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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 793 records · Page 44

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NASA NTRS1993

ROSAT observations of pulsed soft X-ray emission from PSR 1055-52

Utilizing the position-sensitive proportional counter and the high-resolution imager aboard the orbiting X-ray observatory ROSAT, we have detected pulsations at the radio period from the pulsar PSR 1055-52. The pulse shapes are energy-dependent and show a transition at about 0.5 keV where the phase angle of the pulse peak changes by about -120 deg and the pulsed fraction increases from 11 percent to 63 percent toward larger energies. Simple spectral models are found to be unsatisfactory, while multicomponent models, such as a soft blackbody and hard power-law tail, yield better fits to the pulse-height data. The hard power-law tail is consistent with the extension of the recently reported EGRET results and may indicate a common emission mechanism for the X-ray through GeV gamma-ray regime. The soft blackbody component with T(infinity) = (7.5 +/- 0.6) x 10 exp 5 K, if interpreted as the initial cooling of a neutron star, is consistent with standard cooling models and does not require the presence of exotic components.

Oegelman, Hakki↗

NASA NTRS2021

VESIcal Part I: An open-source thermodynamic model engine for mixed volatile (H2O-CO2) solubility in silicate melts

Thermodynamics has been fundamental to the interpretation of geologic data and modeling of geologic systems for decades. However, more recent advancements in computational capabilities and a marked increase in researchers’ accessibility to computing tools has outpaced the functionality and extensibility of currently available modeling tools. Here we present VESIcal (Volatile Equilibria and Saturation Identification calculator): the first comprehensive modeling tool for H 2 O, CO 2 , and mixed (H 2 O-CO 2 ) solubility in silicate melts that: a) allows users access to seven commonly used models, plus easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an excel spreadsheet for simple post-modeling analysis; e) integrates advanced plotting capabilities directly within the tool; and f) provides all of these within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python. The tool is presented within this manuscript, which is a Jupyter notebook containing worked examples accessible to python users with a range of skill levels. The basic functions of VESIcal can also be access via a web app (https://vesical.anvil.app). The VESIcal python library is open-source and available for download at https://github.com/kaylai/VESIcal.

K. Iacovino↗

DOE OSTI2020

Compression behavior of microcrystalline cellulose spheres: Single particle compression and confined bulk compression across regimes

Particle characteristics can drastically influence the process-structure-property-performance aspects of granular materials in compression. In this study, we aim to computationally simulate the mechanical processes of stress redistribution in compacts including the kinematics of particle rearrangement during densification and particle deformation leading to fragmentation. Confined compression experiments are conducted with three sets of commercial microcrystalline cellulose particles nearly spherical in shape with different mean particle size. Experimentally measured compression curves from tall powder columns are fitted with the Kenkre et al. (J. of American Chemical Society, Vol. 79, No. 12) model. This model provides a basis to derive several common two-parameter literature models and as a framework to incorporate statistical representations of critical particle behaviors. We focus on the low-stress compression data and the model comparisons typically not discussed in the literature. Furthermore, additional single particle compressions report fracture strength with particle size for comparison to the apparent particle strength extracted from bulk compression data.

42 ENGINEERING↗

DOE OSTI2023

AmeriFlux BASE data pipeline to support network growth and data sharing

Abstract AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Crack nucleation at forging flaws studied by non-local peridynamics simulations

We present a computational study and framework that allows us to study and understand the crack nucleation process from forging flaws. Forging flaws may be present in large steel rotor components commonly used for rotating power generation equipment including gas turbines, electrical generators, and steam turbines. The service life of these components is often limited by crack nucleation and subsequent growth from such forging flaws, which frequently exhibit themselves as non-metallic oxide inclusions. The fatigue crack growth process can be described by established engineering fracture mechanics methods. However, the initial crack nucleation process from a forging flaw is challenging for traditional engineering methods to quantify as it depends on the details of the flaw, including flaw morphology. We adopt the peridynamics method to describe and study this crack nucleation process. For a specific industrial gas turbine rotor steel, we present how we integrate and fit commonly known base material property data such as elastic properties, yield strength, and S-N curves, as well as fatigue crack growth data into a peridynamic model. The obtained model is then utilized in a series of high-performance two-dimensional peridynamic simulations to study the crack nucleation process from forging flaws for ambient and elevated temperatures in a rectangular simulation cell specimen. The simulations reveal an initial local nucleation at multiple small oxide inclusions followed by micro-crack propagation, arrest, coalescence, and eventual emergence of a dominant micro-crack that governs the crack nucleation process. The dependence on temperature and density of oxide inclusions of both the details of the microscopic processes and cycles to crack nucleation is also observed. The results are compared with fatigue experiments performed with specimens containing forging flaws of the same rotor steel.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Facial Named Entity Recognition by Attention-Based Graph Convolutional Neural Network

In the realm of facial recognition and analysis, the ability to accurately cluster large datasets of facial images stands as a cornerstone for various applications, ranging from security surveillance to user biometric identification. This project evolves a novel approach to facial data clustering by embedding facial images into a high-dimensional vector space using an advanced embedding model trained on separate data and assumes a graph-like structure on the high-dimensional vectors. We find our method works significantly better than common shallow methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective: We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods: We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results: We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions: The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1986

Plasma interactions and surface/material effects

A discussion on plasma interactions and surface/material effects is summarized. The key issues in this area were: (1) the lack of data on the material properties of common spacecraft surface materials; (2) lack of understanding of the contamination and decontamination processes; and (3) insufficient analytical tools to model synergistic phenomena related to plasma interactions. Without an adequate database of material properties, accurate system performance predictions cannot be made. The interdisciplinary nature of the surface-plasma interactions area makes it difficult to plan and maintain a coherent theoretical and experimental program. The shuttle glow phenomenon is an excellent example of an unanticipated, complex interaction involving synergism between surface and plasma effects. Building an adequate technology base for understanding and predicting surface-plasma interactions will require the coordinated efforts of engineers, chemists, and physicists. An interdisciplinary R and D program should be organized to deal with similar problems that the space systems of the 21st century may encounter.

Mandel, M.↗

NASA NTRS1990

Study of the crust and mantle using magnetic surveys by Magsat and other satellites

This article summarizes some of the methodology and results for studies of the earth's mantle and crust. Mantle conductivity studies can be made either by studying signals impressed on the earth from outside, e.g., the ionosphere or magnetosphere, or by studing signals originating in the core and transmitted through the mantle. Crustal field studies begin with a careful selection of the data and subsequent removal of core and external fields by some sort of filtering. Average maps from different local times sometimes differ, presumably due to the remaining presence of fields of external origin. Several techniques for further filtering are discussed. Where large-area aeromagnetic maps are available, crustal maps derived from satellite data can be compared with upward continued data. In general, the comparisons show agreement, with some differences, particularly in and near the auroral belts. The satellite data are further reduced by various methods of inverse and forward modeling, sometimes including reduction to the pole. These techniques are generally unstable at the equator. Common methods of stabilizing the inversions include principle components analysis and ridge regression. Because of the presence of the core field, the entire crustal contribution from the field is not known. Also, there is a basic nonuniqueness to the inverse solutions. Nevertheless, magnetizations that are interpretable can be derived.

Langel, Robert A.↗

NASA NTRS1995

Evaluation of phase-diversity techniques for solar-image restoration

Phase-diversity techniques provide a novel observational method for overcomming the effects of turbulence and instrument-induced aberrations in ground-based astronomy. Two implementations of phase-diversity techniques that differ with regard to noise model, estimator, optimization algorithm, method of regularization, and treatment of edge effects are described. Reconstructions of solar granulation derived by applying these two implementations to common data sets are shown to yield nearly identical images. For both implementations, reconstructions from phase-diverse speckle data (involving multiple realizations of turbulence) are shown to be superior to those derived from conventional phase-diversity data (involving a single realization). Phase-diverse speckle reconstructions are shown to achieve near diffraction-limited resolution and are validated by internal and external consistency tests, including a comparison with a reconstruction using a well-accepted speckle-imaging method.

Paxman, Richard G.↗

NASA NTRS2002

The Software Element of the NASA Portable Electronic Device Radiated Emissions Investigation

NASA Langley Research Center's (LaRC) High Intensity Radiated Fields Laboratory (HIRF Lab) recently conducted a series of electromagnetic radiated emissions tests under a cooperative agreement with Delta Airlines and an interagency agreement with the FAA. The frequency spectrum environment at a commercial airport was measured on location. The environment survey provides a comprehensive picture of the complex nature of the electromagnetic environment present in those areas outside the aircraft. In addition, radiated emissions tests were conducted on portable electronic devices (PEDs) that may be brought onboard aircraft. These tests were performed in both semi-anechoic and reverberation chambers located in the HIRF Lab. The PEDs included cell phones, laptop computers, electronic toys, and family radio systems. The data generated during the tests are intended to support the research on the effect of radiated emissions from wireless devices on aircraft systems. Both tests systems relied on customized control and data reduction software to provide test and instrument control, data acquisition, a user interface, real time data reduction, and data analysis. The software executed on PC's running MS Windows 98 and 2000, and used Agilent Pro Visual Engineering Environment (VEE) development software, Common Object Model (COM) technology, and MS Excel.

Koppen, Sandra V.↗

NASA NTRS2008

Joint Bayesian Component Separation and CMB Power Spectrum Estimation

We describe and implement an exact, flexible, and computationally efficient algorithm for joint component separation and CMB power spectrum estimation, building on a Gibbs sampling framework. Two essential new features are (1) conditional sampling of foreground spectral parameters and (2) joint sampling of all amplitude-type degrees of freedom (e.g., CMB, foreground pixel amplitudes, and global template amplitudes) given spectral parameters. Given a parametric model of the foreground signals, we estimate efficiently and accurately the exact joint foreground- CMB posterior distribution and, therefore, all marginal distributions such as the CMB power spectrum or foreground spectral index posteriors. The main limitation of the current implementation is the requirement of identical beam responses at all frequencies, which restricts the analysis to the lowest resolution of a given experiment. We outline a future generalization to multiresolution observations. To verify the method, we analyze simple models and compare the results to analytical predictions. We then analyze a realistic simulation with properties similar to the 3 yr WMAP data, downgraded to a common resolution of 3 deg FWHM. The results from the actual 3 yr WMAP temperature analysis are presented in a companion Letter.

numerical↗

NASA NTRS2012

Application of the Marsupial Paradigm to Tropical Cyclone Formation from Northwestward-Propagating Disturbances

A wave-tracking algorithm is developed for northwestward-propagating waves that, on occasion, play a role in tropical cyclogenesis over the western oceans. To obtain the Lagrangian flow structure, the frame of reference is translated obliquely at the same propagation speed with the precursor disturbance. Trajectory analysis suggests that streamlines in the obliquely translated frame of reference can be used to approximate flow trajectories. The algorithm was applied to Super Typhoon Nakri (2008), Tropical Cyclone Erika (2009), and a few other examples. Diagnoses of meteorological analyses and satellite-derived moisture and precipitation fields show that the marsupial framework for tropical cyclogenesis in tropical easterly waves is relevant also for northwestward-propagating disturbances as are commonly observed in the tropical western Atlantic, the Gulf of Mexico, and the western North Pacific. Finally, it is suggested that analysis of the global model data and satellite observations in the marsupial framework can provide useful guidance on early tropical cyclone advisories.

Wang, Zhuo↗

NASA NTRSDate not supplied

CFD Simulations of Landing and Takeoff CRM High-Lift Configurations

Numerical simulations have been performed for a conventional high-lift version of the Common Research Model (CRM) model corresponding to landing and take- off configurations. Computed values of lift and drag for the landing configuration are compared with the experimental data acquired in the 14- by 22-Foot Subsonic Tunnel (14×22) at the NASA Langley Research Center (LaRC). Simulations replicated the experimentally observed improvements in the lift characteristics in the presence of a nacelle chine. Flow visualization images indicate that vortices generated by the nacelle chine reduce flow separation regions on the upper surface of the wing at higher angles of attack. Based on such observations, the take-off configuration considered is also equipped with a nacelle chine, and testing of this configuration is planned in the 14×22 tunnel in near future. Preliminary solutions are also presented to explore the feasibility of using a localized flap gap blowing (LFGB) active flow control concept for improving the aerodynamic performance at take-off conditions.

aerodynamics↗

NASA NTRSDate not supplied

CFD Simulations of Landing and Takeoff CRM High-Lift Configurations

aerodynamics↗

NASA NTRSDate not supplied

DEVELOP: Building Capacity in Early Career Individuals to Apply NASA Earth Observations in Health and Air Quality

The NASA DEVELOP National Program builds capacity to use and apply NASA Earth observations to address environmental concerns around the globe. The DEVELOP model builds capacity in both participants (students, recent graduates, and early and transitioning career professionals), who conduct the projects, and partners (decision and policy makers), who are recipients of project methodologies and results. While projects focus on a spectrum of thematic topics, health and air quality related topics made up more than a quarter of DEVELOP’s FY2023 project portfolio. These projects worked in collaboration with over 30 partner organizations throughout the US and internationally to explore how Earth observations could support decision making in areas such as health and air quality, wildland fires, climate, urban development, and transportation and infrastructure. This presentation provides an overview of the DEVELOP model of building capacity to use Earth observation data, environmental decision-making needs identified in health and air quality relevant projects, DEVELOP project case studies, commonly utilized data sources, and lessons learned. Key takeaways include best practices for project development and execution, how to balance learning with delivering impactful results for partners, and water resource relevant decisions guided by project end products.

Remote sensing↗

NASA NTRS2017

Comparison of Satellite Rainfall Estimates and Rain Gauge Measurements in Italy, and Impact on Landslide Modeling

Landslides can be triggered by intense or prolonged rainfall. Rain gauge measurements are commonly used to predict landslides even if satellite rainfall estimates are available. Recent research focuses on the comparison of satellite estimates and gauge measurements. The rain gauge data from the Italian network (collected in the system database Verifica Rischio Frana" (VRF)) are compared with the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) products. For the purpose, we couple point gauge and satellite rainfall estimates at individual grid cells, evaluating the correlation between gauge and satellite data in different morpho-climatological conditions. We then analyze the statistical distributions of both rainfall data types and the rainfall events derived from them. Results show that satellite data underestimates ground data, with the largest differences in mountainous areas. Power-law models, are more appropriate to correlate gauge and satellite data. The gauge and satellite-based products exhibit different statistical distributions and the rainfall events derived from them differ. In conclusion, satellite rainfall cannot be directly compared with ground data, requiring local investigation to account for specific morpho-climatological settings. Results suggest that satellite data can be used for forecasting landslides, only performing a local scaling between satellite and ground data.

landslide; satellite rainfall; TRMM; rain gauges; ↗