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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Methane and Carbon Dioxide Fluxes in a Temperate Tidal Salt Marsh: Comparisons Between Plot and Ecosystem Measurements

Tidal wetlands are comprised of complex interdependent pathways where measurements of carbon exchange are often scale dependent. Common data collection methods (i.e., chambers and eddy covariance) are inherently constrained to different spatial and temporal scales which could generate biased information for applications of carbon accounting, identifying functional relationships and predicting future responses to climate change. Consequently, it is needed to systematically evaluate measurements derived from multiple approaches to identify differences and how techniques complement each other to reconcile interpretations. For our study, to accomplish this, we tested ecosystem-scale eddy covariance with plot-scale chamber measurements within a temperate salt marsh. We found good agreement (R 2 = 0.71–0.95) when comparing measurements of CH 4 emissions and CO 2 exchange but this agreement was dependent upon canopy phenology with discrepancies mainly arising during senescence and dormancy phenophases. The environmental drivers for CH 4 and CO 2 fluxes were mostly preserved across different measurement techniques, but the number of drivers increases while their individual strength decreases at the ecosystem scale. Empirical upscaling models parameterized with chamber measurements overestimated annual net ecosystem exchange (NEE; 108%) and gross primary production (GPP; 12%) while underestimating ecosystem respiration (Reco; 14%) and CH 4 emissions (69%) compared to eddy covariance measurements. Our results suggest that the environmental complexity of CH 4 and CO 2 fluxes in salt marshes may be underestimated by chamber-based measurements, and highlights how different techniques are complementary while considering limitations at each level of measurement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2)

Abstract Earth System Models (ESMs) are increasingly representing agriculture due to its impact on biogeochemical cycles, local and regional climate, and fundamental importance for human society. Realistic large scale simulations may require spatially varying crop parameters that capture crop growth at various scales and among different cultivars, as well as common crop management practices, but their importance is uncertain, and they are often not represented in ESMs. In this study, we examine the impact of using constant versus spatially varying crop parameters using a novel, realistic crop rotation scenario in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2). We implemented crop rotation by using ELMv2's dynamic land unit capability, and then calibrated and validated the model against observations collected at three AmeriFlux sites in the US Midwest with corn soybean rotation. The calibrated model closely captured the magnitude and observed seasonality of carbon and energy fluxes across crops and sites. We performed regional simulations for the US Midwest using the calibrated model and found that spatially varying only a few crop parameters across the region, as opposed to using constant parameters, had a large impact, with the carbon fluxes and energy fluxes both varying by up to 40%. These results imply that large scale ESM simulations using spatially invariant crop parameters may result in biased energy and carbon fluxes estimation from agricultural land, and underline the importance of improving human‐earth systems interactions in ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Soil Moisture Buffers the Impact of Precipitation Variability on Ecosystem Productivity

Water availability governs ecosystem productivity, yet estimates of vegetation sensitivity to water can differ greatly depending on whether the sensitivity is examined spatially or temporally. In particular, the spatial sensitivity is often reported to be much stronger than temporal sensitivities, leading to highly uncertain projections of ecosystem responses to future climate change when using space-for-time substitution. The large difference between spatial and temporal sensitivities remains unexplained. Prior research, however, primarily relied on precipitation as the water availability proxy, whereas vegetation responds to soil moisture. Here, we combined satellite estimates of vegetation productivity with soil moisture data across water-limited ecosystems of the continental United States (CONUS) to identify a convergent sensitivity of productivity to water availability. Using precipitation, we show that temporal sensitivity is 66% lower than spatial sensitivity overall. Our analysis identified the cause of the difference to be primarily driven by the seasonal variability of water availability, rooting depth, and soil properties. When using soil moisture instead of precipitation, we observed widespread convergence in the spatial and temporal sensitivities—that is, the two sensitivities became much more similar in magnitude across all water-limited ecosystems within CONUS. These results show that overlooking soil hydrology can inflate perceived discrepancies between spatial and temporal vegetation sensitivities, leading to biased projections of ecosystem dynamics under future hydro-climatic change.

Wang, Huiqi [University of California, Berkeley, C↗
DOE OSTI2023

Steady-state junction current distribution in p-n GaN diodes measured using low-energy electron microscopy (LEEM)

Here, we report on the measurement of the lateral distribution of the junction current of an electrical biased p-n GaN diode by electron emission microscopy using a low-energy electron microscope. The vacuum level at the surface of the diode was lowered by deposition of cesium to achieve negative electron affinity, allowing overflow electrons at the surface of the biased diodes to be emitted and their spatial distribution imaged. The results were compared to the literature, and a good match with analytical solutions by Joyce and Wemple was obtained.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Development of a 95-Year Solar Dataset for Resource Adequacy Studies

Long-term high-resolution solar data provides enhanced understanding of variability of solar generation and enhances our ability to develop strategies for a resilient and reliable electric grid under high deployment of solar energy. Therefore, it is important to develop long-term synthetic datasets that can provide multiple occurrences of various severe weather scenarios that are expected to test the limits of resource adequacy under scenarios contain various energy generation sources. Examples of such scenarios could be long periods of high temperatures when demand for electricity is high or periods where high winds could lead to a shut-down of transmission lines for long periods of time to ensure fire safety. NREL has developed the first version of such a dataset covering a 95-year period covering 2006-2100 at a 4km hourly resolution. This dataset contains all variables necessary to calculate solar generation. During development of this dataset, we focused on creating unbiased, high-resolution solar irradiance through statistical downscaling methods, using Regional Climate Model (RCM) simulations from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) as input. The National Solar Radiation Database (NSRDB) containing over 25 years of observations was used to calibrate the statistical downscaling models. This presentation will outline the primary steps in developing this dataset, including (1) regridding RCM data to a common grid at 20-km resolution, (2) correcting RCM biases with NSRDB, (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar and ancillary data. Additionally, we will present an evaluation of the downscaled data against the NSRDB across various zones in the CONUS. Lastly, we will present a user guide for accessing the datasets.

14 SOLAR ENERGY↗
DOE OSTI2022

Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution

The efficacy of vegetation dynamics simulations in offline land surface models (LSMs) largely depends on the quality and spatial resolution of meteorological forcing data. In this study, the Princeton Global Meteorological Forcing Data (PMFD) and the high spatial resolution and upscaled China Meteorological Forcing Data (CMFD) were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics (SSiB4/TRIFFID) and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau (TP), a region with complex topography and sparse observations. By comparing the monthly Leaf Area Index (LAI) and Gross Primary Production (GPP) against observations, we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP, reducing RMSEs by 24.3% and 20.5%, respectively. The multi-year averaged GPP decreased from 364.68 gC m –2 yr –1 to 241.21 gC m –2 yr –1 with the percentage bias dropping from 50.2% to –1.7%. When using the high spatial resolution CMFD, the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5% and 9.5%, respectively. This study highlights the importance of more realistic and high-resolution forcing data in simulating vegetation growth and carbon exchange between the atmosphere and biosphere over the TP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The DOE E3SM v1.2 Cryosphere Configuration: Description and Simulated Antarctic Ice‐Shelf Basal Melting

Abstract The processes responsible for freshwater flux from the Antarctic Ice Sheet (AIS), ice‐shelf basal melting and iceberg calving, are generally poorly represented in current Earth System Models (ESMs). Here we document the cryosphere configuration of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) v1.2. This includes simulating Antarctic ice‐shelf basal melting, which has been implemented through simulating the ocean circulation within static Antarctic ice‐shelf cavities, allowing for the ability to calculate ice‐shelf basal melt rates from the associated heat and freshwater fluxes. In addition, we added the capability to prescribe forcing from iceberg melt, allowing for realistic representation of the other dominant mass loss process from the AIS. In standard resolution simulations (using a noneddying ocean) under preindustrial climate forcing, we find high sensitivity of modeled ocean/ice shelf interactions to the ocean state, which can result in a transition to a high basal melt regime under the Filchner‐Ronne Ice Shelf (FRIS), presenting a significant challenge to representing the ocean/ice shelf system in a coupled ESM. We show that inclusion of a spatially dependent parameterization of eddy‐induced transport reduces biases in water mass properties on the Antarctic continental shelf. With these improvements, E3SM produces realistic ice‐shelf basal melt rates across the continent that are generally within the range inferred from observations. The accurate representation of ice‐shelf basal melting within a coupled ESM is an important step toward reducing uncertainties in projections of the Antarctic response to climate change and Antarctica's contribution to global sea‐level rise.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Analytical noise bias correction for precise weak lensing shear inference

Noise bias is a significant source of systematic error in weak gravitational lensing measurements that must be corrected to satisfy the stringent standards of modern imaging surveys in the era of precision cosmology. This paper reviews the analytical noise bias correction method and provides analytical derivations demonstrating that we can recover shear to its second order using the ‘renoising’ noise bias correction approach introduced by METACALIBRATION. We implement this analytical noise bias correction within the AnaCal shear estimation framework and propose several enhancements to the noise bias correction algorithm. We evaluate the improved AnaCal using simulations designed to replicate Rubin Legacy Survey of Space and Time (LSST) imaging data. These simulations feature semi-realistic galaxies and stars, complete with representative distributions of magnitudes and Galactic spatial density. We conduct tests under various observational challenges, including cosmic rays, defective CCD columns, bright star saturation, bleed trails, and spatially variable point spread functions. Our results indicate a multiplicative bias in weak lensing shear recovery of less than a few tenths of a per cent, meeting LSST Dark Energy Science Collaboration requirements without requiring calibration from external image simulations. Additionally, our algorithm achieves rapid processing, handling one galaxy in less than a millisecond.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for >700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

MCCCS-MN

The MCCCS‒MN (Monte Carlo for Complex Chemical Systems‒Minnesota) software is developed by the Siepmann research group at the University of Minnesota. MCCCS‒MN allows for the simulation of multi-component molecular systems in the canonical, isobaric-isothermal (including constant stress for solids), grand-canonical, semi-grand, and Gibbs (NVT, NPT, and more than two simulation boxes) ensembles. It uses the configurational-bias Monte Carlo method to efficiently sample phase space for linear, branched and cyclic chain molecules, the adiabatic nuclear and electronic sampling Monte Carlo method to treat many-body polarization effects, and the aggregation-volume-bias Monte Carlo algorithm to efficiently sample the spatial distribution of associating molecules. MCCCS-MN employs a molecular representation of the system where force fields contain bonded and non-bonded terms. Funding for the development of MCCCS-MN through grants from the National Science Foundation (simulation of fluid phase equilibria and chromatography) and the Department of Energy (simulation of adsorption equilibria) is gratefully acknowledged.

Siepmann, J.Ilja [University of Minnesota - Twin C↗
DOE OSTI2023

Continuum kinetic investigation of the impact of bias potentials in the current saturation regime on sheath formation

In this work, we examine sheath formation in the presence of bias potentials in the current saturation regime for pulsed power fusion experiments. It is important to understand how the particle and heat fluxes at the wall may impact the wall material and affect electrode degradation. Here, simulations are performed using the 1X-1V Boltzmann–Poisson system for a proton–electron plasma in the presence of bias potentials ranging from 0 to 10 kV. The results indicate that the sheath near the anode remains generally the same as that of a classical sheath without the presence of a bias potential. However, the sheath near the cathode becomes more prominent with a larger potential drop, a significant decrease in the electron density, and larger sheath lengths. The spatially constant current density increases to a saturation value with the increasing bias potential. For high bias potentials, the material choice needs to consider that the anode faces significantly larger particle and heat fluxes compared to the cathode. In general, the results trend with theory with differences attributed to the simplified assumptions in the theory and the kinetic effects considered in the simulations. Due to the significant computational cost of a well resolved 1X-2V simulation, only one such simulation is performed for the 5 kV case showing higher current.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

A pair of early- and late-forming galaxy cluster samples: A novel way of studying halo assembly bias assisted by a constrained simulation

The halo assembly bias, a phenomenon referring to dependencies of the large-scale bias of a dark matter halo other than its mass, is a fundamental property of the standard cosmological model. First discovered in 2005 from the Millennium Run simulation, it has been proven very difficult to be detected observationally, with only a few convincing claims of detection so far. The main obstacle lies in finding an accurate proxy of the halo formation time. In this study, by utilizing a constrained simulation that can faithfully reproduce the observed structures larger than 2 Mpc in the local universe, for a sample of 634 massive clusters at z ≤ 0.12, we found their counterpart halos in the simulation and used the mass growth history of the matched halos to estimate the formation time of the observed clusters. This allowed us to construct a pair of early- and late-forming clusters, with a similar mass as measured via weak gravitational lensing, and large-scale biases differing at the ≈3σ level, suggestive of the signature of assembly bias, which is further corroborated by the properties of cluster galaxies, including the brightest cluster galaxy and the spatial distribution and number of member galaxies. Our study paves a way to further detect assembly bias based on cluster samples constructed purely on observed quantities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Subtropical Eastern North Pacific SST bias in earth system models

Karthik Balaguru (PNNL), Luke Van Roekel (LLNL), L. Ruby Leung (PNNL) and Milena Veneziani (LLNL) Abstract: This study systematically evaluates the warm SST bias in the Subtropical Eastern North Pacific (SENP), a problem plaguing most CMIP6 models, using the Energy Exascale Earth System Model version 1 (E3SM). Compared to the model at its standard resolution (1o atmosphere, 30-60 km ocean), the SST bias improves in several regions at high resolution (0.25o atmosphere, 18-6 km ocean) except in subtropical eastern boundary regions, such as the SENP. A mixed layer heat budget analysis reveals that errors in the surface radiative fluxes and horizontal heat advection are primarily responsible for the bias. Analysis of HighResMIP models indicates that the lack of improvement in the SENP SST bias with resolution is not native to E3SM alone. A larger CMIP6 inter-model correlation of SST bias with biases in surface winds than radiation highlights the important role of large-scale circulation biases in the SENP SST bias in coupled models.

Balaguru, Karthik↗
DOE OSTI2021

How can biosphere models simulate enough vegetation biomass in the mountains of the western United States? Implications of meteorological forcing

Most carbon stocks and fluxes in the western United States are found in mountainous terrain, where observations and modeling are difficult. Terrestrial biosphere models generally underestimate above-ground biomass (AGB) over this region. In this report, we identify methods to reduce this underestimation by focusing upon 1) biases in meteorological datasets, 2) model representation of water stress, and 3) spatial resolution. We adopted the widely-used Community Land Model version 4.5 (CLM 4.5) with six different meteorological datasets and found a 6-fold variation in simulated AGB across Utah/Colorado. Simulations underestimated AGB because of warm and dry biases within the meteorological datasets that reduced water availability and restricted plant growth. To eliminate the AGB underestimation we adopted a meteorological dataset designed for complex terrain (gridMET), combined with a representation of plant hydraulic stress (CLM 5.0). Conversely, changes in spatial resolution (meteorological variables and land surface description) had negligible impact on simulated AGB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Impact of Bias Row Noise to Photometric Accuracy: Case Study Based on a Scientific CMOS Detector

Abstract We tested a new model of CMOS detector manufactured by the Gpixel Inc, for potential space astronomical application. In laboratory, we obtain some bias images under the typical application environment. In these bias images, clear random row noise pattern is observed. The row noise also contains some characteristic spatial frequencies. We quantitatively estimated the impact of this feature to photometric measurements, by making simulated images. We compared different bias noise types under strict parameter control. The result shows the row noise will significantly deteriorate the photometric accuracy. It effectively increases the readout noise by a factor of 2–10. However, if it is properly removed, the image quality and photometric accuracy will be significantly improved.

Astronomy & Astrophysics↗
DOE OSTI2025

Electrically‐Driven Metal‐Insulator Transitions Emerging from Localizing Current Density and Temperature

Negative differential resistance (NDR) is a key electronic response enabling two‐terminal artificial neurons that can be achieved through different physical phenomena, including phase‐homogeneous current density and temperature (electro‐thermal) localizations and spatially‐localized metal‐insulator phase transitions (MITs). These two effects have been observed to occur sequentially in select electrically‐biased transition metal oxides. However, it is unknown why and under what conditions localizing behaviors precede MITs, particularly as a function of device length scale. To this end, the interplay between phase‐homogeneous electro‐thermal localizations and MITs is investigated in a 3D multiphysics simulation of a lateral thin film device, using the material properties of the prototype MIT material VO 2 . These findings demonstrate that the MIT is nucleated through dynamically localizing current density and temperature. A critical device width (≈0.7 µm in this study) is identified, below which both the electrically‐induced electro‐thermal and phase inhomogeneities cease to appear. It is demonstrated that the formation of spatial inhomogeneities directly relates to device dimensions, and demonstrate the decoupling of NDR from the MIT through device scaling relationships. These results provide insight into the material phenomena underlying the material's electrical responses, clarifying conditions under which spatial inhomogeneities form in electrically‐biased MIT materials.

artificial neuron↗
DOE OSTI2026

A North Sea in Situ Evaluation of the Fitch Wind Farm Parameterization Within the Mellor-Yamada-Nakanishi-Niino and 3D Planetary Boundary Layer Schemes

Wind resource assessments and wind power forecasts that account for wind farm wakes are sensitive to the choice of planetary boundary layer (PBL) scheme. This work compares the one-dimensional Mellor-Yamada-Nakanishi-Niino (MYNN) PBL scheme with a three-dimensional PBL (3DPBL) scheme, evaluating predictions made with both schemes against two sets of North Sea in situ observations of wind farm wakes. The optimal PBL scheme varies based on the observations (FINO1 tower vs. aircraft), the quantity of interest (wind speed vs. turbulence kinetic energy [TKE]), and the error metric (bias, centered root mean square error [cRMSE], R2, and earth mover's distance [EMD]). Whereas 3DPBL wind speeds outperform MYNN wind speeds with respect to the cRMSE at the FINO1 site located at a single point within the turbine rotor layer, 3DPBL TKE bias is larger than MYNN TKE bias when compared to aircraft observations taken 100 m above a wind farm. Wind speeds in the aircraft region are ambiguous with regard to which PBL scheme is optimal. Aircraft MYNN wind speeds outperform 3DPBL wind speeds with respect to R2 and cRMSE but underperform with respect to bias and EMD. Future evaluations across broader temporal and spatial scales may offer further insight into model differences.

17 WIND ENERGY↗