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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Experiences with Probabilistic Analysis Applied to Controlled Systems

This paper presents a semi-analytic method for computing frequency dependent means, variances, and failure probabilities for arbitrarily large-order closed-loop dynamical systems possessing a single uncertain parameter or with multiple highly correlated uncertain parameters. The approach will be shown to not suffer from the same computational challenges associated with computing failure probabilities using conventional FORM/SORM techniques. The approach is demonstrated by computing the probabilistic frequency domain performance of an optimal feed-forward disturbance rejection scheme.

Kenny, Sean P.↗
NASA NTRS1988

Variability of pigment biomass in the California Current system as determined by satellite imagery. II - Temporal variability

Characteristics of temporal variability in the California Current system are analyzed using a 30-month time series of CZCS imagery. About 20-25 percent of the variance is produced by a periodic annual cycle with peak values in winter. Analysis of ship-based chlorophyll measurements indicates that the winter peak is only characteristic of the upper portion of the euphotic zone and that total water column chlorophyll peaks during the spring upwelling season. Satellite studies of intraannual variability are modulated by strong 5- to 6-day oscillation in the availability of usable imagery, resulting from a combination of satellite orbital dynamics, which produces images of the study area roughly 4 out of every 6 days, and an oscillation in cloud cover, which controls the availability of clear imagery. The cloud cover oscillation, which is also present in coastal winds, undoubtedly affects the ocean surface and biases the data obtained by satellites. Analysis of data using a 5-day time step indicates that the predominant mode of nonseasonal variability is characterized by in-phase fluctuations throughout the southern and central California coastal region.

Michaelsen, Joel↗
NASA NTRS1990

Laser Atmospheric Wind Sounder (LAWS) phase 1. Volume 2

This report summarizes and documents the results of the 12-month phase 1 work effort. The objective of phase 1 was to establish the conceptional definition of the laser atmospheric wind sounder (LAWS) sensor system, including accommodations analyses to ensure compatibility with the Space Station Freedom (SSF) and the Earth Observing System (EOS) Polar Orbiting Platform (POP). Various concepts were investigated with trade studies performed to select the configuration to be carried forward to the phase 2 Preliminary Design Definition. A summary of the LAWS system and subsystem trade studies that were performed leading to the baseline design configuration is presented in the appendix. The overall objective of the LAWS Project is to define, design, and implement an operational space based facility, LAWS, for accurate measurement of Earth wind profiles. Phase 1 addressed three major areas: (1) requirements definition; (2) instrument concepts and configurations; and (3) performance analysis. For the LAWS instrument concepts and configurations, the issues which press the technological state of the art are reliable detector lifetime and laser performance and lifetime. Lag angle compensation, pointing accuracy, satellite navigation, and telescope design are significant technical issues, but they are considered to be currently state of the art. The primary issues for performance analysis concern interaction with the atmosphere in terms of backscatter and attenuation, wind variance, and cloud blockage. The phase 1 tasks were formulated to address these significant technical issues and demonstrate the technical feasibility of the LAWS concept. Primary emphasis was placed on analysis/trade and identification of candidate concepts. Promising configurations were evaluated for performance, sensitivities, risks, and budgetary costs. Lockheed's baseline LAWS configuration is presented.

Source record↗
NASA NTRS1991

Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM

A new diagnostic tool is developed for examining relationships between the synoptic scale circulation and regional temperature distributions in GCMs. The 4 x 5 deg GISS GCM is shown to produce accurate simulations of the variance in the synoptic scale sea level pressure distribution over the U.S. An analysis of the observational data set from the National Meteorological Center (NMC) also shows a strong relationship between the synoptic circulation and grid point temperatures. This relationship is demonstrated by deriving transfer functions between a time-series of circulation parameters and temperatures at individual grid points. The circulation parameters are derived using rotated principal components analysis, and the temperature transfer functions are based on multivariate polynomial regression models. The application of these transfer functions to the GCM circulation indicates that there is considerable spatial bias present in the GCM temperature distributions. The transfer functions are also used to indicate the possible changes in U.S. regional temperatures that could result from differences in synoptic scale circulation between a 1XCO2 and a 2xCO2 climate, using a doubled CO2 version of the same GISS GCM.

Crane, Robert G.↗
NASA NTRS1991

Control of large flexible structures - An experiment on the NASA Mini-Mast facility

The output variance constraint controller design procedure is integrated with model reduction by modal cost analysis. A procedure is given for tuning MIMO controller designs to find the maximal rms performance of the actual system. Controller designs based on a finite-element model of the system are compared with controller designs based on an identified model (obtained using the Q-Markov Cover algorithm). The identified model and the finite-element model led to similar closed-loop performance, when tested in the Mini-Mast facility at NASA Langley.

Hsieh, Chen↗
NASA NTRS2001

The Interaction Between Dynamics and Chemistry of Ozone in the Set-up Phase of the Northern Hemisphere Polar Vortex

The morphology and evolution of the stratospheric ozone (O3) distribution at high latitudes in the Northern Hemisphere (NH) are examined for the late summer and fall seasons of 1999. This time period sets the O3 initial condition for the SOLVE/THESEO field mission performed during winter 1999-2000. In situ and satellite data are used along with a three-dimensional model of chemistry and transport (CTM) to determine the key processes that control the distribution of O3 in the lower-to-middle stratosphere. O3 in the vortex at the beginning of the winter season is found to be nearly constant from 500 to above 800 K with a value at 3 ppmv +/- approx. 10%. Values outside the vortex are up to a factor of 2 higher and increase significantly with potential temperature. The seasonal time series of data from POAM shows that relatively low O3 mixing ratios, which characterize the vortex in late fall, are already present at high latitudes at the end of summer before the vortex circulation sets up. Analysis of the CTM output shows that the minimum O3 and increase in variance in late summer are the result of: 1) stirring of polar concentric O3 gradients by nascent wave-driven transport, and 2) an acceleration of net photochemical loss with decreasing solar illumination. The segregation of low O3 mixing ratios into the vortex as the circulation strengthens through the fall suggests a possible feedback role between O3 chemistry and the vortex formation dynamics. Trajectory calculations from O3 sample points early in the fall, however, show only a weak correlation between initial O3 mixing ratio and potential vorticity later in the season consistent with order-of-magnitude calculations for the relative importance of O3 in the fall radiative balance at high latitudes. The possible connection between O3 chemistry and the dynamics of vortex formation does suggest that these feedbacks and sensitivities need to be better understood in order to make confident predictions of the recovery of NH O3.

Kawa, S. R.↗
NASA NTRSDate not supplied

Probabalistic Risk Analysis and Thermal Margin Process for an Inflatable Aeroshell

Uncertainties always exist in atmospheric entry aeroheating environments and the thermal response of thermal protection system (TPS) material. These uncertainties are mitigated in the design by ap-plying margin and factors of safety to the TPS. Entry vehicle TPS is often conservatively over-sized for the heat loads that are experienced along the entry trajectory by stacking worst-case scenarios together. Additionally, the current TPS design and margin process used by NASA offers very little insight into the risk of over-temperature during flight and the reliability of the heat shield performance [1,3]. A probabilistic margin process can be used to calculate the amount of TPS margin necessary to survive a given entry heat load at a specified level of risk [2,3,4]. The vehicle’s initial entry state (entry velocity, flight path angle, and entry mass) determines the expected atmospheric entry environmental conditions and resulting heat load that the entry vehicle will experience. If there is flexibility in the entry state, then this process can be used to select an appropriate combination of entry state parameters and TPS size to target a desired reentry reliability. This probabilistic margin process allows engineers to make informed aeroshell design, entry-trajectory design, and TPS performance risk trades while preventing excessive TPS margin from being applied. The probabilistic TPS margin process has been performed to determine TPS thickness and entry heating constraints given an acceptable risk level for the Low Earth Orbit Flight Experiment of an Inflatable Decelerator (LOFTID) flight project. The process is used in a manner to size the entry heat load for a given flexible TPS (FTPS) thickness so that it meets project reliability standards while allowing the FTPS and the underlying inflatable structure (IS) to be pushed to adequately high temperatures. Since the LOFTID project is an experimental flight demonstration, it is de-sired to drive the FTPS and IS to temperatures that cover a large range of their thermal response models’ applicability. This will allow the thermal response models to be better improved and validated post-flight using LOFTID’s extensive instrumentation embedded within the aeroshell. The presentation demonstrates how uncertainty analysis is carried out using an end-to-end Monte Carlo process where three separate Monte Carlo simulations are run in sequence. The first Monte Carlo simulation operates on the entry trajectory model to generate trajectory parameter dispersions that are fed into the second Monte Carlo simulation. The second Monte Carlo simulation operates on the aerothermodynamics model to generate aeroheating parameter dispersions that are fed into the third Monte Carlo simulation. The third Monte Carlo simulation operates on the FTPS material thermal response model to generate the final FTPS/IS thermal response dispersions. The end-to-end Monte Carlo simulation propagates the uncertainties of each model into the next to quantify the resulting uncertainty of the FTPS/IS thermal response. The fractional contributions of the uncertain parameters in the trajectory, aerothermal, and thermal response models to the variance in the FTPS/IS thermal response is determined as a byproduct of the Monte Carlo analysis. The structural uncertainty of the FTPS thermal response model is evaluated by flight relevant ground testing and model error analysis using test measurements. This probabilistic TPS margin process had never been applied to an entry vehicle and it is one of the LOFTID project’s goals to demonstrate its merits.

Steven A. Tobin↗
DOE OSTI2024

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. Here, the method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.

42 ENGINEERING↗
NASA NTRS1986

Spatial characteristics of observed precipitation fields: A catalog of summer storms in Arizona, Volume 2

The parameters of the conceptual model are evaluated from the analysis of eight years of summer rainstorm data from the dense raingage network in the Walnut Gulch catchment near Tucson, Arizona. The occurrence of measurable rain at any one of the 93 gages during a noon to noon day defined a storm. The total rainfall at each of the gages during a storm day constituted the data set for a single storm. The data are interpolated onto a fine grid and analyzed to obtain: an isohyetal plot at 2 mm intervals, the first three moments of point storm depth, the spatial correlation function, the spatial variance function, and the spatial distribution of the total storm depth. The description of the data analysis and the computer programs necessary to read the associated data tapes are presented.

Fennessey, N. M.↗
DOE OSTI2025

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Harnessing graph convolutional neural networks for identification of glassy states in metallic glasses

Graph Convolutional Neural Networks (GCNNs) have emerged as powerful tools for analyzing materials. In this study, we employ GCNNs to examine structural characteristics of CuZr metallic glasses (MGs) and identify their states. We use molecular dynamics to simulate the quenching process of CuZr, using cooling rates ranging from 10 9 to 10 15 K/s, to produce six unique glassy states. For each state, we create a dataset comprising 1,800 distinct samples. We evaluate the effectiveness of various GCNNs, including Graph Attention Neural Network (GANN), Graph Sample and AggreGatE (GraphSAGE), Graph Isomorphism Network (GIN), and Relational Graph Convolutional Neural Network (RGCN). GANN and GraphSAGE demonstrate comparable performance, achieving an overall accuracy of 81% in classifying the MG states. Furthermore, these results underscore the potential of GCNNs to detect subtle structural variances in disordered materials and point to broader application of deep learning in the analysis of MGs and other amorphous substances.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Advanced high-temperature fluid reactors, such as sodium-cooled fast reactors (SFRs) and molten salt–cooled reactors (MSCRs), require coolant purification systems to prevent fluid contamination and local freezing that can lead to plugging. Liquid sodium purification can be achieved with a cold trap, where the sodium temperature is reduced to a near-freezing point to precipitate out impurities. Automation of monitoring of the cold trap performance with machine learning algorithms can aid in early detection of incipient anomalies. An efficient approach to loss-of-coolant–type anomaly detection in a cold trap monitored with more than two dozen thermal-hydraulic sensors consists of a long short-term memory (LSTM) autoencoder. This work develops the uncertainty quantification of the LSTM autoencoder performance for cold trap anomaly detection using the Monte Carlo (MC) dropout method. The MC dropout methodology creates a distribution of sister distributions that all slightly differ from each other because of random neurons being turned off for testing. The variances of the sister network distributions are used to make an uncertainty interval. Our analysis shows that the uncertainty in the autoencoder performance is largest near the peak of the anomaly signal. Using the MC dropout method, we investigate the uncertainty in the anomaly detection with missing sensor inputs. This capability allows the reactor operator to evaluate resilience of the anomaly detection system and to make informed decisions about continuity of operation in the event of sensor failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Turbulence and Cloudiness in Cumulus-Topped Marine Boundary Layers

Turbulence and cloudiness in cumulus-topped marine boundary layers are studied using data from the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site. The analysis includes eight periods of nonprecipitating shallow cumulus clouds, spanning 141 h and encompassing 603 individual clouds. On average, the cumulus had bases at 558 m, were 99 m thick, had a chord length of 500 m, and exhibited an hourly base-layer cloudiness of 12%. Changes in cloud fraction were primarily driven by variations in cloud number not chord length. High-resolution Doppler lidar and cloud radar observations were used to estimate updraft and downdraft mass fluxes in the cumulus base layer under both clear and cloudy regions. Cloudy updrafts contributed only ∼23% of the total updraft mass flux at cloud base, indicating that most upward mass flux originated from clear-air eddies. Updraft strength, rather than updraft fraction, was found to predominantly control both clear and cloudy updraft mass fluxes. Cloud-base cloudiness strongly correlated with cloudy updraft mass flux but showed negligible correlation with clear-air updraft mass flux. Clear-air downdraft mass flux exhibited a strong relationship with the ratio of surface buoyancy to inversion strength. Mesoscale analysis revealed that moist patches had greater cloudiness, updraft mass flux, and vertical velocity variance compared to dry patches. Additionally, mesoscale base-layer cloud fraction was highly and significantly correlated with cloudy updraft mass flux in both dry and moist environments. Results presented herein have implications for cumulus parameterization development along with climatological and model evaluation studies conducted at the ENA site.

cumulus clouds↗
NASA NTRS1972

Synchronization using pulsed edge tracking in optical PPM communication system

A pulse position modulated (PPM) optical communication system using narrow pulses of light for data transmission requires accurate time synchronization between transmitter and receiver. The presence of signal energy in the form of optical pulses suggests the use of a pulse edge tracking method of maintaining the necessary timing. The edge tracking operation in a binary PPM system is examined, taking into account the quantum nature of the optical transmissions. Consideration is given first to pure synchronization using a periodic pulsed intensity, then extended to the case where position modulation is present and auxiliary bit decisioning is needed to aid the tracking operation. Performance analysis is made in terms of timing error and its associated statistics. Timing error variances are shown as a function of system signal to noise ratio.

Gagliardi, R.↗
NASA NTRS1982

Jovian winds from Voyager 2. I - Zonal mean circulation

Independent measurements of Jovian cloud motions confirm previously published results on the general structure of Jupiter's zonal mean circulation. The new results are based on Voyager 2 images and measurement techniques which are different from those used in previous studies. The latitudes of the zonal jets agree with previous results, but there are some differences in the measured speed of the jets which exceed uncertainty estimates. These differences may be due to differences in sampling strategies. The structure of the zonal mean meridional velocity profile has still not been clearly resolved: mean meridional velocities generally differ from zero by no more than their estimated uncertainty. An analysis of successive measurements of the same cloud targets shows that most of the variance of individual velocity measurements is due to true variability of the winds.

Limaye, S. S.↗
NASA NTRS1987

Application of spatial Poisson process models to air mass thunderstorm rainfall

Eight years of summer storm rainfall observations from 93 stations in and around the 154 sq km Walnut Gulch catchment of the Agricultural Research Service, U.S. Department of Agriculture, in Arizona are processed to yield the total station depths of 428 storms. Statistical analysis of these random fields yields the first two moments, the spatial correlation and variance functions, and the spatial distribution of total rainfall for each storm. The absolute and relative worth of three Poisson models are evaluated by comparing their prediction of the spatial distribution of storm rainfall with observations from the second half of the sample. The effect of interstorm parameter variation is examined.

Eagleson, P. S.↗
NASA NTRS1998

Dynamic Density: An Air Traffic Management Metric

The definition of a metric of air traffic controller workload based on air traffic characteristics is essential to the development of both air traffic management automation and air traffic procedures. Dynamic density is a proposed concept for a metric that includes both traffic density (a count of aircraft in a volume of airspace) and traffic complexity (a measure of the complexity of the air traffic in a volume of airspace). It was hypothesized that a metric that includes terms that capture air traffic complexity will be a better measure of air traffic controller workload than current measures based only on traffic density. A weighted linear dynamic density function was developed and validated operationally. The proposed dynamic density function includes a traffic density term and eight traffic complexity terms. A unit-weighted dynamic density function was able to account for an average of 22% of the variance in observed controller activity not accounted for by traffic density alone. A comparative analysis of unit weights, subjective weights, and regression weights for the terms in the dynamic density equation was conducted. The best predictor of controller activity was the dynamic density equation with regression-weighted complexity terms.

Laudeman, I. V.↗