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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Ionospheric electron content prediction from SERIES/GPS data

Total electron contents along arbitrary lines-of-sight are derived from dual band SERIES P-code data. At present, in a normal observing session, five Global Positioning Satellites (GPS) can be observed for a time interval of 5 h. A SERIES observing session consists of about 300 line-of-sight measurements. Transmitter offsets are different for each satellite and no reliable a priori data can be obtained at present. The electron content and the satellite offsets are estimated simultaneously by a least squares procedure using a simple empirical model. The present accuracy of this technique is estimated to be 6 x 10 to the 16th el/sq m. The results are compared with Faraday rotation measurements. The method may be used for local or global ionospheric content monitoring.

Lanyi, Gabor↗
NASA NTRS1986

Supercomputer implementation of finite element algorithms for high speed compressible flows

Prediction of compressible flow phenomena using the finite element method is of recent origin and considerable interest. Two shock capturing finite element formulations for high speed compressible flows are described. A Taylor-Galerkin formulation uses a Taylor series expansion in time coupled with a Galerkin weighted residual statement. The Taylor-Galerkin algorithms use explicit artificial dissipation, and the performance of three dissipation models are compared. A Petrov-Galerkin algorithm has as its basis the concepts of streamline upwinding. Vectorization strategies are developed to implement the finite element formulations on the NASA Langley VPS-32. The vectorization scheme results in finite element programs that use vectors of length of the order of the number of nodes or elements. The use of the vectorization procedure speeds up processing rates by over two orders of magnitude. The Taylor-Galerkin and Petrov-Galerkin algorithms are evaluated for 2D inviscid flows on criteria such as solution accuracy, shock resolution, computational speed and storage requirements. The convergence rates for both algorithms are enhanced by local time-stepping schemes. Extension of the vectorization procedure for predicting 2D viscous and 3D inviscid flows are demonstrated. Conclusions are drawn regarding the applicability of the finite element procedures for realistic problems that require hundreds of thousands of nodes.

Thornton, E. A.↗
DOE OSTI2023

Time-dependent SOLPS-ITER simulations of the tokamak plasma boundary for model predictive control using SINDy *

Abstract Time-dependent SOLPS-ITER simulations have been used to identify reduced models with the sparse identification of nonlinear dynamics (SINDy) method and develop model-predictive control of the boundary plasma state using main ion gas puff actuation. A series of gas actuation sequences are input into SOLPS-ITER to produce a dynamic response in upstream and divertor plasma quantities. The SINDy method is applied to identify reduced linear and nonlinear models for the electron density at the outboard midplane n e , s e p O M P and the electron temperature at the outer divertor T e , s e p d i v . Note that T e , s e p d i v is not necessarily the peak value of T e along the divertor. The identified reduced models are interpretable by construction (i.e. not black box), and have the form of coupled ordinary differential equations. Despite significant noise in T e , s e p d i v , the reduced models can be used to predict the response over a range of actuation levels to a maximum deviation of 0.5% in n e , s e p O M P and 5%–10% in T e , s e p d i v for the cases considered. Model retraining using time history data triggered by a preset error threshold is also demonstrated. A model predictive control strategy for nonlinear models is developed and used to perform feedback control of a SOLPS-ITER simulation to produce a setpoint trajectory in n e , s e p O M P using the integrated plasma simulator framework. The developed techniques are general and can be applied to time-dependent data from other boundary simulations or experimental data. Ongoing work is extending the approach to model identification and control for divertor detachment, which will present transient nonlinear behavior from impurity seeding, including realistic latency and synthetic diagnostic signals derived from the full SOLPS-ITER output.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data

Growing vaccine hesitancy is contributing to the decline in immunization rates for highly contagious, vaccine-preventable childhood diseases. Therefore, there has been a significant interest in understanding how hesitancy is spreading at higher spatio-temporal resolutions, enabling more targeted interventions. Motivated by this, we study the problem of prediction of vaccine hesitancy at the ZIP Code level, referred to as the VaxHesitancy problem. A significant challenge for this problem is the lack of high-resolution data that indicates hesitancy. Here, we develop a hybrid VaxHesSTL framework that combines a Graph Neural Network (GNN) and a Recurrent Neural Network (RNN) to address the VaxHesitancy problem. The GNN uses a ZIP Code-level network to capture spatial signals from neighboring areas, while the RNN models the temporal dynamics present in the data. We train and evaluate VaxHesSTL using a large dataset, namely the All-Payer Claims Databases (APCD), for Virginia, consisting of insurance claims from over five million individuals for six years. We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. Experiments demonstrate that VaxHesSTL outperforms a range of state-of-the-art baselines, which rely solely on historical time series data without accounting for spatial relationships. Since hesitancy data at higher spatial resolution is often unavailable or hard to get, we incorporate an active learning approach with our VaxHesSTL framework to optimize the training set without compromising the prediction performance. We find that hesitancy data for only 18% of ZIP Codes selected by active learning allows us to forecast hesitancy for all the ZIP Codes in the Virginia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗
DOE OSTI2023

PVade (PV Aerodynamic Design Engineering) [SWR-23-49]

PV Aerodynamic Design Engineering (PVade) is an open-source fluid-structure interaction (FSI) solver which can accurately simulate wind loads and aerodynamic stability in solar-tracking PV arrays. PVade’s pressure profiles and inertial load time series can be used as inputs into mechanical module models to study degradation mechanisms including cracking of cells, weathering of cracked cells, and glass breakage. This software enables PV plant owners to predict optimal high-wind stow strategies under a variety of weather and site conditions.

Young, Ethan↗
NASA NTRS1986

Rotor design for maneuver performance

A method of determining the sensitivity of helicopter maneuver performance to changes in basic rotor design parameters is developed. Maneuver performance is measured by the time required, based on a simplified rotor/helicopter performance model, to perform a series of specified maneuvers. This method identifies parameter values which result in minimum time quickly because of the inherent simplicity of the rotor performance model used. For the specific case studied, this method predicts that the minimum time required is obtained with a low disk loading and a relatively high rotor solidity. The method was developed as part of the winning design effort for the American Helicopter Society student design competition for 1984/1985.

Berry, John D.↗
NASA NTRS1990

Predicting performance of parallel computations

An accurate and computationally efficient method for predicting the performance of a class of parallel computations running on concurrent systems is described. A parallel computation is modeled as a task system with precedence relationships expressed as a series-parallel directed acyclic graph. Resources in a concurrent system are modeled as service centers in a queuing network model. Using these two models as inputs, the method outputs predictions of expected execution time of the parallel computation and the concurrent system utilization. The method is validated against both detailed simulation and actual execution on a commercial multiprocessor. Using 100 test cases, the average error of the prediction when compared to simulation statistics is 1.7 percent, with a standard deviation of 1.5 percent; the maximum error is about 10 percent.

Mak, Victor W.↗
NASA NTRS2000

The Dynamic Planner: The Sequencer, Scheduler, and Runway Allocator for Air Traffic Control Automation

The Dynamic Planner (DP) has been designed, implemented, and integrated into the Center-TRACON Automation System (CTAS) to assist Traffic Management Coordinators (TMCs), in real time, with the task of planning and scheduling arrival traffic approximately 35 to 200 nautical miles from the destination airport. The TMC may input to the DP a series of current and future scheduling constraints that reflect the operation and environmental conditions of the airspace. Under these constraints, the DP uses flight plans, track updates, and Estimated Time of Arrival (ETA) predictions to calculate optimal runway assignments and arrival schedules that help ensure an orderly, efficient, and conflict-free flow of traffic into the terminal area. These runway assignments and schedules can be shown directly to controllers or they can be used by other CTAS tools to generate advisories to the controllers. Additionally, the TMC and controllers may override the decisions made by the DP for tactical considerations. The DP will adapt to computations to accommodate these manual inputs.

Wong, Gregory L.↗
NASA NTRS2021

Geomagnetic Secular Variation Forecast using the NASA GEMS Ensemble Kalman filter: A Candidate SV Model for IGRF‑13

We have produced a 5-year mean secular variation (SV ) of the geomagnetic field for the period 2020–2025. We use the NASA Geomagnetic Ensemble Modeling System (GEMS), which consists of the NASA Goddard geodynamo model and ensemble Kalman filter (EnKF) with 400 ensemble members. Geomagnetic field models are used as observations for the assimilation, including gufm1 (1590–1960), CM4 (1961–2000) and CM6 (2001–2019). The forecast involves a bias correction scheme that assumes that the model bias changes on timescales much longer than the forecast period, so that they can be removed by successive forecast series. The algorithm was validated on the time period 2010-2015 by comparing with CM6 before being applied to the 2020–2025 time period. This forecast has been sub-mitted as a candidate predictive model of IGRF-13 for the period 2020–2025.

Andrew Tangborn↗
NASA NTRSDate not supplied

Isotopic Tracers for CO 2 Produced During A Planetary Impact Into Limestone Target Rocks

Terrestrial meteorite impacts have been directly linked with multiple mass extinction events throughout Earth’s history. Among these impacts, those that land in sedimentary target rocks are thought to generate large quantities of CO 2 via decarbonation. This injection of CO 2 into the atmosphere has the potential to alter the climate and threaten terrestrial habitability. The magnitude of this change depends upon the net amount of CO 2 released, which is controlled by how much CO 2 is produced by the impact, how much CO 2 is removed by back-reactions after the impact, and other post-impact CO 2 sinks. To interrogate the behavior of CO 2 release into the impact atmosphere, we present carbon (δ 13 C), oxygen (δ 18 O), and clumped (Δ 47 ) isotope results from carbonate clasts preserved within the impact breccia of the Steen River Impact Structure (SRIS) in Alberta, Canada. These clasts exhibit a ~65‰ range in δ 13 C and a ~5‰ variation in δ 18 O. However, while δ 13 C and δ 18 O are positively correlated, Δ 47 unexpectedly has a negative relationship with δ 13 C and δ 18 O. Based on prior assumptions, there would either be (1) no relationship with carbonate Δ 47 and the bulk ratios because Δ 47 would be reset at extreme impact temperatures, or (2) Δ 47 would have a positive correlation with the bulk ratios, reflecting gradual Δ 47 ‘resetting.’ To reconcile the SRIS result with these expectations, we conducted a series of in vacuo heating experiments at temperatures above calcite decomposition. As predicted by Rayleigh fractionation, these heating experiments generated depletions in δ 13 C and δ 18 O that increased with reaction time. Mimicking the SRIS results, these experiments also produced concomitant increases in Δ 47 . Using an adaptation of a mechanistic model for decomposition fractionation (Hayles & Killingsworth, 2022) we hypothesize that these exotic isotope trends are caused by Rayleigh fractionation and a Δ 47 kinetic isotope effect that results from the disproportionation of O to CaO and CO 2 during thermal decomposition. This high Δ 47 CO 2 subsequently exchanges with the residual CaCO 3 . This work highlights a potential pathway for identifying and quantifying CO 2 generation by impacts and builds on the relatively limited literature characterizing the behavior of carbonate clumped isotopes at very high geologic temperatures.

Multi-isotope systems↗
NASA NTRS2023

Development of Solar Energetic Particle Prediction Portal (SEP3)

Robust prediction of Solar Energetic Particle (SEP) events is among the key priorities of the space weather community. In the framework of NASA’s Early Stage Innovation Program, we develop the Solar Energetic Particle Prediction Portal (SEP3: https://sun.njit.edu/SEP3), which hosts web applications that allow the users to retrieve the database records. In particular, SEP3 lists the API examples to query each data source potentially important for the SEP prediction. The Portal has a search page for browsing the events from the most widely used catalogs (https://sun.njit.edu/SEP3/search.php) and a dedicated space to share the most recent achievements of the team. In addition, we have added a CDAW SEP catalog and a LASCO/SOHO CME catalog and introduced the possibility of displaying the properties of the connected events (parental solar flares and CMEs for SEPs) on the search page. The interactive widget has the capability to display GOES soft X-ray and proton flux time series from different satellites with the GOES flare records on top of them. The data portal has been used to evaluate the forecasts of the solar proton events based on the statistical properties of the GOES soft X-ray and proton fluxes and investigate machine-learning approaches to the SEP prediction.

SMD↗
DOE OSTI2025

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation

Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method achieves comparable reconstruction accuracy for interpolation tasks, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability. These results suggest that while the model offers a computationally efficient alternative for datasets with temporal stability, its current formulation is best suited for interpolation rather than for predicting future states in time-evolving systems. This study thus lays the groundwork for further refinement of neural network-based approaches to low-rank matrix factorization in high-dimensional plasma simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Single electron in a storage ring: a probe into the fundamental properties of synchrotron radiation and a powerful diagnostic tool

An experimental study of undulator radiation generated by a single electron was carried out at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab. Photons were detected with a single-photon avalanche diode (SPAD) at an average rate of 20 kHz, corresponding to one detection every few hundred revolutions. Events were continuously recorded by a picosecond event timer for as long as 5 minutes at a time. The collected data were used to directly test the predictions of classical Poisson photostatistics. The study was motivated in part by a previous observation of sub-Poissonian statistics in a similar experiment with synchrotron radiation. In addition, we show that the time series of recorded events can be used to study the synchrotron motion of a single electron, yielding dynamical parameters such as the phase jitter of the radiofrequency cavity and the dependence of the synchrotron period on amplitude. Research plans for further experiments are also discussed.

47 OTHER INSTRUMENTATION↗
NASA NTRS1973

Evaluation of reusable surface insulation for space shuttle over a range of heat-transfer rate and surface temperature

Reusable surface insulation materials, which were developed as heat shields for the space shuttle, were tested over a range of conditions including heat-transfer rates between 160 and 620 kW/sq m. The lowest of these heating rates was in a range predicted for the space shuttle during reentry, and the highest was more than twice the predicted entry heating on shuttle areas where reusable surface insulation would be used. Individual specimens were tested repeatedly at increasingly severe conditions to determine the maximum heating rate and temperature capability. A silica-base material experienced only minimal degradation during repeated tests which included conditions twice as severe as predicted shuttle entry and withstood cumulative exposures three times longer than the best mullite material. Mullite-base materials cracked and experienced incipient melting at conditions within the range predicted for shuttle entry. Neither silica nor mullite materials consistently survived the test series with unbroken waterproof surfaces. Surface temperatures for a silica and a mullite material followed a trend expected for noncatalytic surfaces, whereas surface temperatures for a second mullite material appeared to follow a trend expected for a catalytic surface.

Chapman, A. J.↗
NASA NTRS1979

Correlation of asperity contact-time fraction with elastohydrodynamic film thickness in a 20-millimeter-bore ball bearing

Elastohydrodynamic film thicknesses and asperity contact-time fractions were measured for a 20 mm bore ball bearing by using the capacitance and conductance techniques. The bearing was thrust loaded to 90, 445, and 778 N. The corresponding maximum stresses on the inner race were 1.28, 2.09, and 2.45 GPa. The test speed ranged from 400 to 15,000 rpm. The test bearing was mist lubricated with a MIL-L-23699A turbine oil. The temperature was 27 C. The experimental results were correlated to give the percent film (no-contact-time fraction) as a function of measured film thickness. Measurements were made for three test series that represented the test bearing in various conditions of run-in. The measurements show that the percent film changes with bearing run-in time. The experimental results agreed well with theoretical predictions based on surface trace analysis for a new bearing. For the run-in state, they agreed with previously reported experimental results. The results show that asperity contact existed at a film thickness-roughness ratio lambda of 6.0 or less for a new bearing. After run-in, no asperity contact occurred at a lambda of 3.5 or greater.

Coy, J. J.↗
NASA NTRS1996

Nonlinear dynamics of the magnetosphere and space weather

The solar wind-magnetosphere system exhibits coherence on the global scale and such behavior can arise from nonlinearity on the dynamics. The observational time series data were used together with phase space reconstruction techniques to analyze the magnetospheric dynamics. Analysis of the solar wind, auroral electrojet and Dst indices showed low dimensionality of the dynamics and accurate prediction can be made with an input/output model. The predictability of the magnetosphere in spite of the apparent complexity arises from its dynamical synchronism with the solar wind. The electrodynamic coupling between different regions of the magnetosphere yields its coherent, low dimensional behavior. The data from multiple satellites and ground stations can be used to develop a spatio-temporal model that identifies the coupling between different regions. These nonlinear dynamical models provide space weather forecasting capabilities.

Sharma, A. Surjalal↗