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At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bias Characterization, Vertical Interpolation, and Horizontal Interpolation for Distributed Wind Siting Using Mesoscale Wind Resource Estimates

Much like their counterparts in utility-scale wind energy, developers of industrial, small-scale and distributed wind turbine deployments need to understand and accurately characterize the wind resource to properly assess the power generation and financial ramifications during siting and planning. National Renewable Energy Laboratory’s WIND (Wind Integration National Dataset) Toolkit (WTK) provides a best-in-class wind resource dataset generated using the Weather Research and Forecasting (WRF) model. This dataset includes parameters such as the wind speed, wind direction, and temperature at various heights, plus atmospheric stability near the surface. This data is available at 2-km spatial resolution and five-minute temporal resolution across 7 years, from 2007 to 2013 through a publicly accessible API interface. The Tools Assessing Performance (TAP) project seeks to extend this dataset to allow long term resource estimates and leverage it to better equip distributed wind equipment manufacturers, owner-operators, and installation professionals with better tools for practical siting applications. In this report, we present the results from our investigation within the TAP project focused on characterization of bias in WTK-based wind speed estimates and evaluation of vertical and horizontal interpolation techniques. We discuss the tradeoffs between different techniques and their combinations, as well as describe the lower bounds we determine for the studied validation errors. While the specific estimates we present are specific to WTK and the validation dataset we have chosen for this investigation (NREL's Wind Resource Meteorological Database), the overall analysis and the studied techniques are general enough to be applied to a broader set of wind datasets, both simulation-based and observational.

17 WIND ENERGY↗
DOE OSTI2020

Multipactor in Coaxial Line Induced by DC Bias

High DC voltage applied between outer and inner electrodes of coaxial transmission lines is considered and actually is an effective remedy against multipactor in these lines. However, the high voltage bias being below certain level itself initiates multipacting. In this technical note the experimental manifestation and numerical simulations of the phenomenon are presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Physical Mechanisms and Electric-Bias Control of Phase Transitions in Quasi-2D Charge-Density-Wave Quantum Materials

The goals of this fundamental science project, aimed at understanding the physical mechanisms and developing methods for electric-bias control of phase transitions in quasi-2D CDW materials, have been achieved. We focused on 1T-TaS2, one of the most interesting materials of this type, and demonstrated electrical gating of the I-V characteristics and hysteresis in this material. The demonstration of electrical gating of CDW phases in quasi-2D material was performed at RT. We have conducted experiments to separate the electric-field CDW switching from Joule heating-induced switching. This was an important development for 2D CDW materials. The project has led to a better understanding of the physical mechanisms behind the phase transitions and CDW depinning in quasi-2D van der Waals materials. We established that the CDW domain depinning in 1T-TaS2 does not lead to a strong increase in the collective current and accompanying narrow-band noise. We developed a technique that utilized the low-frequency noise measurements in such materials for monitoring the CDW phase transitions. In the experiments where 1T-TaS2 flakes were used in polymeric matrices, we verified the robustness of the phase transitions between the nearly commensurate and incommensurate CDW phases.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A 32-Channel Cryo-CMOS ASIC for SNSPD Biasing and Readout with Picosecond Timing

Superconducting nanowire single-photon detectors (SNSPD) are a promising technology for particle detection. Although SNSPDs have demonstrated picosecond timing accuracy, scaling up large arrays has proved challenging. In this work, we introduce a 32-channel cryo-CMOS application-specifc integrated circuit (ASIC) that can be tightly integrated with SNSPD arrays. The ASIC is designed to operate at a temperature of 4K and can perform up to 32 simultaneous timing measurements with a root-mean-square (RMS) accuracy of 8.0ps. The ASIC includes on-chip circuitry for externally biasing superconducting devices, low-noise amplifers for reading superconducting devices, high-resolution time-to-digital converters (TDC) for time-tagging events, and serializers for transmitting data to room-temperature electronics. The ASIC is manufactured in a 22nm FDSOI process and occupies an area of 4.0mm x 1.0mm. The performance of the ASIC was verifed using custom cryogenic device models internally developed for the 22nm SOI process. Measurement results will be presented at the conference.

Fredenburg, Jeff↗
DOE OSTI2017

The Physiological Functions and Structural Determinants of Catalytic Bias in the [FeFe]-Hydrogenases CpI and CpII of Clostridium pasteurianum Strain W5

Here, the first generation of biochemical studies of complex, iron-sulfur-cluster-containing [FeFe]-hydrogenases and Mo-nitrogenase were carried out on enzymes purified from Clostridium pasteurianum (strain W5). Previous studies suggested that two distinct [FeFe]-hydrogenases are expressed differentially under nitrogen-fixing and non-nitrogen-fixing conditions. As a result, the first characterized [FeFe]-hydrogenase (CpI) is presumed to have a primary role in central metabolism, recycling reduced electron carriers that accumulate during fermentation via proton reduction. A role for capturing reducing equivalents released as hydrogen during nitrogen fixation has been proposed for the second hydrogenase, CpII. Biochemical characterization of CpI and CpII indicated CpI has extremely high hydrogen production activity in comparison to CpII, while CpII has elevated hydrogen oxidation activity in comparison to CpI when assayed under the same conditions. This suggests that these enzymes have evolved a catalytic bias to support their respective physiological functions. Using the published genome of C. pasteurianum (strain W5) hydrogenase sequences were identified, including the already known [NiFe]-hydrogenase, CpI, and CpII sequences, and a third hydrogenase, CpIII was identified in the genome as well. Quantitative real-time PCR experiments were performed in order to analyze transcript abundance of the hydrogenases under diazotrophic and non-diazotrophic growth conditions. There is a markedly reduced level of CpI gene expression together with concomitant increases in CpII gene expression under nitrogen-fixing conditions. Structure-based analyses of the CpI and CpII sequences reveal variations in their catalytic sites that may contribute to their alternative physiological roles. This work demonstrates that the physiological roles of CpI and CpII are to evolve and to consume hydrogen, respectively, in concurrence with their catalytic activities in vitro , with CpII capturing excess reducing equivalents under nitrogen fixation conditions. Comparison of the primary sequences of CpI and CpII and their homologs provides an initial basis for identifying key structural determinants that modulate hydrogen production and hydrogen oxidation activities.

09 BIOMASS FUELS↗
DOE OSTI2020

On Aerosol Liquid Water and Sulfate Associations: The Potential for Fine Particulate Matter Biases

In humid locations of the Eastern U.S., sulfate is a surrogate for aerosol liquid water (ALW), a poorly measured particle constituent. Regional and seasonal variation in ALW–sulfate relationships offers a potential explanation to reconcile epidemiology and toxicology studies regarding particulate sulfur and health endpoints. ALW facilitates transfer of polar species from the gas phase to the particle phase and affects particle pH and metal oxidation state. Though abundant and a potential indicator of adverse health endpoints, ALW is largely removed in most particulate matter measurement techniques, including in routine particulate matter (PM 2.5 ) networks that use federal reference method (FRM) monitors, which are used in epidemiology studies. We find that in 2004, a typical year in the available record, ambient ALW mass is removed during sampling and filter equilibration to standard laboratory conditions at most (94%) sites, up to 85% of the ambient water mass. The removal of ALW can induce the evaporation of other semi-volatile compounds present in PM 2.5 , such as ammonium nitrate and numerous organics. This produces an artifact in the PM mass measurements that is, importantly, not uniform in space or time. This suggests that PM 2.5 epidemiology studies that exclude ALW are biased. This work provides a plausible explanation to resolve multi-decade discrepancies regarding ambient sulfate and health impacts in some epidemiological and toxicological studies

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Bright and faint ends of Lyα luminosity functions at z = 2 determined by the Subaru survey: implications for AGNs, magnification bias, and ISM H I evolution

We present the Lyα luminosity functions (LFs) derived by our deep Subaru narrowband survey that identifies a total of 3137 Lyα emitters (LAEs) at z = 2.2 in five independent blank fields. This sample of LAEs is the largest to date and covers a very wide Lyα luminosity range of logL{sub Lyα}=41.7--44.4 erg s{sup −1}. We determine the Lyα LF at z = 2.2 with unprecedented accuracy and obtain the best-fit Schechter parameters of L{sub Lyα}{sup ∗}=5.29{sub −1.13}{sup +1.67}×10{sup 42} erg s{sup −1}, ϕ{sub Lyα}{sup ∗}=6.32{sub −2.31}{sup +3.08}×10{sup −4} Mpc{sup −3}, and α=−1.75{sub −0.09}{sup +0.10}, showing a steep faint-end slope. We identify a significant hump at the LF bright end (logL{sub Lyα}>43.4 erg s{sup −1}). Because all of the LAEs in the bright-end hump have a bright counterpart(s) in either the X-ray, UV, or radio data, this bright-end hump is not made by gravitational lensing magnification bias but by active galactic nuclei (AGNs). These AGNs allow us to derive the AGN UV LF at z ∼ 2 down to the faint magnitude limit of M {sub UV} ≃ −22.5 and to constrain the faint-end slope of the AGN UV LF, α {sub AGN} = −1.2 ± 0.1, which is flatter than those at z > 4. Based on the Lyα and UV LFs from our and previous studies, we find an increase of Lyα escape fraction f{sub esc}{sup Lyα} from z ∼ 0 to 6 by two orders of magnitude. This large f{sub esc}{sup Lyα} increase can be explained neither by the evolution of stellar population nor by outflow alone, but by the evolution of neutral hydrogen H i density in the interstellar medium that enhances dust attenuation for Lyα by resonance scattering. Our uniform expanding shell models suggest that the typical H i column density decreases from N{sub HI}∼7×10{sup 19} (z ∼ 0) to ∼1 × 10{sup 18} cm{sup −2} (z ∼ 6) to explain the large f{sub esc}{sup Lyα} increase.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

The concentration dependence of the galaxy–halo connection: Modeling assembly bias with abundance matching

Empirical methods for connecting galaxies to their dark matter halos have become essential for interpreting measurements of the spatial statistics of galaxies. In this work, we present a novel approach for parameterizing the degree of concentration dependence in the abundance matching method. Furthermore, this new parameterization provides a smooth interpolation between two commonly used matching proxies: the peak halo mass and the peak halo maximal circular velocity. This parameterization controls the amount of dependence of galaxy luminosity on halo concentration at a fixed halo mass. Effectively this interpolation scheme enables abundance matching models to have adjustable assembly bias in the resulting galaxy catalogs. With the new $400\,\mathrm{Mpc}\,{h}^{-1}$ DarkSky Simulation, whose larger volume provides lower sample variance, we further show that low-redshift two-point clustering and satellite fraction measurements from SDSS can already provide a joint constraint on this concentration dependence and the scatter within the abundance matching framework.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Understanding Type Ia Supernova Distance Biases by Simulating Spectral Variations

In the next decade, transient searches from the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope will increase the sample of known Type Ia supernovae (SNe Ia) from ~10 3 to 10 5 . With this reduction of statistical uncertainties on cosmological measurements, new methods are needed to reduce systematic uncertainties. Here, characterizing the underlying spectroscopic evolution of SN Ia remains a major systematic uncertainty in current cosmological analyses, motivating a new simulation tool for the next era of SN Ia cosmology: Build Your Own Spectral Energy Distribution (byosed). byosed is used within the SNANA framework to simulate light curves by applying spectral variations to model SEDs, enabling flexible testing of possible systematic shifts in SN Ia distance measurements. We test the framework by comparing a nominal Roman SN Ia survey simulation using a baseline SED model to simulations using SEDs perturbed with byosed, and investigating the impact of ignoring specific SED features in the analysis. These features include semiempirical models of two possible, predicted relationships: between SN ejecta velocity and light-curve observables, and a redshift-dependent relationship between SN Hubble residuals and host-galaxy mass. We analyze each byosed simulation using the SALT2 and BEAMS with Bias Corrections framework, and estimate changes in the measured value of the dark-energy equation-of-state parameter, w. We find a difference of Δw = –0.023 for SN velocity and Δw = 0.021 for redshift-evolving host mass when compared to simulations without these features. By using byosed for SN Ia cosmology simulations, future analyses (e.g., the Rubin and Roman SN Ia samples) will have greater flexibility to constrain or reduce such SN Ia modeling uncertainties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The Dark Energy Camera Plane Survey 2 (DECaPS2): More Sky, Less Bias, and Better Uncertainties

Abstract Deep optical and near-infrared imaging of the entire Galactic plane is essential for understanding our Galaxy’s stars, gas, and dust. The second data release of the Dark Energy Camera (DECam) Plane Survey extends the five-band optical and near-infrared survey of the southern Galactic plane to cover 6.5% of the sky, ∣ b ∣ ≤ 10°, and 6° > ℓ > −124°, complementary to coverage by Pan-STARRS1. Typical single-exposure effective depths, including crowding effects and other complications, are 23.5, 22.6, 22.1, 21.6, and 20.8 mag in g , r , i , z , and Y bands, respectively, with around 1″ seeing. The survey comprises 3.32 billion objects built from 34 billion detections in 21,400 exposures, totaling 260 hr open shutter time on the DECam at Cerro Tololo. The data reduction pipeline features several improvements, including the addition of synthetic source injection tests to validate photometric solutions across the entire survey footprint. A convenient functional form for the detection bias in the faint limit was derived and leveraged to characterize the photometric pipeline performance. A new postprocessing technique was applied to every detection to debias and improve uncertainty estimates of the flux in the presence of structured backgrounds, specifically targeting nebulosity. The images and source catalogs are publicly available at http://decaps.skymaps.info/ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

As a Matter of Force—Systematic Biases in Idealized Turbulence Simulations

Many astrophysical systems encompass very large dynamical ranges in space and time, which are not accessible by direct numerical simulations. Thus, idealized subvolumes are often used to study small-scale effects including the dynamics of turbulence. These turbulent boxes require an artificial driving in order to mimic energy injection from large-scale processes. In this paper, we show and quantify how the autocorrelation time of the driving and its normalization systematically change the properties of an isothermal compressible magnetohydrodynamic flow in the sub- and supersonic regime and affect astrophysical observations such as Faraday rotation. For example, we find that δ-in-time forcing with a constant energy injection leads to a steeper slope in kinetic energy spectrum and less-efficient small-scale dynamo action. In general, we show that shorter autocorrelation times require more power in the acceleration field, which results in more power in compressive modes that weaken the anticorrelation between density and magnetic field strength. Thus, derived observables, such as the line-of-sight (LOS) magnetic field from rotation measures, are systematically biased by the driving mechanism. Finally, we argue that δ-in-time forcing is unrealistic and numerically unresolved, and conclude that special care needs to be taken in interpreting observational results based on the use of idealized simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Correcting for filter-based aerosol light absorption biases at the Atmospheric Radiation Measurement program's Southern Great Plains site using photoacoustic measurements and machine learning

Abstract. Measurement of light absorption of solar radiation by aerosols is vital for assessing direct aerosol radiative forcing, which affects local and global climate. Low-cost and easy-to-operate filter-based instruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosols on a filter and measure light attenuation through the filter are widely used to infer aerosol light absorption. However, filter-based absorption measurements are subject to artifacts that are difficult to quantify. These artifacts are associated with the presence of the filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (Babs). However, the inability of these algorithms to incorporate into their formulations the complex matrix of influencing parameters such as particle asymmetry parameter, particle size, and particle penetration depth results in prediction of particle-phase absorption coefficients with relatively low accuracy. The analytical forms of corrections also suffer from a lack of universal applicability: different corrections are required for rural and urban sites across the world. In this study, we analyzed and compared 3 months of high-time-resolution ambient aerosol absorption data collected synchronously using a three-wavelength photoacoustic absorption spectrometer (PASS) and PSAP. Both instruments were operated on the same sampling inlet at the Department of Energy's Atmospheric Radiation Measurement program's Southern Great Plains (SGP) user facility in Oklahoma. We implemented the two most commonly used analytical correction algorithms, namely, Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)–Bond et al. (1999) as well as a random forest regression (RFR) machine learning algorithm to predict Babs values from the PSAP's filter-based measurements. The predicted Babs was compared against the reference Babs measured by the PASS. The RFR algorithm performed the best by yielding the lowest root mean square error of prediction. The algorithm was trained using input datasets from the PSAP (transmission and uncorrected absorption coefficient), a co-located nephelometer (scattering coefficients), and the Aerosol Chemical Speciation Monitor (mass concentration of non-refractory aerosol particles). A revised form of the Virkkula (2010) algorithm suitable for the SGP site has been proposed; however, its performance yields approximately 2-fold errors when compared to the RFR algorithm. To generalize the accuracy and applicability of our proposed RFR algorithm, we trained and tested it on a dataset of laboratory measurements of combustion aerosols. Input variables to the algorithm included the aerosol number size distribution from the Scanning Mobility Particle Sizer, absorption coefficients from the filter-based Tricolor Absorption Photometer, and scattering coefficients from a multiwavelength nephelometer. The RFR algorithm predicted Babs values within 5 % of the reference Babs measured by the multiwavelength PASS during the laboratory experiments. Thus, we show that machine learning approaches offer a promising path to correct for biases in long-term filter-based absorption datasets and accurately quantify their variability and trends needed for robust radiative forcing determination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Assessment of errors and biases in retrievals of XCO2, XCH4, XCO, and XN2O from a 0.5 cm–1 resolution solar-viewing spectrometer

Abstract. Bruker™ EM27/SUN instruments are commercial mobile solar-viewing near-IR spectrometers. They show promise for expanding the global density of atmospheric column measurements of greenhouse gases and are being marketed for such applications. They have been shown to measure the same variations of atmospheric gases within a day as the high-resolution spectrometers of the Total Carbon Column Observing Network (TCCON). However, there is little known about the long-term precision and uncertainty budgets of EM27/SUN measurements. In this study, which includes a comparison of 186 measurement days spanning 11 months, we note that atmospheric variations of Xgas within a single day are well captured by these low-resolution instruments, but over several months, the measurements drift noticeably. We present comparisons between EM27/SUN instruments and the TCCON using GGG as the retrieval algorithm. In addition, we perform several tests to evaluate the robustness of the performance and determine the largest sources of errors from these spectrometers. We include comparisons of XCO2, XCH4, XCO, and XN2O. Specifically we note EM27/SUN biases for January 2015 of 0.03, 0.75, –0.12, and 2.43 % for XCO2, XCH4, XCO, and XN2O respectively, with 1σ running precisions of 0.08 and 0.06 % for XCO2 and XCH4 from measurements in Pasadena. We also identify significant error caused by nonlinear sensitivity when using an extended spectral range detector used to measure CO and N2O.

Hedelius, Jacob K. (ORCID:0000000320257519)↗