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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Building a Healthy Urban Design Index (HUDI): how to promote health and sustainability in European cities

As global urbanisation accelerates, alongside declining environmental quality and increasing climate challenges, it is increasingly vital for urban planners and policy makers to integrate health and wellbeing considerations into urban planning. This study introduces the Healthy Urban Design Index (HUDI), a high-resolution spatial index developed for European cities. HUDI combines policy-relevant indicators related to urban design, sustainable transportation, environmental quality, and greenspace accessibility—key factors influencing human health and well-being. Unlike existing indices, which often focus on few or large metropolitan cities and lack spatial granularity, HUDI offers high resolution and extends its scope to small-sized and medium-sized cities, home to over 50% of Europe's population.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Machine learning for precise hit position reconstruction in Resistive Silicon Detectors

RSDs are LGAD silicon sensors with 100% fill factor, based on the principle of AC-coupled resistive read-out. Signal sharing and internal charge multiplication are the RSD key features to achieve picosecond-level time resolution and micron-level spatial resolution, thus making these sensors promising candidates as 4D-trackers for future experiments. This paper describes the use of a neural network to reconstruct the hit position of ionizing particles, an approach that can boost the performance of the RSD with respect to analytical models. The neural network has been trained in the laboratory and then validated on test beam data. The device-under-test in this work is a 450 μm-pitch matrix from the FBK RSD2 production, which achieved a resolution of about 65 μm at the DESY Test Beam Facility, a 50% improvement compared to a simple analytical reconstruction method, and a factor two better than the resolution of a standard pixel sensor of equal pitch size with binary read-out. The test beam result is compatible with the laboratory ones obtained during the neural network training, confirming the ability of the machine learning model to provide accurate predictions even in environments very different from the training one. Prospects for future improvements are also discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Temperature Mapping of Stacked Silicon Dies from X-Ray-Diffraction Intensities

Increasing power densities in integrated circuits have led to an increased prevalence of thermal hotspots in integrated circuits. Tracking these thermal hotspots is imperative to prevent circuit failures. In three- dimensional (3D) integrated circuits, conventional surface techniques like infrared thermometry are unable to measure the 3D temperature distribution, and optical and magnetic resonance techniques are difficult to apply due to the presence of metals and large current densities. X-rays offer a high penetration depth and can be used to probe 3D structures. We report a method utilizing the temperature dependence of x-ray-diffraction intensity via the Debye-Waller factor to simultaneously map the temperature of individual silicon dies in a stack. Here, utilizing beamline 1-ID-E at the Advanced Photon Source (Argonne), we demonstrate, for each individual silicon die, a temperature resolution of 3 K, a spatial resolution of 100 × 400 μm 2 , and a temporal resolution of 20 s. Utilizing a sufficiently high-intensity laboratory source, e.g., from a liquid-anode source, this method can be scaled down to laboratories for noninvasive temperature mapping of 3D integrated circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

NOW-23: the 2023 National Offshore Wind Data Set

In this report, we present the latest wind resource data specifically tailored for offshore regions in the United States. The data set, known as the 2023 National Offshore Wind data set (NOW-23), has been developed by the National Renewable Energy Laboratory (NREL) and its partners, and surpasses the previous resource data set, the Wind Integration National Dataset (WIND) Toolkit, which was released approximately ten years ago, in its offshore component. The WIND Toolkit has been widely utilized by stakeholders involved in wind resource assessments across the continental United States. However, with significant advancements in numerical weather prediction modeling over the past decade, the NOW-23 data set incorporates the latest research and development progress to provide stakeholders with an updated and cutting-edge resource for offshore wind analysis. The NOW-23 data set is created using the Weather Research and Forecasting (WRF) model and its output is available, as for its predecessor, at 5-minute time resolution and 2-kilometer horizontal spatial resolution. However, the NOW-23 data set improves upon the WIND Toolkit through: 1. A modeling period of at least 20 years (and as long as 23 years in selected regions), starting in 2000 (compared to the 7-year 2007–2013 modeling period in the WIND Toolkit). 2. For several offshore regions, a region-specific sensitivity analysis, driven by an ensemble of WRF simulations, to assess the most adequate region-specific WRF setup. 3. An updated WRF model, from Version 3.4 used in the WIND Toolkit to Version 4.2.1 used for the NOW-23 data set, which incorporates significant research advancements. 4. The use of the state-of-the-art reanalysis product ERA5 (which supersedes the older ERA-Interim used in the WIND Toolkit) to provide atmospheric forcing at the WRF domain boundaries. Figure 1 shows the NOW-23 mean wind speed at 160 m above sea level (asl), which we use as proxy for hub-height of a commercial offshore wind turbine in this report across all regions.

17 WIND ENERGY↗
DOE OSTI2021

A parameterization of sub-grid topographical effects on solar radiation in the E3SM Land Model (version 1.0): implementation and evaluation over the Tibetan Plateau

Abstract. Topography exerts significant influences on the incoming solar radiation at the land surface. A few stand-alone regional and global atmospheric models have included parameterizations for sub-grid topographic effects on solar radiation. However, nearly all Earth system models (ESMs) that participated in the Coupled Model Intercomparison Project (CMIP6) use a plane-parallel (PP) radiative transfer scheme that assumes that the terrain is flat. In this study, we incorporated a well-validated sub-grid topographic (TOP) parameterization in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) version 1.0 to quantify the effects of sub-grid topography on solar radiation flux, including the shadow effects and multi-scattering between adjacent terrain. We studied the role of sub-grid topography by performing ELM simulations with the PP and TOP schemes over the Tibetan Plateau (TP). Additional ELM simulations were performed at multiple spatial resolutions to investigate the role of spatial scale on sub-grid topographic effects on solar radiation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to compare with the ELM simulations. The results show that topography has non-negligible effects on surface energy budget, snow cover, snow depth, and surface temperature over the TP. The absolute differences in surface energy fluxes for net solar radiation, latent heat flux, and sensible heat flux between TOP and PP exceed 20, 10, and 5 W m−2, respectively. The differences in land surface albedo, snow cover fraction, snow depth, and surface temperature between TOP and PP exceed 0.1, 0.1, 10 cm, and 1 K, respectively. The magnitude of the sub-grid topographic effects is dependent on seasons and elevations and is also sensitive to the spatial scales. Although the sub-grid topographic effects on solar radiation are larger with more spatial details at finer spatial scales, they cannot be simply neglected at coarse spatial scales. When compared to MODIS data, incorporating the sub-grid topographic effects overall reduces the biases of ELM in simulating surface energy balance, snow cover, and surface temperature, especially in the high-elevation and snow-covered regions over the TP. The inclusion of sub-grid topographic effects on solar radiation parameterization in ELM will contribute to advancing our understanding of the role of the surface topography on terrestrial processes over complex terrain.

Hao, Dalei (ORCID:0000000334979774)↗
DOE OSTI2023

A Method for Projecting Cloud Shadows Onto a Central Receiver Field to Predict Receiver Damage

This work demonstrates methods of mapping high-spatial-resolution direct normal irradiance (DNI) data from satellites, Total Sky Imagers (TSIs), and analogous data sources onto a heliostat field for characterizing the spatial and temporal variation of the incident flux on a central receiver tower during cloud transient events. The mapping methods are incorporated into an optical software module that interfaces with CoPylot–SolarPILOT’s python API– to provide computationally efficient optical simulation of the heliostat field and the solar power tower. Eventually, this optical model will be incorporated into optimization models whereby a plant operator can understand the effects of cloud transient events on overall power production and receiver lifetime due to creep-fatigue damage and therefore make better informed decisions about receiver shutdown events. By more accurately modelling the effects of cloud events on receiver flux maps, this work may determine the magnitude and frequency of thermal cycling on receiver tubes and panels using actual or realistic cloud shapes instead of averaged DNI values–which may undercount the total cycle number. This work may also prevent unnecessary plant shutdowns due to overly precautionary control strategies and characterize the relative impact of various cloud types on receiver life. We plan to eventually integrate this methodology into the System Advisor Model (SAM) to improve performance model accuracy during periods of cloudiness. In this paper, we demonstrate generating DNI maps and mapping them to a solar field in CoPylot using 10 m resolution data from publicly available Sentinel-2 satellite data over the Crescent Dunes plant.

Mullin, Matthew↗
DOE OSTI2021

Fast and accurate learned multiresolution dynamical downscaling for precipitation

Abstract. This study develops a neural-network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computational cost. The key idea is to use combination of low- and high-resolution simulations (that differ not only in spatial resolution but also in geospatial patterns) to train a neural network to map from the former to the latter. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and we train each CNN type both with a conventional loss function, such as mean square error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high-resolution precipitation results with precipitation generated from original high-resolution simulations, a bilinear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique. Results show that the SR technique produces results similar to those of the bilinear interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than the original high-resolution simulations. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique do, their predictions are still biased from the original high-resolution simulations. The CNNs trained by CGAN generate more realistic and physically reasonable results, better capturing not only data variability in time and space but also extremes such as intense and long-lasting storms. The new proposed CNN-based downscaling approach can downscale precipitation from 50 to 12 km in 14 min for 30 years once the network is trained (training takes 4 h using 1 GPU), while the conventional dynamical downscaling would take 1 month using 600 CPU cores to generate simulations at the resolution of 12 km over the contiguous United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Improving OFDR Distributed Fiber Sensing by Fibers With Enhanced Rayleigh Backscattering and Image Processing

This article investigates using optical fibers with enhanced backscattering profiles to improve distributed fiber sensor performance and reduce instrumentation costs. Using a femtosecond (fs) laser direct writing technique, the Rayleigh backscattering profile of a standard telecom fiber was enhanced by more than 40 dB to improve the signal-to-noise ratio (SNR) for optical frequency domain reflectometry (OFDR). The enhanced backscattering signals enable effectively distributed strain measurements using a low-cost tunable laser (TL). Median filtering is applied to denoise cross correlation results to further improve measurement outcomes. Results presented in this article show that a TL with a 1-nm tuning range, which is far less than the tuning range used in commercial OFDR interrogators, can perform effectively distributed strain measurements using sensing fibers with enhanced backscattering profiles. The sensing fiber with over 40-dB backscattering enhancement achieved 4.8-cm spatial resolution in strain measurements with a root mean square accuracy of less than 2.70 με when 10–50 με were exerted to the sensing fiber. In conclusion, results presented in this article reveal both the potential and limitations of sensing fibers with enhanced backscattering for OFDR-based distributed fiber sensors.

42 ENGINEERING↗
DOE OSTI2021

HAT-P-68b: A Transiting Hot Jupiter around a K5 Dwarf Star

We report the discovery by the ground-based Hungarian-made Automated Telescope Network (HATNet) survey of the transiting exoplanet HAT-P-68b, which has a mass of 0.724 ± 0.043 M {sub J}, and radius of 1.072 ± 0.012 R {sub J}. The planet is in a circular P = 2.2984 day orbit around a moderately bright V = 13.937 ± 0.030 magnitude K-dwarf star of mass 0.673{sub −0.014}{sup +0.020} M {sub ⊙}, and radius 0.6726 ± 0.0069 R {sub ⊙}. The planetary nature of this system is confirmed through follow-up transit photometry obtained with the Fred L. Whipple Observatory (FLWO) 1.2 m telescope, high-precision radial velocities measured using Keck I/High Resolution Echelle Spectrometer (HIRES), FLWO 1.5 m/Tillinghast Reflector Echelle Spectrograph (TRES), and Observatoire de Haute-Provence (OHP) 1.9 m/Sophie, and high-spatial-resolution speckle imaging from WIYN 3.5 m/DSSI. HAT-P-68 is at an ecliptic latitude of +3° and outside the field of view of both the NASA Transiting Exoplanet Survey Satellite primary mission and the K2 mission. The large transit depth of 0.036 mag (r band) makes HAT-P-68b a promising target for atmospheric characterization via transmission spectroscopy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Multi-site assessment of the potential of fine resolution red-edge vegetation indices for estimating gross primary production

Gross primary production (GPP) models driven by fine resolution remote sensing data characterize the spatial and temporal heterogeneities in plant photosynthesis, which is largely dependent on biome-specific maximum photosynthetic capacity. The red-edge reflectance, sensitive to leaf chlorophyll content, is a good proxy of maximum photosynthetic capacity. More importantly, studies show that the red-edge reflectance-related chlorophyll content index (CIr) multiplied by the incident photosynthetic active radiation (PAR in ) strongly correlates to GPP estimated at carbon flux towers (GPP flux ). Yet, to the best of our knowledge, there is no systematic study investigating the general relationship between fine spatial resolution CIr and GPP among biomes and the relationship between CIr and maximum photosynthetic capacity in GPP models. To provide an overview on incorporating space-borne CIr into a GPP model, we applied fine resolution Sentinel-2-derived CIr and GPP flux over 57 flux sites representative of 10 biomes. We investigated the relationship between CIr and GPP flux , and the spatio-temporal relationship between CIr and ecosystem maximum photosynthetic capacity indicated by the potential ecosystem light use efficiency (LUE pot ). We also evaluated the relationship between other five vegetation indices (VIs) and GPP flux . Results showed that the CIr multiplied by PARin has a higher agreement (R 2 > 0.5) with GPP than other VIs. A universal relationship exists between the CIr multiplied by PARin and GPP, except for forest biomes. The CIr also strongly (R 2 > 0.5) relates to the LUE pot during the peak of the growing season. The CIr has a low spatial variance (CV = 0.25) among biomes, highlighting that CIr can be a proxy of maximum photosynthetic capacity in GPP models that do not require biome-dependent coefficients. This study provides insight for incorporating CIr into GPP models and better quantifying global terrestrial photosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Giant nonlinear optical responses from photon-avalanching nanoparticles

Avalanche phenomena use steeply nonlinear dynamics to generate disproportionately large responses from small perturbations, and are found in a multitude of events and materials. Photon avalanching enables technologies such as optical phase-conjugate imaging, infrared quantum counting and efficient upconverted lasing. However, the photon-avalanching mechanism underlying these optical applications has been observed only in bulk materials and aggregates, limiting its utility and impact. In this study we report the realization of photon avalanching at room temperature in single nanostructures-small, Tm 3+ -doped upconverting nanocrystals-and demonstrate their use in super-resolution imaging in near-infrared spectral windows of maximal biological transparency. Avalanching nanoparticles (ANPs) can be pumped by continuous-wave lasers, and exhibit all of the defining features of photon avalanching, including clear excitation-power thresholds, exceptionally long rise time at threshold, and a dominant excited-state absorption that is more than 10,000 times larger than ground-state absorption. Beyond the avalanching threshold, ANP emission scales nonlinearly with the 26th power of the pump intensity, owing to induced positive optical feedback in each nanocrystal. This enables the experimental realization of photon-avalanche single-beam super-resolution imaging with sub-70-nanometre spatial resolution, achieved by using only simple scanning confocal microscopy and without any computational analysis. Pairing their steep nonlinearity with existing super-resolution techniques and computational methods, ANPs enable imaging with higher resolution and at excitation intensities about 100 times lower than other probes. The low photon-avalanching threshold and excellent photostability of ANPs also suggest their utility in a diverse array of applications, including sub-wavelength imaging and optical and environmental sensing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Resolution requirements for numerical modeling of neutrino quantum kinetics

Neutrino quantum kinetics is a rapidly evolving field in computational astrophysics, with a primary focus on collective neutrino oscillations in core-collapse supernovae and postmerger phases of binary neutron star mergers. In recent years, there has been considerable debate concerning resolution dependence in numerical simulations. In this paper, we conduct a comprehensive resolution study in both angular- and spatial directions by using two independent schemes of quantum kinetic neutrino transport: finite volume and pseudospectral methods. We complement our discussion by linear stability analysis including inhomogeneous modes. Our result suggests that decreasing spatial resolutions underestimates the growth of flavor instability, and then leads to wrong asymptotic states of flavor conversions, which potentially has a critical impact on astrophysical consequences. We further delve into numerical results of low resolution simulations, that reveals the underlying mechanism responsible for numerical artifacts caused by insufficient resolutions. Finally, this study settles the debate on requirements of resolutions and serves as a guideline for numerical modeling of quantum kinetic neutrino transport.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

X-Ray Characterization of Real Fuel Sprays for Gasoline Direct Injection

Here, the effects of fuel blend properties on spray and injector performance has been investigated in a side-mount injector for gasoline direct injection (GDI) using two certification fuel blends: Euro 5 and Euro 6. Several X-ray diagnostic techniques were conducted to characterize the injector and spray morphology. Detailed internal geometry of the GDI injector was resolved to 1.8 μm, through the use of hard X-ray tomography. The geometry characterization of this six-hole GDI, side mount injector, quantifies relevant hole and counterbore dimensions and reveals the intricate details within the flow passages, including surface roughness and micron-sized features. Internal valve motion was measured with a temporal resolution of 20 μs and a spatial resolution of 2.0 μs, for three injection pressures and several injector energizing strategies. The needle motion for both fuels exhibits similar lift profiles for common energizing commands. A combination of X-ray radiography and ultra-small-angle X-ray scattering (USAXS) was used to characterize the fuel mass distribution and the droplet sizing, respectively. Tomographic spray radiography revealed the near-nozzle distribution of fuel mass for each of the fuels and the asymmetry produced by the angled nozzles. Under evaporative conditions, the two fuels show minor differences in peak fuel mass distribution during steady injection, though both exhibit fluctuations in injection during the early, transient phase. USAXS measurements of the path-specific surface area of the spray indicated lower peak values for the more evaporative conditions in the near nozzle region.

09 BIOMASS FUELS↗
DOE OSTI2020

Single-frame far-field diffractive imaging with randomized illumination

We introduce a single-frame diffractive imaging method called randomized probe imaging (RPI). In RPI, a sample is illuminated by a structured probe field containing speckles smaller than the sample’s typical feature size. Quantitative amplitude and phase images are then reconstructed from the resulting far-field diffraction pattern. The experimental geometry of RPI is straightforward to implement, requires no near-field optics, and is applicable to extended samples. When the resulting data are analyzed with a complimentary algorithm, reliable reconstructions which are robust to missing data are achieved. To realize these benefits, a resolution limit associated with the numerical aperture of the probe-forming optics is imposed. RPI therefore offers an attractive modality for quantitative X-ray phase imaging when temporal resolution and reliability are critical but spatial resolution in the tens of nanometers is sufficient. We discuss the method, introduce a reconstruction algorithm, and present two proof-of-concept experiments: one using visible light, and one using soft X-rays.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Non-Invasive Functional-Brain-Imaging with an OPM-based Magnetoencephalography System

A non-invasive functional-brain-imaging system based on optically-pumped-magnetometers (OPM) is presented. The OPM-based magnetoencephalography (MEG) system features 20 OPM channels conforming to the subject’s scalp. We have conducted two MEG experiments on three subjects: assessment of somatosensory evoked magnetic field (SEF) and auditory evoked magnetic field (AEF) using our OPM-based MEG system and a commercial MEG system based on superconducting quantum interference devices (SQUIDs). We cross validated the robustness of our system by calculating the distance between the location of the equivalent current dipole (ECD) yielded by our OPM-based MEG system and the ECD location calculated by the commercial SQUID-based MEG system. We achieved sub-centimeter accuracy for both SEF and AEF responses in all three subjects. Due to the proximity (12 mm) of the OPM channels to the scalp, it is anticipated that future OPM-based MEG systems will offer enhanced spatial resolution as they will capture finer spatial features compared to traditional MEG systems employing SQUIDs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

LGAD-Based Silicon Sensors for 4D Detectors

Low-Gain Avalanche Diodes (LGAD) are a class of silicon sensors developed for the fast detection of Minimum Ionizing Particles (MIPs). The development was motivated by the need of resolving piled-up tracks of charged particles emerging from several vertexes originating from the same bunch-crossing in High-Energy Physics (HEP) collider experiments, which, however, are separated not only in space but also in time by a few tens of picoseconds. Built on thin silicon substrates and featuring an internal moderate gain, they provide fast signals for excellent timing performance, which are therefore useful to distinguish the different tracks. Unfortunately, this comes at the price of poor spatial resolution. To overcome this limitation, other families of LGAD-based silicon sensors which can deliver in the same substrate both excellent timing and spatial information are under development. Such devices are, to name a few, capacitively coupled LGADs (AC-LGAD), deep-junction LGADs (DJ-LGAD) and trench-isolated LGADs (TI-LGADs). These devices can be fabricated by even small-scale research-focused clean rooms for faster development within the scientific community. However, to scale up production, efforts towards integrating these sensor concepts in CMOS substrates, with the obvious advantage of the possibility of integrating part of the read-out electronics in the same substrate, have begun.

spatial resolution↗
DOE OSTI2022

Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data

Recent developments in “headline-making” deep neural networks (DNNs), specifically convolutional neural networks (CNNs), along with advancements in computational power, open great opportunities to integrate massive amounts of real-time observations to characterize spatiotemporal structures of surface precipitation. This study aims to develop a CNN algorithm, named Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW) brightness temperatures (Tbs) at emission and scattering frequencies combined with infrared (IR) Tbs from geostationary satellites and surface information to automatically extract geospatial features related to the precipitable clouds. These features allow the end-to-end Deep-STEP algorithm to instantaneously map surface precipitation intensities with a spatial resolution of 4 km. The main advantages of Deep-STEP, as compared to current state-of-the-art techniques, are 1) it learns and estimates complex precipitation systems directly from raw measurements in near–real time, 2) it uses the automatic spatial neighborhood feature extraction approach, and 3) it fuses coarse-resolution PMW footprints with IR images to reliably retrieve surface precipitation at a high spatial resolution. We anticipate our proposed DNN algorithm to be a starting point for more sophisticated and efficient precipitation retrieval systems in terms of accuracy, fine spatial pattern detection skills, and computational costs.

54 ENVIRONMENTAL SCIENCES↗