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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Central-place foraging poses variable constraints year-round in a neotropical migrant

Background: “Central-place foragers” are constrained in their habitat selection and foraging range by the frequency with which they need to return to a central place. For example, chick-rearing songbirds that must feed their offspring hourly might be expected to have smaller foraging ranges compared to non-breeding songbirds that return nightly to a roost. Methods: We used GPS units to compare the foraging behaviour of an aerial insectivorous bird, the purple martin (Progne subis), during the breeding season in three regions across North America, as well as the non-breeding season in South America. Specifically, we tested foraging range size and habitat selection. Results: Foraging range did not vary among regions during breeding (14.0 ± 39.2 km 2 ) and was larger during the nonbreeding period (8840 ± 8150 km 2 ). Purple martins strongly preferred aquatic habitats to other available habitats year-round and in the Amazon commuted from night roosts in low productivity sediment-poor water, where risk of predation was probably low, to daytime foraging sites in productive sediment-rich water sites. Conclusions: We provide the first estimates for foraging range size in purple martins and demonstrate foraging preference for aquatic habitats throughout two stages of the annual cycle. Understanding foraging constraints and habitat of aerial insectivores may help plan conservation actions throughout their annual cycle. Future research should quantify foraging behaviour during the post-breeding period and during migration.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

Simulation of Crack Propagation in Engine Rotating Components under Variable Amplitude Loading

The crack propagation life of tested specimens has been repeatedly shown to strongly depend on the loading history. Overloads and extended stress holds at temperature can either retard or accelerate the crack growth rate. Therefore, to accurately predict the crack propagation life of an actual component, it is essential to approximate the true loading history. In military rotorcraft engine applications, the loading profile (stress amplitudes, temperature, and number of excursions) can vary significantly depending on the type of mission flown. To accurately assess the durability of a fleet of engines, the crack propagation life distribution of a specific component should account for the variability in the missions performed (proportion of missions flown and sequence). In this report, analytical and experimental studies are described that calibrate/validate the crack propagation prediction capability ]or a disk alloy under variable amplitude loading. A crack closure based model was adopted to analytically predict the load interaction effects. Furthermore, a methodology has been developed to realistically simulate the actual mission mix loading on a fleet of engines over their lifetime. A sequence of missions is randomly selected and the number of repeats of each mission in the sequence is determined assuming a Poisson distributed random variable with a given mean occurrence rate. Multiple realizations of random mission histories are generated in this manner and are used to produce stress, temperature, and time points for fracture mechanics calculations. The result is a cumulative distribution of crack propagation lives for a given, life limiting, component location. This information can be used to determine a safe retirement life or inspection interval for the given location.

Bonacuse, P. J.↗
NASA NTRS1999

Simulation of Crack Propagation in Engine Rotating Components Under Variable Amplitude Loading

The crack propagation life of tested specimens has been repeatedly shown to strongly depend on the loading history. Overloads and extended stress holds at temperature can either retard or accelerate the crack growth rate. Therefore, to accurately predict the crack propagation life of an actual component, it is essential to approximate the true loading history. In military rotorcraft engine applications, the loading profile (stress amplitudes, temperature, and number of excursions) can vary significantly depending on the type of mission flown. To accurately assess the durability of a fleet of engines, the crack propagation life distribution of a specific component should account for the variability in the missions performed (proportion of missions flown and sequence). In this report, analytical and experimental studies are described that calibrate/validate the crack propagation prediction capability for a disk alloy under variable amplitude loading. A crack closure based model was adopted to analytically predict the load interaction effects. Furthermore, a methodology has been developed to realistically simulate the actual mission mix loading on a fleet of engines over their lifetime. A sequence of missions is randomly selected and the number of repeats of each mission in the sequence is determined assuming a Poisson distributed random variable with a given mean occurrence rate. Multiple realizations of random mission histories are generated in this manner and are used to produce stress, temperature, and time points for fracture mechanics calculations. The result is a cumulative distribution of crack propagation lives for a given, life limiting, component location. This information can be used to determine a safe retirement life or inspection interval for the given location.

P J Bonacuse↗
NASA NTRS2012

Conceptual Problems in Land Surface Data Assimilation

A land data assimilation system (LDAS) merges observations (or satellite retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, several conceptual problems can interfere with realizing the potential improvements from data assimilation. Of particular concern is the frequent mismatch between the assimilated observations and the land surface model variables of interest. The seminar will discuss recent research with the ensemble-based NASA GEOS-S LDAS to address various aspects of this mismatch. These aspects include (i) the assimilation of coarse-scale observations into higher-resolution land surface models, (ii) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (iii) the forward modeling of microwave brightness temperatures over land for radiance-based land surface data aSSimilation, and (iv) the selection of the most relevant types of observations for the analysis of a specific water cycle variable (such as root zone soil moisture). At its core, the solution to the above challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Reichle, Rolf↗
DOE OSTI2022

ML Pipeline (Machine Learning Pipeline) [SWR-22-35]

The Machine Learning Pipeline (ML Pipeline) allows the user to fit a model to predict a dependent variable, y, based on a feature set, X. ML Pipeline gives the ability to automatically generate features with its Feature Engineering function, automatically select the most important features with its Feature Selection function, fit the model with its Fit function, and evaluate the model with its Evaluate function. ML Pipeline's functionality can not only help the user predict values for the desired variable it also gives the user a better understanding of which features are important and how effective the model is. ML Pipeline accomplishes all this, while remaining simple to use and interpret.

Schiek, Andrew↗
NASA NTRS1985

Results of Analysis on the Design Variable Cells

Sealed Nickel-Cadmium aerospace cells play a very important role in the mission of the satellite. There has been a constant effort to improve the cycle life, energy density, and reliability of these cells by many manufacturers and users. The Design Variable Cell Program is one systematic approach started by NASA in collaboration with General Electric (GE) towards that goal. Nine important designs were selected for evaluation. Fifty-two nickel-cadmium cells each of 12 Ah nominal capacity manufactured by GE were sent to the Navel weapons Support Center for evaluation of different design variables with which these cells were built.

Vasanth, K. L.↗
NASA NTRS1984

Interdisciplinary research in global biogeochemical cycling Nitrous oxide in terrestrial ecosystems

NASA has begun an interdisciplinary research program to investigate various aspects of Global Biology and Global Habitability. An important element selected for the study of global phenomena is related to biogeochemical cycling. The studies involve a collaboration with recognized scientists in the areas of plant physiology, microbiology, nutrient cycling theory, and related areas. Selected subjects of study include nitrogen cycling dynamics in terrestrial ecosystems with special attention to biosphere/atmosphere interactions, and an identification of sensitive response variables which can be used in ecosystem models based on parameters derived from remotely sensed variables. A description is provided of the progress and findings over the past two years. Attention is given to the characteristics of nitrous oxide emissions, the approach followed in the investigations, the selection of study sites, radiometric measurements, and research in Sequoia.

Norman, S. D.↗
NASA NTRS2013

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Land data assimilation↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Investigating Mountain Watershed Headwater‐To‐Groundwater Connections, Water Sources, and Storage Selection Behavior With Dynamic‐Flux Particle Tracking

Abstract Climate change will impact mountain watershed streamflow both directly—with changing precipitation amounts and variability—and indirectly—through temperature shifts altering snowpack, melt, and evapotranspiration. To understand how these complex processes will affect ecosystem functioning and water resources, we need tools to distinguish connections between water sources (rain/snowmelt), groundwater storage, and exit fluxes (streamflow/evapotranspiration), and to determine how these connections change seasonally and as climate shifts. Here, we develop novel watershed‐scale approaches to understand water source, storage, and exit flux connections using a dynamic‐flux particle tracking model (EcoSLIM) applied in California's Cosumnes Watershed, which connects the Sierra Nevada and Central Valley. This work develops new visualizations and applications to provide mechanistic understanding that underpins the interpretation of isotopic field data at watershed scales to distinguish sources, flow paths, residence times, and storage selection. In our simulations, streamflow comes primarily from snow‐derived water while evapotranspiration generally comes from rain. Most streamflow starts above 1,000 m while evapotranspiration is sourced relatively evenly across the watershed and is generally younger than streamflow. Modeled streamflow consists primarily of water sourced from precipitation in the previous 5 years but before the current water year, while ET consists primarily of water from precipitation in the current water year. ET, and to a lesser extent streamflow, are both younger than water in groundwater storage. However, snowmelt‐derived streamflow preferentially discharges older water from snow‐derived storage. Dynamic‐flux particle tracking and new approaches presented here enable novel model‐tracer comparisons in large‐scale watersheds to better understand watershed behavior in a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ZTF SN Ia DR2: Improved SN Ia colors through expanded dimensionality with SALT3+

Context. Type Ia supernovae (SNe Ia) are a key probe in modern cosmology, as they can be used to measure luminosity distances at gigaparsec scales. Models of their light curves are used to project heterogeneous observed data onto a common basis for analysis. Aims. The SALT model currently used for SN Ia cosmology describes SNe as having two sources of variability, accounted for by a color parameter c , and a “stretch” parameter x 1 . We extend the model to include an additional parameter we label x 2 , to investigate the cosmological impact of currently unaddressed light-curve variability. Methods. We constructed a new SALT model, that we dub “SALT3+”. This model was trained by an improved version of the SALTshaker code, using training data combining a selection of the second data release of cosmological SNe Ia from the Zwicky Transient Facility and the existing SALT3 training compilation. Results. We find additional, coherent variability in supernova light curves beyond SALT3. Most of this variation can be described as phase-dependent variation in g − r and r − i color curves, correlated with a boost in the height of the secondary maximum in i -band. These behaviors correlate with spectral differences, particularly in line velocity. We find that fits with the existing SALT3 model tend to address this excess variation with the color parameter, leading to less informative measurements of supernova color. We find that neglecting the new parameter in light-curve fits leads to a trend in Hubble residuals with x 2 of 0.039 ± 0.005 mag, representing a potential systematic uncertainty. However, we find no evidence of a bias in current cosmological measurements. Conclusions. We conclude that extended SN Ia light-curve models promise mild improvement in the accuracy of color measurements, and corresponding cosmological precision. However, models with more parameters are unlikely to substantially affect current cosmological results.

Kenworthy, W. D. (ORCID:0000000251535983)↗
DOE OSTI2025

Thermal stress in degraded forests in the Brazilian Amazon Arc of Deforestation

Understanding thermal stress in tropical forests has taken on new urgency in light of accelerating climate change and expansion of deforestation and forest degradation. Degraded tropical forests in particular may be approaching critical temperature thresholds even more rapidly than intact forests, with implications for tree survival and ecosystem recovery. We investigate thermal stress in degraded tropical forests within the Brazilian Amazon Arc of Deforestation. Using land surface temperature data from the ECOsystem Spaceborne Thermal Radiometer Experiment on the international Space Station (ECOSTRESS), we compared canopy temperatures of intact, selectively logged, and burned forests in Feliz Natal, Mato Grosso, Brazil. Upper canopy temperatures in previously burned forests were 4.1% higher (mean = 36.5 °C) and 50.9% more variable compared to intact and logged forests, which showed remarkably similar temperature distributions (means of 34.9 °C and 35.1 °C, respectively). Modeled leaf temperature distributions based on canopy temperature measurements from one of the warmest days in a 2 year dry season record indicated that 87% of leaves in the warmest burned forest patches exceeded the temperature threshold where respiration surpasses photosynthesis, compared to approximately 72%–74% in intact and logged forests, respectively, in the same time period. After controlling for environmental factors, burned forests were predicted to be 2.6 °C warmer on average [1.39 °C–3.96 °C, 95% credible interval] than intact forests across a 5–40 m canopy height range. Burned forests showed modest thermal recovery over time, with temperatures decreasing by approximately 1.2 °C over a 30 year recovery period. In contrast, logged forests showed minimal thermal differences from intact forests (−0.353 on average, [−1.125–0.274, 95% credible interval]) and negligible change (0.15 °C) over the same timeframe. While the absolute probabilities of exceeding damaging thermal thresholds remain low across all forest types under current climate conditions, the probability of leaves exceeding temperatures that cause permanent leaf damage was ten times higher in burned forests, with implications for the future of burned forest regeneration in water-limited regions of the Amazon basin. In particular, the combination of higher mean temperatures, greater temperature variability, and more frequent exposure to damaging thermal thresholds implies that burned tropical forests will experience substantially higher mortality rates and slower biomass recovery compared to intact and selectively logged forests, especially in water-limited regions where trees cannot rely on evaporative cooling to moderate canopy temperatures.

Brazilian Amazon Arc of Deforestation↗
NASA NTRS2017

Sensitivity of CONUS Summer Rainfall to the Selection of Cumulus Parameterization Schemes in NU-WRF Seasonal Simulations

This study investigates the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall fields were simulated at 24-km resolution using the NASA-Unified Weather Research and Forecasting (NU-WRF) Model for June-August 2000. Four cumulus parameterization schemes and two options for shallow cumulus components in a specific scheme were tested. The spread in the domain-mean rainfall rates across the parameterization schemes was generally consistent between the entire CONUS and most subregions. The selection of the shallow cumulus component in a specific scheme had more impact than that of the four cumulus parameterization schemes. Regional variability in the performance of each scheme was assessed by calculating optimally weighted ensembles that minimize full root-mean-square errors against reference datasets. The spatial pattern of the seasonally averaged rainfall was insensitive to the selection of cumulus parameterization over mountainous regions because of the topographical pattern constraint, so that the simulation errors were mostly attributed to the overall bias there. In contrast, the spatial patterns over the Great Plains regions as well as the temporal variation over most parts of the CONUS were relatively sensitive to cumulus parameterization selection. Overall, adopting a single simulation result was preferable to generating a better ensemble for the seasonally averaged daily rainfall simulation, as long as their overall biases had the same positive or negative sign. However, an ensemble of multiple simulation results was more effective in reducing errors in the case of also considering temporal variation.

Iguchi, Takamichi↗
DOE OSTI2025

Computationally Selected Multivalent HIV-1 Subtype C Vaccine Protects Against Heterologous SHIV Challenge

Background: The RV144 trial in Thailand is the only HIV-1 vaccine efficacy trial to date to demonstrate any efficacy. Genetic signatures suggested that antibodies targeting the variable loop 2 (V2) of the HIV-1 envelope played an important protective role. The ALVAC prime and protein boost follow-up trial in southern Africa (HVTN702) failed to show any efficacy. One hypothesis for this is the greater diversity of subtype C viruses in southern Africa relative to CRF01_AE in Thailand. Methods: Here, we determined whether an ALVAC prime with computationally selected gp120 boost immunogens maximizing coverage of diversity of subtype C viruses in the variable V1 and V2 regions (V1V2) improved the protection of non-human primates (NHPs) from a heterologous subtype C SHIV challenge compared to more traditional regimens. Results: An ALVAC prime with Trivalent subtype C gp120 boosts resulted in statistically significant protection from repeated intrarectal SHIV challenges compared to the control. Evaluation of the immunogenicity of each vaccine regimen at the time of challenge demonstrated that different gp120 combination boosts elicited similar high magnitudes of gp120 and breadth of V1V2-binding antibodies, as well as strong Fc-mediated immune responses. Low-to-no neutralization of the challenge virus was detected. A Cox proportional hazard analysis of five pre-selected immune parameters at the time of challenge identified ADCC against the challenge envelope as a correlate of protection. Systems serology analysis revealed that immune responses elicited by the different vaccine regimens were distinct and identified further correlates of resistance to infection. Conclusions: Computationally designed vaccines with maximized subtype C V1V2 coverage mediated protection of NHPs from a heterologous Tier-2 subtype C SHIV challenge.

Immunology↗
DOE OSTI2024

Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models

Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Snyder, Abigail C.↗
NASA NTRS2017

Multispectral Snapshot Imagers Onboard Small Satellite Formations for Multi-Angular Remote Sensing

Multispectral snapshot imagers are capable of producing 2D spatial images with a single exposure at selected, numerous wavelengths using the same camera, therefore operate differently from push broom or whiskbroom imagers. They are payloads of choice in multi-angular, multi-spectral imaging missions that use small satellites flying in controlled formation, to retrieve Earth science measurements dependent on the targets Bidirectional Reflectance-Distribution Function (BRDF). Narrow fields of view are needed to capture images with moderate spatial resolution. This paper quantifies the dependencies of the imagers optical system, spectral elements and camera on the requirements of the formation mission and their impact on performance metrics such as spectral range, swath and signal to noise ratio (SNR). All variables and metrics have been generated from a comprehensive, payload design tool. The baseline optical parameters selected (diameter 7 cm, focal length 10.5 cm, pixel size 20 micron, field of view 1.15 deg) and snapshot imaging technologies are available. The spectral components shortlisted were waveguide spectrometers, acousto-optic tunable filters (AOTF), electronically actuated Fabry-Perot interferometers, and integral field spectrographs. Qualitative evaluation favored AOTFs because of their low weight, small size, and flight heritage. Quantitative analysis showed that waveguide spectrometers perform better in terms of achievable swath (10-90 km) and SNR (greater than 20) for 86 wavebands, but the data volume generated will need very high bandwidth communication to downlink. AOTFs meet the external data volume caps well as the minimum spectral (wavebands) and radiometric (SNR) requirements, therefore are found to be currently feasible in spite of lower swath and SNR.

Nag, Sreeja↗