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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software (Extended Analysis)

Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and the tensor elements are nonnegative count data. SparTen is a high-performance C++ library which computes a low-rank decomposition using different solvers: a first-order quasi-Newton or a second-order damped Newton method, along with the appropriate choice of runtime parameters. Since default parameters in SparTen are tuned to experimental results in prior published work on a single real-world dataset conducted using MATLAB implementations of these methods, it remains unclear if the parameter defaults in SparTen are appropriate for general tensor data. Furthermore, it is unknown how sensitive algorithm convergence is to changes in the input parameter values. This report addresses these unresolved issues with large-scale experimentation on three benchmark tensor data sets. Experiments were conducted on several different CPU architectures and replicated with many initial states to establish generalized profiles of algorithm convergence behavior.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Uncertainty Quantification and Sensitivity Analysis of Low-Dimensional Manifold via Co-Kurtosis PCA in Combustion Modeling

For multi-scale multi-physics applications e.g., the turbulent combustion code Pele, robust and accurate dimensionality reduction is crucial to solving problems at exascale and beyond. A recently developed technique, Co-Kurtosis based Principal Component Analysis (CoK-PCA) which leverages principal vectors of co-kurtosis, is a promising alternative to traditional PCA for complex chemical systems. To improve the effectiveness of this approach, we employ Artificial Neural Networks for reconstructing thermo-chemical scalars, species production rates, and overall heat release rates corresponding to the full state space. Our focus is on bolstering confidence in this deep learning based non-linear reconstruction through Uncertainty Quantification (UQ) and Sensitivity Analysis (SA). UQ involves quantifying uncertainties in inputs and outputs, while SA identifies influential inputs. One of the noteworthy challenges is the computational expense inherent in both endeavors. To address this, we employ the Monte Carlo methods to effectively quantify and propagate uncertainties in our reduced spaces while managing computational demands. Our research carries profound implications not only for the realm of combustion modeling but also for a broader audience in UQ. By showcasing the reliability and robustness of CoK-PCA in dimensionality reduction and deep learning predictions, we empower researchers and decision-makers to navigate complex combustion systems with greater confidence.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years

The Model of Emissions of Gases and Aerosols from Nature (MEGANv2.1) together with the Modern-Era Retrospective Analysis for Research and Applications (MERRA) meteorological fields were used to create a global emission dataset of biogenic VOCs available on a monthly basis for the time period of 1980 - 2010. This dataset is called MEGAN-MACC. The model estimated mean annual total BVOC emission of 760 Tg(C) yr1 consisting of isoprene (70%), monoterpenes (11%), methanol (6%), acetone (3%), sesquiterpenes (2.5%) and other BVOC species each contributing less than 2 %. Several sensitivity model runs were performed to study the impact of different model input and model settings on isoprene estimates and resulted in differences of * 17% of the reference isoprene total. A greater impact was observed for sensitivity run applying parameterization of soil moisture deficit that led to a 50% reduction of isoprene emissions on a global scale, most significantly in specific regions of Africa, South America and Australia. MEGAN-MACC estimates are comparable to results of previous studies. More detailed comparison with other isoprene in ventories indicated significant spatial and temporal differences between the datasets especially for Australia, Southeast Asia and South America. MEGAN-MACC estimates of isoprene and*-pinene showed a reasonable agreement with surface flux measurements in the Amazon andthe model was able to capture the seasonal variation of emissions in this region.

Sindelarova, K.↗
DOE OSTI2020

Atmospheric condition identification in multivariate data through a metric for total variation

Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation.Atmospheric conditions relevant for wind energy research include stationary conditions, given the need for well-converged statistics for model validation, as well as conditions observed less frequently, such as extreme atmospheric events, which are used in wind turbine and wind plant design.Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of atmospheric conditions.Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation set the stage for more advanced model validation and the development of real-time control and operational strategies.The current work explores a single metric for variation in a multivariate data sample that quantifies variability within each channel as well as covariance between channels.The total variation is used to identify conditions of interest that conform to desired objective functions, such as stationary conditions, ramps or waves of wind speed, and changes in wind direction.Total variation is somewhat sensitive to the presence of outliers in the input data, and the method is best complemented by quality-control procedures to ensure reliable results.The direct detection and classification of events or conditions of interest within atmospheric data sets is vital to developing our understanding of wind plant response and to the formulation of forecasting and control models.

17 WIND ENERGY↗
NASA NTRS1972

Solar electric propulsion breadboard thrust subsystem test results.

Work providing a firm basis for building an engineering model thrust subsystem (TSS) is described. A 1500-hr functional demonstration test revealed the impact of each element on TSS integration, operation, and future development. The primary conclusions are: accurate and specific definition of interfaces is essential to a timely system integration program. In addition to normal operating requirements, this definition must include requirements for preflight checkout, self-protection of all interfaces, capability expansion, failure modes, maximum input power, interelement power losses, noise and transient sensitivity and production, packaging for vacuum, thermal control, electrical grounding, cabling, mounting, and handling. In any future design, each phase of the TSS operation (startup, throttling, switching, etc.) should be considered in detail to anticipate and account for possible in-flight variations in element characteristics and performance.

Masek, T. D.↗
NASA NTRS1984

The adaptation of vestibulo-spinal reflexes as a function of spaceflight and their relationship to space motion sickness

The hypothesis that exposure to prolonged free fall is a form of sensorymotor rearrangement rather than a direct change in otolith sensitivity or sensory compensation for a reduced otolith input is discussed. Data from Spacelab-1 experiment 1NS-104 are presented to support an otolith reinterpretation hypothesis. This experiment measured vestibulo-spinal reflex changes as a function of sustained free fall. Findings indicate that when a monosynaptic reflex (H-reflex), measured from the major postural muscles (soleus) is used, adaptation to space flight includes a change in how the central nervous system interprets a fall. In a normal gravity environment a sudden unexpected fall produces a potentiated H-reflex. After 7 days inflight, an equivalent fall does not potentiate the reflex. Postflight a greatly increased reflex is observed in those crewmen most susceptible to space motion sickness.

Reschke, M. F.↗
NASA NTRS1986

Pseudolog Digital-to-Analog Converter

Sensitivity decreases by 10 at beginning of each input decade. Method conceived to convert binary-coded data to suitable linear form for stripchart recording. Strip-chart recordings obtained from typical pressure readings in a vacuum system during pumpdown. In reading curve, BCD digital vacuum-gage output processed by analog-to-digital converter in such way that only reading digits (but not range) appear in output. In range and reading, range also converted to analog and placed as most significant digit.

Gooder, S. T.↗
NASA NTRS1986

Unstalled flutter stability predictions and comparisons to test data for a composite prop-fan model

The aeroelastic stability analyses for three graphite/epoxy composite Prop-Fan designs and post-test stability analysis for one of the designs, the SR-3C-X2 are presented. It was shown that Prop-Fan stability can be effectively analyzed using the F203 modal aeroelastic stability analysis developed at Hamilton Standard and that first mode torsion-bending coupling has a direct effect on blade stability. Positive first mode torsion-bending coupling is a destabilizing factor and the minimization of this parameter will increase Prop-Fan stability. It was also shown that Prop-Fan stability analysis using F203 is sensitive to the blade modal data used as input. Calculated blade modal properties varied significantly with the structural analysis used, and these variations are reflected in the F203 calculations.

Turnberg, J. E.↗
NASA NTRS1989

Global sensitivity analysis in control-augmented structural synthesis

In this paper, an integrated approach to structural/control design is proposed in which variables in both the passive (structural) and active (control) disciplines of an optimization process are changed simultaneously. The global sensitivity equation (GSE) method of Sobieszczanski-Sobieski (1988) is used to obtain the behavior sensitivity derivatives necessary for the linear approximations used in the parallel multidisciplinary synthesis problem. The GSE allows for the decoupling of large systems into smaller subsystems and thus makes it possible to determine the local sensitivities of each subsystem's outputs to its inputs and parameters. The advantages in using the GSE method are demonstrated using a finite-element representation of a truss structure equipped with active lateral displacement controllers, which is undergoing forced vibration.

Bloebaum, Christina L.↗
NASA NTRS1992

Experiment specific processing of residual acceleration data

To date, most Spacelab residual acceleration data collection projects have resulted in data bases that are overwhelming to the investigator of low-gravity experiments. This paper introduces a simple passive accelerometer system to measure low-frequency accelerations. Model responses for experiments using actual acceleration data are produced and correlations are made between experiment response and the accelerometer time history in order to test the idea that recorded acceleration data and experimental responses can be usefully correlated. Spacelab 3 accelerometer data are used as input to a variety of experiment models, and sensitivity limits are obtained for particular experiment classes. The modeling results are being used to create experiment-specific residual acceleration data processing schemes for interested investigators.

Rogers, Melissa J. B.↗
NASA NTRS2004

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

Neural Networks (Computer)↗
NASA NTRS2007

NEXT Ion Propulsion System Configurations and Performance for Saturn System Exploration

The successes of the Cassini/Huygens mission have heightened interest to return to the Saturn system with focused robotic missions. The desire for a sustained presence at Titan, through a dedicated orbiter and in-situ vehicle, either a lander or aerobot, has resulted in definition of a Titan Explorer flagship mission as a high priority in the Solar System Exploration Roadmap. The discovery of active water vapor plumes erupting from the tiger stripes on the moon Enceladus has drawn the attention of the space science community. The NASA's Evolutionary Xenon Thruster (NEXT) ion propulsion system is well suited to future missions to the Saturn system. NEXT is used within the inner solar system, in combination with a Venus or Earth gravity assist, to establish a fast transfer to the Saturn system. The NEXT system elements are accommodated in a separable Solar Electric Propulsion (SEP) module, or are integrated into the main spacecraft bus, depending on the mission architecture and performance requirements. This paper defines a range of NEXT system configurations, from two to four thrusters, and the Saturn system performance capability provided. Delivered mass is assessed parametrically over total trip time to Saturn. Launch vehicle options, gravity assist options, and input power level are addressed to determine performance sensitivities. A simple two-thruster NEXT system, launched on an Atlas 551, can deliver a spacecraft mass of over 2400 kg on a transfer to Saturn. Similarly, a four-thruster system, launched on a Delta 4050 Heavy, delivers more than 4000 kg spacecraft mass. A SEP module conceptual design, for a two thruster string, 17 kW solar array, configuration is characterized.

Benson, Scott W.↗
NASA NTRS2005

An Analysis of Spacecraft Localization from Descent Image Data for Pinpoint Landing on Mars and other Cratered Bodies Data Acquisition

A pinpoint landing capability will be a critical component for many planned NASA missions to Mars and beyond. Implicit in the requirement is the ability to accurately localize the spacecraft with respect to the terrain during descent. In this paper, we present evidence that a vision-based solution using craters as landmarks is both practical and will meet the requirements of next generation missions. Our emphasis in this paper is on the feasibility of such a system in terms of (a) localization accuracy and (b) applicability to Martian terrain. We show that accuracy of well under 100 meters can be expected under suitable conditions. We also present a sensitivity analysis that makes an explicit connection between input data and robustness of our pose estimate. In addition, we present an analysis of the susceptibility of our technique to inherently ambiguous configurations of craters. We show that probability of failure due to such ambiguity is becoming increasingly small.

landforms↗
NASA NTRS2017

GC13I-0857: Designing a Frost Forecasting Service for Small Scale Tea Farmers in East Africa

Kenya is the third largest tea exporter in the world, producing 10% of the world's black tea. Sixty percent of this production occurs largely by small scale tea holders, with an average farm size of 1.04 acres, and an annual net income of $1,075. According to a recent evaluation, a typical frost event in the tea growing region causes about $200 dollars in losses which can be catastrophic for a small holder farm. A 72-hour frost forecast would provide these small-scale tea farmers with enough notice to reduce losses by approximately 80 USD annually. With this knowledge, SERVIR, a joint NASA-USAID initiative that brings Earth observations for improved decision making in developing countries, sought to design a frost monitoring and forecasting service that would provide farmers with enough lead time to react to and protect against a forecasted frost occurrence on their farm. SERVIR Eastern and Southern Africa, through its implementing partner, the Regional Centre for Mapping of Resources for Development (RCMRD), designed a service that included multiple stakeholder engagement events whereby stakeholders from the tea industry value chain were invited to share their experiences so that the exact needs and flow of information could be identified. This unique event allowed enabled the design of a service that fit the specifications of the stakeholders. The monitoring service component uses the MODIS Land Surface Temperature product to identify frost occurrences in near-real time. The prediction component, currently under testing, uses the 2-m air temperature, relative humidity, and 10-m wind speed from a series of high-resolution Weather Research and Forecasting (WRF) numerical weather prediction model runs over eastern Kenya as inputs into a frost prediction algorithm. Accuracy and sensitivity of the algorithm is being assessed with observations collected from the farmers using a smart phone app developed specifically to report frost occurrences, and from data shared through our partner network developed at the stakeholder engagement meeting. This presentation will illustrate the efficacy of our frost forecasting algorithm, and a way forward for incorporating these forecasts in a meaningful way to the key decision makers - the small-scale farmers of East Africa.

frost↗
NASA NTRS2017

Sensitivity of Impact Risk to Uncertainty in Asteroid Properties and Entry Parameters

A central challenge in evaluating the threat posed by asteroids striking Earth is the large amount of uncertainty in potential asteroid properties and entry parameters, which can vary the resulting ground damage and affected population by orders of magnitude. We are using our Probabilistic Asteroid Impact Risk (PAIR) model to investigate the sensitivity of asteroid impact damage to these uncertainties. To assess the risk sensitivity, we alternately fix or vary the different input parameters and compare the damage distributions produced. In this study, we consider local ground damage from blast waves or thermal radiation for impactors 50-500m in diameter. The ongoing goal of this work is to help guide future efforts in asteroid characterization and model refinement by determining which properties most significantly affect the potential risk.

Wheeler, Lorien↗
NASA NTRS2006

A comparison of global estimates of marine primary production from ocean color

The third primary production algorithm round robin (PPARR3) compares output from 24 models that estimate depth-integrated primary production from satellite measurements of ocean color, as well as seven general circulation models (GCMs) coupled with ecosystem or biogeochemical models. Here we compare the global primary production fields corresponding to eight months of 1998 and 1999 as estimated from common input fields of photosynthetically-available radiation (PAR), sea-surface temperature (SST), mixed-layer depth, and chlorophyll concentration. We also quantify the sensitivity of the ocean-color-based models to perturbations in their input variables. The pair-wise correlation between ocean-color models was used to cluster them into groups or related output, which reflect the regions and environmental conditions under which they respond differently. The groups do not follow model complexity with regards to wavelength or depth dependence, though they are related to the manner in which temperature is used to parameterize photosynthesis. Global average PP varies by a factor of two between models. The models diverged the most for the Southern Ocean, SST under 10 C, and chlorophyll concentration exceeding 1mg Chlm-3. Based on the conditions under which the model results diverge most, we conclude that current ocean-color-based models are challenged by high-nutrient low-chlorophyll conditions, and extreme temperatures or chlorophyll concentrations. The GCM-based models predict comparable primary production to those based on ocean color: they estimate higher values in the Southern Ocean, at low SST, and in the equatorial band, while they estimate lower values in eutrophic regions (probably because the area of high chlorophyll concentrations is smaller in the GCMs). Further progress in primary production modeling requires improved understanding of the effect of temperature on photosynthesis and better parameterization of the maximum photosynthetic rate.

Yamanaka, Yasuhiro↗
DOE OSTI2016

UQ Toolkit v. 3.0

The Uncertainty Quantification (UQ) Toolkit is a software library for the characterization and propagation of uncertainties in computational models. This library provides Bayesian inference tools to infer uncertain model parameters, Bayesian compressive sensing methods for discovering sparse representations of high-dimensional input-output response surfaces, methods for constructing Karhunen-Loève representations of stochastic processes, and global sensitivity analysis tools used to compute Sobol indices in order to characterize the importance of uncertain inputs or the interactions between them. The basis for many of these methods relies on representing random variables with Polynomial Chaos expansions (PCEs). This library implements several spectral basis function types (e.g. Hermite basis functions in terms of Gaussian random variables or Legendre basis functions in terms of uniform random variables) that can be used to represent random variables with PCEs. For the propagation of uncertainty, tools are provided to propagate PCEs that describe the input uncertainty through the computational model using either intrusive methods (Galerkin projection of equations onto basis functions) or non-intrusive methods (perform deterministic operation at sampled values of the random values and project the obtained results onto basis functions).

Sargsyan, Khachik↗