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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Wastewater reuse benefits for municipal complete retention lagoons: Life cycle assessment and dynamic modeling

Complete retention lagoons with wastewater reuse for agricultural purposes may offer sustainability advantages over alternative systems for small communities in semiarid regions. This study quantifies the environmental life cycle impact of adopting agriculture water reuse systems using case study data to estimate operating and building infrastructure impacts and spatial–temporal modeling to quantify resource trade-offs. Water reuse system benefits are highly dependent on supply–storage–demand dynamics. The relative size of irrigated agricultural land to the lagoon size was the most significant factor influencing site water application rates. The benefits are sensitive to changes in air emissions occurring from the agricultural land and further emphasize the importance of proper fertilizer management when adopting water reuse systems. Wastewater reuse from complete retention lagoons reduce life cycle GHG emissions, primarily through excavation reductions, offset fertilizer use, and especially from increased crop yields from wastewater reuse at previously rainfed sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Comparative Study of Physics‐Informed and Data‐Driven Neural Networks for Compound Flood Simulation at River‐Ocean Interfaces: A Case Study of Hurricane Irene

Simulating compound flooding (CF) at the river-ocean interface within large-scale Earth System Models (ESMs) presents significant challenges due to complex interactions between river discharge, storm surge, and tides. This study assesses the comparative advantages of physics-informed and data-driven machine learning (ML) approaches for enhancing local ESM performance. We systematically compare data-driven neural network models (i.e., CNNs, U-Net, Long Short-Term Memory (LSTM), Gated Recurrent Unit), and physics-informed neural network (PINN) models, including vanilla PINN and a finite-difference-based PINN (FD-PINN). Specifically, FD-PINN is introduced to enhance computational efficiency, accelerating vanilla PINNs by ∼6.5 times while improving accuracy. To enhance data-driven model training, a new data-generation approach is developed to sample historical fluvial and coastal flood events, which ensures a robust data set for extreme event prediction. The models are evaluated using a realistic one-dimensional river domain extracted from an ESM's river mesh and the Hurricane Irene event as an independent test case. Results show that FD-PINN achieves accurate predictions with significantly reduced computational costs relative to vanilla PINNs. Among data-driven models, the best overall performance is achieved by a CNN-LSTM hybrid, which balances accuracy and efficiency. While a fully connected CNN (CNN-FC) provides the best accuracy, it incurs high computational cost. Architectures lacking strong temporal modeling tend to underperform on unseen events. These findings highlight the importance of sequence-aware designs for robust generalization. This study reveals the trade-offs between physics-informed and data-driven models and proposes an adaptive hybrid framework for integrating ML into ESMs to enhance local flood simulations.

Earth Systems Modeling↗
NASA NTRS1972

Predicted trapped particle radiation levels for Tiros-N

Vehicle encountered electron and proton fluxes were calculated for a set of nominal TIROS-N trajectories with new computational methods and new electron environment models. Temporal variations in the electron data were considered and partially accounted for. Estimates of energetic solar proton fluxes are given for the lifetime of the satellite at selected integral energies from 10 to 100 MeV. Field strength calculations were performed with an extrapolated model on the basis of linear secular variation predictions. Orbital flux integration results are presented in graphical and tabular form.

Stassinopoulos, E. G.↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
NASA NTRS1992

Real-time antenna fault diagnosis experiments at DSS 13

Experimental results obtained when a previously described fault diagnosis system was run online in real time at the 34-m beam waveguide antenna at Deep Space Station (DSS) 13 are described. Experimental conditions and the quality of results are described. A neural network model and a maximum-likelihood Gaussian classifier are compared with and without a Markov component to model temporal context. At the rate of a state update every 6.4 seconds, over a period of roughly 1 hour, the neural-Markov system had zero errors (incorrect state estimates) while monitoring both faulty and normal operations. The overall results indicate that the neural-Markov combination is the most accurate model and has significant practical potential.

Mellstrom, 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↗
DOE OSTI2022

Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps

Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Lower Mekong Hydrological Decision Support system

The Lower Mekong Hydrological Decision Support system (LMHDSs) is a environmental data analysis tool developed at the NASA Goddard Space Flight Center with funding from the SERVIR Applied Sciences Team and technical support from SERVIR Science Coordination Office (SCO). The web application allows stakeholders and decision-makers to view and download the inputs and outputs to the Soil and Water Assessment Tool(SWAT) model temporally and spatially. The front end is developed using JavaScript libraries like OpenLayers and Stock charts and the backend uses Django, a Python-based web framework. The web app provides several features, including visualizing map products, time-series plots, land-use/land-cover and associated soil information, and a data cart for downloading data. In addition, LMHDSs incorporates the NASAaccess software package, which provides seamless access to various climate and weather data products from NASA’s Earth observations portfolio. The application is region agnostic (any valid SWAT model can be used), modular (different components of the applications can be customized), and open (anyone can download and run it on their end). The web app is currently in use by the Mekong River Commission (MRC), a treaty-based regional intergovernmental organization that is made up of Mekong countries, as part of its hydrological decision support.

Hydrology↗
NASA NTRS1981

Saturn's magnetic tail - Structure and dynamics

Voyager 1 magnetic field observations have provided evidence of a Saturnian magnetic tail. Tail current system distributions are inferred through comparison of the observations with a realistic magnetotail current system model. Temporal variations observed in the tail were probably produced by solar wind variations.

Behannon, K. W.↗
NASA NTRS1985

Monitoring real-time navigation processes using the automated reasoning tool (ART)

An expert system is described for monitoring and controlling navigation processes in real-time. The ART-based system features data-driven computation, accommodation of synchronous and asynchronous data, temporal modeling for individual time intervals and chains of time intervals, and hypothetical reasoning capabilities that consider alternative interpretations of the state of navigation processes. The concept is illustrated in terms of the NAVEX system for monitoring and controlling the high speed ground navigation console for Mission Control at Johnson Space Center. The reasoning processes are outlined, including techniques used to consider alternative data interpretations. Installation of the system has permitted using a single operator, instead of three, to monitor the ascent and entry phases of a Shuttle mission.

Maletz, M. C.↗
NASA NTRS1990

Stochastic estimation of tropospheric path delays in global positioning system geodetic measurements

Water vapor radiometric and surface meteorological measurements taken during three GPS geodetic experiments are used to calculate process noise levels for random walk and first-order Gauss-Markov temporal models of tropospheric path delays. Entire wet and combined wet and dry zenith delays at each network site are estimated simultaneously with the geodetic parameters without prior calibration. The path delays and corresponding baseline estimates are compared to those obtained with calibrated data and stochastic residual delays. In this manner, the marginal utility of a priori tropospheric calibration is assessed given the ability to estimate the path delays directly using only the GPS data. Estimation of total zenith path delays with appropriate random walk or Gauss-Markov models yields baseline repeatabilities of a few parts in 100 million. Results suggest that GPS data alone have sufficient strength to resolve centimeter-level zenith path delay fluctuations over periods of a few minutes.

Tralli, David M.↗
NASA NTRS1991

N-Decane Droplet Combustion in the NASA-Lewis 5 Second Zero-Gravity Facility - Results in Test Gas Environments Other than Air

The burning rate of single droplets of n-decane in a microgravity environment of the NASA-Lewis 5 Second Zero-Gravity Facility was investigated as a function of time, together with the flame diameter/droplet diameter ratio, for a wide range of test environments other than normal air conditions, using an engineering model of the flight experiment. Oxygen mole fractions were varied from 18 to 50 percent, the total test chamber pressure was varied from 0.5 to 2 atmospheres, and the initial droplet diameter was varied from 0.98 to 2.41 mm. Measurements showed that the average burning rates for n-decane droplets exhibited the same qualitative trends as are found in two current models. Temporal analysis of the local burning rates showed variable rates of change in local burning as the droplet combustion progressed. The causes and implications of these findings are discussed.

Haggard, John B.↗
NASA NTRS1992

Direct numerical simulation of laminar breakdown in high-speed, axisymmetric boundary layers

Temporal direct numerical simulation of laminar breakdown via subharmonic secondary instability in high-speed axisymmetric boundary layers has been accomplished using a highly accurate, fully explicit algorithm which combines spectral collocation and high-order compact-difference techniques. Numerical test cases confirm that subharmonic secondary instability is confirmed to be a viable path to transition in high-speed boundary-layer flow. Secondary instability is shown to account for peaks in the Reynolds stresses at or near the critical layer which are not possible from the second-mode primary instability alone. Reynolds stresses spatially reconstructed from the temporal model via the Gaster transformation show a 'spreading angle' of about 12 deg, in qualitative agreement with experimental findings. The rate of broadening of the Reynolds stress peak is a strongly nonlinear phenomenon which cannot be reproduced by secondary instability theory.

Pruett, C. D.↗
NASA NTRS1993

WNN 92; Proceedings of the 3rd Workshop on Neural Networks: Academic/Industrial/NASA/Defense, Auburn Univ., AL, Feb. 10-12, 1992 and South Shore Harbour, TX, Nov. 4-6, 1992

The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.

Padgett, Mary L.↗
NASA NTRS1993

Spatial simulation of secondary instability in plane channel flow - Comparison of K- and H-type disturbances

This study involves a numerical simulation of spatially evolving secondary instability in plane channel flow. The computational algorithm integrates the time-dependent, 3D, incompressible Navier-Stokes equations by a mixed finite-difference/spectral technique. In particular, we are interested in the differences between instabilities instigated by Klebanoff (K-) type and Herbert (H-) type inflow conditions, and in comparing the present spatial results with previous temporal models. It is found that for the present inflow conditions, H-type instability is biased towards one of the channel walls, while K-type instability evolves on both walls. For low initial perturbation amplitudes, H-type instability exhibits higher growth rates than K-type instability, while higher initial amplitudes lead to comparable growth rates of both H- and K-type instability. In H-type instability, spectral analysis reveals the presence of the subharmonic 2D mode which promotes the growth of the 3D spanwise and fundamental modes through nonlinear interactions. An intermodal energy transfer study demonstrates that there is a net energy transfer from the 3D modes to the 2D mode. This analysis also indicates that the mean mode transfers net energy to the 2D subharmonic mode and to the 3D modes.

Saiki, E. M.↗
NASA NTRS1995

Dynamic sensitivity analysis of frictional contact/impact response of axisymmetric composite structures

A computational procedure is presented for evaluating the sensitivity coefficients of the dynamic frictional contact/impact response of axisymmetric composite structures. The structures are assumed to consist of an arbitrary number of perfectly bonded homogeneous anisotropic layers. The material of each layer is assumed to be hyperelastic, and the effect of geometric nonlinearity is included. The sensitivity coefficients measure the sensitivity of the response to variations in different material, lamination and geometric parameters of the structure. A displacement finite element model is used for the discretization. The normal contact conditions are incorporated into the formulation by using a perturbed Lagrangian approach with the fundamental unknowns consisting of the nodal displacements, and the Lagrange multipliers associated with the contact conditions. The Lagrange multipliers are allowed to be discontinuous at interelement boundaries. Tangential contact conditions are incorporated by using a penalty method in conjunction with the classical Coulomb's friction model. Temporal integration is performed by using Newmark method. The Newton-Raphson iterative scheme is used for the solution of the resulting nonlinear algebraic equations, and for the determination of the contact region, contact conditions (sliding or sticking), and the contact pressures. The sensitivity coefficients are evaluated by using a direct differentiation approach. Numerical results are presented from the frictional contact/impact response of a composite spherical cap impacting on a rigid plate.

Karaoglan, Levent↗
NASA NTRS2006

An Unusual Precursor Burst with Oscillations from SAX J1808.4-3658

We report the finding of an unusual, weak precursor to a thermonuclear X-ray burst from the accreting millisecond pulsar SAX 51808.4-3658. The burst in question was observed on Oct. 19, 2002 with the Rossi X-Ray Timing Explorer (RXTE) proportional counter array (PCA). The precursor began approx. equal to 1 s prior to the onset of a strong radius expansion burst, lasted for about 0.4 s, and exhibited strong oscillations at the 401 Hz spin frequency. Oscillations are not detected in the approx. equal to 0.5 s interval between the precursor and the main burst. The estimated peak photon flux and energy fluence of the precursor are about 1/25, and 1/500 that of the main burst, respectively. From joint spectral and temporal modeling, we find that an expanding burning region with a relatively low temperature on the spinning neutron star surface can explain the oscillations, as well as the faintness of the precursor with respect to the main part of the burst. We discuss some of the implications of our findings for the ignition and spreading of thermonuclear flames on neutron stars.

Bhattacharyya, Sudip↗