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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Improved Test Planning and Analysis Through the Use of Advanced Statistical Methods

The goal of this work is, through computational simulations, to provide statistically-based evidence to convince the testing community that a distributed testing approach is superior to a clustered testing approach for most situations. For clustered testing, numerous, repeated test points are acquired at a limited number of test conditions. For distributed testing, only one or a few test points are requested at many different conditions. The statistical techniques of Analysis of Variance (ANOVA), Design of Experiments (DOE) and Response Surface Methods (RSM) are applied to enable distributed test planning, data analysis and test augmentation. The D-Optimal class of DOE is used to plan an optimally efficient single- and multi-factor test. The resulting simulated test data are analyzed via ANOVA and a parametric model is constructed using RSM. Finally, ANOVA can be used to plan a second round of testing to augment the existing data set with new data points. The use of these techniques is demonstrated through several illustrative examples. To date, many thousands of comparisons have been performed and the results strongly support the conclusion that the distributed testing approach outperforms the clustered testing approach.

Green, Lawrence L.↗
NASA NTRS1990

Surface characterizations of color threshold

The paper evaluates how well three different parametric shapes, ellipsoids, rectangles, and parallelograms, serve as models of three-dimentional detection contours. The constraints of the procedures for deriving the best-fitting shapes on inferences about the theoretical visual detection mechanisms are described. Results of two statistical tests show that only the parallelogram fits the data with more precision than the variance in repeated threshold measurements, and thus provides a slightly better fit than the other two shapes. Nevertheless it does not serve as a better guide than the ellipsoidal model for interpolating from the measurements to thresholds in novel color directions.

Poirson, Allen B.↗
NASA NTRS1983

Standard ozone profiles from balloon and satellite data sets

Standard profiles based on upper level averaged profiles from BUV and lower level averaged profiles from balloon measurements are presented in a parametric representation as a function of time of year and latitude. The representation is a simple 4-parameter function representing the ozone amount (m-atm-cm) in each of 12 atmospheric layers defined following the standard Umkehr convention. The same parameterization is applied to the Nimbus-7 SBUV data and is compared to the BUV/balloon parameterization. The ozone variance unaccounted for by the representation is presented and discussed. The season-latitude representation reduces considerably the ozone variance at all levels and explains much of the correlation between layers. This simple representation and corresponding covariance matrix have been used as a priori information in the ozone vertical profile inversion of the Nimbus-7 SBUV experimental measurements.

Klenk, K. F.↗
NASA NTRS1995

The lognormal distribution as a model for bio-optical variability in the sea

The lognormal distribution is presented as a useful model for bio-optical variability at a variety of spatial and temporal scales. A parametric statistical framework is presented for using the lognormal model to assess the effects of heterogeneity and scale on closure. Variability at small scales affects but is unresolved by large-scale measurements. An assumed lognormal distribution allows one to integrate over small-scale variability to predict large-scale measurements. Examples are presented to demonstrate how knowledge of the variance can be incorporated into models to relate measurements made at different scales.

Campbell, Janet W.↗
DOE OSTI2024

A principle of maximum ignorance for semiclassical gravity

The principle of maximum ignorance posits that the coarse-grained description of a system is maximally agnostic about its underlying microscopic structure. We briefly review this principle for random matrix theory and for the eigenstate thermalization hypothesis. We then apply this principle in holography to construct ensembles of random mixed states. This leads to an ensemble of microstates which models our microscopic ignorance, and which on average reproduces the effective semiclassical physics of a given bulk state. We call this ensemble the state-averaging ansatz. The output of our model is a prediction for semiclassical contributions to variances and higher statistical moments over the ensemble of microstates. The statistical moments provide coarse-grained — yet gravitationally non-perturbative — information about the microstructure of the individual states of the ensemble. We show that these contributions exactly match the on-shell action of known wormhole configurations of the gravitational path integral. These results strengthen the view that wormholes simply parametrize the ignorance of the microstructure of a fundamental state, given a fixed semiclassical bulk description.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A new multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential processes under process model and parametric uncertainty

Process-based models have been widely used for hydrologic modeling, and it is a common practice to use sensitivity analysis methods for excluding non-influential hydrologic processes from further investigation and/or model improvement. This study develops a new method called multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential system processes and parameters. MMADS is conceptually similar to the Morris method for addressing parametric uncertainty, but has a unique feature to address both process model uncertainty (i.e., a process may be represented by multiple process models) and process model parameter uncertainty (i.e., parameters associated with a process model are random). MMADS first evaluates absolute differences of a quantity of interest (i.e., a system model output) by varying process models and/or process model parameter values, and then calculates the mean and variance of the differences for investigating process influence. The mean measures overall influence of the process on the quantity of interest, and the variance estimates influence of nonlinear effects of the process and/or its interactions with other processes. MMADS is an extension of the Morris method from a parameter space to a joint parameter-model space for explicitly addressing both process model uncertainty and model parameter uncertainty. The performance of MMADS is evaluated by using two numerical experiments. One experiment is based on Sobol’s G*-function with ten product elements, and has analytical solutions of the MMADS mean and variance of absolute differences. The other experiment is for groundwater flow modeling which considers three processes (i.e., recharge, geology, and snowmelt) that interact with each other. Finally, results indicate that MMADS is computationally efficient and can identify non-influential processes of complex hydrological systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Subseasonal Tropical Convection Characteristics in the Energy Exascale Earth System Model Version 2

Accurate simulation of subseasonal tropical moist convection remains a key challenge for Earth system models. The difficulties stem from the reliance of cumulus cloud processes on model parameterizations and the need to represent the multiscale nature of interactions among clouds, radiation, moisture, circulation, and surface energy fluxes. Equatorial convection drives circulation anomalies that can affect weather patterns and extremes globally, motivating efforts to better understand and simulate these tropical disturbances. Here, a detailed review of subseasonal tropical convective behavior as simulated in the Energy Exascale Earth System Model version 2 (E3SMv2) is presented, with comparison to its predecessor version 1 (E3SMv1) and reference data sets. Model structural changes to the deep convective trigger function and surface fluxes, along with parametric tuning of the cloud and microphysics schemes, together result in an improved depiction of organized tropical convection across scales. In particular, E3SMv2 exhibits a more realistic Madden‐Julian oscillation (MJO) and low‐frequency Kelvin waves—owing to a sharper time mean equatorial meridional moisture gradient and improved convection‐circulation coupling —as well as a better depiction of MJO Northern Hemisphere teleconnections. Despite these improvements, subseasonal precipitation variance continues to be strongly underestimated in E3SMv2. Use of a cloud plume model also reveals that the coupling between daily averaged tropical precipitation and lower tropospheric instability in E3SM is inconsistent with observations, a bias that could potentially impact the simulation of intraseasonal disturbances.

54 ENVIRONMENTAL SCIENCES↗
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↗
NASA NTRS2016

Simple Parametric Model for Intensity Calibration of Cassini Composite Infrared Spectrometer Data

Accurate intensity calibration of a linear Fourier-transform spectrometer typically requires the unknown science target and the two calibration targets to be acquired under identical conditions. We present a simple model suitable for vector calibration that enables accurate calibration via adjustments of measured spectral amplitudes and phases when these three targets are recorded at different detector or optics temperatures. Our model makes calibration more accurate both by minimizing biases due to changing instrument temperatures that are always present at some level and by decreasing estimate variance through incorporating larger averages of science and calibration interferogram scans.

Brasunas, J.↗
DOE OSTI2024

Exploration with Scalable Gaussian Process Reinforcement Learning

Exploration is a challenging problem in reinforcement learning (RL), especially in environments with sparse rewards. Quantifying and utilizing the parametric uncertainty has been shown to be paramount for successful exploration [Osband et al., 2018]. Bayesian, or approximately Bayesian, methods present a principled means of estimating the parametric uncertainty in RL problems. Gaussian processes, nonparametric Bayesian models, are often impractical due to poor scalability and computational bottlenecks. We introduce a scalable Gaussian process RL (GPRL) method which directly induces sparsity in the covariance matrix to facilitate faster computation. This is a departure from previous GPRL methods which instead rely on data reduction and subsampling. We compare various covariance-based exploration techniques (Thompson sampling, upper confidence bound, and probabilistic maximum variance) which leverage our scalable GP framework in sparse reward environments. Finally, we show favorable comparison against the bootstrapped deep Q-Network.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Use of Taguchi design of experiments to optimize and increase robustness of preliminary designs

The research performed this summer includes the completion of work begun last summer in support of the Air Launched Personnel Launch System parametric study, providing support on the development of the test matrices for the plume experiments in the Plume Model Investigation Team Project, and aiding in the conceptual design of a lunar habitat. After the conclusion of last years Summer Program, the Systems Definition Branch continued with the Air Launched Personnel Launch System (ALPLS) study by running three experiments defined by L27 Orthogonal Arrays. Although the data was evaluated during the academic year, the analysis of variance and the final project review were completed this summer. The Plume Model Investigation Team (PLUMMIT) was formed by the Engineering Directorate to develop a consensus position on plume impingement loads and to validate plume flowfield models. In order to obtain a large number of individual correlated data sets for model validation, a series of plume experiments was planned. A preliminary 'full factorial' test matrix indicated that 73,024 jet firings would be necessary to obtain all of the information requested. As this was approximately 100 times more firings than the scheduled use of Vacuum Chamber A would permit, considerable effort was needed to reduce the test matrix and optimize it with respect to the specific objectives of the program. Part of the First Lunar Outpost Project deals with Lunar Habitat. Requirements for the habitat include radiation protection, a safe haven for occasional solar flare storms, an airlock module as well as consumables to support 34 extra vehicular activities during a 45 day mission. The objective for the proposed work was to collaborate with the Habitat Team on the development and reusability of the Logistics Modules.

Carrasco, Hector R.↗
NASA NTRS1994

Parameter dimension of turbulence-induced phase errors and its effects on estimation in phase diversity

The method of phase diversity has been used in the context of incoherent imaging to estimate jointly an object that is being imaged and phase aberrations induced by atmospheric turbulence. The method requires a parametric model for the phase-aberration function. Typically, the parameters are coefficients to a finite set of basis functions. Care must be taken in selecting a parameterization that properly balances accuracy in the representation of the phase-aberration function with stability in the estimates. It is well known that over parameterization can result in unstable estimates. Thus a certain amount of model mismatch is often desirable. We derive expressions that quantify the bias and variance in object and aberration estimates as a function of parameter dimension.

Thelen, Brian J.↗
DOE OSTI2023

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity, via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias–variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Furthermore, our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.

42 ENGINEERING↗
DOE OSTI2023

Mixing across stable density interfaces in forced stratified turbulence

Understanding how turbulence enhances irreversible scalar mixing in density-stratified fluids is a central problem in geophysical fluid dynamics. While isotropic overturning regions are commonly the focus of mixing analyses, we here investigate whether significant mixing may arise in anisotropic statically stable regions of the flow. Focusing on a single forced direct numerical simulation of stratified turbulence, we analyse spatial correlations between the vertical density gradient $\partial \rho /\partial z$ and the dissipation rates of kinetic energy $\epsilon$ and scalar variance $\chi$ , the latter quantifying scalar mixing. The domain is characterized by relatively well-mixed density layers separated by sharp stable interfaces that are correlated with high vertical shear. While static instability is most prevalent within the mixed layers, much of the scalar mixing is localized to the intervening interfaces, a phenomenon not apparent if considering local static instability or $\epsilon$ alone. While the majority of the domain is characterized by the canonical flux coefficient $\varGamma \equiv \chi /\epsilon =0.2$ , often assumed in ocean mixing parametrizations, extreme values of $\chi$ within the statically stable interfaces, associated with elevated $\varGamma$ , strongly skew the bulk statistics. Our findings suggest that current parametrizations of turbulent mixing may be biased by undersampling, such that the most common, but not necessarily the most significant, mixing events are overweighted. Having focused here on a single simulation of stratified turbulence, it is hoped that our results motivate a broader investigation into the role played by stable density interfaces in mixing, across a wider range of parameters and forcing schemes representative of ocean turbulence.

Couchman, Miles M. P. (ORCID:0000000246676829)↗
NASA NTRS1994

Mean climate and transience in the tropics of the UGAMP GCM: Sensitivity to convective parametrization

The sensitivity of the UK Universities Global Atmospheric Modelling Programme (UGAMP) General Circulation Model (UGCM) to two very different approaches to convective parametrization is described. Comparison is made between a Kuo scheme, which is constrained by large-scale moisture convergence, and a convective-adjustment scheme, which relaxes to observed thermodynamic states. Results from 360-day integrations with perpetual January conditions are used to describe the model's tropical time-mean climate and its variability. Both convection schemes give reasonable simulations of the time-mean climate, but the representation of the main modes of tropical variability is markedly different. The Kuo scheme has much weaker variance, confined to synoptic frequencies near 4 days, and a poor simulation of intraseasonal variability. In contrast, the convective-adjustment scheme has much more transient activity at all time-scales. The various aspects of the two schemes which might explain this difference are discussed. The particular closure on moisture convergence used in this version of the Kuo scheme is identified as being inappropriate.

Slingo, Julia↗
NASA NTRS2015

Random Predictor Models for Rigorous Uncertainty Quantification: Part 2

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean, the variance, and the range of the model's parameter, thus of the output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, is bounded rigorously.

Crespo, Luis G.↗
DOE OSTI2022

A Conditional Autoencoder for Galaxy Photometric Parameter Estimation

Astronomical photometric surveys routinely image billions of galaxies, and traditionally infer the parameters of a parametric model for each galaxy. This approach has served us well, but the computational expense of deriving a full posterior probability distribution function is a challenge for increasingly ambitious surveys. In this paper, we use deep learning methods to characterize galaxy images, training a conditional autoencoder on mock data. The autoencoder can reconstruct and denoise galaxy images via a latent space engineered to include semantically meaningful parameters, such as brightness, location, size, and shape. Our model recovers galaxy fluxes and shapes on mock data with a lower variance than the Hyper Suprime-Cam photometry pipeline, and returns reasonable answers even for inputs outside the range of its training data. When applied to data in the training range, the regression errors on all extracted parameters are nearly unbiased with a variance near the Cramr-Rao bound.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2015

Random Predictor Models for Rigorous Uncertainty Quantification: Part 1

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean and the variance of the model's parameters, thus of the predicted output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, can be bounded tightly and rigorously.

Crespo, Luis G.↗