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At least 1,063 records · Page 59

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NASA NTRS2023

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While the InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model’s accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017, in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Urban flooding↗

NASA NTRS2023

Uncertainty Analysis of Slug Calorimeters in the HyMETS Arc-Jet Facility

The objective of this work is to perform an uncertainty analysis of the deduced stagnation heat flux environment on a slug calorimeter for conditions that span the performance envelope of the Hypersonic Materials Environmental Test System arc-jet facility located at NASA Langley Research Center. Analytical solutions are developed for boundary-value problems on the slug element accounting for non-ideal effects, including spatial variation in the slug heat flux, multi-dimensional thermal conduction, and back-face losses, which departs from the state-of-the-art method derived from the American Society of Testing and Materials. Boundary-value problem definitions are informed by preliminary finite element thermal analysis of the slug calorimeter assembly (including both slug and housing) and just the slug element. The analytical solutions are presented in a general sense and in a truncated form from error analysis. Results are shown in optimizing and validating the analytical models against available slug back-face thermal data. The optimization results indicate that the appropriate epistemic uncertainty of the deduced stagnation heat flux on the slug calorimeter is at most±2.5% for both a high-and low-enthalpy test condition. In addition, a numerical approach is used to determine the aleatory (probabilistic) uncertainty component in the slug stagnation heat flux by applying a marching least-squares slope routine through the steady-state portion of the slug back-face thermal response. Results indicate a compromise between the number of samples and the filter frequency of slug back-face thermal data points when evaluating the standard deviation of the deduced stagnation heat flux statistics. When combining the mixed uncertainty, both aleatory and epistemic, the interval of uncertainty in the deduced stagnation heat flux is determined to be up to ±4%, which is at least a 60% reduction from the standard uncertainty used in the state-of-the-art method.

uncertainty↗

NASA NTRS2023

Remotely Sensed Soil Moisture Can Capture Dynamics Relevant to Plant Water Uptake

A frequently expressed viewpoint across the Earth science community is that global soil moisture estimates from satellite L-band (1.4 GHz) measurements represent moisture only in a shallow surface layer (0–5 cm) and consequently are of limited value for studying global terrestrial ecosystems because plants use water from deeper rootzones. Using this argumentation, many observation-based land surface studies avoid satellite-observed soil moisture. Here, based on peer-reviewed literature across several fields, we argue that such a viewpoint is overly limiting for two reasons. First, microwave soil emission depth considerations and statistical considerations of vertically correlated soil moisture information together indicate that L-band measurements carry information about soil moisture extending below the commonly referenced 5 cm in many conditions. However, spatial variations of effective depths of representation remain uncertain. Second, in reviewing isotopic tracer field studies of plant water uptake, we find a prevalence of vegetation that primarily draws moisture from these upper soil layers. This is especially true for grasslands and croplands covering more than a third of global vegetated surfaces. Even some deeper-rooted species (i.e., shrubs and trees) preferentially or seasonally draw water from the upper soil layers. Therefore, L-band satellite soil moisture estimates are more relevant to global vegetation water uptake than commonly appreciated (i.e., relevant beyond only shallow soil processes like soil evaporation). Our commentary encourages the application of satellite soil moisture across a broader range of terrestrial hydrosphere and biosphere studies while urging more rigorous estimates of its effective depth of representation.

Andrew F. Feldman↗

NASA NTRSDate not supplied

Establishing and Maintaining the Digital Thread of Additively Manufactured Materials and Applications

Additive Manufacturing (AM) and Integrated Computational Materials Engineering (ICME) are complementary enabling technologies for design and manufacturing of “fit-for-purpose” materials. Both technologies will impact rapid material design, reduction in cost- and time-to-market for new applications, and discovery and implementation of new materials. An ICME approach to design, however, requires experimentally validated material models at multiple length and time scales, an integrated framework that can connect analysis tools with one another to ensure the digital thread of an application is maintained, and the manufacturing (e.g., AM) capability to leverage processing-structure-property-performance (PSPP) relationships to achieve spatially varying material properties where desired. AM enables the implementation of the design of an optimized, spatially varying microstructure through careful selection of the processing parameters used during an additively manufactured build. In order to establish these PSPP relations, a large amount of data is necessary, and that data must be properly captured, analyzed and maintained in an information management system that can establish the required traceability between various aspects of the design process to ensure an application’s digital thread is maintained (from design to end of life). Such an information management system must be able to capture feedstock material pedigree, resulting microstructure from various build parameters, subsequent mechanical properties derived from testing, developed material models, and enable spatial variations in material assignment in an engineering application. Furthermore, the information management system should be easily integrated with traditionally engineered materials in a single, centralized platform to enable an ICME optimization tool to explore both types of manufacturing processes. At NASA GRC, a robust, 21st century materials information management system has been previously developed with a focus towards enabling ICME. In this work, GRC’s ICME schema is extended to accommodate additively manufactured materials, enabling storage of both traditionally and additively manufactured materials in the same construct. The methodology for properly capturing additively manufactured materials across the entire material lifecycle is presented, following the previously established database best practices, as a potential framework for establishing PSPP relationships for additively manufactured materials and applying them to engineering applications.

Data management↗

NASA NTRS2024

Alaska Ecological Conservation II: Using NASA Earth Observations to Identify Recent Changes in Vegetation Phenology and Its Impacts on Caribou Calving and Migration

Caribou are known for their long-distance migrations from wintering grounds to specified calving zones. Notably, the Western Arctic Herd (WAH) is the largest caribou herd in Northwestern Alaska that exhibits this behavior. These calving zones are highly predictable, and research suggests that calving and the availability of high-quality vegetation are correlated. We partnered with the U.S. National Park Service in Northwestern Alaska to use Terra and Aqua Moderate Resolution Imaging Spectroradiometer(MODIS)and Harmonized Landsat and Sentinel-2 (HLS) imagery to derive Normalized Difference Vegetated Index (NDVI) values and assess vegetation phenology in calving zones from 2000 –2024. Our partners seek to understand if observed changes in caribou migration patterns and calving events correlate with shifts in the availability of nutrient-rich vegetation. Our results showed spatial variations of highly vegetated zones and annual shifts in the timing and length of growing seasons. While limitations existed in the MODIS and HLS datasets, we demonstrated feasibility using National Aeronautics and Space Administration (NASA)Earth observations for NDVI analysis over the study region. Using location data, our results may further research into caribou calving behaviors.

NDVI↗

NASA NTRS2024

Discounting Water for Optimal Carbon Gain as a Basis of Stomatal Closure

The exchange of carbon dioxide and water vapor between terrestrial ecosystems and the atmosphere is regulated by stomata (small pores in the leaves of plants). Unsurprisingly, environmental factors controlling the opening and closure of stomata has been sought as early as 1800. One approach, popularized in the early 1970s, is a stomatal optimization framework. This framework is based on the hypothesis that plants optimize carbon gain subject to water loss or water availability constraints. This constraint optimization problem was solved in various forms assuming instantaneous adjustments of stomatal aperture to maximize a reward function with no future foresight or legacy effects. Holtzman et al. (2024, https://doi.org/10.1029/2023av001113) offers a novel approach that can diagnose the effective timescale over which the reward function maximization must be time-integrated. The developed method thus optimizes an integrated carbon gain function but adjusted by a discount factor subject to water availability in the root zone. The discount factor considers how the plant values carbon gain to save water and its timescale can be inferred from observations because the model is analytically tractable. The results suggest that the most important climate factor that determines this discount timescale is multi-annual mean of the longest dry period during the growing season. The findings highlight how local climate traits influence the spatial variation in ecosystem-level water use strategies. This sets the stage for expanding such a framework to cases where multiple constraints act in concert while operating at distinct time scales.

stomata↗

DOE OSTI2017

Characterizing U.S. Heat Demand Market for Potential Application of Geothermal Direct Use

In this paper, we assess the U.S. demand for low-temperature thermal energy at the county resolution for four major end-use sectors: residential buildings, commercial buildings, manufacturing facilities, and agricultural facilities. Existing, publicly available data on the U.S. thermal demand market are characterized by coarse spatial resolution, with assessments typically at the state-level or larger. For many uses, these data are sufficient; however, our research was motivated by an interest in assessing the potential demand for direct use (DU) of low-temperature (30 degrees to 150 degrees C) geothermal heat. The availability and quality of geothermal resources for DU applications are highly spatially heterogeneous; therefore, to assess the potential market for these resources, it is necessary to understand the spatial variation in demand for low-temperature resources at a local resolution. This paper presents the datasets and methods we used to develop county-level estimates of the thermal demand for the residential, commercial, manufacturing, and agricultural sectors. Although this analysis was motivated by an interest in geothermal energy deployment, the results are likely to have broader applications throughout the energy industry. The county-resolution thermal demand data developed in this study for four major U.S. sectors may have far-reaching implications for building technologies, industrial processes, and various distributed renewable energy thermal resources (e.g. biomass, solar).

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

Parallel Multi-cycle LES of an Optical Pent-roof DISI Engine Under Motored Operating Conditions

The use of Large-eddy Simulations (LES) has increased due to their ability to resolve the turbulent fluctuations of engine flows and capture the resulting cycle-to-cycle variability. One drawback of LES, however, is the requirement to run multiple engine cycles to obtain the necessary cycle statistics for full validation. The standard method to obtain the cycles by running a single simulation through many engine cycles sequentially can take a long time to complete. Recently, a new strategy has been proposed by our research group to reduce the amount of time necessary to simulate the many engine cycles by running individual engine cycle simulations in parallel. With modern large computing systems this has the potential to reduce the amount of time necessary for a full set of simulated engine cycles to finish by up to an order of magnitude. In this paper, the Parallel Perturbation Methodology (PPM) is used to simulate up to 35 engine cycles of an optically accessible, pent-roof Directinjection Spark-ignition (DISI) engine at two different motored engine operating conditions, one throttled and one un-throttled. Comparisons are made against corresponding sequential-cycle simulations to verify the similarity of results using either methodology. Mean results from the PPM approach are very similar to sequential-cycle results with less than 0.5% difference in pressure and a magnitude structure index (MSI) of 0.95. Differences in cycle-to-cycle variability (CCV) predictions are larger, but close to the statistical uncertainty in the measurement for the number of cycles simulated. PPM LES results were also compared against experimental data. Mean quantities such as pressure or mean velocities were typically matched to within 5- 10%. Pressure CCVs were under-predicted, mostly due to the lack of any perturbations in the pressure boundary conditions between cycles. Velocity CCVs for the simulations had the same average magnitude as experiments, but the experimental data showed greater spatial variation in the root-mean-square (RMS). Conversely, circular standard deviation results showed greater repeatability of the flow directionality and swirl vortex positioning than the simulations.

I.C. Engines↗

DOE OSTI2018

Thermal Design of Hybrid Materials Produced via Ultrasonic Additive Manufacturing

Here, ultrasonic additive manufacturing (UAM) is a technique for producing three-dimensional components through the metallurgical bonding of successive layers of metal foil. Due to its ability to process at low temperatures, UAM is uniquely suited for building hybrid materials. In this study, AA6061+water hybrid blocks with varying water cavity sizes were produced and their thermal performances were measured. Nonoptimal processing parameters were used to understand the interaction between interlayer porosity and thermal diffusivity of bulk material containing both solid and liquid. Flash thermography was used to characterize the spatial distribution of thermal diffusivity for each part. The average thermal diffusivity was successfully compared to a simplified thermal resistor model. The spatial variation in thermal properties was also correlated to specific interlayer features and related back to interactions between process characteristics and the component geometry.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Subsurface Seismic Structure Around the Sanford Underground Research Facility

To address the fundamental challenges of understanding processes associated with enhanced or engineered geothermal systems (EGS), teams from the EGS Collab SIGMA-V project are conducting stimulations at the Sanford Underground Research Facility. Various modalities of geophysical observations are collected and analyzed, and different types of parameters are modeled to help investigate these critical processes. Among these parameters, the three-dimensional stress field is one of the primary objectives. Seismic velocity models have been used to estimate the 3D stress field for other regions. A detailed seismic velocities and density model can help us infer spatial variations of the stress field in the study area. We inverted the 3D seismic structure around the site by using body-wave travel times, surface-wave dispersions and gravity observations simultaneously. The resulting model will be a starting point for more detailed investigations and could also be used to study subsurface geological and stress field variations of the broad area.

Chai, Chengping↗

DOE OSTI2019

Subsurface Seismic Structure Around the Sanford Underground Research Facility

Chai, Chengping↗

DOE OSTI2019

Bifacial PV System Mismatch Loss Estimation

Nonuniform rear-irradiance on bifacial PV systems can cause additional mismatch loss, which may not be appropriately captured in PV energy production estimates and software. A simplified empirical relationship is presented that links the spatial variation of irradiance (to the resulting mismatch loss factor).

14 SOLAR ENERGY↗

DOE OSTI2019

Bifacial PV System Mismatch Loss Estimation & Parameterization

Non-uniform irradiance on the rear-side of bifacial PV systems can cause additional mismatch loss which may not be appropriately captured in PV energy production estimates and software. We have evaluated several rooftop mounted systems over high albedo reflective roofs. Mismatch losses of up to 2% annual loss for very close-mounted (0.15 m) rooftop systems were found, but losses for high ground-clearance rooftop systems were lower (<0.5%). A simplified empirical relationship was found that links the spatial variation of irradiance (specifically the mean absolute difference of irradiance) to the resulting mismatch loss factor, with a R2 fit better than 0.985. This approximate relationship has been validated by experiments on mismatched PV modules, and provides a basis for fast estimation of bifacial mismatch loss factors for use in hourly PV performance simulations such as PVSyst or SAM.

14 SOLAR ENERGY↗

DOE OSTI2019

Bifacial PV System Mismatch Loss Estimation and Parameterization: Preprint

Non-uniform irradiance on the rear-side of bifacial PV systems can cause additional mismatch loss which may not be appropriately captured in PV energy production estimates and software. We have evaluated several scenarios including horizontal single-axis tracking (HSAT) and rooftop mounted systems over high albedo reflective roofs. Mismatch losses of up to 2% annual loss for very close-mounted (0.15 m) rooftop systems were found, but losses for HSAT systems and high ground-clearance rooftop systems were lower (<0.5%). A simplified empirical relationship was found that links the spatial variation of irradiance (specifically the mean absolute difference of irradiance) to the resulting mismatch loss factor, with a R2 fit better than 0.985. This approximate relationship has been validated by experiments on mismatched PV modules, and provides a basis for fast estimation of bifacial mismatch loss factors for use in hourly PV performance simulations such as PVSyst or SAM.

14 SOLAR ENERGY↗

DOE OSTI2019

Scanning Kelvin Probe Microscopy: A Tool to Investigate Nano-Scale Doping Non-Uniformities in Poly-Si/SiOx Contacts: Preprint

Monocrystalline Si (c-Si) solar cells with passivated contacts based on the ultrathin SiOx and doped polycrystalline Si (poly-Si) layers in a poly-Si/SiOx/c Si structure show high solar cell efficiencies that are ~26%. Excellent surface passivation using these contacts is achieved via the combined effects of chemical passivation of the SiOx/c-Si interface by the SiOx layer and field-effect passivation from the heavily doped poly-Si layer. These contacts give best performance only when annealed to temperatures higher than 850 degrees C. Structural changes in the SiOx layer and dopant diffusion from poly-Si into the underlying c-Si wafer occur during this step which are hard to investigate using conventional characterization techniques. In this work we investigate poly-Si/SiOx contacts with both a 1.5 (tunneling transport) and 2.2 (pinhole transport) nm SiOx layer using atomic force microscopy techniques. Conductive AFM on n+-poly-Si/SiOx/p-Si structures show significant spatial variations for both contact types, likely due to non-uniformities in the poly-Si layer itself. The electrical and structural variations deeper into the contact were revealed by scanning Kelvin probe microscopy after precisely etching away the poly-Si and SiOx layers and few nanometers of c-Si surface. This etching was performed using tetramethylammonium hydroxide and dilute HF solutions. The resulting surface potential maps appear similar for both contacts, and show less than 500 nm size heavily-doped regions. However, further etching of the c-Si surface reveals these heavily-doped regions to be less than 200 nm deep for the 2.2 nm SiOx contact and greater than 200 nm deep for the 1.5 nm SiOx contact.

14 SOLAR ENERGY↗

DOE OSTI2020

Deep Learning Prediction of Interspecies Interactions from Self-organized Spatiotemporal Patterns of Co-evolving Organisms

Microorganisms colonizing natural habits such as soils co-evolve to form specific spatial patterns through interspecies interactions. These self-organized patterns are a key ecological phenotype, which provides critical information on their interaction mechanisms. However, conventional network inference techniques that analyze species population data in bulk have yet to be extended to account for such spatial heterogeneity. Here we proposed supervised deep learning as a new network inference tool for predicting interspecies interactions from spatiotemporal patterns of microbial evolution. Due to lack of biological imaging data that can be used for training deep learning networks, we used in silico data generated from high-fidelity agent-based models to determine model structure and parameters. Even though networks were trained under simple configurations where interaction coefficients are assumed to be spatially invariant, we demonstrated that the resulting model can be utilized to successfully predict spatial variation of interactions in more complex domains (i.e., configured with a context-dependent mixture of interaction coefficients) as well as in simple domains without further training. In the further test against real biological data obtained through imaging experiments of a binary consortium (Pseudomonas fluorescens and a mutant of Escherichia coli), our model also predicted the dramatic shifts in interactions of the two organisms across different environmental contexts. Through various successful demonstrations in this work, the combined use of the agent-based model and machine learning algorithm provides a means to use new type of data - microscopic images - for extracting microbial interactions, therefore presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Lee, Joon-Yong↗

DOE OSTI2023

An Interpretable Index of Social Vulnerability to Environmental Hazards

Index-based measures of social vulnerability to environmental hazards are commonly modeled from composites of population-level risk factors. These models overlook individual context in communities' experiences of environmental hazards, producing metrics that may hinder spatial decision support for mitigating and responding to hazards. This paper introduces an interpretable, high-resolution model for generating an individual-oriented social vulnerability index (IOSVI) for the United States built on synthetic populations that couples individual and social determinants of vulnerability. The IOSVI combines an individual vulnerability index (IVI) that ranks individuals in an area’s synthetic population based on intersecting risk factors, with a social vulnerability index (SVI) based on the population’s cumulative distribution of IVI scores. Interpretability of the IOSVI procedure is demonstrated through examples of national, metropolitan, and neighborhood (census tract) level spatial variation in index scores and IVI themes, as well as an exploratory analysis examining risk factors affecting a specific sub-population (military veterans) in areas of high social and environmental vulnerability.

Tuccillo, Joe↗

DOE OSTI2023

The Atacama Cosmology Telescope: High-resolution component-separated maps across one-third of the sky

Observations of the millimeter sky contain valuable information on a number of signals, including the blackbody cosmic microwave background (CMB), Galactic emissions, and the Compton-$y$ distortion due to the thermal Sunyaev-Zel'dovich (tSZ) effect. Extracting new insight into cosmological and astrophysical questions often requires combining multi-wavelength observations to spectrally isolate one component. In this work, we present a new arcminute-resolution Compton-$y$ map, which traces out the line-of-sight-integrated electron pressure, as well as maps of the CMB in intensity and E-mode polarization, across a third of the sky (around 13,000 sq.~deg.). We produce these through a joint analysis of data from the Atacama Cosmology Telescope (ACT) Data Release 4 and 6 at frequencies of roughly 93, 148, and 225 GHz, together with data from the \textit{Planck} satellite at frequencies between 30 GHz and 545 GHz. We present detailed verification of an internal linear combination pipeline implemented in a needlet frame that allows us to efficiently suppress Galactic contamination and account for spatial variations in the ACT instrument noise. These maps provide a significant advance, in noise levels and resolution, over the existing \textit{Planck} component-separated maps and will enable a host of science goals including studies of cluster and galaxy astrophysics, inferences of the cosmic velocity field, primordial non-Gaussianity searches, and gravitational lensing reconstruction of the CMB.

79 ASTRONOMY AND ASTROPHYSICS↗