SEARCH · Engineering Papers

Results for “Spatial resolution”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,639 records · Page 91

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Preliminary Results from ADRIANO2 Test Beams

A novel high-granularity, dual-readout calorimetric technique (ADRIANO2) is under development as part of the research program of T1604 Collaboration. (Talk Presented at the 19th International Conference on Calorimetry in Particle Physics (CALOR 2022), University of Sussex, Sussex, UK, 16–20 May 2022). The building block of such a calorimeter consists of a pair of optically isolated, small size tiles made of scintillating plastic and lead glass. The prompt Čerenkov light from the glass can be exploited to perform high resolution timing measurements, while the high granularity provides good resolution of the spatial components of the shower. Dual-readout compensation and particle flow techniques can be applied simultaneously to the scintillation and to the Čerenkov section, providing excellent energy resolution as well as PID particle identification. These characteristics make ADRIANO2 a 6-D detector, suited for High Energy as well as High Intensity experiments. A report on the status of the ADRIANO2 project, preliminary measurements of light yield, and current and future R&D plans by T1604 Collaboration are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Longitudinal Patterns in Nitrate Revealed Through Drone‐Based Measurements in an Agriculturally Influenced Midwestern U.S. River

Nitrate concentrations in streams and rivers in the Midwestern United States are often elevated, reflecting the predominance of agriculture in the surrounding landscape. Recent advances in technology, including surface water drones and more precise sensors, provide opportunities to investigate nitrate dynamics with high spatial and temporal resolution. We deployed an aquatic drone, the AquaBOT, in a sixth-order, agriculturally influenced river to examine longitudinal patterns in water quality. Our goal was to measure the spatial and temporal heterogeneity in nitrate and nitrate removal processes and determine the influence of tributary inputs on main stem chemistry. We navigated the drone along a 12-km reach of the Des Moines River (Iowa, USA) nine times between June 2021 and August 2022. Across the deployments, mean nitrate concentration was positively related to discharge and was nearly two orders of magnitude higher in spring than summer. We observed contrasting patterns in main stem nitrate, which decreased downstream during some runs (e.g., 3.1–2.7 mg N L −1 in June 2021), demonstrating net nitrate uptake along the reach, and remained constant on other dates. Similarly, tributaries to the Des Moines had a varied influence on riverine nitrate. Tributaries either increased or decreased main stem nitrate concentrations depending on the tributary and the date. Nitrate removal rates were spatially and temporally variable but showed some consistency at the subreach (2 km) scale, with two subreaches often showing elevated rates of nitrate removal across dates. In conclusion, our study reveals nuanced heterogeneity in nitrate dynamics of the Des Moines River despite the homogeneity of agricultural land cover in the watershed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Further adoption of conservation tillage can increase maize yields in the western US Corn Belt

Conservation tillage can reduce soil erosion, increase soil health, and decrease labor and fuel input costs. Despite these benefits, potential yield impacts remain an important concern for farmers considering adoption. Previous research suggests that conservation tillage is likely to have the largest yield benefits in more arid conditions, but a lack of field-level analyses across climatic, management and soil conditions limits confidence in such predictions. Satellite imagery provides the opportunity to monitor agricultural lands at sub-field resolution across large spatial scales and wide environmental gradients. Here we investigate the maize yield impacts of conservation tillage in the semi-arid western US Corn Belt, using sub-field resolution datasets on tillage practices and crop yields derived from satellite data spanning four states (Nebraska, Kansas, South Dakota, and North Dakota) between 2008 and 2020. On these datasets, we estimate heterogenous yield outcomes for several thousand maize fields across gradients in climate, soil quality and irrigation status by using a causal forests analysis, an adaptation of the random forests machine-learning algorithm for causal inference on observational data. We find that long-term adoption of conservation tillage increased rainfed maize yields by an average of 9.9% in the region. Impacts on irrigated yields were small and not statistically significant. These results, along with an analysis of variables related to greater than average yield benefits, indicate that improved water infiltration and retention are the primary reasons for conservation tillage benefits. Despite yield benefits, many fields estimated to see increased yields under long term low till have not adopted the practice. Therefore, we identify specific counties likely to benefit most from increased levels of adoption. Our results strengthen the understanding of the impacts of conservation agriculture on crop yields and help define environments and counties most likely to benefit from conservation tillage.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

High Resolution Ion Energy Distribution Measurements for Energization Studies on Madison Symmetric Torus RFP Plasmas

We propose to continue a comprehensive and aggressive collaborative study of the critical issues of RFP and fusion physics at MST by enhancing the inferred ion distribution function and the high (spatial and temporal) resolution of magnetic fluctuation measurements. For this, we propose to make novel use of a retarding potential before a neutral particle analyzer as well as a novel millimeter magnetic probe. The measurement of relationships between ion and electron temperatures and magnetic field fluctuations can provide otherwise unobtainable insights on the role of turbulence in fusion plasmas with special focus on the impact of magnetic field turbulence on plasma confinement and scaling. These insights can be particularly valuable when they are coupled with an increased understanding of the issues which drive the turbulence onset, enhance steady-state sustainment, and enable turbulent dissipation. With these new insights, MST will be particularly well positioned to provide important insights in a manner portable to a broad class of fusion devices. We propose to exploit this opportunity and, at the same time, take full advantage of the associated educational enhancements for plasma physics students as FAMU continues to expand the diversity of the fusion scientific workforce.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

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 OSTI2024

Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network

The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2019

kazr2cfrpr (a1)

The Ka-band ARM Zenith Radar (KAZR) remotely probes the extent and composition of clouds at millimeter wavelengths. The KAZR is a zenith-pointing Doppler radar that operates at a frequency of approximately 35 GHz. The main purpose of this radar is to determine the first three Doppler moments (reflectivity, vertical velocity, and spectral width) at a range resolution of approximately 30 meters from near-ground to nearly 20 kilometers in altitude. The KAZR replaces the millimeter-wavelength cloud radar (MMCR) and uses a new digital receiver that provides higher spatial and temporal resolution than the MMCR. In addition, spectral artifacts in the data are significantly reduced in the KAZR, allowing researchers to study cloud dynamics much more closely than with the MMCR. KAZR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

kazr2cfrge (a1)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

kazr2cfrmd (a1)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Resolved H i in two ultra-diffuse galaxies from contrasting non-cluster environments

ABSTRACT We report on the first resolved H i observations of two blue ultra-diffuse galaxies (UDGs), using the Giant Metrewave Radio Telescope (GMRT). These observations add to the so-far limited number of UDGs with resolved H i data. The targets are from contrasting non-cluster environments: UDG-B1 is projected in the outskirts of Hickson Compact Group 25 and Secco-dI-2 (SdI-2) is an isolated UDG. These UDGs also have contrasting effective radii with Re of 3.7 kpc (similar to the Milky Way) and 1.3 kpc, respectively. SdI-2 has an unusually large $\frac{M_{\rm H\,{\small I}}}{M_*}$ ratio = 28.9, confirming a previous single dish H i observation. Both galaxies display H i morphological and kinematic signatures consistent with a recent tidal interaction, which is also supported by observations from other wavelengths, including optical spectroscopy. Within the limits of the observations’ resolution our analysis indicates that SdI-2 is dark matter dominated within its H i radius and this is also likely to be the case for UDG-B1. Our study highlights the importance of high spatial and spectral resolution H i observations for the study of the dark matter properties of UDGs.

Scott, T. C.↗

DOE OSTI2023

Dielectrics Under Extreme Electric Fields: In Situ Studies on Nanoscale Mechanisms (Final Technical Report)

The lack of materials that can function reliably under extreme electric fields is a critical roadblock to achieving higher energy efficiency and extended service lifetime of devices in electricity generation, transmission, and consumption. Materials typically fail at two or three orders of magnitude below their theoretical dielectric breakdown strength. Identifying the origins of premature dielectric breakdown using state-of-the-art characterization tools will, therefore, enable the design and discovery of transformational materials capable of approaching their intrinsic limits. The vast improvement in the resolution power of imaging tools, combined with the recently developed in situ electric biasing technique, has made high resolution observations of dielectric breakdown as it happens possible. The overall goal of the project is to directly image the dynamic processes of defect formation, accumulation, and interaction with preexisting defects (e.g., dislocations, domain walls, and grain boundaries in crystalline dielectric films) under electric fields up to 1,000 MV/m with a temporal resolution better than 5 microseconds. Using the newly acquired, custom-made Hysitron PI95 transmission electron microscopy (TEM) specimen holder, in situ TEM observations of breakdown at the sub nanometer resolution will be made on representative dielectric compounds with progressive complexities: the linear dielectric SiO 2 , CuO, and TiO 2 , the ferroelectric BaTiO 3 nanocrystals and nanocubes. Through direct observation of the breakdown event at high spatial and temporal resolutions, the nanoscale mechanisms for the failure of dielectrics under intense electric fields will be identified. The results of the project will be applied to the design and processing of new dielectric materials with higher efficiency, improved reliability, and a prolonged lifetime. Such new dielectrics are urgently needed for modernizing energy infrastructures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

The Jefferson Lab Eta Factory Experiment and Applications of PbWO4 Calorimeters in Future Experimental Facilities

The goal of the new JLab Eta Factory (JEF) experiment, conducted with the GlueX detector in Hall D at Jefferson Lab, is to perform measurements of various ¿(') decays with a primary focus on rare neutral modes. The experiment’s physics program ranges from precision tests of low-energy QCD to searches for gauge bosons with masses below 1 GeV that could couple the Standard Model (SM) sector to the dark sector. The experiment will collect a high-statistics data sample of ¿(') mesons produced via a beam of tagged photons. The GlueX detector features a large, nearly uniform acceptance for both neutral and charged particles, enabling efficient identification of complex multi-particle final states. To meet the requirements of the JEF experiment, the inner section of the forward lead-glass calorimeter in the GlueX detector has been upgraded with lead tungstate (PbWO4) scintillating crystals. PbWO4 offers exceptional characteristics, such as a small radiation length and Molire radius, and large light yield, that make it ideal for constructing high- granularity, high-resolution, radiation-hard detectors. These properties enable excellent spatial separation and energy resolution of reconstructed electromagnetic showers, establishing PbWO4 as the material of choice for many high-precision experiments. The JEF experiment began data collection in April 2025 and will operate concurrently with the GlueX experiment, whose primary objective is the search for gluonic excitations in the meson spectrum. I will give an overview of the JEF experiment, the GlueX detector, and the feasibility of further upgrades to support future ¿ physics studies. Special attention will be given to the newly constructed PbWO4 scintillating calorimeter and recent advancements in calorimeter instrumentation.

Somov, Alexander↗

DOE OSTI2021

Grid resolution requirement for resolving rare and high intensity wall-shear stress events in direct numerical simulations

Turbulent signals are intermittent with large instantaneous fluctuations. Such large fluctuations lead to small Kolmogorov scales that are hard to resolve in numerical simulations [P. K. Yeung, K. R. Sreenivasan, and S. B. Pope, Effects of finite spatial and temporal resolution in direct numerical simulations of incompressible isotropic turbulence, Phys. Rev. Fluids 3, 064603 (2018)]. Here, the present paper follows the above basic logic, but instead of dissipation events in isotropic turbulence, we study wall-shear stress events in plane channel flow. Wall-shear stress fluctuations are increasingly more intermittent as the Reynolds number increases. Hence, one has to employ higher grid resolutions as the Reynolds number increases in order to resolve a given percentage of wall-shear stress events. The objective of this paper is to quantify effects of the grid resolutions on the rare and high intensity wall-shear stress events. We find that the standard grid resolution resolves about 99% of the wall-shear stress events at Re τ = 180. A slightly higher grid resolution has to be employed in order to resolve 99% of the wall-shear stress events at higher Reynolds numbers, and if the standard grid resolution is used for, e.g., a Re τ = 10000 channel flow, one resolves about 90%–95% wall-shear stress events.

42 ENGINEERING↗

DOE OSTI2024

Phase-resolving spin-wave microscopy using infrared strobe light

The need for sensitively and reliably probing magnetization dynamics has been increasing in various contexts such as studying novel hybrid magnonic systems, in which the spin dynamics strongly and coherently couple to other excitations, including microwave photons, light photons, or phonons. Recent advances in quantum magnonics also highlight the need for employing the magnon phase as quantum state variable, which is to be detected and mapped out with high precision in on-chip micro- and nanoscale magnonic devices. Here, in this study, we demonstrate a facile optical technique that can directly perform concurrent spectroscopic and imaging functionalities with spatial and phase resolutions, using infrared strobe light operating at 1550-nm wavelength. To showcase the methodology, we spectroscopically studied the phaseresolved spin dynamics in a bilayer of Permalloy and yttrium iron garnet Y 3 Fe 5 O 12 (YIG), and spatially imaged the backward-volume spin-wave modes of YIG in the dipolar spin-wave regime. Using the strobe light probe, the detected precessional phase contrast can be directly used to construct the map of the spin wave's wave front, in the continuous-wave regime of spin-wave propagation and in the stationary state, without needing any optical reference path. By selecting the applied field, frequency, and detection phase, the spin-wave images can be made sensitive to the precession amplitude and phase. Our results demonstrate that infrared optical strobe light can serve as a versatile platform for magneto-optical probing of magnetization dynamics, with potential implications in investigating hybrid magnonic systems.

Xiong, Yuzan [University of North Carolina, Chapel↗

DOE OSTI2024

Localizing Low-Grade Heat Using Hybrid Photonic-Phononic Materials

Creating next-generation devices capable of transducing waste heat into useful work will require building blocks designed to direct low-grade thermal energy at length scales well below the diffraction limit. Characterizing thermally induced energy transfer at the nanoscale, however, is a formidable task due to the simultaneous demand of high spatial and spectral resolution at infrared energies. Leveraging recent advancements in electron energy loss (EEL) and gain (EEG) spectroscopy, we reveal that individual nanostructures can concentrate energy by the resonant thermal excitation of their photonic Fabry–Pérot modes. Specifically, we show that the spatially localized gain signal increases upon in situ heating. Finally, theoretical modeling elucidates the mechanism for these observations by showing that a Purcell enhancement in the local photonic density of states drives the increased rate of infrared energy transfer from the ambient environment to each nanostructure.

36 MATERIALS SCIENCE↗

DOE OSTI2026

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding

Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. In this work, we proposed F-Hash, a novel feature-based multi-resolution Tesseract encoding architecture to greatly enhance the convergence speed compared with existing input encoding methods for modeling time-varying volumetric data. The proposed design incorporates multi-level collision-free hash functions that map dynamic 4D multi-resolution embedding grids without bucket waste, achieving high encoding capacity with compact encoding parameters. Our encoding method is agnostic to time-varying feature detection methods, making it a unified encoding solution for feature tracking and evolution visualization. Experiments show the F-Hash achieves state-of-the-art convergence speed in training various time-varying volumetric datasets for diverse features. We also proposed an adaptive ray marching algorithm to optimize the sample streaming for faster rendering of the time-varying neural representation.

deep learning↗

DOE OSTI2026

A Data-Driven Method for Synthetic Extreme Weather Generation and Solar Impact Assessment: Preprint

High-resolution, high-fidelity weather datasets are essential for testing and evaluating the resilience of power systems, particularly under extreme weather conditions. However, existing extreme weather datasets are typically derived from historical events that are localized and may lack the spatial and temporal resolution or scenario diversity needed to test largescale power systems. In this work, we propose a synthetic extreme weather simulation approach capable of generating targeted extreme events, such as hurricanes, using publicly available data sources. Preliminary results demonstrate the impact of a simulated Category 1 hurricane on renewable generation and critical infrastructure in California. The work aims to provide a flexible approach for creating multiple types of extreme weather scenarios across different regions, enabling comprehensive system stress testing, training, and resilience assessment.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Wind Resource Data for Southeast Asia Using a Hybrid Numerical Weather Prediction with Machine Learning Super Resolution Approach

In this work we develop and present a machine learning based downscaling approach using generative adversarial networks (GANs). GANs learn to distinguish the relationships between low-resolution and high-resolution simulations and generate accurate high-resolution output from low-resolution input (Stengel, Glaws, Hettinger, & King, 2020). Low-resolution numerical weather prediction (NWP) simulations at 9-km spatial and 60-minute temporal resolution were executed over Southeast Asia to provide input to the GANs model. GANs for wind, temperature, and pressure were trained on coarsened WIND Toolkit data with a diverse sampling of terrain and meteorological conditions. After training, the NWP simulations over Southeast Asia were enhanced by 3x along each horizontal spatial dimension and 4x along the temporal dimension. This novel downscaling approach generated 15-year high-resolution wind, temperature, and pressure data from January 2007 through December 2021 at multiple hub heights over Southeast Asia at 3-km spatial and 15-minute temporal resolution with a 16x reduction in compute time over standard dynamical downscaling.

17 WIND ENERGY↗