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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Pelagic Habitat Analysis Module (PHAM) for GIS Based Fisheries Decision Support

We have assembled a system that integrates satellite and model output with fisheries data We have developed tools that allow analysis of the interaction between species and key environmental variables Demonstrated the capacity to accurately map habitat of Thresher Sharks Alopias vulpinus & pelagicus. Their seasonal migration along the California Current is at least partly driven by the seasonal migration of sardine, key prey of the sharks.We have assembled a system that integrates satellite and model output with fisheries data We have developed tools that allow analysis of the interaction between species and key environmental variables Demonstrated the capacity to accurately map habitat of Thresher Sharks Alopias vulpinus nd pelagicus. Their seasonal migration along the California Current is at least partly driven by the seasonal migration of sardine, key prey of the sharks.

geographic information system (GIS)↗
NASA NTRS2021

Spatiotemporal Associations Between Social Vulnerability, Environmental Measurements, and COVID-19 in the Conterminous United States

This study summarizes the results from fitting a Bayesian hierarchical spatiotemporal model to coronavirus disease 2019 (COVID-19) cases and deaths at the county level in the United States for the year 2020. Two models were created, one for cases and one for deaths, utilizing a scaled Besag, York, Mollié model with Type I spatial-temporal interaction. Each model accounts for 16 social vulnerability and 7 environmental variables as fixed effects. The spatial pattern between COVID-19 cases and deaths is significantly different in many ways. The spatiotemporal trend of the pandemic in the United States illustrates a shift out of many of the major metropolitan areas into the United States Southeast and Southwest during the summer months and into the upper Midwest beginning in autumn. Analysis of the major social vulnerability predictors of COVID-19 infection and death found that counties with higher percentages of those not having a high school diploma, having non-White status and being Age 65 and over to be significant. Among the environmental variables, above ground level temperature had the strongest effect on relative risk to both cases and deaths. Hot and cold spots, areas of statistically significant high and low COVID-19 cases and deaths respectively, derived from the convolutional spatial effect show that areas with a high probability of above average relative risk have significantly higher Social Vulnerability Index composite scores. The same analysis utilizing the spatiotemporal interaction term exemplifies a more complex relationship between social vulnerability, environmental measurements, COVID-19 cases, and COVID-19 deaths.

spatial epidemiology↗
DOE OSTI2023

Juvenile life history diversity is associated with lifetime individual heterogeneity in a migratory fish

Abstract Differences in the life history pathways (LHPs) of juvenile animals are often associated with differences in demographic rates in later life stages. For migratory animals, different LHPs often result in animals from the same population occupying distinct habitats subjected to different environmental drivers. Understanding how demographic rates differ among animals expressing different LHPs may reveal fitness trade‐offs that drive the expression of alternative LHPs and enable better prediction of population dynamics in a changing environment. To understand how demographic outcomes and their relationships with environmental variables differ among animals with different LHPs, we analyzed a long‐term (2006–2021) mark–recapture dataset for Chinook salmon ( Oncorhynchus tshawytscha ) from the Wenatchee River, Washington, USA. Distinct LHPs represented in this population include either remaining in the natal stream until emigrating to the ocean as a 1‐year‐old (natal‐reach rearing) or emigrating from the natal stream and rearing in downstream habitats for several months before completing the emigration to the ocean as a 1‐year‐old (downstream rearing). We found that downstream‐rearing fish emigrated to the ocean 19 days earlier on average and returned as adults from the ocean at higher rates. We detected a positive correlation between rate of return from the ocean by downstream‐rearing fish and coastal upwelling in their spring of outmigration, whereas for natal‐reach‐rearing fish we detected a positive correlation with sea surface temperature during their first marine summer. Different responses to environmental variability should lead to asynchrony in adult abundance among juvenile LHPs. A higher proportion of downstream‐rearing fish returned at younger ages compared with natal‐reach‐rearing fish, which contributed to variability in age at reproduction and greater mixing across generations. Our results demonstrate how diversity in juvenile LHPs is associated with heterogeneity in demographic rates during subsequent life stages, which can in turn affect variance in aggregate population abundance and response to environmental change. Our findings underscore the importance of considering life history diversity in demographic analyses and provide insights into the effects of life history diversity on population dynamics and trade‐offs that contribute to the maintenance of life history diversity.

Sorel, Mark H.↗
DOE OSTI2026

Phosphorus sorption and its environmental predictors across pantropical forest soils sampled over the past decade

Tropical forest productivity is frequently constrained by soil phosphorus (P) availability, yet global Land Surface Model (LSM), which are used to simulate ecosystem processes, still represent P cycling in tropical regions only in a limited way, largely because of scarce observational data. Phosphorus adsorption and desorption of dissolved inorganic P to and from soil minerals (hereafter termed sorption), is an important process for predicting how much P is available to plants. This dataset was created to improve predictions of soil P sorption in tropical soils by identifying the isotherm equation that best describes pantropical soils. It includes raw measurements of environmental variables, such as soil properties and climate, together with P sorption data collected from 40 forest soil pits from 9 Forest Global Earth Observatory (ForestGEO) sites across 7 tropical countries during 2018-2022. The data are organized by site, country, and continent. Each site may include several soil pits. For each pit, P sorption was measured across a range of soil P concentrations to build sorption isotherm curves, typically with about 6 to 8 measurements per curve. While sorbed P varies across these concentration levels, the other environmental variables remain constant at the plot level.

Aluminum oxide↗
DOE OSTI2025

Multivariate environmental and trait-based controls of transpiration in the Central Amazon Rainforest

Tropical forest tree mortality is increasing due to more severe droughts, yet our understanding of how tree traits and life strategies are linked to drought stress has been limited by measurement scarcity. The BIONTE (BIOmass and NuTrient Experiment) near Manaus, Brazil hosts one of the world’s largest sap flow installations, with sensors in 90 canopy trees across a wood density gradient monitored since June 2022. The 2023 El Niño drought provided a unique opportunity to evaluate how water availability impacts tree transpiration. An interpretable machine learning framework was used to study the complex interactions between transpiration and multiple environmental variables such as soil water availability and vapor pressure deficit (VPD), and how these interactions vary with wood density and individual trees. We found varying responses of transpiration from different trees during the El Niño drought. Transpiration generally increased with temperature, with stronger effects in wetter areas and in trees with low to medium wood density. However, this response was modulated by stomatal sensitivity to VPD, which constrained transpiration under high atmospheric demand, particularly in intermediate-moisture area. The inflection in transpiration rate at high temperatures (>32°C) underscores the role of stomatal and hydraulic regulation in limiting water loss and protecting trees from excessive evaporative demand. Analysis of soil water contribution to transpiration revealed unimodal patterns in wetter area, with peak contributions near 0.45 cm 3 cm -3 of surface soil water and declining or flat responses beyond that threshold, suggesting a shift from water- to energy-limited transpiration. In contrast, drier areas exhibited limited transpiration sensitivity to soil water conditions and minimal trait-based variation in VPD responses, indicating supply-limited conditions. Despite higher wood density trees being generally more resilient, this study shows diverse tree drought resilience, prompting further investigation into the specific traits and dynamics between environmental variables in regulating transpiration and other physiological processes in trees.

Drought↗
DOE OSTI2024

A Semi‐Mechanistic Model for Partitioning Evapotranspiration Reveals Transpiration Dominates the Water Flux in Drylands

Abstract Popular evapotranspiration (ET) partitioning methods make assumptions that might not be well‐suited to dryland ecosystems, such as high sensitivity of plant water‐use efficiency (WUE) to vapor pressure deficit (VPD). Our objectives were to (a) create an ET partitioning model that can produce fine‐scale estimates of transpiration (T) in drylands, and (b) use this approach to evaluate how climate controls T and WUE across ecosystem types and timescales along a dryland aridity gradient. We developed a novel, semi‐mechanistic ET partitioning method using a Bayesian approach that constrains abiotic evaporation using process‐based models, and loosely constrains time‐varying WUE within an autoregressive framework. We used this method to estimate daily T and weekly WUE across seven dryland ecosystem types and found that T dominates ET across the aridity gradient. Then, we applied cross‐wavelet coherence analysis to evaluate the temporal coherence between focal response variables (WUE and T/ET) and environmental variables. At yearly scales, we found that WUE at less arid, higher elevation sites was primarily limited by atmospheric moisture demand, and WUE at more arid, lower elevation sites was primarily limited by moisture supply. At sub‐yearly timescales, WUE and VPD were sporadically correlated. Hence, ecosystem‐scale dryland WUE is not always sensitive to changes in VPD at short timescales, despite this being a common assumption in many ET partitioning models. This new ET partitioning method can be used in dryland ecosystems to better understand how climate influences physically and biologically driven water fluxes.

Environmental Sciences & Ecology↗
DOE OSTI2024

Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools

Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS2003

The Role of Design-of-Experiments in Managing Flow in Compact Air Vehicle Inlets

It is the purpose of this study to demonstrate the viability and economy of Design-of-Experiments methodologies to arrive at microscale secondary flow control array designs that maintain optimal inlet performance over a wide range of the mission variables and to explore how these statistical methods provide a better understanding of the management of flow in compact air vehicle inlets. These statistical design concepts were used to investigate the robustness properties of low unit strength micro-effector arrays. Low unit strength micro-effectors are micro-vanes set at very low angles-of-incidence with very long chord lengths. They were designed to influence the near wall inlet flow over an extended streamwise distance, and their advantage lies in low total pressure loss and high effectiveness in managing engine face distortion. The term robustness is used in this paper in the same sense as it is used in the industrial problem solving community. It refers to minimizing the effects of the hard-to-control factors that influence the development of a product or process. In Robustness Engineering, the effects of the hard-to-control factors are often called noise , and the hard-to-control factors themselves are referred to as the environmental variables or sometimes as the Taguchi noise variables. Hence Robust Optimization refers to minimizing the effects of the environmental or noise variables on the development (design) of a product or process. In the management of flow in compact inlets, the environmental or noise variables can be identified with the mission variables. Therefore this paper formulates a statistical design methodology that minimizes the impact of variations in the mission variables on inlet performance and demonstrates that these statistical design concepts can lead to simpler inlet flow management systems.

Anderson, Bernhard H.↗
DOE OSTI2021

The effects of food quantity, light, and temperature on clearance rates in freshwater bivalves (Cyrenidae and Unionidae)

Assessing the environmental variables that influence freshwater bivalve filtration activity is key to better understand the functioning of stream ecosystems. Here, the effects of light (0 ± 0 µmol m –2 s –1 and 19 ± 2 µmol m –2 s –1 ) and temperature (10, 15, 20 and 25°C) on the clearance rates of two bivalve species: the Asian clam Corbicula fluminea (Cyrenidae) and pocketbook Lampsilis ovata (Unionidae) were assessed under controlled laboratory conditions at two different concentrations of algal food (i.e., 10,000 cells ml –1 and 100,000 cells ml –1 ). Clearance rates for C. fluminea varied from 0 to 491 ml g –1 h –1 , while the observed range for L. ovata was 0 to 905 ml g –1 h –1 . We found that the relative contribution of the tested variables was species-dependent. While temperature plays a major role in the clearance rates of C. fluminea , food concentration was the most significant variable in the clearance rate of L. ovata . Our results confirm the complex interactions between abiotic and biotic factors in freshwater bivalve filtration activity. Overall, clearance rates are highly variable especially in C. fluminea and presumably regulated by other untested factors suggesting high plasticity in filtration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Meteoric 10Be Flux Calibration Data for the East River Watershed, Colorado, USA

This data package contains tabular and geospatial data used to quantify and model meteoric beryllium-10 fluxes in the East River watershed, Colorado, USA. The tabular component includes calibration-site data from five glacial moraine sites and includes environmental variables used to evaluate spatial controls on meteoric 10Be delivery, including elevation, mean annual precipitation (MAP), mean snow depth, and mean snow water equivalent (SWE). These site-level data were used to compare observed fluxes with environmental gradients across the watershed and to evaluate the effects of erosion correction on flux estimates. The package also includes supporting slope and curvature values used to assess topographic inputs to the erosion analysis. A second component of the data package contains updated manuscript tables and regression outputs used to summarize the relationships between meteoric 10Be flux and environmental predictors. These tables include meteoric 10Be sample information and AMS results, site-level environmental values, site-level meteoric 10Be inventory and flux values, watershed-averaged predicted fluxes, soil bulk density measurements, fine-fraction values, soil pH measurements, and regression statistics including slope, intercept, coefficient of determination, and p-value. The regression products include both standard linear regressions and regressions in which the intercept is constrained to pass through zero, and they support the analyses presented in the companion manuscript. Together, these tabular files provide the numerical basis for the manuscript tables and the regression-based interpretation of meteoric 10Be flux variability in a snow-dominated mountain watershed. The geospatial component of the package consists of GeoTIFF raster files used to generate the map products presented in Figures 2 and 6 of the companion manuscript. These rasters represent watershed-scale spatial layers for environmental variables and regression-based predictions of meteoric 10Be flux. This dataset contains comma-separated values files (.csv), Microsoft Excel files (.xlsx), GeoTIFF raster files (.tif), and upporting metadata files, including CSV data dictionaries and readme text files (.csv, .txt). The tabular files can be opened with standard spreadsheet software, and the raster files can be viewed and analyzed in GIS software such as ArcGIS Pro or QGIS. Together, these files document the numerical and spatial datasets used to calibrate and predict meteoric 10Be delivery in the East River watershed.

East River↗
DOE OSTI2021

Linking habitat suitability with a longleaf pine-hardwood model: Building a species-predictive fire-land management framework

Active management of fire-dependent ecosystems for specific species leads to complex tradeoffs, which affect conservation outcomes to other species. Therefore a multi-species evaluation of management actions is required. Habitat Suitability Models (HSMs) can help in predicting the likelihood of species occurrence using corresponding environmental variables and empirical relationships that link occurrence with specific environmental conditions. Incorporating multiple species into HSMs and relating them to habitat dynamics is crucial for ecosystems that require active management with prescribed fire. To address this issue, we developed multi-species HSM driven within an existing population model of the longleaf pine-hardwood ecosystem to assess the suitability of an ecosystem given different fire management strategies and environmental conditions. The population model used in this study provides spatial and temporal changes of longleaf pine-hardwood habitat structure in response to fire. These habitat values are used by the HSM to calculate habitat suitability for three threatened and endangered faunal species of this ecosystem, which all thrive with frequent fire, but have unique habitat requirements. Transient habitat conditions are traced to predict longleaf pine ecosystem trajectories under various management strategies, thereby evaluating current land management actions, such as thinning or prescribed fire frequencies. We tested a suite of environmental conditions to emphasize the sensitivity of the species to different fire management actions. The results of our modeling suggest that maximum suitable habitat for all three species can be achieved with fire frequency occurring at approximately once every three years. The modeling results support current management actions and provide a new habitat assessment tool that incorporates ecological factors for multiple species, thus providing for habitat optimization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Developing a simulator-based satellite dataset for using machine learning techniques to derive aerosol-cloud-precipitation interactions in models and observations in a consistent framework

Aerosol-cloud-precipitation interactions (ACPI) remain a major uncertainty in understanding the Earth’s radiation budget and water cycle (including extremes). After decades of active research, various observationally based metrics have been developed to constrain ACPI in Earth System Models (ESMs), but direct comparison of model and data estimates can confound scientific understanding because limitations and uncertainties in sampling and retrieval procedures may combine with model deficiencies in process representations of ACPI to obstruct understanding. Furthermore, conventional ACPI metrics often vary from one regime to another, and the ACPI process representation in ESMs is also typically derived based on only a limited area/regime (even though the parameterization applies globally). To bridge the gap between models and data and to correctly describe ACPI in all regimes, we propose to construct a new CALIPSO-CloudSat merged dataset that is produced by the same algorithms used in satellite simulators in ESMs, and to use machine learning techniques to derive new ACPI metrics that can be accurately estimated by satellites and can provide meaningful constraints on cloud microphysical process representations in ESMs. The dataset will include measured and retrieved variables for aerosol, cloud, and precipitation from CALIPSO and CloudSat, and environmental variables from meteorological reanalysis. The data will be used to train a neural network to construct the ACPI metrics as a function of environmental conditions. The new ACPI formula will be used to constrain the ACPI in the Energy Exascale Earth System Model (E3SM), and to augment/reformulate the ACPI process representation in the E3SM to improve the simulation of the evolution of the atmosphere under different environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impact of the built, social, and food environment on long‐term weight loss within a behavioral weight loss intervention

Abstract Background Behavioral weight loss interventions can lead to an average weight loss of 5%–10% of initial body weight, however there is wide individual variability in treatment response. Although built, social, and community food environments can have potential direct and indirect influences on body weight (through their influence on physical activity and energy intake), these environmental factors are rarely considered as predictors of variation in weight loss. Objective Evaluate the association between built, social, and community food environments and changes in weight, moderate‐to‐vigorous physical activity (MVPA), and dietary intake among adults who completed an 18‐month behavioral weight loss intervention. Methods Participants included 93 adults (mean ± SD; 41.5 ± 8.3 years, 34.4 ± 4.2 kg/m 2 , 82% female, 75% white). Environmental variables included urbanicity, walkability, crime, Neighborhood Deprivation Index (includes 13 social economic status factors), and density of convenience stores, grocery stores, and limited‐service restaurants at the tract level. Linear regressions examined associations between environment and changes in body weight, waist circumference (WC), MVPA (SenseWear device), and dietary intake (3‐day diet records) from baseline to 18 months. Results Grocery store density was inversely associated with change in weight ( β = −0.95; p = 0.02; R 2 = 0.062) and WC ( β = −1.23; p < 0.01; R 2 = 0.109). Participants living in tracts with lower walkability demonstrated lower baseline MVPA and greater increases in MVPA versus participants with higher walkability (interaction p = 0.03). Participants living in tracts with the most deprivation demonstrated greater increases in average daily steps ( β = 2048.27; p = 0.02; R 2 = 0.039) versus participants with the least deprivation. Limited‐service restaurant density was associated with change in % protein intake ( β = 0.39; p = 0.046; R 2 = 0.051). Conclusion Environmental factors accounted for some of the variability (<11%) in response to a behavioral weight loss intervention. Grocery store density was positively associated with weight loss at 18 months. Additional studies and/or pooled analyses, encompassing greater environmental variation, are required to further evaluate whether environment contributes to weight loss variability.

Tewahade, Selam↗
NASA NTRS2010

Reducing Tick-Borne Disease in Alabama: Linking Health Risk Perception with Spatial Analysis Using the NASA Earth Observing System

Lyme disease (LD) accounts for most vector-borne disease reports in the U.S., and although its existence in Alabama remains controversial, other tick-borne illnesses (TBI) such as Southern Tick-Associated Rash Illness (STARI) pose a health concern in the state. Phase One of the Marshall Space Flight Center-UAB DEVELOP study of TBI identified the presence of the chain of infection for LD (Ixodes scapularis ticks carrying Borrelia burgdorferi bacteria) and STARI (Amblyomma americanum ticks and an as-yet-unconfirmed agent) in Alabama. Both LD and STARI are associated with the development of erythema migrans rashes around an infected tick bite, and while treatable with oral antibiotics, a review of educational resources available to state residents revealed low levels of prevention information. To improve prevention, recognition, and treatment of TBI in Alabama, Phase Two builds a health communication campaign based on vector habitat mapping and risk perception assessment. NASA Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery identified likely tick habitats using remotely sensed measurements of vegetation vigor (Normalized Difference Vegetation Index) and soil moisture. Likely tick habitats, identified as those containing both high vegetation density and soil moisture, included Oak Mountain State Park, Bankhead National Forest, and Talladega National Forest. To target a high-risk group -- outdoor recreation program participants at Alabama universities -- the study developed a behavior survey instrument based on existing studies of LD risk factors and theoretical constructs from the Social Ecological Model and Health Belief Model. The survey instrument was amended to include geographic variables in the assessment of TBI knowledge, attitudes, and prevention behaviors, and the vector habitat model will be expanded to incorporate additional environmental variables and in situ data. Remotely sensed environmental data combined with risk perception assessments inform an ongoing outreach campaign consisting of stakeholder meetings and educational seminars.

Hemmings, S.↗
NASA NTRS1975

Use of remote sensing in agriculture

The remote sensing studies of (a) cultivated peanut areas in Southeastern Virginia; (b) studies at the Virginia Truck and Ornamentals Research Station near Painter, Virginia, the Eastern Virginia Research Station near Warsaw, Virginia, the Tidewater Research and Continuing Education Center near Suffolk, Virginia, and the Southern Piedmont Research and Continuing Education Center Blackstone, Virginia; and (c) land use classification studies at Virginia Beach, Virginia are presented. The practical feasibility of using false color infrared imagery to detect and determine the areal extent of peanut disease infestation of Cylindrocladium black rot and Sclerotinia blight is demonstrated. These diseases pose a severe hazard to this major agricultural food commodity. The value of remote sensing technology in terrain analyses and land use classification of diverse land areas is also investigated. Continued refinement of spectral signatures of major agronomic crops and documentation of pertinent environmental variables have provided a data base for the generation of an agricultural-environmental prediction model.

Pettry, D. E.↗
NASA NTRS1992

Prediction of space sickness in astronauts from preflight fluid, electrolyte, and cardiovascular variables and Weightless Environmental Training Facility (WETF) training

Nine preflight variables related to fluid, electrolyte, and cardiovascular status from 64 first-time Shuttle crewmembers were differentially weighted by discrimination analysis to predict the incidence and severity of each crewmember's space sickness as rated by NASA flight surgeons. The nine variables are serum uric acid, red cell count, environmental temperature at the launch site, serum phosphate, urine osmolality, serum thyroxine, sitting systolic blood pressure, calculated blood volume, and serum chloride. Using two methods of cross-validation on the original samples (jackknife and a stratefied random subsample), these variables enable the prediction of space sickness incidence (NONE or SICK) with 80 percent sickness and space severity (NONE, MILD, MODERATE, of SEVERE) with 59 percent success by one method of cross-validation and 67 percent by another method. Addition of a tenth variable, hours spent in the Weightlessness Environment Training Facility (WETF) did not improve the prediction of space sickness incidences but did improve the prediction of space sickness severity to 66 percent success by the first method of cross-validation of original samples and to 71 percent by the second method. Results to date suggest the presence of predisposing physiologic factors to space sickness that implicate fluid shift etiology. The data also suggest that prior exposure to fluid shift during WETF training may produce some circulatory pre-adaption to fluid shifts in weightlessness that results in a reduction of space sickness severity.

Simanonok, K.↗
NASA NTRS1979

A method for correlating performance data of a terrestrial solar cell array

An analytical method was proposed for characterizing array power output, in the region of maximum power, as a function of environmental variables. The correlation provided a way of evaluating the output of an array under environmental conditions that differ from those encountered during testing. Power data obtained at one location was used to predict array performance at other locations.

Simon, F. F.↗