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DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL’s 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis of the causes of high wind speed bias in the 20-year wind resource data set for offshore California that the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California Outer Continental Shelf, near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a 1-year period.

17 WIND ENERGY↗

DOE OSTI2021

Anaerobic Fungal Mevalonate Pathway Genomic Biases Lead to Heterologous Toxicity Underpredicted by Codon Adaptation Indices

Anaerobic fungi are emerging biotechnology platforms with genomes rich in biosynthetic potential. Yet, the heterologous expression of their biosynthetic pathways has had limited success in model hosts like E. coli. We find one reason for this is that the genome composition of anaerobic fungi like P. indianae are extremely AT-biased with a particular preference for rare and semi-rare AT-rich tRNAs in E coli, which are not explicitly predicted by standard codon adaptation indices (CAI). Native P. indianae genes with these extreme biases create drastic growth defects in E. coli (up to 69% reduction in growth), which is not seen in genes from other organisms with similar CAIs. However, codon optimization rescues growth, allowing for gene evaluation. In this manner, we demonstrate that anaerobic fungal homologs such as PI.atoB are more active than S. cerevisiae homologs in a hybrid pathway, increasing the production of mevalonate up to 2.5 g/L (more than two-fold) and reducing waste carbon to acetate by ~90% under the conditions tested. This work demonstrates the bioproduction potential of anaerobic fungal enzyme homologs and how the analysis of codon utilization enables the study of otherwise difficult to express genes that have applications in biocatalysis and natural product discovery.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Low-Temperature Annealing of CdZnTeSe under Bias

We performed a gradual low-temperature annealing up to 360 K on a CdZnTeSe radiation detector equipped with gold and indium electrodes under bias at both polarities. We observed significant changes in the detector’s resistance and space-charge accumulation. This could potentially lead to the control and improvement of the electronic properties of the detector because the changes are accompanied with the reduction in the bulk dark current and surface leakage current. In this article, we present the results of a detailed study of the internal electric field and conductivity changes in CdZnTeSe detector for various annealing steps under bias taking into account different polarities during annealing and subsequent characterization. We observed that low-temperature annealing results in an increase in the barrier height at the contacts that, in general, reduces the dark current and decreases the positive space charge present in the sample compared to the pre-annealed condition.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

The Dependence of the Type Ia Supernova Host Bias on Observation or Fitting Technique

More luminous Type Ia supernovae prefer less massive hosts and regions of higher star formation. This correlation is inverted during width–color–luminosity light-curve standardization resulting in step-like biases of distance measurements with respect to host properties. Using the PMAS/PPak Integral-field Supernovahosts COmpilation (PISCO) supernova host sample and Sloan Digital Sky Survey, Galaxy Evolution Explorer, and Two Micron All Sky Survey photometry, we compare host stellar mass and specific star-formation rate (sSFR) from different observation methods, including local versus global, and fitting techniques to measure their impact on the host step biases. Mass-step measurements for all our mass samples are consistent within a 1σ significance from –0.03 ± 0.02 mag to –0.04 ± 0.02 mag. Including or excluding UV information had no effect on measured mass-step size or location. sSFR step sizes are more significant than mass-step measurements and varied from 0.05 ± 0.03 mag (Hα) and 0.06 ± 0.02 mag (UV) for a 51 host sample. The sSFR step location is influenced by the mass sample used to normalize star formation and by sSFR tracer choice. The step size is reduced to 0.04 ± 0.03 mag when using all available 73 hosts with Hα measurements. This 73 PISCO host subsample overall lacked a clear step signal, but here we are searching for whether different choices of mass or sSFR estimation can create a step signal. We find no evidence that different observation or fitting techniques choices can create a distance measurement step in either mass or sSFR.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Study of CdZnTeSe Gamma-Ray Detector under Various Bias Voltages

Cadmium zinc telluride selenide (CdZnTeSe) is a new semiconductor material for gamma-ray detection and spectroscopy applications at room temperature. It has very high crystal quality compared to similar materials such as cadmium telluride and cadmium zinc telluride. The consistency of peak position in radiation detection devices is important to practical applications. In this paper, we have characterized a CdZnTeSe planar detector for bias voltages in the range of -20 V to -200 V and amplifier shaping time of 2, 3 and 6 μs. The peak position of the 59.6-keV gamma line of 241 Am becomes more stable as the absolute value of the applied voltage increases. The best energy resolution of 8.5% was obtained for the 59.6-keV gamma peak at -160 V bias voltage and 3-μs shaping time. The energy resolution was relatively stable in the -120 V to -200 V range for a 6-μs shaping time. Future work will be focused on the study of the peak position and energy resolution over time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Model bias in simulating major chemical components of PM 2.5 in China

High concentrations of PM 2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm) in China have caused severe visibility degradation. Accurate simulations of PM 2.5 and its chemical components are essential for evaluating the effectiveness of pollution control strategies and the health and climate impacts of air pollution. In this study, we compared the GEOS-Chem model simulations with comprehensive datasets for organic aerosol (OA), sulfate, nitrate, and ammonium in China. Model results are evaluated spatially and temporally against observations. The new OA scheme with a simplified secondary organic aerosol (SOA) parameterization significantly improves the OA simulations in polluted urban areas, highlighting the important contributions of anthropogenic SOA from semivolatile and intermediate-volatility organic compounds. The model underestimates sulfate and overestimates nitrate for most of the sites throughout the year. More significant underestimation of sulfate occurs in winter, while the overestimation of nitrate is extremely large in summer. The model is unable to capture some of the main features in the diurnal pattern of the PM 2.5 chemical components, suggesting inaccuracies in the presented processes. Potential model adjustments that may lead to a better representation of the boundary layer height, the precursor emissions, hydroxyl radical concentrations, the heterogeneous formation of sulfate and nitrate, and the wet deposition of nitric acid and nitrate have been tested in the sensitivity analysis. The results show that uncertainties in chemistry perhaps dominate the model biases. The proper implementation of heterogeneous sulfate formation and the good estimates of the concentrations of sulfur dioxide, hydroxyl radical, and aerosol liquid water are essential for the improvement of the sulfate simulation. The update of the heterogeneous uptake coefficient of nitrogen dioxide significantly reduces the modeled concentrations of nitrate. However, the large overestimation of nitrate concentrations remains in summer for all tested cases. The possible bias in the chemical production and the wet deposition of nitrate cannot fully explain the model overestimation of nitrate, suggesting issues related to the atmospheric removal of nitric acid and nitrate. A better understanding of the atmospheric nitrogen budget, in particular, the role of the photolysis of particulate nitrate, is needed for future model developments. Moreover, the results suggest that the remaining underestimation of OA in the model is associated with the underrepresented production of SOA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Analysis of secondary organic aerosol simulation bias in the Community Earth System Model (CESM2.1)

Organic aerosol (OA) has been considered as one of the most important uncertainties in climate modeling due to the complexity in presenting its chemical production and depletion mechanisms. To better understand the capability of climate models and probe into the associated uncertainties in simulating OA, we evaluate the Community Earth System Model version 2.1 (CESM2.1) configured with the Community Atmosphere Model version 6 (CAM6) with comprehensive tropospheric and stratospheric chemistry representation (CAM6-Chem) through a long-term simulation (1988–2019) with observations collected from multiple datasets in the United States. We find that CESM generally reproduces the interannual variation and seasonal cycle of OA mass concentration at surface layer with a correlation of 0.40 compared to ground observations and systematically overestimates (69%) in summer and underestimates (-19%) in winter. Through a series of sensitivity simulations, we reveal that modeling bias is primarily related to the dominant fraction of monoterpene-formed secondary organic aerosol (SOA), and a strong positive correlation of 0.67 is found between monoterpene emission and modeling bias in the eastern US during summer. In terms of vertical profile, the model prominently underestimates OA and monoterpene concentrations by 37%–99% and 82%–99%, respectively, in the upper air (>500 m) as validated against aircraft observations. Our study suggests that the current volatility basis set (VBS) scheme applied in CESM might be parameterized with monoterpene SOA yields that are too high, which subsequently results in strong SOA production near the emission source area. We also find that the model has difficulty in reproducing the decreasing trend of surface OA in the southeastern US probably because of employing pure gas VBS to represent isoprene SOA which is in reality mainly formed through multiphase chemistry; thus, the influence of aerosol acidity and sulfate particle change on isoprene SOA formation has not been fully considered in the model. This study reveals the urgent need to improve the SOA modeling in climate models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations

There has been a growing concern that most climate models predict precipitation that is too frequent, likely due to lack of reliable subgrid variability and vertical variations in microphysical processes in low-level warm clouds. In this study, the warm-cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017–2018. The 8-month single-column model (SCM) simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary layer cloud structure, major macrophysical properties, and their transition. The improvement in warm-cloud properties from the Community Atmospheric Model 5 and 6 (CAM5 to CAM6) physics can be found through comparison with the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft-observed ones in the summer but are overestimated by a factor of 2 in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations in droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of subgrid variations in cloud properties in CAM6 cloud microphysics, which further suppresses the scheme's sensitivity to individual warm-rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces an aerosol indirect effect that is too strong. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Combining regional mesh refinement with vertically enhanced physics to target marine stratocumulus biases as demonstrated in the Energy Exascale Earth System Model version 1

Abstract. In this paper we develop a novel framework aimed to significantly reduce biases related to marine stratocumulus clouds in general circulation models (GCMs) while circumventing excessive computational cost requirements. Our strategy is to increase the horizontal resolution using a regionally refined mesh (RRM) over our region of interest in addition to using the Framework for Improvement by Vertical Enhancement (FIVE) to increase the vertical resolution only for specific physical processes that are important for stratocumulus. We apply the RRM off the coast of Peru in the southeastern Pacific, a region that climatologically contains the most marine stratocumulus in the subtropics. We find that our new modeling framework is able to replicate the results of our high-resolution benchmark simulation with much fidelity, while reducing the computational cost by several orders of magnitude. In addition, this framework is able to greatly reduce the long-standing biases associated with marine stratocumulus in GCMs when compared to the standard-resolution control simulation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The Digital Mirror Langmuir Probe: Field Programmable Gate Array Implementation of Real-Time Langmuir Probe Biasing

High bandwidth, high spatial resolution measurements of electron temperature, density and plasma potential are valuable for resolving turbulence in the boundary plasma of tokamaks. While conventional Langmuir probes can provide such measurements either their temporal or spatial resolution is limited: the former by the sweep rate necessary for obtaining I-V characteristics and the latter by the need to use multiple electrodes, as is the case in triple and double probe configurations. The Mirror Langmuir Probe (MLP) bias technique overcomes these limitations by rapidly switching the voltage on a single electrode cycling between three bias states, each dynamically optimised for the local plasma conditions. The MLP system on Alcator C-Mod used analog circuitry to perform this function, measuring Te, VF, and Isat at 1.1 MSPS. Recently, a new prototype digital MLP controller has been implemented on a Red Pitaya Field Programmable Gate Array (FPGA) board which reproduces the functionality of the original controller and performs all data acquisition. There is also the potential to provide the plasma parameters externally for use with feedback control systems. The use of FPGA technology means the system is readily customisable at a fraction of the development time and implementation cost. A second Red Pitaya was used to test the MLP by simulating the current response of a physical probe using C-Mod experimental measurements. This project is available as a git repository to facilitate extensibility (e.g. real-time control outputs, more voltage states) and scalability through collaboration.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Increasingly minimal bias routing

A system and algorithm configured to generate diversity at the traffic source so that packets are uniformly distributed over all of the available paths, but to increase the likelihood of taking a minimal path with each hop the packet takes. This is achieved by configuring routing biases so as to prefer non-minimal paths at the injection point, but increasingly prefer minimal paths as the packet proceeds, referred to herein as Increasing Minimal Bias (IMB).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A Clustering-based biased Monte Carlo Approach to Protein Titration Curve Prediction

We develop and implement a novel approach to computing the ensemble averages in systems characterized by pair-wise interactions between the entities. Methods involving full enumeration of the configuration space result in exponential complexity. Sampling methods such as Markov Chain Monte Carlo (MCMC) algorithms have been proposed to tackle the exponential complexity of these problems. In certain scenarios where significant energetic coupling exists between the entities, the accuracy of the such algorithms can be diminished. We propose a strategy to improve the accuracy of the MCMC runs by taking advantage of the cluster structure in the interaction energy matrix. We propose two different schemes for performing the biased MCMC runs on the partitioned systems and show that they are valid MCMC schemes. We then apply these algorithms to the problem of computing the protonation fractions and hence the titration curves of titratable protein residues that constitute a given protein. We leverage both synthesized and real-world systems and show the improved performance of our biased MCMC methods when compared to the regular MCMC method.

Visweswara Sathanur, Arun↗

DOE OSTI2021

Qualifying AL-800 Garnet For The Fermilab Booster Perpendicular Biased 2nd Harmonic RF Cavity

Al-800 garnet was evaluated for the perpendicular biased 2nd harmonic cavity for the Fermilab Booster accelerator. A test apparatus was designed to evaluate the magnetic permeability and batch consistency of small witness samples of Al-800. Fully assembled AL-800 garnet rings were tested on the garnet ring test stand to measure the resonant frequency and quality factor of each ring. The garnet witness samples and assembled garnet rings showed acceptable consistency within the parameters set by the cavity design team. Based on the test results, AL-800 garnet was deemed suitable for use in the perpendicular biased 2nd harmonic cavity.

Kuharik, John C.↗

DOE OSTI2021

Methods and devices configured to operated scanning tunneling microscopes using out-of-bandwidth frequency components added to bias voltage and related software

In the system and method disclosed, an ultrahigh vacuum (UHV) scanning tunneling microscope (STM) tip is used to selectively desorb hydrogen atoms from the Si(100)-2X1:H surface by injecting electrons at a negative sample bias voltage. A new lithography method is disclosed that allows the STM to operate under imaging conditions and simultaneously desorb H atoms as required. A high frequency signal is added to the negative sample bias voltage to deliver the required energy for hydrogen removal. The resulted current at this frequency and its harmonics are filtered to minimize their effect on the operation of the STM's feedback loop. This approach offers a significant potential for controlled and precise removal of hydrogen atoms from a hydrogen-terminated silicon surface and thus may be used for the fabrication of practical silicon-based atomic-scale devices.

Moheimani, Seyed Omid Reza↗

DOE OSTI2023

Methods and devices configured to operated scanning tunneling microscopes using out-of-bandwidth frequency components added to bias voltage and related software

Moheimani, Seyed Omid Reza↗

DOE OSTI2024

The National Climate Data Base (NCDB): A Bias-Corrected High-Resolution Climate Dataset

Assessing renewable energy resources under future climate scenarios has been highlighted in recent years to analyze and understand potential impacts of future change in renewable generation on the power sector. Solar energy is well-known as the most plentiful among various renewable resources and usually converted to electricity using photovoltaics (PV) technologies, and the global deployment of PV technology has increased rapidly in recent decades. In this study, we develop a statistical technique to downscale the future projection of solar irradiance for PV energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the proposed downscaling method and future extension of this research.

climate data↗

DOE OSTI2021

On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

Physics-informed neural networks (PINNs) are demonstrating remarkable promise in integrating physical models with gappy and noisy observational data, but they still struggle in cases where the target functions to be approximated exhibit high-frequency or multi-scale features. Here in this work we investigate this limitation through the lens of Neural Tangent Kernel (NTK) theory and elucidate how PINNs are biased towards learning functions along the dominant eigen-directions of their limiting NTK. Using this observation, we construct novel architectures that employ spatio-temporal and multi-scale random Fourier features, and justify how such coordinate embedding layers can lead to robust and accurate PINN models. Numerical examples are presented for several challenging cases where conventional PINN models fail, including wave propagation and reaction–diffusion dynamics, illustrating how the proposed methods can be used to effectively tackle both forward and inverse problems involving partial differential equations with multi-scale behavior.

42 ENGINEERING↗

DOE OSTI2022

Exchange bias and interface-related effects in two-dimensional van der Waals magnetic heterostructures: Open questions and perspectives

The exchange bias (EB) effect is known as a fundamentally and technologically important magnetic property of a magnetic bilayer film. It is manifested as a horizontal shift in a magnetic hysteresis loop of a film subject to cooling in the presence of a magnetic field. The EB effect in van der Waals (vdW) heterostructures offers a novel approach for tuning the magnetic properties of the newly discovered single-layer magnets, as well as adds a new impetus to magnetic vdW heterostructures. Indeed, intriguing EB effects have recently been reported in a variety of low-dimensional vdW magnetic systems ranging from a weakly interlayer-coupled vdW magnet (e.g., Fe 3 GeTe 2 ) to a bilayer composed of two different magnetic vdW materials (e.g., Fe 3 GeTe 2 /CrCl 3 , Fe 3 GeTe 2 /FePS 3 , Fe 3 GeTe 2 /MnPS 3 , Fe 3 GeTe 2 /CrSe, Fe 3 GeTe 2 /CrOCl, Fe 3 GeTe 2 /CoPc, Fe 5 GeTe 2 /FePS 3 ), to bilayers of two different vdW defective magnets (e.g., VSe 2 /MoS 2 ), or to metallic ferromagnet/vdW defective magnet interfaces (e.g., Fe/MoS 2 ). Despite their huge potential in spintronic device applications, the physical origins of the observed EB effects have remained elusive to researchers. Here, we present here a critical review of the EB effect and associated phenomena such as magnetic proximity (MP) in various vdW heterostructure systems and propose approaches to addressing some of the emerging fundamental questions.

2D magnets↗