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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Performance Before and After Irradiation of Pixelated 3D Silicon Sensors for the HL-LHC CMS Tracker

The Large Hadron Collider (LHC) particle accelerator at European Center for Nuclear Research (CERN) will be shut down starting in 2026 to achieve the High Luminosity Large Hadron Collider (HL-LHC) upgrade. The upgrade will allow for higher fluences, in order to increase the probability of detecting increasingly rare particles, and to obtain higher precision measurements of known particles. To accommodate the new accelerator conditions, many aspects of the Compact Muon Solenoid (CMS) detector will be upgraded; of particular interest for this thesis are the silicon pixel detectors located in the inner tracker. These will be replaced and upgraded to accommodate the higher fluences of the HL-LHC upgrade, as well as to replace existing sensors which have sustained radiation damage. In order for the new sensors to operate under high luminosity conditions, they must be increasingly radiation hard, and in order to detect rare particles, they must be increasingly more precise. The performance of one Centro Nacional de Microelectronica (CNM) 3D silicon sensor before and after undergoing irradiation at fluences similar to those which will be observed at the HL-LHC was investigated to determine radiation hardness and precision. Data was collected at Fermi National Laboratory (Fermilab), in the Fermi National Laboratory Test Beam Facility (FTBF) silicon tracker telescope, which can be used to determine the number of particles, and tracks made by high energy protons passing through. The sensor was also irradiated at Fermilab in the Irradiation Test Area (ITA). Prior to data collection a tuning procedure is carried out to determine ideal bias voltage operating conditions, mask noisy and dead pixels, adjust to the ideal threshold, and map sensor gain. Data is then collected at the FTBF, where the sensor is installed in the center of the FTBF silicon telescope. Variables, including angle and bias voltage, are varied throughout data collection. Data is then processed using an alignment software to determine the exact telescope geometry, along with the tracks which were observed passing through the sensor and telescope. Sensor performance was found to be comparable before and after irradiation, with irradiated results showing slightly lower efficiencies and cluster sizes. Position resolution is comparable both before and after irradiation, and similar distributions of cluster shape are observed. After irradiation, the sensor shows increasing collected charge with bias, an indication of increased width of the depletion region. Peak charge pre-irradiation is higher than post-irradiation peak charge, indicating the irradiated results are not taken under fully-depleted conditions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A Conterminous USA-Scale Map of Relative Tidal Marsh Elevation

Abstract Tidal wetlands provide myriad ecosystem services across local to global scales. With their uncertain vulnerability or resilience to rising sea levels, there is a need for mapping flooding drivers and vulnerability proxies for these ecosystems at a national scale. However, tidal wetlands in the conterminous USA are diverse with differing elevation gradients, and tidal amplitudes, making broad geographic comparisons difficult. To address this, a national-scale map of relative tidal elevation ( Z * MHW ), a physical metric that normalizes elevation to tidal amplitude at mean high water (MHW), was constructed for the first time at 30 × 30-m resolution spanning the conterminous USA. Contrary to two study hypotheses, watershed-level median Z * MHW and its variability generally increased from north to south as a function of tidal amplitude and relative sea-level rise. These trends were also observed in a reanalysis of ground elevation data from the Pacific Coast by Janousek et al. (Estuaries and Coasts 42 (1): 85–98, 2019). Supporting a third hypothesis, propagated uncertainty in Z * MHW increased from north to south as light detection and ranging (LiDAR) errors had an outsized effect under narrowing tidal amplitudes. The drivers of Z * MHW and its variability are difficult to determine because several potential causal variables are correlated with latitude, but future studies could investigate highest astronomical tide and diurnal high tide inequality as drivers of median Z * MHW and Z * MHW variability, respectively. Watersheds of the Gulf Coast often had propagated Z * MHW uncertainty greater than the tidal amplitude itself emphasizing the diminished practicality of applying Z * MHW as a flooding proxy to microtidal wetlands. Future studies could focus on validating and improving these physical map products and using them for synoptic modeling of tidal wetland carbon dynamics and sea-level rise vulnerability analyses.

Holmquist, James R. (ORCID:0000000325466766)↗
DOE OSTI2025

Pyrogenic Organic Matter Laboratory Experiment: Aerobic Respiration and Geochemistry from Variably Inundated Stream Sediments (v3)

This dataset supports a broader study examining the effects of variable inundation and pyrogenic organic matter on ecosystem respiration. The dataset provides data generated from a laboratory batch experiment investigating the interaction between variable inundation conditions (wet and dry sediment) and pyrogenic organic matter (burned and unburned treatments). The contents include time series dissolved oxygen, sediment geochemistry data, and field metadata (including qualitative information on instream and river corridor characteristics). This data package was originally published in November 2025. It was updated in April 2026 (v2; new and modified files) and May 2026 (v3; modified files). See the change history section in the readme for more details For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) international generic sample number (IGSN) mapping file; (5) readme; (6) field protocol; (7) sample name metadata; (8) an environmental context picture for the dry and inundated sampling locations; and (9) a subfolder with sample data from the sediment incubation experiment. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC); (2) total nitrogen (TN); (3) gravimetric moisture; (4) partial pressure and production rates of carbon dioxide, methane, and nitrous oxide; (5) field wet sediment mass, dry sediment mass, water mass, and field wet sediment volume in incubation and sediment NPOC/TN vials; (6) methods codes; (7) respiration rates, pH, and temperature from after the incubation, raw time series dissolved oxygen and temperature, and a subfolder containing associated plots and scripts; (8) ions; (9) FTICR-MS methods; and (10) a subfolder of 12 Tesla (12T) FTICR-MS data. This folder contains the CoreMS processed data and three subfolders, one containing the .xml files, one containing the CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .html, .Rmd, .py, .cal, .json, or .jpg.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A deep learning-based direct forecasting of CO 2 plume migration

Accurate and timely forecasts of CO 2 plume evolution in geological reservoirs are crucial for CO 2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO 2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO 2 plume distributions. In this work, we demonstrate the effectiveness and accuracy of our method in predicting the CO 2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Spring Dust in Colorado Plateau: Transport Pathways and Interannual Variability Derived From Two Decades of MERRA‐2 Reanalysis

Spring dust storms in the U.S. Southwest significantly impact environmental and human systems, yet their climatological patterns and driving mechanisms remain poorly understood. Using two decades of MERRA-2 reanalysis data and self-organizing map clustering, we identified four distinct dust transport pathways from surrounding and remote deserts for the top 10% of spring dust events impacting the Colorado Plateau: (a) intense southwesterly near-surface transport where eastward upward transport is blocked by topography, (b) substantial remote transport plus weaker surface emissions, (c) moderate southwesterly transport with stronger upper-level transport, and (d) rare northwesterly pattern linked to the Great Basin sources, which accounts for 38%, 14%, 37%, and 11% of total events, respectively. The frequency and intensity of these events strongly correlate with large-scale climate variability, with increased dust events during La Niña. Our results provide a robust framework for improving seasonal dust forecasts and understanding future dust dynamics under climate change.

aerosol transport↗
DOE OSTI2023

Solving Seismic Wave Equations on Variable Velocity Models With Fourier Neural Operator

Here, in the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning (DL) enables solving partial differential equations (PDEs), including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. This approach can be faster than traditional numerical methods when numerous instances are to be solved. Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted in practice. Instead, inspired by the idea of operator learning, this work leverages the Fourier neural operator (FNO) to effectively learn the frequency domain seismic wavefields under the context of variable velocity models. We also propose a new framework paralleled FNO (PFNO) for efficiently training the FNO-based solver given multiple source locations and frequencies. Numerical experiments demonstrate the high accuracy of both FNO and PFNO with complicated velocity models in the OpenFWI datasets. Furthermore, the cross-dataset generalization test verifies that PFNO adapts to out-of-distribution velocity models. Finally, PFNO admits higher computational efficiency on large-scale testing datasets than the traditional finite-difference method. The aforementioned advantages endow the FNO-based solver with the potential to build powerful models for research on seismic waves.

58 GEOSCIENCES↗
DOE OSTI2024

Large Ensemble Exploration of Global Energy Transitions Under National Emissions Pledges

Global climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries' nationally determined contributions and long-term strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large-ensemble methods and analyses mapping a wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using the Global Change Analysis Model. Eleven energy-related uncertainties are systematically varied, representing national mitigation pledges, institutional factors, and techno-economic parameters, among others. The resulting ensemble maps how uncertainties impact common energy system metrics used to characterize national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable energy transitions as measured by multiple metrics, including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO 2 removal globally determines how much the energy system can continue to emit, but the relative role of different CO 2 removal options in meeting decarbonization goals varies across regions. Previous studies characterizing uncertainty have typically focused on a few scenarios, and other large-ensemble work has not (to our knowledge) combined this framework with national emissions pledges or institutional factors. Our results underscore the value of large-ensemble scenario discovery for decision support as countries begin to design strategies to meet their goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Grad–Shafranov equilibria via data-free physics informed neural networks

A large number of magnetohydrodynamic (MHD) equilibrium calculations are often required for uncertainty quantification, optimization, and real-time diagnostic information, making MHD equilibrium codes vital to the field of plasma physics. In this paper, we explore a method for solving the Grad–Shafranov equation by using physics-informed neural networks (PINNs). For PINNs, we optimize neural networks by directly minimizing the residual of the partial differential equation as a loss function. We show that PINNs can accurately and effectively solve the Grad–Shafranov equation with several different boundary conditions, making it more flexible than traditional solvers. This method is flexible as it does not require any mesh and basis choice, thereby streamlining the computational process. We also explore the parameter space by varying the size of the model, the learning rate, and boundary conditions to map various tradeoffs such as between reconstruction error and computational speed. Additionally, we introduce a parameterized PINN framework, expanding the input space to include variables such as pressure, aspect ratio, elongation, and triangularity in order to handle a broader range of plasma scenarios within a single network. Parameterized PINNs could be used in future work to solve inverse problems such as shape optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Accuracy of kinetic equilibrium reconstruction of NSTX and NSTX-U plasmas and its impact on the transport and stability analysis

An accurate magnetohydrodynamic (MHD) equilibrium reconstruction is an essential starting point for stability and transport plasma analysis. Herein this work describes an approach for obtaining kinetic equilibrium reconstructions using the OMFIT framework, which has been applied for the first time to spherical tokamak data from NSTX and NSTX-U. The EFIT equilibrium solver is integrated with experimental data analysis procedures and subsequent TRANSP transport simulations to enhance the accuracy of the reconstruction, in particular, at the edge region, by adding constraints on the total pressure and current density profiles, based on the transport code solution. The accuracy of the equilibrium reconstruction depends on the uncertainty and number of constraints, as well as the choice of basis functions to represent the pressure and current density profiles. Improved fidelity of the equilibrium reconstruction is demonstrated by reducing the variability of the magnetic axis and boundary locations from several centimeters, for reconstructions based on magnetic and experimental pressure constraints, to only several millimeters, for kinetic reconstructions based on transport code constraints, when different representations of basis functions were tested. The variability of the safety factor on axis was reduced ten times in the same sensitivity study. The accuracy of the equilibrium reconstruction and subsequent mapping of the experimental kinetic profile data have a significant impact on the trapped gyro Landau fluid and linear CGYRO turbulence simulations, which predict different spectra of unstable modes and turbulent fluxes for cases with different numbers of constraints in the equilibrium reconstruction. Conversely, the stability analysis performed using the GATO code shows plasmas that are stable to n = 1 MHD modes in both equilibria using magnetic and experimental pressure constraints as well as the transport code constrained equilibrium. However, a scan of parameters away from these conditions shows considerable deviation in the threshold of unstable modes between these reconstructions. Therefore, for reliable plasma analysis and use in turbulence and stability calculations, a high-fidelity equilibrium reconstruction with accurate kinetic constraints based on transport code solutions is necessary.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Spacetime quantum mechanics for bosonic and fermionic systems

We provide a Hilbert space approach to quantum mechanics where space and time are treated on an equal footing. Our approach replaces the standard dependence on an external classical time parameter with a spacetime-symmetric algebraic structure, thereby unifying the axioms that traditionally distinguish the treatment of spacelike and timelike separations. Standard quantum evolution can be recovered from timelike correlators, defined by means of a quantum action operator, a quantum version of the action of classical mechanics. The corresponding map also provides an alternative perspective on the path integral formulation that, in the case of fermions, does not require the use of Grassmann variables. In addition, the formalism can be interpreted in terms of generalized quantum states, codifying both the conventional information of a quantum system at a given time and its evolution. We show that these states are solutions to a quantum principle of stationary action grounded in timelike correlations and pseudo-entropies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

RatXcan: A framework for cross-species integration of genome-wide association and gene expression data

Genome-wide association studies (GWAS) have implicated specific alleles and genes as risk factors for numerous complex traits. However, translating GWAS results into biologically and therapeutically meaningful discoveries remains extremely challenging. Most GWAS results identify noncoding regions of the genome, suggesting that differences in gene regulation are the major driver of trait variability. To better integrate GWAS results with gene regulatory polymorphisms, we previously developed PrediXcan (also known as “transcriptome-wide association studies” orTWAS), which maps SNPs to predicted gene expression using GWAS data. In this study, we developed RatXcan, a framework that extends this methodology to outbred heterogeneous stock (HS) rats. RatXcan accounts for the close familial relationships among HS rats by modeling the relatedness with a random effect that encodes the genetic relatedness. RatXcan also corrects for polygenic-driven inflation because of the equivalence between a relatedness random effect and the infinitesimal polygenic model. To develop RatXcan, we trained transcript predictors for 8,934 genes using reference genotype and expression data from five rat brain regions. We found that the cis genetic architecture of gene expression in both rats and humans was sparse and similar across brain tissues. We tested the association between predicted expression in rats and two example traits (body length and BMI) using phenotype and genotype data from 5,401 densely genotyped HS rats and identified a significant enrichment between the genes associated with rat and human body length and BMI. Thus, RatXcan represents a valuable tool for identifying the relationship between gene expression and phenotypes across species and paves the way to explore shared biological mechanisms of complex traits.

Genetics & Heredity↗
DOE OSTI2025

Challenges in quantifying unparameterized spatial uncertainties in deep geologic repositories for nuclear waste

Spatially heterogeneous uncertainties are prevalent in geophysical modeling applications, such as probabilistic post-closure performance assessment (PA) of deep geologic repositories for nuclear waste. Such uncertainties are often highly influential to model outputs, so it is desirable to identify the most important mechanisms by which they influence model predictions. However, these uncertainties are often unparameterized in the sense that there is no set of parameters that can be specified to yield a particular realization of the uncertainty. Additionally, the uncertainty is not intrinsically endowed with a parameterization that captures a realization’s mechanistic influence on model outputs. Therefore, in this work we present a novel methodology to develop and assess a set of proxy variables that aim to represent this influence. We show how they can be computed, downselected, and incorporated into the construction of statistical surrogate models mapping model inputs to outputs. We present our methodology in the context of a motivating application problem in deep geologic repository PA and discuss the challenges in capturing the effects of these spatial heterogeneities in uncertainty analyses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Multivariate Bayesian Optimization of CoO Nanoparticles for CO 2 Hydrogenation Catalysis

The hydrogenation of CO 2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO 2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO 2 and assessed for thermocatalytic CO 2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO 2 and a conventionally prepared catalyst. The optimized CoO/SiO 2 catalyst consistently exhibited higher activity and CH 4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Luxury iron uptake and storage in pennate diatoms from the equatorial Pacific Ocean

Abstract Iron is a key micronutrient for ocean phytoplankton, and the availability of iron controls primary production and community composition in large regions of the ocean. Pennate diatoms, a phytoplankton group that responds to iron additions in low-iron areas, can have highly variable iron contents, and some groups such as Pseudo-nitzschia, are known to use ferritin to store iron for later use. We quantified and mapped the intracellular accumulation of iron by a natural population of Pseudo-nitzschia from the Fe-limited equatorial Pacific Ocean. A total of 48 h after iron addition, nearly half of the accumulated iron was localized in storage bodies adjacent to chloroplasts believed to represent ferritin. Over the subsequent 48 h, stored iron was distributed to the rest of the cell through subsequent growth and division, partially supporting the iron contents of the daughter cells. This study provides the first quantitative view into the cellular trafficking of iron in a globally relevant phytoplankton group and demonstrates the unique capabilities of synchrotron-based element imaging approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model’s rule set. The model’s internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2021

A HPC Theory-Guided Machine Learning Cyberinfrastructure for Communicating Hydrometeorological Data Across Scales

High-resolution predictions of hydrometeorological variables are critical for supporting hydropower generation decisions and flood control at hydroelectric power plants. Traditional climate and hydrologic models rely on the numerical simulation of detailed physical processes. Therefore, running these simulations is time-, labor-, and computation-intensive. Improving the spatial and temporal resolution in these modeling outputs could lead to cubic increases in both the simulation time and computational demands, rendering high-resolution hydrometeorological predictions expensive and impractical. Many past studies apply the super resolution (SR) technique to downscale climate models using deep learners. However, deep learners are deemed “black-boxes,” as their derivation processes from low-resolution outputs to high-resolution outputs are often hidden. Their results are difficult for domain scientists to interpret and validate. Thus, there is a need for an exploratory machine learning approach that can partially integrate domain-specific theory and knowledge into the data-driven mapping process between simulation outputs of different spatial scales. The domain-specific theory and knowledge can be incorporated into the data model through an inductive approach in which process-related environmental variables are used and analyzed as key drivers (i.e., environmental surrogates) to reflect the complex physical processes. Many of these variables, such as land use land cover, soil types, topography, digital elevation, air temperature, and various watershed characteristics, can be directly measured through sensors or remote sensing techniques. Additionally, SR applications that can downscale hydrological and hydrodynamics models to efficiently produce high-resolution (1 m) flood depth grids are still rare. Since the flood depth grid can be used to support critical decisions for flood control operation at hydroelectric power plants, it is crucial to enable an SR-based capability for interpolating high-resolution flood inundation maps.

13 HYDRO ENERGY↗
DOE OSTI2021

Stellar and Accretion Disk Parameters of the Close Binary HD 50526

We present a photometric and spectroscopic study of HD 50526, an ellipsoidal binary member of the group Double Periodic Variable stars. Performing data mining in photometric surveys and conducting new spectroscopic observations with several spectrographs during 2008–2015, we obtained orbital and stellar parameters of the system. The radial velocities were analyzed with the genetic PIKAIA algorithm, whereas Doppler tomography maps for the Hα and Hβ lines were constructed with the Total Variation Minimization code. An optimized simplex algorithm was used to solve the inverse problem adjusting the light curve with the best stellar parameters for the system. We find an orbital period of 6.ͩ701 ± 0.ͩ001 and a long photometric cycle of 191 ± 2 days. We detected the spectral features of the coldest star and modeled it with a logg=2.79±0.02 dex giant of mass 1.13 ± 0.02 M {sub ⊙} and effective temperature 10500 ± 125 K. In addition, we determine a mass ratio q = 0.206 ± 0.033 and that the hot star is a B-type dwarf of mass 5.48 ± 0.02 M {sub ⊙}. The V-band orbital light curve can be modeled including the presence of an accretion disk around the hotter star. This fills the Roche lobe of the hotter star and has a radius 14.74 ± 0.02 R {sub ⊙} and the temperature at the outer edge is 9400 K. Two bright spots located in the disk account for the global morphology of the light curve. The Doppler tomography maps of Hα and Hβ reveal complex structures of mass fluxes in the system.

79 ASTRONOMY AND ASTROPHYSICS↗