Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model generation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Galilean satellites - Observations of mutual occultations and eclipses in 1973

Seven Galilean satellite mutual events, two occultations and two eclipses of Europa and three eclipses of Io, were observed at three wavelengths (0.35, 0.50, 0.91 micrometers) with a time resolution of 0.1 sec. Preliminary model fits to the light curves are presented. Model satellites with different albedo distributions (uniform disk, bright solar caps, bright quadrant) are used in generating model occultation and eclipse curves to demonstrate the sensitivity of observed light curves to the brightness distribution on the surface of the occulted or eclipsed satellite. At the present the observations yield no conclusive information of the limb-darkening of Io. The best data for Europa indicate that the satellite is limb-darkened at both 0.50 and 0.91 micrometers.

Wasserman, L. H.↗
NASA NTRS1985

Estimation of soil hydraulic properties with microwave techniques

Useful quantitative information about soil properties may be obtained by calibrating energy and moisture balance models with remotely sensed data. A soil physics model solves heat and moisture flux equations in the soil profile and is driven by the surface energy balance. Model generated surface temperature and soil moisture and temperature profiles are then used in a microwave emission model to predict the soil brightness temperature. The model hydraulic parameters are varied until the predicted temperatures agree with the remotely sensed values. This method is used to estimate values for saturated hydraulic conductivity, saturated matrix potential, and a soil texture parameter. The conductivity agreed well with a value measured with an infiltration ring and the other parameters agreed with values in the literature.

Oneill, P. E.↗
NASA NTRS1993

Advanced X ray Astrophysics Facility-Imaging (AXAF-I) thermal analyses using Integrated Thermal Analysis System (ITAS) program

The complex geometry and stringent thermal requirements associated with the Advanced X-ray Astrophysics Facility - Imaging (AXAF-I) necessitate a detailed and accurate thermal analysis of the proposed system. A brief description of said geometry and thermal requirements is included. Among the tools considered for the aforementioned analysis is a PC-compatible version of the Integrated Thermal Analysis System (ITAS). Several bench-mark studies were performed to evaluate the capabilities of ITAS and to compare the corresponding results with those obtained using TRASYS and SINDA. Comparative studies were conducted for a typical Space Station module. Four models were developed using various combinations of the available software packages (i.e. ITAS, SINDA, and TRASYS). Orbital heating and heat transfer calculations were performed to determine the temperature distributions along the surfaces of this module. A comparison of the temperature distributions obtained for each of the four cases is presented. Results of this investigation were used to verify the different ITAS modules including those used for model generation, steady state and transient orbital heating analyses, radiative and convective heat flow analyses, and SINDA/TRASYS model translation. The results suggest that ITAS is well suited to subsequent analyses of the AXAF-I.

Ghaffarian, Benny↗
NASA NTRS2021

Development of Spaceborne SoOp Reflectometry Model for Complex Terrains

Following the launch of multiple global navigation satel-lite system (GNSS) reflectometry (GNSS-R) missions, theSignals of Opportunity (SoOp) method has proven to be apowerful tool for geophysical parameter retrieval for land ap-plications such as soil moisture. Having demonstrated thefeasibility of the SoOp techniques at P- and S-band, the devel-opment of SoOp measurements beyond the GNSS frequencyregime is highly anticipated. The SoOp Coherent Bistatic(SCoBi) model and simulator, developed in 2017 and open-sourced in 2018, has been made available to provide multi-frequency, fully polarimetric SoOp simulations for ground-based applications through the joint use of analytical wavetheory and distorted Borne approximation to evaluate landcontributions from multilayer dielectric profiles composed ofsoil moisture, vegetation, and surface roughness effects. Thispaper describes the advancement of SCoBi from a ground-and airborne-based model to a spaceborne model. This ex-tension allows for fully polarimetric, complex delay-Dopplermap (DDM) simulations through evaluation of the coherentsuperposition of electric fields emerging from a grid of ori-ented facets. The model generates a grid of facets by de-termining the geometry of contributing elements from digitalelevation models, with each element providing its contribu-tion under a flat-earth assumption. This module will enablethe analysis of fully polarimetric scattering from frequenciesavailable across the ultra-high frequency (UHF) regime.

D R Boyd↗
NASA NTRS2006

Using Apex To Construct CPM-GOMS Models

process for automatically generating computational models of human/computer interactions as well as graphical and textual representations of the models has been built on the conceptual foundation of a method known in the art as CPM-GOMS. This method is so named because it combines (1) the task decomposition of analysis according to an underlying method known in the art as the goals, operators, methods, and selection (GOMS) method with (2) a model of human resource usage at the level of cognitive, perceptual, and motor (CPM) operations. CPM-GOMS models have made accurate predictions about behaviors of skilled computer users in routine tasks, but heretofore, such models have been generated in a tedious, error-prone manual process. In the present process, CPM-GOMS models are generated automatically from a hierarchical task decomposition expressed by use of a computer program, known as Apex, designed previously to be used to model human behavior in complex, dynamic tasks. An inherent capability of Apex for scheduling of resources automates the difficult task of interleaving the cognitive, perceptual, and motor resources that underlie common task operators (e.g., move and click mouse). The user interface of Apex automatically generates Program Evaluation Review Technique (PERT) charts, which enable modelers to visualize the complex parallel behavior represented by a model. Because interleaving and the generation of displays to aid visualization are automated, it is now feasible to construct arbitrarily long sequences of behaviors. The process was tested by using Apex to create a CPM-GOMS model of a relatively simple human/computer-interaction task and comparing the time predictions of the model and measurements of the times taken by human users in performing the various steps of the task. The task was to withdraw $80 in cash from an automated teller machine (ATM). For the test, a Visual Basic mockup of an ATM was created, with a provision for input from (and measurement of the performance of) the user via a mouse. The times predicted by the automatically generated model turned out to approximate the measured times fairly well (see figure). While these results are promising, there is need for further development of the process. Moreover, it will also be necessary to test other, more complex models: The actions required of the user in the ATM task are too sequential to involve substantial parallelism and interleaving and, hence, do not serve as an adequate test of the unique strength of CPM-GOMS models to accommodate parallelism and interleaving.

John, Bonnie↗
NASA NTRS1979

Monte Carlo simulation of single accident airport risk profile

A computer simulation model was developed for estimating the potential economic impacts of a carbon fiber release upon facilities within an 80 kilometer radius of a major airport. The model simulated the possible range of release conditions and the resulting dispersion of the carbon fibers. Each iteration of the model generated a specific release scenario, which would cause a specific amount of dollar loss to the surrounding community. By repeated iterations, a risk profile was generated, showing the probability distribution of losses from one accident. Using accident probability estimates, the risks profile for annual losses was derived. The mechanics are described of the simulation model, the required input data, and the risk profiles generated for the 26 large hub airports.

Source record↗
DOE OSTI2024

A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration

Abstract Calibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Software Surface Modeling and Grid Generation Steering Committee

It is a NASA objective to promote improvements in the capability and efficiency of computational fluid dynamics. Grid generation, the creation of a discrete representation of the solution domain, is an essential part of computational fluid dynamics. However, grid generation about complex boundaries requires sophisticated surface-model descriptions of the boundaries. The surface modeling and the associated computation of surface grids consume an extremely large percentage of the total time required for volume grid generation. Efficient and user friendly software systems for surface modeling and grid generation are critical for computational fluid dynamics to reach its potential. The papers presented here represent the state-of-the-art in software systems for surface modeling and grid generation. Several papers describe improved techniques for grid generation.

Smith, Robert E.↗
NASA NTRS1988

Four-Dimensional Global Reference-Atmosphere Model

Four-Dimensional Global Reference Atmosphere Model (GRAM) computer program developed from empirical atmospheric model generating values for pressure, density, temperature, and winds, from ground to orbital altitudes. Is amalgamation of two empirical atmospheric models for low and high atmosphere with newly-developed latitude-and longitude-dependent model for middle atmosphere. UNIVAC version written in UNIVAC FORTRAN. DEC VAX version of GRAM written in FORTRAN 77. Applications include simulation of reentry trajectories of external tanks, studies of global circulation and diffusion and generation of plots or data for comparison.

Johnson, Dale↗
DOE OSTI2019

Design and analysis of machine learning exchange-correlation functionals via rotationally invariant convolutional descriptors

Here, we explore the potential of a data-driven approach to the design of exchange-correlation (xc) functionals. The approach, inspired by convolutional filters in computer vision and surrogate functions from optimization, utilizes convolutions of the electron density to form a feature space to represent local electronic environments and neural networks to map the features to the exchange-correlation energy density. These features are orbital free, and provide a systematic route to including information at various length scales. This work shows that convolutional descriptors are theoretically capable of an exact representation of the electron density, and proposes Maxwell-Cartesian spherical harmonic kernels as a class of rotationally invariant descriptors for the construction of machine learned functionals. The approach is demonstrated using data from the B3LYP functional on a number of small molecules containing C, H, O, and N along with a neural network regression model. The machine learned functionals are compared to standard physical approximations and the accuracy is assessed for the absolute energy of each molecular system as well as formation energies. The results indicate that it is possible to reproduce the exchange-correlation portion of B3LYP formation energies to within chemical accuracy using orbital-free descriptors with a spatial extent of 0.2 Å. The findings provide empirical insight into the spatial range of electron exchange, and suggest that the combination of convolutional descriptors and machine learning regression models is a promising framework for xc functional design, although challenges remain in obtaining training data and generating models consistent with pseudopotentials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data

A restricted Boltzmann machine (RBM) is a generative model that could be used in effectively balancing a cybersecurity dataset because the synthetic data a RBM generates follows the probability distribution of the training data. RBM training can be performed using contrastive divergence (CD) and quantum annealing (QA). QA-based RBM training is fundamentally different from CD and requires samples from a quantum computer. We present a real-world application that uses a quantum computer. Specifically, we train a RBM using QA for cybersecurity applications. The D-Wave 2000Q has been used to implement QA. RBMs are trained on the ISCX data, which is a benchmark dataset for cybersecurity. For comparison, RBMs are also trained using CD. CD is a commonly used method for RBM training. Our analysis of the ISCX data shows that the dataset is imbalanced. We present two different schemes to balance the training dataset before feeding it to a classifier. The first scheme is based on the undersampling of benign instances. The imbalanced training dataset is divided into five sub-datasets that are trained separately. A majority voting is then performed to get the result. Our results show the majority vote increases the classification accuracy up from 90.24% to 95.68%, in the case of CD. For the case of QA, the classification accuracy increases from 74.14% to 80.04%. In the second scheme, a RBM is used to generate synthetic data to balance the training dataset. We show that both QA and CD-trained RBM can be used to generate useful synthetic data. Balanced training data is used to evaluate several classifiers. Among the classifiers investigated, K-Nearest Neighbor (KNN) and Neural Network (NN) perform better than other classifiers. They both show an accuracy of 93%. Our results show a proof-of-concept that a QA-based RBM can be trained on a 64-bit binary dataset. The illustrative example suggests the possibility to migrate many practical classification problems to QA-based techniques. Further, we show that synthetic data generated from a RBM can be used to balance the original dataset.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Digital simulation of continuous error models with application to an instrument landing system error

A digital simulation of the continuous error of the localized beam of a conventional instrument landing system is discussed. The digital simulation was developed during the analysis of space shuttle navigation capabilities. A discrete mathematical model for use on a digital computer is described. The model generates an output random sequence which is equivalent, for simulation purposes, to the desired random process. The model is a system of difference equations driven by a zero-mean Gaussian random sequence.

Merrick, R. B.↗
NASA NTRS1988

A multiple-vortex-ring model of the DFW microburst

A multiple-vortex-ring model of the winds associated with a microburst is verified by matching the model-generated winds to those encountered at the Dallas-Ft. Worth (DFW) microburst. The basis of the model consists of time-invariant vortex ring filaments embedded in irrotational flow. Each ring's viscous core is modeled by distributing the vorticity over a small distance (relative to the ring diameter) radially from the filaments. Parameters such as the size and strength of the vortex rings are identified using a modified Newton-Raphson technique. The parameters identified from the analysis of the DFW microburst encounter indicate a large ring with a radius of 8500 ft and a smaller ring with a radius of 1700 ft.

Schultz, Thomas A.↗
NASA NTRS1998

Monte Carlo Calculations of Polarized Microwave Radiation Emerging from Cloud Structures

The last decade has seen tremendous growth in cloud dynamical and microphysical models that are able to simulate storms and storm systems with very high spatial resolution, typically of the order of a few kilometers. The fairly realistic distributions of cloud and hydrometeor properties that these models generate has in turn led to a renewed interest in the three-dimensional microwave radiative transfer modeling needed to understand the effect of cloud and rainfall inhomogeneities upon microwave observations. Monte Carlo methods, and particularly backwards Monte Carlo methods have shown themselves to be very desirable due to the quick convergence of the solutions. Unfortunately, backwards Monte Carlo methods are not well suited to treat polarized radiation. This study reviews the existing Monte Carlo methods and presents a new polarized Monte Carlo radiative transfer code. The code is based on a forward scheme but uses aliasing techniques to keep the computational requirements equivalent to the backwards solution. Radiative transfer computations have been performed using a microphysical-dynamical cloud model and the results are presented together with the algorithm description.

Kummerow, Christian↗
NASA NTRS1995

Program Helps Generate Boundary-Element Mathematical Models

Composite Model Generation-Boundary Element Method (COM-GEN-BEM) computer program significantly reduces time and effort needed to construct boundary-element mathematical models of continuous-fiber composite materials at micro-mechanical (constituent) scale. Generates boundary-element models compatible with BEST-CMS boundary-element code for anlaysis of micromechanics of composite material. Written in PATRAN Command Language (PCL).

Goldberg, R. K.↗
DOE OSTI2021

Explicit physics-informed neural networks for nonlinear closure: The case of transport in tissues

In upscaling methods, closures for nonlinear problems present a well-known challenge. While a number of theoretical methods have been proposed for handling such closures, nonlinearities still remain a significant obstacle for many problems. In this work, we use a combination of formal upscaling and data-driven machine learning for explicitly closing a nonlinear transport and reaction process in multiscale tissues. The classical effectiveness factor model is used to formulate the macroscale reaction kinetics. We train a multilayer perceptron network using training data generated by direct numerical simulations over microscale examples. Once trained, the network is used in an algorithm for numerically solving the upscaled (coarse-grained) differential equation describing mass transport and reaction in two example tissues. The network is described as being explicit in the sense that the network is trained using macroscale concentrations and gradients of concentration as components of the feature space rather than incorporating them as part of a constraint in the optimization process. Network training and solutions to the macroscale transport equations were computed for two different tissues. The two tissue types (brain and liver) exhibit markedly different geometrical complexity and spatial scale (cell size and sample size). The upscaled solutions for the average concentration are compared with numerical solutions derived from the microscale concentration fields by a posteriori averaging. There are three outcomes of this work of particular note. 1) Our overall approach results in an upscaled nonlinear PDE. The PDE is closed using a neural network, and our approach results in the definition of the classical effectiveness factor for effecting closure. 2) We identify particular source terms for the closure problem that are important for representing the structure of the closure. These source terms involve macroscale concentrations and their gradients. We adopt these source terms to use as explicit features in the learning algorithm. We find the trained networks that include the macroscale source terms generate models that are able to predict the correction factor with increased fidelity over those that do not. 3) We find that the trained network exhibits good generalizability, and it is able to predict the effectiveness factor with high fidelity for realistically-structured tissues despite the significantly different scale and geometrical complexity of the two example tissue types. This latter result emphasizes our purposeful connection between conventional averaging methods with the use of machine learning for closure; this contrasts with some machine learning methods for upscaling where the exact form of the macroscale equation remains unknown.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Generative electronic background music system

In this short paper-extended abstract the new approach to generation of electronic background music has been presented. The Generative Electronic Background Music System (GEBMS) has been located between other related approaches within the musical algorithm positioning framework proposed by Woller et al. The music composition process is performed by a number of mini-models parameterized by further described properties. The mini-models generate fragments of musical patterns used in output composition. Musical pattern and output generation are controlled by container for the mini-models - a host-model. General mechanism has been presented including the example of the synthesized output compositions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗