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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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208 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Spatially Resolved, In Situ Carbon Isotope Analysis of Archean Organic Matter

Spatiotemporal variability in the carbon isotope composition of sedimentary organic matter (OM) preserves information about the evolution of the biosphere and of the exogenic carbon cycle as a whole. Primary compositions, and imprints of the post-depositional processes that obscure them, exist at the scale of individual sedimentary grains (mm to micron). Secondary ion mass spectrometry (SIMS) (1) enables analysis at these scales and in petrographic context, (2) permits morphological and compositional characterization of the analyte and associated minerals prior to isotopic analysis, and (3) reveals patterns of variability homogenized by bulk techniques. Here we present new methods for in situ organic carbon isotope analysis with sub-permil precision and spatial resolution to 1 micron using SIMS, as well as new data acquired from a suite of Archean rocks. Three analytical protocols were developed for the CAMECA ims1280 at WiscSIMS to analyze domains of varying size and carbon concentration. Average reproducibility (at 2SD) using a 6 micron spot size with two Faraday cup detectors was 0.4 %, and 0.8 % for analyses using 1 micron and 3 micron spot sizes with a Faraday cup (for C-12) and an electron multiplier (for C-13). Eight coals, two ambers, a shungite, and a graphite were evaluated for micron-scale isotopic heterogeneity, and LCNN anthracite (delta C-13 = -23.56 +/- 0.1 %, 2SD) was chosen as the working standard. Correlation between instrumental bias and H/C was observed and calibrated for each analytical session using organic materials with H/C between 0.1 and 1.5 (atomic), allowing a correction based upon a C-13H/C-13 measurement included in every analysis. Matrix effects of variable C/SiO2 were evaluated by measuring mm to sub-micron graphite domains in quartzite from Bogala mine, Sri Lanka. Apparent instrumental bias and C-12 count rate are correlated in this case, but this may be related to a crystal orientation effect in graphite. Analyses of amorphous Archean OM suggest that instrumental bias is consistent for 12C count rates as low as 10% relative to anthracite. Samples from the ABDP-9 (n=3; Mount McRae Shale, approximately 2.5 Ga), RHDH2a (n=2; Carrawine Dolomite and Jeerinah Fm, approximately 2.6 Ga), WRL1 (n=3; Wittenoom Fm, Marra Mamba Iron Formation, and Jeerinah Fm, approximately 2.6 Ga), and SV1 (n=1; Tumbiana Fm, approximately 2.7 Ga) drill cores, each previously analyzed for bulk organic carbon isotope composition, yielded 100 new, in situ data from Neoarchean sedimentary OM. In these samples, delta C-13 varies between -53.1 and -28.3 % and offsets between in situ and bulk compositions range from -8.3 to 18.8%. In some cases, isotopic composition and mode of occurrence (e.g. morphology and mineral associations) are statistically correlated, enabling the identification of distinct reservoirs of OM. Our results support previous evidence for gradients of oxidation with depth in Neoarchean environments driven by photosynthesis and methane metabolism. The relevance of these findings to questions of bio- and syngenicity as well as the alteration history of previously reported Archean OM will be discussed.

Williford, Kenneth H.↗
DOE OSTI2026

Citizen science coupled with machine learning to quantify green-blue infrastructure cooling potential in Maricopa County, Arizona

Here, this study investigates the spatiotemporal cooling performance of green and blue infrastructure (GBI) in the Dobson Ranch urban neighborhood in Phoenix, Arizona. We leveraged citizen science near-surface (2 m) air temperature (Tair) measurements to train a highly accurate Tair predicting LightGBM machine learning model (R 2 : 0.986, MAE: 0.251 °C, RMSE: 0.585 °C). On June 16, 2024, the park area exhibited approximately 1 °C cooling effect (relative to the neighborhood mean) during both day and night. In contrast, the nearby artificial lake exhibited a stronger cooling effect of 2.4 °C during the day but a slight warming of 0.3 °C at night. At 00:00, locations 50 m downwind of the park were 0.3 °C warmer than the park, while locations 50 m upwind were 0.8 °C warmer. At 11:00, we observed that the downwind area is 0.8 °C cooler and the upwind area is 0.6 °C warmer—at the same 50 m distances relative to the park. We also observed 1 °C cooler and warmer effects respectively at the same 50 m downwind and upwind locations at 19:00 on June 17, 2024. Our data-driven analysis highlights potential limitations of car-traverse measurements, showing that failure to account for temporal variations during the traverse can lead to overestimation of Tair at night and underestimation during the day. Our analysis also showed only a weak correlation (coefficient: 0.48) between Landsat-derived land surface temperature (LST) and model predicted Tair at the time of the local Landsat overpass (∼11.00). This highlights the potential error of relying solely on LST for human thermal exposure analysis—particularly within the heterogenous built-environment.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Direction of Moving Plaids is Biased by Asymmetric Viewing Windows

Directionally selective V1 neurons are tuned to particular spatiotemporal frequencies and respond to local 1-D edge motion. At least some MT neurons however appear to respond to the actual velocity of moving 2D patterns (Movshon et al., EBR, 11:117, 1986). To better understand how the 1D local motion information available from V1 is integrated to derive a 2D velocity signal we investigated human perception of moving plaids, 2-D patterns composed of the sum of two 1-D sinusoidal gratings of different orientations. We measured the effect of the shape of the viewing window on the perceived direction of plaid motion. The plaids were spatially windowed by 2-D Gaussians with unequal standard deviations (sigma l, sigma 2). Four observers indicated perceived direction by adjusting a pointer. Direction errors were measured as a function of the difference between window orientation and true plaid direction (delta theta) for several grating spatial frequencies (SF = 0.3, 0.6, 1.2 c/d) and window aspect ratios (AR = sigma 1/sigma 2 = 1, 1.4, 2,4). Observers showed systematic errors in perceived direction (approx. 15 degrees for R = 4 and SF = 0.6 c/d) that peaked at delta theta approximately 40 degrees. The errors increased for increasing aspect ratio and decreased for increasing spatial frequencies (or number of cycles). These results show that despite the unambiguous motion of the plaids, within asymmetric windows, human can systematically misperceive plaid direction. These data constrain models of motion integration within extrastriate cortex and, in particular, are inconsistent with algorithms that use either the Intersection of Constraints rule or cross correlation to compute velocity.

Beutter, Brent Robert↗
NASA NTRS2022

An Evaluation of the Liquid Cloud Droplet Effective Radius Derived from MODIS, Airborne Remote Sensing, and in Situ Measurements from CAMP2Ex

The cloud drop effective radius (Re) of the drop size distribution derived from passive satellite sensors is a key variable used in climate research. Validation of these satellite products has often taken place under stratiform cloud conditions that favor the assumption of cloud horizontal homogeneity used by the retrieval techniques. However, many studies have noted concerns with respect to significant biases in retrieved Re arising from cloud heterogeneity, for example, in cumulus cloud fields. Here, we examine data collected during the 2019 “Cloud, Aerosol and Monsoon Processes Philippines Experiment” (CAMP2Ex), which, in part, targeted the objective of providing the first detailed evaluation of Re retrieved across multiple platforms and techniques in a cumulus and congestus cloud region. Our evaluation consists of cross-comparisons of Re between the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, the Research Scanning Polarimeter (RSP) onboard the NASA P-3 aircraft, and in situ measurements from both the NASA P-3 and Learjet aircraft that are all taken in close spatiotemporal proximity to the same cloud fields. A particular advantage of our approach lies in the capability of the RSP to retrieve Re using a bi-spectral MODIS approach and a polarimetric approach, which allows for the evaluation of bi-spectral and polarimetric Re retrievals from an airborne perspective using the same samples. Averaged over all P-3 flight segments examined here for warm clouds, the RSP polarimetric method, the in situ method, and the bias-adjusted MODIS method of Fu et al. (2019) show a comparable median (mean ± standard deviation) for the Re samples of 9.6 (10.2 ± 4.0) µm, 11.0 (13.6 ± 11.3) µm, and 10.4 (10.8 ± 3.8) µm, respectively. These values are far lower than the values of 15.1 (16.2 ± 5.5) µm and 17.2 (17.7 ± 5.7) µm from the bi-spectral retrievals of RSP and MODIS, respectively. Similar results are observed when Re is segregated by cloud-top height and in detailed case studies. The clouds sampled during CAMP2Ex consist of mostly small (mean transect length ∼ 1.4 km) and low clouds (mean cloud-top height ∼ 1 km), which had more numerous small clouds than the trade wind cumuli sampled in past field campaigns such as Rain in Shallow Cumulus over the Ocean (RICO) and the Indian Ocean Experiment (INDOEX). The overestimates of Re from the RSP bi-spectral technique compared with the polarimetric technique increased as cloud size and cloud optical depth decreased. Drizzle, cloud-top bumpiness, and solar zenith angle, however, are not closely correlated with the overestimate of bi-spectral Re. For shallow clouds that dominated the liquid cloud cover for the CAMP2Ex region and period, we show that 3-D radiative transfer and cloud heterogeneity, particularly for the optically thin and small clouds, appear to be the leading cause of the large positive biases in bi-spectral retrievals. Because this bias varies with the underlying structure of the cloud field, caution continues to be warranted in studies that use bi-spectral Re retrievals in cumulus cloud fields.

Cloud microphysics↗
NASA NTRS2019

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm.

high mountain Asia↗
DOE OSTI2020

Spatiotemporal Analyses of Groundwater and Shoreline Cr(VI) Concentrations in the 100 Areas at Hanford

Cleanup efforts have been ongoing since the late 1990s to remediate contaminated waste sites and groundwater in the 100 Areas at the U.S. Department of Energy (DOE) Hanford Site. One of the primary contaminants of concern is hexavalent chromium (Cr(VI)), which was used as a corrosion inhibitor in cooling water for nuclear reactors that formerly operated along the shoreline of the Columbia River. Cleanup efforts have included 1) removal, treatment (as needed), and disposal of contaminated sediments; 2) in situ redox manipulation as a permeable reactive barrier; 3) pump-and-treat; 4) soil flushing; and 5) monitored natural attenuation. DOE’s annual groundwater monitoring reports document the significant reductions in Cr(VI) plume areas that have occurred over the past 10 years or more as a result of these cleanup efforts. The Record of Decision for the 100-HR-3 operable unit specified a cleanup level (CUL) for Cr(VI) in groundwater of 48 µg/L to protect human receptors, and a surface water CUL of 10 µg/L to protect aquatic organisms in the Columbia River. The Record of Decision did not specify point-of-compliance locations for the surface water CUL. Data for 2019 from the six groundwater operable units (OUs) in the 100 Areas indicate that the 48 μg/L groundwater CUL has been achieved in 100% of the wells in the 100-BC and 100-NR OUs, and in 89- 97% of the wells in the other OUs (100-KR, 100-HR-D, 100-HR-H, 100-FR). Data for 2019 indicate that 100% of the aquifer tubes monitored for Cr(VI) in the 100 Areas have concentrations below the 48 μg/L groundwater CUL. However, the 10 μg/L standard has not yet been consistently achieved for both inland groundwater monitoring wells and shoreline aquifer tubes. This report describes a series of data analyses performed to identify consistent relationships, if any, between inland well and shoreline Cr(VI) concentrations within the 100 Areas. To this end, select monitoring data for Cr(VI) measured in groundwater and aquifer tubes at the 100 Areas were analyzed for a 10-year period—2010 to 2019. Relationships between inland groundwater plumes and surface-water points of discharge in and along the Columbia River were examined through several analyses that included inland well and aquifer tube concentrations as a function of distance from the shoreline, evaluation of cumulative probability plots, trend analysis, correlation analysis, cluster analysis, and identification of plume trajectories for each of the 100 Areas. The analyses did not identify consistent relationships between inland groundwater Cr(VI) concentrations and shoreline concentrations within the 100 Areas due to several confounding factors influencing groundwater flow directions and Cr(VI) concentrations. The proximity of groundwater Cr(VI) plumes to the river, and the highly dynamic nature of the river, influence the transport behavior of the plumes and create challenges for quantifying attenuation of Cr(VI) between the inland monitoring wells and shoreline concentrations. Other factors contributing to temporal and spatial Cr(VI) concentrations, as supported by some of the data analyses, include the presence of vadose zone sources, variable sorption behavior, and complexities associated with Cr(VI) mass transfer between the upper and lower aquifers and their interactions with the river. Hence, monitoring to assess compliance with target CULs will need to be determined for each area individually since several factors influence Cr(VI) concentrations in the 100 Areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.

42 ENGINEERING↗
DOE OSTI2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Abstract. Quantifying the spatiotemporal dynamics in subsurface hydrological flows over a long time window usually employs a network of monitoring wells. However, such observations are often spatially sparse with potential temporal gaps due to poor quality or instrument failure. In this study, we explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. We use a well network that monitors the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer at the U.S. Department of Energy's Hanford site. This 10-year-long dataset contains hourly temperature, specific conductance, and groundwater table elevation measurements from 42 wells with gaps of various lengths. We employ a long short-term memory (LSTM) model to capture the temporal variations in the observed system behaviors needed for gap filling. The performance of the LSTM-based gap-filling method was evaluated against a traditional autoregressive integrated moving average (ARIMA) method in terms of error statistics and accuracy in capturing the temporal patterns of river corridor wells with various dynamics signatures. Our study demonstrates that the ARIMA models yield better average error statistics, although they tend to have larger errors during time windows with abrupt changes or high-frequency (daily and subdaily) variations. The LSTM-based models excel in capturing both high-frequency and low-frequency (monthly and seasonal) dynamics. However, the inclusion of high-frequency fluctuations may also lead to overly dynamic predictions in time windows that lack such fluctuations. The LSTM can take advantage of the spatial information from neighboring wells to improve the gap-filling accuracy, especially for long gaps in system states that vary at subdaily scales. While LSTM models require substantial training data and have limited extrapolation power beyond the conditions represented in the training data, they afford great flexibility to account for the spatial correlations, temporal correlations, and nonlinearity in data without a priori assumptions. Thus, LSTMs provide effective alternatives to fill in data gaps in spatially distributed time-series observations characterized by multiple dominant frequencies of variability, which are essential for advancing our understanding of dynamic complex systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

In-Operando Study of Shape Retention and Microstructure Development in a Hydrolyzing Sol–Gel Ink during 3D-Printing

3D printing of amorphous and crystalline ceramics is of paramount importance for the fabrication of a wide range of devices with applications across different technology fields. Printed ceramics are remarkably enabled by the sol-gel synthesis method in conjunction with continuous filament direct ink writing. During printing, multiple processes contribute to the evolution of inks including shape retention, chemical conversion, solidification, and microstructure formation. Traditionally, depending on the ink composition and printing environment, several mechanisms have been associated with the shape retention and solidification of 3D printed structures: gelation, rapid solvent evaporation, energy-driven phase transformation, and chemical-driven phase transformation. Understanding the fundamental differences between these mechanisms becomes key since they strongly influence the spatiotemporal evolution of the materials, as the out-of-equilibrium processes inherent to the extrusion, relaxation, and solidification of printed materials have significant effects on the materials properties. Herein, we investigate the shape retention mechanism and the hydrolysis-induced material conversion and microstructure formation during the 3D printing of a water reactive sol-gel ink that transforms into titanium dioxide-based ceramic. This study aims at identifying characteristic mechanisms associated with the material transformation, establishing connections between the microstructure development and the timescales associated with solidification under operando 3D-printing conditions. The investigation of this material's out-of-equilibrium pathways under processing conditions is enabled by time-resolved coherent X-ray scattering, providing simultaneous access to temporospatially resolved microstructural and dynamics information. Furthermore, we explore X-ray speckle tracking as a tool to resolve deformations of the microstructure in a printed filament associated with the deposition of consecutive filaments. Through this work, we aim at providing a fundamental understanding of the relationships behind these transformative processes in 3D printing and their timescales as the basis for achieving unprecedented control over printed materials microstructure.

36 MATERIALS SCIENCE↗
NASA NTRS2019

Surface erythemal UV irradiance in the continental United States derived from ground-based and OMI observations: quality assessment, trend analysis and sampling issues

Surface full-sky erythemal dose rate (EDR) from the Ozone Monitoring Instrument (OMI) at both satellite overpass time and local noon time is evaluated against ground measurements at 31 sites from the US Department of Agriculture's (USDA) UV-B Monitoring and Research Program (UVMRP) over the period of 2005–2017. We find that both OMI overpass and solar noon time EDR are highly correlated with the measured counterparts (with a linear correlation coefficient of 0.90 and 0.88, respectively). Although the comparison statistics are improved with a longer time window (0.5–1.0 h) for pairing surface and OMI measurements, both OMI overpass and local noon time EDRs have 7 % overestimation that is larger than 6 % uncertainty in the ground measurements and show different levels of dependence on solar zenith angle (SZA) and to lesser extent on cloud optical depth. The ratio of EDR between local noon and OMI overpass time is often (95 % in frequency) larger than 1 with a mean of 1.18 in the OMI product; in contrast, the same ratio from surface observation is normally distributed with 22 % of the times less than 1 and a mean of 1.38. This contrast in part reflects the deficiency in the OMI surface UV algorithm that assumes constant atmospheric conditions between overpass and noon time. The probability density functions (PDFs) for both OMI and ground measurements of noontime EDR are in statistically significant agreement, showing dual peaks at ∼20 and ∼200 mW/sq. m, respectively; the latter is lower than 220 mW/ sq. m, the value at which the PDF of daily EDR from ground measurements peaks, and this difference indicates that the largest EDR value for a given day may not often occur at local noon. Lastly, statistically significant positive trends of EDR are found in the northeastern US in OMI data, but opposite trends are found within ground-based data (regardless of sampling for either noontime or daily averages). While positive trends are consistently found between OMI and surface data for EDR over the southern Great Plains (Texas and Oklahoma), their values are within the uncertainty of ground measurements. Overall, no scientifically sound trends can be found among OMI data for aerosol total and absorbing optical depth, cloud optical depth and total ozone to explain coherently the surface UV trends revealed either by OMI or ground-based estimates; these data also cannot reconcile trend differences between the two estimates (of EDR from OMI and surface observations). Future geostationary satellites with better spatiotemporal resolution data should help overcome spatiotemporal sampling issues inherent in OMI data products and therefore improve the estimates of surface UV flux and EDR from space.

Ozone Monitoring Instrument (OMI)↗