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Results for “Temporal modeling”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

RANS Predictions of Inlet/Isolator Unstart at Mach 4

This paper describes several approaches to back pressuring an inlet/isolator model at Mach 4.0 and the differences in unstart predictions using common RANS simulation methods. The CFD codes OVERFLOW and CREATE-AV Kestrel were used for this work. Verification and validation was conducted on a relevant shock-wave-boundary-layer-interaction case with both solvers. Using aftmounted back pressure flaps did not produce a stable pre-combustion shock train. An unstart comparison between CFD codes OVERFLOW and CREATE-AV Kestrel as well as common 2-equation turbulence models was conducted with numerical back pressure. Unstart predictions using a novel louver back pressure mechanism was done with OVERFLOW. There is notable differences in shock train travel and predicted unstart limit due to turbulence modeling, temporal resolution, and use of the 2D simplification.

CFD↗
NASA NTRS2024

The Lunar Environment Heliophysics X-Ray Imager (LEXI) Mission

The Lunar Environment heliospheric X-ray Imager (LEXI) is a wide field-of-view soft Xray telescope developed to study solar wind-magnetosphere coupling. LEXI is part of the Blue Ghost 1 mission comprised of 10 payloads to be deployed on the lunar surface. LEXI monitors the dayside magnetopause position and shape as a function of time by observing soft X-rays (0.1–2 keV) emitted from solar wind charge-exchange between exospheric neutrals and high charge-state solar wind plasma in the dayside magnetosheath. Measurements of the shape and position of the magnetopause are used to test temporal models of mesoand macro-scale magnetic reconnection. To image the boundary, LEXI employs lobster-eye optics to focus X-rays to a microchannel plate detector with a 9.1◦ × 9.1◦ field of view.

Lunar↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗
NASA NTRS2008

Spatial and Temporal Low-Dimensional Models for Fluid Flow

A document discusses work that obtains a low-dimensional model that captures both temporal and spatial flow by constructing spatial and temporal four-mode models for two classic flow problems. The models are based on the proper orthogonal decomposition at two reference Reynolds numbers. Model predictions are made at an intermediate Reynolds number and compared with direct numerical simulation results at the new Reynolds number.

Kalb, Virginia↗
NASA NTRS2014

The Role of Temporal Evolution in Modeling Atmospheric Emissions from Tropical Fires

Fire emissions associated with tropical land use change and maintenance influence atmospheric composition, air quality, and climate. In this study, we explore the effects of representing fire emissions at daily versus monthly resolution in a global composition-climate model. We find that simulations of aerosols are impacted more by the temporal resolution of fire emissions than trace gases such as carbon monoxide or ozone. Daily-resolved datasets concentrate emissions from fire events over shorter time periods and allow them to more realistically interact with model meteorology, reducing how often emissions are concurrently released with precipitation events and in turn increasing peak aerosol concentrations. The magnitude of this effect varies across tropical ecosystem types, ranging from smaller changes in modeling the low intensity, frequent burning typical of savanna ecosystems to larger differences when modeling the short-term, intense fires that characterize deforestation events. The utility of modeling fire emissions at a daily resolution also depends on the application, such as modeling exceedances of particulate matter concentrations over air quality guidelines or simulating regional atmospheric heating patterns.

aerosols↗
NASA NTRS1990

Model checking for linear temporal logic: An efficient implementation

This report provides evidence to support the claim that model checking for linear temporal logic (LTL) is practically efficient. Two implementations of a linear temporal logic model checker is described. One is based on transforming the model checking problem into a satisfiability problem; the other checks an LTL formula for a finite model by computing the cross-product of the finite state transition graph of the program with a structure containing all possible models for the property. An experiment was done with a set of mutual exclusion algorithms and tested safety and liveness under fairness for these algorithms.

Sherman, Rivi↗
DOE OSTI2024

Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains

Abstract In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high‐resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station‐based SWE observations are used as the prediction target. All DL models produce higher median Nash‐Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1983

A mathematical model for crop spectral-temporal trajectories based on a plant growth model

The Kubelka-Munk radiative transfer model is combined with an approximation of Kauth-Thomas greeness and brightness transforms to derive approximate closed form expressions for crop greeness and brightness surrogates in terms of canopy biomass. The greeness relation derived resembles an existing empirical relation between leaf area index and greeness. A simple growth model based on interception and utilization of photosynthetically active radiation is developed and used to describe the time evolution of greeness and brightness. The model developed does not yet yield definitive profile calculations but suggests a conceptual framework which may be found useful for further profile analysis.

Woolford, T. L.↗
NASA NTRS1984

A regression model for the temporal development of soil pipes and associated gullies in the alluvial-fill valley of the Rio Puerco, central New Mexico

On Mars, the association of gullied escarpments and chaotic terrain is evidence for failure and scarp retreat of poorly consolidated materials. Some martian gullies have no surface outlets and may have drained through subterranean channels. Similar features, though on a much smaller scale, can be seen in alluvium along terrestrial river banks in semiarid regions, such as the Rio Puerco Valley of central New Mexico. Many of the escarpments along the Rio Puerco are developing through formation of collapse gullies, which drain through soil pipes. Gully development can be monitored on aerial photographs taken in 1935, 1962, and 1980. A regression model was developed to quantify gully evolution over a known time span. Soil pipes and their associated collapse gullies make recognizable signatures on the air photos. The areal extent of this signature can be normalized to the scarp length of each pipe-gully system, which makes comparisons between systems possible.

Condit, C. D.↗
NASA NTRS1979

GEOS-3 altimetry - Temporal variations in models of the Sargasso Sea

The Sargasso Sea test area lies to the east of the United States. Of the three possible modes, only the method of regional solutions and the method of overlapping passes are capable of using GEOS-3 altimetry data for oceanographic studies at the minimal plus or minus 50 cm resolution required for mapping of ocean eddies. In the present paper, the results obtained by these two methods are summarized, and the problems to be overcome in the recovery of quasi-stationary sea surface topography maintaining the steady-state component of the Gulf Stream are discussed.

Mather, R. S.↗
NASA NTRS1990

A theoretical model for the temporal evolution of HF-enhanced plasma lines

The HF-enhanced plasma lines (HFPLs) observed in the Arecibo heating experiments refer to the radar returns at frequencies near the sum and difference of the radar frequency and the HF-heatear frequency. Those enhanced spectral lines are caused by backscatter of radar signals from parametrically excited plasma waves having a wavenumber of 18 n. A nonlinear theory was developed to describe the temporal evolution of those specified plasma waves and their originating altitude interval of HFPLs observed at Arecibo, Puerto Rico are explained. The theoretical resultsagree well with the observation (Djuth and Sulzer, 1989).

Kuo, S. P.↗
DOE OSTI2021

A semiparametric latent factor model for large scale temporal data with heteroscedasticity

Large scale temporal data have flourished in a vast array of applications, and their sophisticated structures, especially the heteroscedasticity among subjects with inter- and intra-temporal dependence, have fueled a great demand for new statistical models. In this paper, with covariate information, we consider a flexible model for large scale temporal data with subject-specific heteroscedasticity. Formally, the model employs latent semiparametric factors to simultaneously account for the subject-specific heteroscedasticity and the contemporaneous and/or serial correlations. The subject-specific heteroscedasticity is modeled as the product of the unobserved factor process and subject’s covariate effect, which is further characterized via additive models. For estimation, we propose a two-step procedure. First, the latent factor process and nonparametric loading are recovered through projection-based methods, and following, we estimate the regression components by approaches motivated from the generalized least squares. By scrupulously examining the non-asymptotic rates for recovering the factor process and its loading, we show the consistency and efficiency of estimated regression coefficients in the absence of prior knowledge of latent factor process and subject’s covariate effect. Here, the statistical guarantees remain valid even for finite time points that makes our method particularly appealing when the subjects significantly outnumber the observation time points. Using comprehensive simulations, we demonstrate the finite sample performance of our method, which corroborates the theoretical findings. Finally, we apply our method to a data set of air quality and energy consumption collected at 129 monitoring sites in the United States in 2015.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

Temporal variations in regional models of the Sargasso Sea from GEOS-3 altimetry

The paper considers temporal variations in regional models of the Sargasso sea from GEOS-3 telemetry. The methods of regional models and the analysis of overlapping passes are utilized, and short-wave maxima and minima in the regional surface models are examined for correlations with surface and remote sensed infrared temperature data supplemented with subsurface expendable bathythermograph data (XBT). The analysis of overlapping passes provide a better picture of instanteneous sea surface height (SSH) variability through wavelengths greater than 30 km. Correlation studies with cyclonic and anticyclonic ocean eddies from infrared imagery and XBT data indicate satisfactory agreement with equivalent SSH features 98% of the time if the time varying factors are allowed for.

Mather, R. S.↗
NASA NTRS1989

Model interpretation of type III radio burst characteristics. II - Temporal aspects

A model of the radio emission region for kilometric type II bursts is used to interpret systematic variations observed in the temporal behavior of the burst parameters. The temporal behavior of the burst parameters observed by the ISEE-3 spacecraft is reviewed, and it is pointed out that the phase and modulation of the antenna signal vary in a systematic way with both time and observing frequency. The source azimuth is observed to drift with time, with the magnitude and sense of the drift depending on the location of the radio source relative to the observer. The modulation factor usually decreases uniformly with time and is frequently peaked near the burst onset. The model of the radio emission region is developed and used to obtain the intensity, phase, and modulation of the radio signal. Model results are used to show how the behavior of the burst parameters are related to attributes of the source region. It is shown that the temporal behavior of the radio parameters for an observed type II burst is well represented by the model.

Reiner, M. J.↗
NASA NTRSDate not supplied

Predicting Team Functioning in Long Term Space Missions Using Acoustic and Linguistic Measures

Maintaining optimal team functioning is critical for long-duration space exploration missions, yet traditional monitoring methods, such as self-reports and wearable sensors, often impose operational burdens or suffer from bias. This paper investigates a non-intrusive speech-based artificial intelligence (AI) framework to predict degradations in team functioning using data from the Human Exploration Research Analog (HERA) of the U.S. National Aeronautics and Space Administration (NASA). Using acoustic features, linguistic descriptors, and semantic embeddings, we evaluate static non-linear and temporal machine learning models to predict both objective (task accuracy) and subjective (self-reported efficacy and cohesion) team functioning outcomes. Results indicate that temporal models outperform static approaches, with prediction of objective task accuracy in Team Interaction Battery (TIB) improving from near chance to 71%. Self-reported outcomes, including team efficacy and cohesion, are predicted more reliably than task performance, achieving balanced accuracies of up to 85.56% and 78.12%, respectively, and are found to be most strongly associated with acoustic features. In a second interdependent task, the MMSEV–EVA, accuracies of up to 78% are achieved using temporal models with acoustic features. Furthermore, incorporating just 1–2 days of team-specific historical data systematically improved performance, and acoustic markers from informal pre-task interactions provided modest predictive gains. Finally, while automated preprocessing yielded viable accuracy, humancorrected data provided moderate performance gains, though transcription error rates did not significantly correlate with model performance. These findings highlight the potential of speech as a passive, high-fidelity monitoring tool for autonomous habitats.

Temporal modeling↗