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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Ray-Tracing Simulations Characterising the Performance of the Proposed 2024 HFIR HB4 Main Shutter

The Main Shutter at HB4 will serve two purposes after the HFIR Beryllium Reflector Replacement planned to take place in 2024. First as the primary certified safety control controlling the passage of neutrons from the cold source in the HFIR pressure vessel into the cold guide hall, and second as the first set of reflecting surfaces used to guide neutrons from the source and into the individual guide starts for each instrument in the cold guide hall.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Roundtable Report: Terahertz and 6G Wireless Communications in Science and Extreme Environments

The main objective of the roundtable is to bring together computer scientists and wireless communications experts from industry, academia, and government to explore the electromagnetic spectrum in the Terahertz (THz) bands for ultra-high-speed short-distance wireless communications in extreme environments. This new generation of THz wireless communications is the key to future machine-to-machine communications critical for the mass deployment of an intelligent Internet of Things (IoT). The roundtable discussion explores THz and 6G wireless technologies along with Visible Light Communications (VLC), intelligent reflection surfaces, free-space quantum and optical communications, and nano communications. The roundtable considers the suitability of these technologies for extreme environments from the nanoscale to the mesoscale for the following: scientific facilities and associated instruments, as well as industry, and manufacturing. Such use cases are not traditionally the primary customer of commercial wireless networks which are focused on personal broadband voice and data communications. Finally, the discussion identifies high-level opportunities and challenges for leveraging THz and 6G wireless communications and related Artificial Intelligence (AI) that can catalyze the advancement of the development of hyperconnected smart scientific instruments and facilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Acoustic Travel-Time Tomography for Wind Energy

New remote sensing technologies for direct observation of the atmospheric flow in the proximity of operating wind turbines are necessary for the advancement of wind turbine design, wind plant control, and the validation of high-fidelity numerical models; fundamental goals of the DOE Wind Energy Technology Office's (WETO's) A2e program. Modern remote sensing technologies for commercial and research applications rely on the backscatter of light or sound waves, which limits spatial and temporal resolution and cannot produce reliable flow estimates near solid bodies or reflective surfaces. Acoustic tomography (AT) is an innovative sensing technology based on fundamental thermodynamic properties of the atmosphere and an inverse algorithm that can reconstruct both velocity and temperature fields within a network of acoustic transducers. AT relies on the direct travel path of acoustic signals between speakers and microphones in the network and can achieve much higher resolutions than conventional backscatter technologies. For wind energy R&D applications, AT represents an extremely economical option over other remote sensing technologies, integrating mature and commonly available acoustic and signal processing hardware. This report is intended as an update on the development of the AT array on the NREL Flatirons Campus (FC) and includes a brief review of the instrumentation, the calibration of equipment, signal processing, and resulting flow field estimates. Designs for future AT systems and applications are detailed in this report, including summaries of new capabilities, engineering and theoretical challenges, and anticipated costs help to outline future directions for AT technology R\&D. The potential for application of AT to utility-scale turbines is also discussed. Investing in the development of the proposed instrument will establish AT as a transformational RS technique that represents a significant advance in measurement instrumentation, enabling new fundamental insights into the structure of the atmospheric boundary layer and supporting applications for wind energy, pollution transport, and resource characterization. AT is also an extremely economical option over other RS instruments, integrating mature and commonly available acoustic and signal processing hardware.

17 WIND ENERGY↗
DOE OSTI2025

Ray-Tracing Simulations Characterizing the Performance of the Proposed 2028 HFIR HB4 Main Shutter

The Main Shutter at HB4 will serve two purposes after the HFIR Beryllium Reflector Replacement, planned to take place in 2028. First as the primary certified safety control permitting the passage of neutrons from the cold source in the HFIR pressure vessel into the cold guide hall, and second as the first set of reflecting surfaces used to guide neutrons from the source and into the individual guide starts for each instrument in the cold guide hall. This report outlines a the simulations and analyses used to conclude that the HB4 Main Shutter Insert alignment is critical to associated instrument performance. In general, the alignment of these surfaces to within +/-0.25 millimeters and +/-0.05° across all dimensions will ensure losses are no worse than 1% for any of the guide starts.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

SLOPE daily and 250 m gross primary productivity (GPP) for the CONUS, 2000-2019, Carbon Monitoring System (CMS)

The SatelLite Only Photosynthesis Estimation (SLOPE) GPP product is a daily, 250 m GPP dataset covering the Contiguous United States (CONUS) from 2000 to present with 1 day latency. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time, and observation-based GPP products. This product has been developed to address this critical gap, leveraging a number of MODIS land and atmosphere products. There are three distinct features of the SLOPE GPP production algorithm. (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two slope parameters to estimate GPP along with a quantitative uncertainty on a per-pixel and daily basis. The slope GPP product has a R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1, evaluated against from 50 AmeriFlux eddy covariance sites (332 site-years). Archived data from 2000 to 2019 are publicly available at the NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Data from 2020 are available from the authors upon request. All data are projected in the standard MODIS Land Integerized Sinusoidal tile map projection. Each processing tile is in size of 4800 pixels by 4800 pixels, representing approximately 1200 km by 1200 km land region. In addition to the GPP product, SLOPE PAR, SANIRV, and C4 fraction, along with their uncertainties, are also released.

Jiang, Chongya↗
DOE OSTI2020

Challenging a Global Land Surface Model in a Local Socio-Environmental System

Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson’s R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI; phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

Forest disturbance and regrowth are key processes in forest dynamics, but detailed information on these processes is difficult to obtain in remote forests such as the Amazon. We used chronosequences of Landsat satellite imagery (Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus) to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clear-cut, and clear-cut and burned (cut + burn) and their successional pathways of forest regrowth in the Central Amazon. We also assessed whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clear-cut. The results show that all spectral bands from the Landsat satellites were sensitive to the disturbances but after 3 to 6 years only the near-infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR values decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR values will return to pre-disturbance values in about 39, 36, and 56 years for windthrow, clear-cut, and cut + burn disturbances, respectively. The NIR band captured the observed, and different, successional pathways of forest regrowth after windthrow, clear-cut, and cut + burn. Consistent with inferences from the NIR observations, ELM-FATES predicted higher peaks of biomass and stem density after clear-cuts than after windthrows. ELM-FATES also predicted recovery of forest structure and canopy coverage back to pre-disturbance conditions in 38 years after windthrows and 41 years after clear-cut. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clear-cut to those of the NIR results suggests the NIR band can be used to benchmark forest regrowth in ecosystem models. Our results show the potential of Landsat imagery data for mapping forest regrowth from different types of disturbances, benchmarking, and the improvement of forest regrowth models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using automated machine learning for the upscaling of gross primary productivity

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r 2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

BESS-STAIR: a framework to estimate daily, 30m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt

Abstract. With increasing crop water demands and drought threats, mapping andmonitoring of cropland evapotranspiration (ET) at high spatial and temporalresolutions become increasingly critical for water management andsustainability. However, estimating ET from satellites for precise waterresource management is still challenging due to the limitations in bothexisting ET models and satellite input data. Specifically, the process of ETis complex and difficult to model, and existing satellite remote-sensing datacould not fulfill high resolutions in both space and time. To address theabove two issues, this study presents a new high spatiotemporal resolution ETmapping framework, i.e., BESS-STAIR, which integrates a satellite-drivenwater–carbon–energy coupled biophysical model, BESS (Breathing Earth SystemSimulator), with a generic and fully automated fusion algorithm, STAIR(SaTallite dAta IntegRation). In this framework, STAIR provides daily 30'mmultispectral surface reflectance by fusing Landsat and MODIS satellite datato derive a fine-resolution leaf area index and visible/near-infrared albedo,all of which, along with coarse-resolution meteorological and CO 2 data, are used to drive BESS to estimate gap-free 30 m resolution daily ET.We applied BESS-STAIR from 2000 through 2017 in six areas across the US CornBelt and validated BESS-STAIR ET estimations using flux-tower measurementsover 12 sites (85 site years). Results showed that BESS-STAIR daily ETachieved an overall R2=0.75, with root mean square error RMSE=0.93 mm d -1 and relative error RE =27.9 % when benchmarkedwith the flux measurements. In addition, BESS-STAIR ET estimations capturedthe spatial patterns, seasonal cycles, and interannual dynamics well indifferent sub-regions. The high performance of the BESS-STAIR frameworkprimarily resulted from (1) the implementation of coupled constraints onwater, carbon, and energy in BESS, (2) high-quality daily 30 m data from theSTAIR fusion algorithm, and (3) BESS's applicability under all-skyconditions. BESS-STAIR is calibration-free and has great potentials to be areliable tool for water resource management and precision agricultureapplications for the US Corn Belt and even worldwide given the globalcoverage of its input data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Measuring surface albedo for various crops

Surface albedo can affect the energy budget and subsequently cause localized warming or cooling of the climate. When we convert a substantial portion of lands to agriculture, land surface properties are consequently altered, including albedo. Through crop selection and management, one can increase crop albedo to obtain higher levels of localized cooling effects to mitigate global warming. Still, there is little understanding about how distinctive features of a cropping system may be responsible for elevated albedo and consequently for the cooling potential of cultivated lands. To address this pressing issue, we conducted seasonal measurements of surface reflectivity during five growing seasons on annual crops of corn-soybean–winter wheat

albedo↗
DOE OSTI2020

Vertically stacked photovoltaic and thermal solar cell

According to some embodiments, the present invention provides a novel photovoltaic solar cell system from photovoltaic modules that are vertically arrayed in a stack format using thin film semiconductors selected from among organic and inorganic thin film semiconductors. The stack cells may be cells that are produced in a planar manner, then vertically oriented in an angular form, also termed herein tilted, to maximize the light capturing aspects. The use of a stack configuration system as described herein allows for the use of a variety of electrode materials, such as transparent materials or semitransparent metals. Light concentration can be achieved by using fresnel lens, parabolic mirrors or derivatives of such structures. The light capturing can be controlled by being reflected back and forth in the photovoltaic system until significant quantities of the resonant light is absorbed. Light that passes to the very end and can be reflected back through the device by beveling or capping the end of the device with a different refractive index material, or alternatively using a reflective surface. The contacting between stacked cells can be done in series or parallel. According to some embodiments, the present invention uses a concentrator architecture where the light is channeled into the cells that contain thermal fluid channels (using a transparent fluid such as water) to absorb and hence reduce the thermal energy generation.

14 SOLAR ENERGY↗
DOE OSTI2024

A Non-Intrusive Optical (NIO) Method to Measure Optical Errors of in-situ Heliostats in Utility-Scale Power Tower Plants: Detecting Uncertainties in Heliostat Geometry

Heliostat optical errors can account for significant losses in efficiency of power tower concentrating solar power (CSP) plants. Accurately measuring heliostat optical errors can help to improve plant performance. A Non-Intrusive Optical (NIO) method has been developed to efficiently measure heliostat optical errors from UAS collected images of the mirror surface reflection [1]–[3]. In some cases, plant data of heliostat geometry can be incomplete or contain inaccuracies, in which case field collected data can be used to detect and correct uncertainties, which is valuable information for plant operators.

Mitchell, Rebecca↗
DOE OSTI2024

Improvements in Optical Surface Measurement Using Reflected Computer Vision Targets

Since 2021, NREL has been developing a system to measure heliostats by measuring the deflection of printed computer vision targets, called the Reflected Target Non-intrusive Assessment (ReTNA) [6], [7]. While this system will have lower resolution than a fringe deflectometry system, it has several important advantages that make it a complimentary technology: 2D surface slope measurement can be generated from a single image, it can operate in ambient lighting, target points can be directly located in 3D space with photogrammetry allowing for a non-flat target, and it's well suited to using a smaller target, and multiple images to measure larger optical surfaces. ReTNA has undergone several significant changes and improvements, described below. This talk will summarize new system layouts designed for commercial use, new computer vision algorithms used to automate the analysis process and validation campaigns for the ReTNA software.

computer vision↗
DOE OSTI2020

Multi-cation perovskites prevent carrier reflection from grain surfaces

The composition of perovskite has been optimized combinatorially such that it often contains six components (A x B y C 1-x-y PbX z Y 3-z ) in state-of-art perovskite solar cells. Questions remain regarding the precise role of each component, and the lack of a mechanistic explanation limits the practical exploration of the large and growing chemical space. Here, aided by transient photoluminescence microscopy, we find that, in perovskite single crystals, carrier diffusivity is in fact independent of composition. In polycrystalline thin films, the different compositions play a crucial role in carrier diffusion. In this work, we report that methylammonium (MA)-based films show a high carrier diffusivity of 0.047 cm 2 s -1 , while MA-free mixed caesium-formamidinium (CsFA) films exhibit an order of magnitude lower diffusivity. Elemental composition studies show that CsFA grains display a graded composition. This curtails electron diffusion in these films, as seen in both vertical carrier transport and surface potential studies. Incorporation of MA leads to a uniform grain core-to-edge composition, giving rise to a diffusivity of 0.034 cm 2 s -1 in CsMAFA films. A model that invokes competing crystallization processes allows us to account for this finding, and suggests further strategies to achieve homogeneous crystallization for the benefit of perovskite optoelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Where Are White Roofs More Effective in Cooling the Surface?

Solar reflective roofs (also called white or cool roofs) are a common heat mitigation strategy. By reflecting more sunlight than regular roofs, cool roofs experience lower surface temperatures and produce less heating of the surrounding air. The effectiveness of cool roofs, quantified by the surface temperature difference between cool roofs and regular roofs ??????, is known to vary spatially. A common perception is that ?????? is controlled by solar radiation that reaches the roof surface. Here we use an Earth System Model and a surface energy balance model to show that the spatial variability of ??????, when normalized by the albedo difference between cool roofs and regular roofs (????), is in fact more controlled by an energy distribution factor that encodes the efficiencies of surface energy balance components in dissipating heat. Our results suggest that painting the roof white is more effective in reducing the roof surface temperature when the roof has less water holding capacity and smaller thermal admittance and is located in places with more solar radiation, less precipitation, and lower wind speed.

Wang, Linying↗
DOE OSTI2021

Reflection absorption infrared spectroscopy of the surface chemistry of furfural on Pd(111)

The hydrolysis and subsequent acidic dehydration of biomass leads to the production of smaller oxygenates, including furfural, which can undergo subsequent reactions such as hydrogenation to produce value-added products. Palladium has been found to be an active catalyst for this process. As a result, the surface chemistry of furfural is investigated on a Pd(111) single-crystal surface using reflection-absorption infrared spectroscopy as a basis for understanding the catalytic conversion of furfural to value-added products. Following adsorption at 90 K, furfural adopts a flat-lying geometry at low coverages, but converts to a tilted species as the coverage approaches saturation. Heating to ~175 K forms a tilted η 1 (O) species that appears to deprotonate on heating to above 200 K to form an intermediate with a tilted furyl ring and a carbonyl group close to parallel to the surface. Further heating to ~250 K and above caused this species to decarbonylate to form adsorbed carbon monoxide and an infrared invisible furyl intermediate. This can then undergo a ring-opening reaction to produce further CO and form a C 3 H 3 intermediate that can hydrogenate to produce propylene. Finally, this reaction sequence is in good agreement with previous density functional theory calculations and with the products observed in temperature-programmed desorption.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A canonical purification for the entanglement wedge cross-section

In AdS/CFT we consider a class of bulk geometric quantities inside the entanglement wedge called reflected minimal surfaces. The areas of these surfaces are dual to the entanglement entropy associated to a canonical purification (the GNS state) that we dub the reflected entropy. From the bulk point of view, we show that half the area of the reflected minimal surface gives a reinterpretation of the notion of the entanglement wedge cross-section. We prove some general properties of the reflected entropy and introduce a novel replica trick in CFTs for studying it. The duality is established using a recently introduced approach to holographic modular flow. We also consider an explicit holographic construction of the canonical purification, introduced by Engelhardt and Wall; the reflected minimal surfaces are simply RT surfaces in this new spacetime. We contrast our results with the entanglement of purification conjecture, and finally comment on the continuum limit where we find a relation to the split property: the reflected entropy computes the von Neumann entropy of a canonical splitting type-I factor introduced by Doplicher and Longo.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Sub-micrometer random-pyramid texturing of silicon solar wafers with excellent surface passivation and low reflectance

Select emerging solar cell technologies, ranging from very narrow screen-printed fingers to perovskite/silicon tandems, would benefit from a reduction of the height of the standard pyramidal texture of monocrystalline silicon wafers to below 1 μm. However, fabricating such small surface features usually compromises the requisite low reflectance and high passivation quality. Through systematic design of experiments, we demonstrate that conventional alkaline-based wet-chemical etching processes can be tuned to produce dense, spatially homogeneous, and uniformly sized sub-micrometer pyramids. Precise texture size can be further controlled by adding potassium silicate to the texturing solution to effectively slow the pyramid growth rate and promote nucleation. With pyramid size distribution data—extracted from multiple atomic force microscopy images across each wafer—we demonstrate 16 nanotextures with average pyramid sizes ranging from 62 to 512 nm, with the largest having only 0.3% of its pyramids taller than 1 μm. Six of the nanotextures exhibit light trapping and surface defect densities comparable to state-of-the-art microtextured wafers. In conclusion, the best nanotexture achieved an AM1.5G-weighted reflectance of 11.8% and a minority-carrier lifetime of 3.4 ms after passivation with amorphous silicon, demonstrating that smaller textures need not deteriorate solar cell performance.

14 SOLAR ENERGY↗