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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Metamodels for Computer-Based Engineering Design: Survey and Recommendations

The use of statistical techniques to build approximations of expensive computer analysis codes pervades much of todays engineering design. These statistical approximations, or metamodels, are used to replace the actual expensive computer analyses, facilitating multidisciplinary, multiobjective optimization and concept exploration. In this paper we review several of these techniques including design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We survey their existing application in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of statistical approximation techniques in given situations and how common pitfalls can be avoided.

Simpson, Timothy W.↗
NASA NTRS2010

Solar Radiation Transport in the Cloudy Atmosphere: A 3D Perspective on Observations and Climate Impacts

The interplay of sunlight with clouds is a ubiquitous and often pleasant visual experience, but it conjures up major challenges for weather, climate, environmental science and beyond. Those engaged in the characterization of clouds (and the clear air nearby) by remote sensing methods are even more confronted. The problem comes, on the one hand, from the spatial complexity of real clouds and, on the other hand, from the dominance of multiple scattering in the radiation transport. The former ingredient contrasts sharply with the still popular representation of clouds as homogeneous plane-parallel slabs for the purposes of radiative transfer computations. In typical cloud scenes the opposite asymptotic transport regimes of diffusion and ballistic propagation coexist. We survey the three-dimensional (3D) atmospheric radiative transfer literature over the past 50 years and identify three concurrent and intertwining thrusts: first, how to assess the damage (bias) caused by 3D effects in the operational 1D radiative transfer models? Second, how to mitigate this damage? Finally, can we exploit 3D radiative transfer phenomena to innovate observation methods and technologies? We quickly realize that the smallest scale resolved computationally or observationally may be artificial but is nonetheless a key quantity that separates the 3D radiative transfer solutions into two broad and complementary classes: stochastic and deterministic. Both approaches draw on classic and contemporary statistical, mathematical and computational physics.

Davis, Anthony B.↗
NASA NTRS1981

APPLEPIPS /Apple Personal Image Processing System/ - An interactive digital image processing system for the Apple II microcomputer

Recent developments related to microprocessor-based personal computers have made low-cost digital image processing systems a reality. Image analysis systems built around these microcomputers provide color image displays for images as large as 256 by 240 pixels in sixteen colors. Descriptive statistics can be computed for portions of an image, and supervised image classification can be obtained. The systems support Basic, Fortran, Pascal, and assembler language. A description is provided of a system which is representative of the new microprocessor-based image processing systems currently on the market. While small systems may never be truly independent of larger mainframes, because they lack 9-track tape drives, the independent processing power of the microcomputers will help alleviate some of the turn-around time problems associated with image analysis and display on the larger multiuser systems.

Masuoka, E.↗
NASA NTRS1994

Direct simulation of a self-similar plane wake

Direct simulations of two time-developing turbulent wakes have been performed. Initial conditions for the simulations were obtained from two realizations of a direct simulation of a turbulent boundary layer at momentum thickness Reynolds number 670. In addition, extra two dimensional disturbances were added in one of the cases to mimic two dimensional forcing. The unforced wake is allowed to evolve long enough to attain self similarity. The mass-flux Reynolds number (equivalent to the momentum thickness Reynolds number in spatially developing wakes) is 2000, which is high enough for a short k(exp -5/3) range to be evident in the streamwise one dimensional velocity spectrum. Several turbulence statistics have been computed by averaging in space and over the self-similar period in time. The growth rate in the unforced flow is low compared to experiments, but when this growth-rate difference is accounted for, the statistics of the unforced case are in reasonable agreement with experiments. However, the forced case is significantly different. The growth rate, turbulence Reynolds number, and turbulence intensities are as much as ten times larger in the forced case. In addition, the forced flow exhibits large-scale structures similar to those observed in transitional wakes, while the unforced flow does not.

Moser, Robert D.↗
NASA NTRS1994

Direct simulation of a self-similar plane wake

Direct simulations of two time-developing turbulent wakes have been performed. Initial conditions for the simulations were obtained from two realizations of a direct simulation of a turbulent boundary layer at momentum thickness Reynolds number 670. In addition, extra two-dimensional disturbances were added in one of the cases to mimic two-dimensional forcing. The unforced wake is allowed to evolve long enough to attain self-similarity. The mass-flux Reynolds number (equivalent to the momentum thickness Reynolds number in spatially developing wakes) is 2000, which is high enough for a short kappa(exp -5/3) range to be evident in the streamwise one-dimensional velocity spectrum. Several turbulence statistics have been computed by averaging in space and over the self-similar period in time. The growth rate in the unforced flow is low compared to experiments, but when this growth-rate difference is accounted for, the statistics of the unforced case are in reasonable agreement with experiments. However, the forced case is significantly different. The growth rate, turbulence Reynolds number, and turbulence intensities are as much as ten times larger in the forced case. In addition, the forced flow exhibits large-scale structures similar to those observed in transitional wakes, while the unforced flow does not.

Moser, Robert D.↗
NASA NTRS2019

Scale-Resolving Simulations of Laminar-to-Turbulent Transition in Swept-Wing Flow

Aerodynamic shape optimization of swept Natural Laminar Flow (NLF) wing design plays an important role for the development of future energy-efficient commercial transport aircraft. Lack of predictive laminar-to-turbulent transition modeling capabilities is still limiting the extent to which design can rely on computational tools, while making costly wind tunnel and flight tests a necessity. Prediction of transition on practical 3D wing geometries using the e (sup N) method coupled with laminar boundary layer and linear stability codes or local statistical correlation CFD (Computational Fluid Dynamics) models such as the gamma - Re (sub theta) Langtry-Menter model have proven challenging, whereas transitional hybrid RANS-LES solvers have shown encouraging but still mitigated results.

Scale-resolving↗
NASA NTRS1989

Chi-squared and C statistic minimization for low count per bin data

Results are presented from a computer simulation comparing two statistical fitting techniques on data samples with large and small counts per bin; the results are then related specifically to X-ray astronomy. The Marquardt and Powell minimization techniques are compared by using both to minimize the chi-squared statistic. In addition, Cash's C statistic is applied, with Powell's method, and it is shown that the C statistic produces better fits in the low-count regime than chi-squared.

Nousek, John A.↗
NASA NTRS2021

Computational Inference of Vibratory System with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains

Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.

K Zhou↗
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 NTRS2017

A Non-Intrusive Algorithm for Sensitivity Analysis of Chaotic Flow Simulations

We demonstrate a novel algorithm for computing the sensitivity of statistics in chaotic flow simulations to parameter perturbations. The algorithm is non-intrusive but requires exposing an interface. Based on the principle of shadowing in dynamical systems, this algorithm is designed to reduce the effect of the sampling error in computing sensitivity of statistics in chaotic simulations. We compare the effectiveness of this method to that of the conventional finite difference method.

Blonigan, Patrick J.↗
NASA NTRS1973

Computation of launch vehicle system requirements using hybrid computer.

Formulating an adequate statistical statement concerning space vehicle dynamic states requires the combination of the statistics of the environment and the vehicle's basic parameters. The basic ingredient of the environment for the Space Shuttle launch phase is the winds, which are represented by an ensemble of measured winds (150/month), which today constitute the best statistical representation. The problem treated in this paper then becomes twofold: (1) how can the vehicle response be analyzed using wind ensembles, and (2) how can the vehicle parameter variations be treated in conjunction with wind ensembles.

Ryan, R. S.↗
NASA NTRS1991

Estimating Rain Attenuation In Satellite Communication Links

Attenuation computed with help of statistical model and meteorological data. NASA Lewis Research Center Satellite Link Attenuation Model (SLAM) program QuickBASIC computer program evaluating static and dynamic statistical assessment of impact of rain attenuation on communication link established between Earth terminal and geosynchronous satellite. Application in specification, design, and assessment of satellite communication links for any terminal location in continental United States. Written in Microsoft QuickBASIC.

Manning, R. M.↗
DOE OSTI2020

Probabilistic learning and updating of a digital twin for composite material systems

This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.

42 ENGINEERING↗
NASA NTRSDate not supplied

Launch Vehicle and Spacecraft Transmitter Electromagnetic Compatibility

The Launch Services Program at NASA’s Kennedy Space Center (KSC) is the primary gate for acquiring commercial vehicles to provide a cost effective ride to space for NASA spacecraft. With the lunar Gateway, more human tended elements are planned for launch. One challenge facing the space industry is the proliferation of communication and science transmitters at frequencies beyond the qualification of space avionics and instruments. Studies have ensued to examine the intricacies of performing radiated susceptibility testing and analysis above 18 GHz. Changes in the launch vehicle communications interface to the range have also lead to new launch vehicle antenna systems and more reliance on GPS and telemetry systems. Finally, research initiated at KSC in the area of predicted electric field distributions in launch vehicle payload fairings have spawned Small Business Technology Transfer initiatives for industry to investigate statistical algorithm and computational improvements in large payload fairing modeling of transmitters at frequencies in the GHz range. These topics, along with electromagnetic compatibility testing for launch vehicles will be discussed.

Electromagnetic Testing Cavity Statistics↗
DOE OSTI2023

Extrapolation of the Rainflow-Counted Load Ranges for Fatigue Assessment of the Wind Turbine's Blades

Wind turbine design standards recommend the use of statistical modeling coupled with extrapolation of the short-term load data to long-term periods for fatigue reliability assessment. However, statistical error and computational expense can limit the accuracy of such approaches. In the case of wind turbine blades, the errors are more significant because of the high material fatigue exponent that makes the damage estimations more sensitive to variations. In addition, due to different excitation sources, the flapwise load range histogram is not unimodal, and thus its statistical modeling is complex. In the present work, we provide three methods for statistical modeling of the flapwise bending moment ranges including a novel approach based on frequency-based separation of the modes. The first two methods are simplified approaches for modeling the most crucial load ranges using unimodal distributions and the third method involves multimodal distribution fitting. The research is based on 3600 10-minute aeroelastic simulations of DTU 10MW case study wind turbine from which a benchmark damage equivalent load (DEL) is calculated. The DEL calculated by each of the three proposed methods is compared to this reference. The results show that the conventional approach based on using 6 seeds as well as using mixture models fitted on the limited data lead to under-conservative results with errors up to 23%. On the other hand, the simplified unimodal approaches provided in this work can provide conservative estimations of the fatigue damage with mean values 5% and 12% higher than the benchmark. However, the variability of the DEL estimates is higher when using unimodal extrapolation of the load ranges, and the data can be conservative by 17.5%. The proposed unimodal fits suggested for modeling and extrapolation of the blade's load ranges provide less errors relatively and most importantly conservative DEL estimations while maintaining computational efficiency.

blade fatigue↗
NASA NTRS2020

Direct Numerical Simulations of Acoustic Disturbances in Various Rectangular Nozzle Configurations

We perform Direct Numerical Simulations (DNS) to study the acoustic freestream disturbances radiating from the turbulent boundary layers along the contoured nozzle walls of a hypersonic wind tunnel with a rectangular test section. To begin with, the effects of the spanwise end walls are suppressed by confining the spanwise computational domain to a finite segment of the overall nozzle cross section and by imposing periodic boundary conditions across that spanwise domain. Besides providing a building-block configuration to reveal par- tial effects of the enclosed acoustic environment within the wind tunnel, these computations serve as a stepping stone toward the goal of fully-3D computations including the nozzle end walls. Building upon the earlier simulations of Deegan et al. (2018), we show that the computed acoustic characteristics in the spanwise periodic simulations are insensitive to changes in the grid resolution parameters (e.g., x+ and z+). This is relative to the previous simulations that involved a coarser resolution in the streamwise and wall-normal directions, especially up- stream of the test section. Furthermore, we outline a comparison of the pressure fluctuations induced by the turbulent boundary layers over the contoured nozzle walls and several other calculations involving boundary layers over a single flat plate at nearly the same value of the edge Mach number. We also show the impact of having only one turbulent boundary layer instead of two within the computational setup. Various flow statistics, including the first and second moments of the unsteady flow field, are computed for the different flow configurations. Good comparisons of the statistics of the nozzle-wall boundary-layer turbulence and of the freestream acoustic disturbances between the simulations of coarser and finer grids confirms DNS procedure, but also the insensitivity of noise characteristics t section to the inflow turbulence generation technique.

Hildebrand, Nathaniel↗
DOE OSTI2024

Physics-informed machine learning of the correlation functions in bulk fluids

The Ornstein–Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine learning models, notably physics-informed neural networks and physics-informed neural operator networks, are explored to solve the OZ equation. The physics-informed machine learning models demonstrate great accuracy and high efficiency in solving the forward and inverse OZ problems of various bulk fluids. The results highlight the significant potential of physics-informed machine learning for applications in thermodynamic state theory.

97 MATHEMATICS AND COMPUTING↗