dark energy is consistent with a cosmological constant to within ∼2 σ . Systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified SN analyses.
79 ASTRONOMY AND ASTROPHYSICS↗
The lepton+jets Selection and Determination of the Lepton Fake Rate with the Full RunIIb Data Set
This thesis presents the combined single top and $ t\overline{ }\ t $ lepton+jets selection for the full RunIIb dataset of the DØ detector. The selection uses the newest soft- ware versions including all standard central object identifications and corrections and has various additions and improvements compared to the previous 7 . 3 fb - 1 $ t\overline{ }\ t $ selection and the previous single top selection in order to accommodate even more different analyses. The lepton fake rate $\epsilon_{\rm QCD}$ and the real lepton efficiency $\epsilon_{\rm sig}$ are estimated using the matrix method and different variations are considered in order to determine the systematic errors. The calculation has to be done for each run period and every set of analysis cuts separately. In addition the values for the exclusive jet bins and for the new single top analysis cuts have been derived and the thesis shows numerous control plots to demonstrate the excellent agreement between data and Monte Carlo.
Meister, Daniel↗
Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years
The Model of Emissions of Gases and Aerosols from Nature (MEGANv2.1) together with the Modern-Era Retrospective Analysis for Research and Applications (MERRA) meteorological fields were used to create a global emission dataset of biogenic VOCs available on a monthly basis for the time period of 1980 - 2010. This dataset is called MEGAN-MACC. The model estimated mean annual total BVOC emission of 760 Tg(C) yr1 consisting of isoprene (70%), monoterpenes (11%), methanol (6%), acetone (3%), sesquiterpenes (2.5%) and other BVOC species each contributing less than 2 %. Several sensitivity model runs were performed to study the impact of different model input and model settings on isoprene estimates and resulted in differences of * 17% of the reference isoprene total. A greater impact was observed for sensitivity run applying parameterization of soil moisture deficit that led to a 50% reduction of isoprene emissions on a global scale, most significantly in specific regions of Africa, South America and Australia. MEGAN-MACC estimates are comparable to results of previous studies. More detailed comparison with other isoprene in ventories indicated significant spatial and temporal differences between the datasets especially for Australia, Southeast Asia and South America. MEGAN-MACC estimates of isoprene and*-pinene showed a reasonable agreement with surface flux measurements in the Amazon andthe model was able to capture the seasonal variation of emissions in this region.
Sindelarova, K.↗