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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Temporal prepulse contrast degradation in high-intensity CPA lasers from anisotropy of amplifier gain media

Here, we present a study of the temporal pre-pulse contrast degradation of high focused intensity pulses produced in CPA laser systems due to imperfections in amplifier design, alignment of amplifier components, and crystal inhomogeneity. Using an extended cross-polarized imaging technique, we demonstrate the presence of multiple crystal domains inside Ti:sapphire slabs with ≈10 cm diameter. Furthermore, the results of our numerical calculations show that crystalline c-axis orientation inhomogeneity caused by these crystal domains can lead to generation of pre-pulses with relative contrast >10 -10 within several picoseconds before the main pulse. In a multiple-slab amplifier head configuration sometimes used in high repetition rate systems, the misalignment of the amplifier slabs crystalline c-axes with respect to each other can lead to the generation of pre-pulses with relative contrast as high as 10 -6 , depending on the magnitude of misalignment.

47 OTHER INSTRUMENTATION↗
NASA NTRS1982

A Temporal Approach to Monitor Surface Mine Reclamation Progress via LANDSAT

Using LANDSAT satellite imagery, the mine reclamation process can be studied on a temporal and continuing basis. Not only can the progress of reclamation be readily monitored, but also a breakdown in the mining reclamation process can be detected. In viewing reclamation, it is important to monitor the mined site well past initial revegation stages. With present mining law and bonding procedures, fast revegetational growth is encouraged, often leading to poor soil fertilizing and inappropriate stabilizing species. As a result, the initial reclamation may exhibit good qualities for one or two years but then may experience vegetational deterioration after the state has relinquished the mining company from it's responsibility. It is this small-scale breakdown in the reclamation process that was detected using an unsupervised classification technique with eight-year temporal LANDSAT imagery coverage.

Davis, A. L.↗
DOE OSTI2024

Privacy-Preserving Real-Time Action Detection in Intelligent Vehicles Using Federated Learning-Based Temporal Recurrent Network

This study introduces a privacy-preserving approach for the real-time action detection in intelligent vehicles using a federated learning (FL)-based temporal recurrent network (TRN). This approach enables edge devices to independently train models, enhancing data privacy and scalability by eliminating central data consolidation. Our FL-based TRN effectively captures temporal dependencies, anticipating future actions with high precision. Extensive testing on the Honda HDD and TVSeries datasets demonstrated robust performance in centralized and decentralized settings, with competitive mean average precision (mAP) scores. The experimental results highlighted that our FL-based TRN achieved an mAP of 40.0% in decentralized settings, closely matching the 40.1% in centralized configurations. Notably, the model excelled in detecting complex driving maneuvers, with mAPs of 80.7% for intersection passing and 78.1% for right turns. These outcomes affirm the model’s accuracy in action localization and identification. The system showed significant scalability and adaptability, maintaining robust performance across increased client device counts. The integration of a temporal decoder enabled predictions of future actions up to 2 s ahead, enhancing the responsiveness. Our research advances intelligent vehicle technology, promoting safety and efficiency while maintaining strict privacy standards.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

The traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Raman Spectroscopy

Raman spectroscopy is one of the mostly utilized optical spectroscopic tools for revealing both the catalyst structure and surface chemistry in heterogeneous catalysis. It has recently seen increasing role in catalysis research, thanks to the development of new Raman instrumentations, reactors, and combination with other techniques, leading to in situ and operando studies with significant temporal and spatial resolutions. This chapter aimed to provide a general overview of the applications of Raman spectroscopy in heterogeneous catalysis. It starts with an introduction to the fundamentals of Raman scattering including theory and pros and cons for catalysis research; followed by a description of the typical setup of a Raman system and recent advances in Raman instrumentations; then a chronology of the applications of Raman spectroscopy for ex situ, in situ, and operando studies of catalysis; elucidations of the advances in improving the temporal and spatial resolution of Raman spectroscopy of catalysis; Raman application case studies related to catalyst synthesis, treatments, and function under reaction conditions; illustrations of the power of multimodal approach including Raman spectroscopy in catalysis research; and ended with a brief summary and a future outlook.

Braatz, Jisue↗
DOE OSTI2023

Raman Spectroscopy

Raman spectroscopy is one of the mostly utilized optical spectroscopic tools for revealing both the catalyst structure and surface chemistry in heterogeneous catalysis. It has recently seen increasing role in catalysis research, thanks to the development of new Raman instrumentations, reactors, and combination with other techniques, leading to in situ and operando studies with significant temporal and spatial resolutions. This chapter aimed to provide a general overview of the applications of Raman spectroscopy in heterogeneous catalysis. It starts with an introduction to the fundamentals of Raman scattering including theory and pros and cons for catalysis research; followed by a description of the typical setup of a Raman system and recent advances in Raman instrumentations; then a chronology of the applications of Raman spectroscopy for ex situ, in situ, and operando studies of catalysis; elucidations of the advances in improving the temporal and spatial resolution of Raman spectroscopy of catalysis; Raman application case studies related to catalyst synthesis, treatments, and function under reaction conditions; illustrations of the power of multimodal approach including Raman spectroscopy in catalysis research; and ended with a brief summary and a future outlook.

Braatz, Jisue↗
DOE OSTI2025

Machine Learned Empirical Numerical Integrator from Simulated Data

Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Comparative study of energetic heliospheric electrons at wind and geotail

The current fleet of both heliospheric and magnetospheric spacecraft provides a unique opportunity of studying spatio-temporal plasma phenomena. Among the numerous topics that can be addressed by such a fleet, figures the 3D study of energetic (greater than 20 keV) solar electron) solar electron events. WIND and GEOTAIL are particulary interesting for such analysis: more than two years of data; up to 3/4 continuous days spent by GEOTAIL in the solar wind; and comparable experiments. During solar electron events, rapid electron flux changes can be associated with these structures. Sometimes they are seen at both spacecraft with a time difference corresponding to the convection time, but sometimes they are observed at one spacecraft but not at the other. Several preliminary conclusions can be drawn from such events: (1) there is direct evidence that, within distances lower than 1.5 x 10(exp 6) km at 1 A (approximately 7000 km at the sun), there are significant spatial variations of (a) the magnetic field line connections to the source region and probably of (b) the particle propagation features; (2) magnetic field lines inside and outside particle propagation structures can keep distinct access to the source region for more than one hour; (3) flux waves, of probable local origin, were observed; and (4) within one hour, spatial flux discrepancies, separated by magnetic structures, may remain or disappear.

Chaizy, P. A.↗
NASA NTRS1986

Time dependent wind fields

Two tasks were performed: (1) determination of the accuracy of Seasat scatterometer, altimeter, and scanning multichannel microwave radiometer measurements of wind speed; and (2) application of Seasat altimeter measurements of sea level to study the spatial and temporal variability of geostrophic flow in the Antarctic Circumpolar Current. The results of the first task have identified systematic errors in wind speeds estimated by all three satellite sensors. However, in all cases the errors are correctable and corrected wind speeds agree between the three sensors to better than 1 ms sup -1 in 96-day 2 deg. latitude by 6 deg. longitude averages. The second task has resulted in development of a new technique for using altimeter sea level measurements to study the temporal variability of large scale sea level variations. Application of the technique to the Antarctic Circumpolar Current yielded new information about the ocean circulation in this region of the ocean that is poorly sampled by conventional ship-based measurements.

Chelton, D. B.↗
NASA NTRS2010

Large-Scale Aspects and Temporal Evolution of Pulsating Aurora

Pulsating aurora is a common phenomenon generally believed to occur mainly in the aftermath of a, substorm, where dim long-period pulsating patches appear. The study determines the temporal and spatial evolution of pulsating events using two THEN IIIS ASI stations, at Gillam (66.18 mlat, 332.78 mlon, magnetic midnight at 0634 UT) and Fort Smith, (67.38 mlat, 306.64 mlon, magnetic midnight at, 0806 UT) along roughly the same invariant latitude. Parameters have been calculated from a database of 74 pulsating aurora events from 119 days of good optical data within the period from September 2007 through March 2008 as identified with the Gillam camera. It is shown that the source region of pulsating aurora drifts or expands eastward, away from magnetic midnight, for pre-midnight onsets and that the spatial evolution is more complicated for post midnight onsets, which has implications for the source mechanism. The most probable duration of a pulsating aurora event is roughly 1.5 hours while the distribution of possible event durations includes many long (several hours) events. This may suggest that pulsating aurora is not strictly a substorm recovery phase phenomenon but rather a persistent, long-lived phenomenon that may be temporarily disrupted by auroral substorms. Observations from the Gillam station show that in fact, pulsating aurora is quite common with the occurrence rate increasing to around 60% for morning hours, with 6910 of pulsating aurora onsets occurring after substorm breakup.

Jones, S. L.↗
DOE OSTI2020

Pattern formation in a coupled membrane-bulk reaction-diffusion model for intracellular polarization and oscillations

Reaction-diffusion systems have been widely used to study spatio-temporal phenomena in cell biology, such as cell polarization. Coupled bulk-surface models naturally include compartmentalization of cytosolic and membrane-bound polarity molecules. Here we study the distribution of the polarity protein Cdc42 in a mass-conserved membrane-bulk model, and explore the effects of diffusion and spatial dimensionality on spatio-temporal pattern formation. We first analyze a one-dimensional (1-D) model for Cdc42 oscillations in fission yeast, consisting of two diffusion equations in the bulk domain coupled to nonlinear ODEs for binding kinetics at each end of the cell. In 1-D, our analysis reveals the existence of symmetric and asymmetric steady states, as well as anti-phase relaxation oscillations typical of slow-fast systems. We then extend our analysis to a two-dimensional (2-D) model with circular bulk geometry, for which species can either diffuse inside the cell or become bound to the membrane and undergo a nonlinear reaction-diffusion process. We also consider a nonlocal system of PDEs approximating the dynamics of the 2-D membrane-bulk model in the limit of fast bulk diffusion. In all three model variants we find that mass conservation selects perturbations of spatial modes that simply redistribute mass. In 1-D, only anti-phase oscillations between the two ends of the cell can occur, and in-phase oscillations are excluded. In higher dimensions, no radially symmetric oscillations are observed. Instead, the only instabilities are symmetry-breaking, either corresponding to stationary Turing instabilities, leading to the formation of stationary patterns, or to oscillatory Turing instabilities, leading to traveling and standing waves. Codimension-two Bogdanov—Takens bifurcations occur when the two distinct instabilities coincide, causing traveling waves to slow down and to eventually become stationary patterns. Our work clarifies the effect of geometry and dimensionality on behaviors observed in ma.ss-conserved cell polarity models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

MemFriend: Understanding Memory Performance with Spatial-Temporal Affinity

In HPC applications, memory access behavior is one of the main factors affecting performance. Improving an application’s memory access behavior involves optimizing data layout and/or restructuring code, and requires studying spatial-temporal data locality. Existing data locality analyses focus on single-location metrics and are restricted to evaluating temporal locality. We introduce spatial-temporal affinity metrics that quantify temporal access proximity, forward access correlation, and nearby access correlation between pairs of memory locations. We describe methods for distinguishing between potential vs. realized affinity and for reasoning about affinity at multiple resolutions (3D, 2D, 1D). Finally, we construct spatial-temporal affinity signatures that classify memory behavior and that be used to reason about changes in software (data relayout, code refactoring) or hardware (caching, prefetching). We describe methods for signature visualization, interpretation, and quantitative comparison of signatures. We evaluate our methodology using applications with variants that contrast data structures, data layouts and algorithms. We show that spatial-temporal affinity analysis provides novel insights and enables predictive reasoning about application performance when contrasted with reuse distance analysis.

Suriyakumar, Yasodhadevi↗
NASA NTRS2001

Gravitational Signal of Mass Redistribution Due to Interannual Meteorological Oscillations in Atmosphere and Ocean

Interannual meteorological oscillations (ENSO, QBO, NAO, etc.) have demonstrable influences on Earth's rotation. Here we study their effects on global gravitational field, whose temporal variations are being studied using SLR (satellite laser ranging) data and in anticipation of the new space mission GRACE. The meteorological oscillation modes are identified using the EOF (empirical orthogonal function)/PC (principal component) decomposition of surface fields (in which we take care of issues associated with the area-weighting and non-zero mean). We examine two fields, one for the global surface pressure field for the atmosphere obtained from the NCEP reanalysis (for the past 40 years), one for the surface topography field for the ocean from the Topex/Poseidon (T/P) data (for the past 8 years). We use monthly maps, and remove the mean-monthly ("climatology") values from each grid point, hence focusing only on non-seasonal signals. The T/P data were first subject to a steric correction where the steric contribution to the ocean surface topography was removed according to output of the numerical POCM model. The respective atmospheric and oceanic contributions to the gravitational variation, in terms of harmonic Stokes coefficients, are then combined mode-by-mode. Since the T/P data already contain the oceanic response to overlying atmospheric pressure, no regards to the inverted-barometer behavior for the ocean need be considered. Results for the lowest-degree Stokes coefficients can then be compared with space geodetic observations including the Earth's rotation and the SLR data mentioned above, to identify the importance of each meteorological oscillations in gravitational variation signals.

Chao, B. F.↗
DOE OSTI2021

A validated thermal and biological model for predicting algal productivity in large scale outdoor cultivation systems

Proper assessment of the sustainability of algal products requires an understanding of algal growth rate with regional and temporal resolution. This study developed a bulk growth model that utilizes geospatial data, reactor geometry inputs, and a maximum of six strain-specific parameters to predict algal productivity with temporal and regional resolution. The model requires local weather data to calculate the time-resolved culture temperature which is combined with a biological model based on maximum theoretical carbon fixation to predict the time-resolved algal concentration. Validation of the model against experimental data illustrates an average ac- curacy of 0.27% ± 5.32% when comparing algal concentration at harvest over 45 days of cultivation data for Galdieria sulphuraria 5587.1, and Galdieria sulphuraria Soos grown in photobioreactors (PBRs) in Mesa, AZ (33.4152° N, 111.8315° W). For open raceway ponds (ORPs), an accuracy of 0.9% ± 2.35% was achieved when comparing algal concentration at harvest over 50 days of cultivation data for Chlorella vulgaris, Desmodesmus intermedius, Nannochloropsis oceanica grown in Atlanta, GA (33.7490° N, 84.3880° W) and Mesa, AZ (33.4152° N, 111.8315° W). The validation trials spanned three seasons (winter, spring, and summer) to ensure model ac- curacy throughout the year, and concentration between harvests was modeled with hourly resolution. The validated model is used to assess the productivity at several case-study locations in the United States with Chlorella vulgaris, grown in open raceway ponds and Galdieria sulphuraria grown in vertical flat panel photo- bioreactors. In conclusion, the model has been published as an open-source tool, in an effort to increase the fidelity of future studies that rely on outdoor algae cultivation for feedstock production and allow for time resolved results for future techno-economic and life cycle assessments.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

Nitrogen nutrition and temporal effects of enhanced carbon dioxide on soybean growth

Plants grown on porous media at elevated CO2 levels generally have low concentrations of tissue N and often appear to require increased levels of external N to maximize growth response. This study determines if soybean [Glycine max (L.) Merr. Ransom'] grown hydroponically at elevated CO2 requires increases in external NO3- concentrations beyond levels that are optimal at ambient CO2 to maintain tissue N concentrations and maximize the growth response. This study also investigates temporal influences of elevated CO2 on growth responses by soybean. Plants were grown vegetatively for 34 d in hydroponic culture at atmospheric CO2 concentrations of 400, 650, and 900 microliters L-1 and during the final 18 d at NO3- concentrations of 0.5, 1.0, 5.0 and 10.0 mM in the culture solution. At 650 and 900 microliters L-1 CO2, plants had maximum increases of 31 and 45% in dry weight during the experimental period. Plant growth at 900 microliters L-1 CO2 was stimulated earlier than at 650 microliters L-1. During the final 18 d of the experiment, the relative growth rates (RGR) of plants grown at elevated CO2 declined. Elevated CO2 caused increases in total N and total NO3(-)-N content and leaf area but not leaf number. Enhancing CO2 levels also caused a decrease in root:shoot ratios. Stomatal resistance increased by 2.1- and 2.8-fold for plants at the 650 and 900 microliters L-1 CO2, respectively. Nitrate level in the culture solutions had no effect on growth or on C:N ratios of tissues, nor did increases in CO2 levels cause a decrease in N concentration of plant tissues. Hence, increases in NO3- concentration of the hydroponic solution were not necessary to maintain the N status of the plants or to maximize the growth response to elevated CO2.

NASA Program CELSS↗
DOE OSTI2020

Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools

We report the overarching hypothesis of this study was that temporal microbial potentiometric sensor (MPS) signal patterns could be used to predict changes in commonly monitored water quality parameters by using artificial intelligence/machine learning tools. To test this hypothesis, the study first examines a proof of concept by correlating between MPS's signals and high algae concentrations in an algal cultivation pond. Then, the study expanded upon these findings and examined if multiple water quality parameters could be predicted in real surface waters, like irrigation canals. Signals generated between the MPS sensors and other water quality sensors maintained by an Arizona utility company, including algae and chlorophyll, were collected in real time at time intervals of 30 min over a period of 9 months. Data from the MPS system and data collected by the utility company were used to train the ML/AI algorithms and compare the predicted with actual water quality parameters and algae concentrations. Based on the composite signal obtained from the MPS, the ML/AI was used to predict the canal surface water's turbidity, conductivity, chlorophyll, and blue-green algae (BGA), dissolved oxygen (DO), and pH, and predicted values were compared to the measured values. Initial testing in the algal cultivation pond revealed a strong linear correlation (R 2 = 0.87) between mixed liquor suspended solids (MLSS) and the MPSs' composite signals. The Normalized Root Mean Square Error (NRMSE) between the predicted values and measured values were <6.5%, except for the DO, which was 10.45%. The results demonstrate the usefulness of MPSs to predict key surface water quality parameters through a single composite signal, when the ML/AI tools are used conjunctively to disaggregate these signal components. The maintenance-free MPS offers a novel and cost-effective approach to monitor numerous water quality parameters at once with relatively high accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Malicious Cyber Activity Detection using Zigzag Persistence

In this study we synthesize zigzag persistence from topological data analysis with autoencoder-based approaches to detect malicious cyber activity, and derive analytic insights. Cybersecurity aims to safeguard computers, networks, and servers from various forms of malicious attacks, including network damage, data theft, and activity monitoring. We focus on the cybersecurity domain and investigate the detection of malicious activity using log data. We consider the dynamics of the log data and explore the changing topology of a hypergraph representation of this data to gain insights into the underlying activity. These hypergraphs capture complex interactions between processes, together with their temporal information. To study the changing topology we use zigzag persistence, which captures how topological features persist at multiple dimensions over time. We observe that this detects malicious activity in a cyber data set. To automate this detection we implement an autoencoder trained on a vectorization of the resulting zigzag persistence barcodes. Our experimental results demonstrate the effectiveness of the autoencoder in detecting malicious activity. Overall, this study highlights the potential of zigzag persistence and its combination with temporal hypergraphs for analyzing cybersecurity log data and detecting malicious behavior.

hypergraphs, temporal hypergraph, topological data↗