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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Theory Manual (V.6.15)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (V.6.16 User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 Theory Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota’s iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

Robe Development for Electrical Conductivity Analysis in an Electron Gun Produced Helium Plasma

The use of magnetohydrodynamic (MHD) power conversion systems, potentially coupled with a fission power source, is currently being investigated as a driver for an advanced propulsion system, such as a plasma thruster. The efficiency of a MHD generator is strongly dependent on the electrical conductivity of the fluid that passes through the generator; power density increases as fluid conductivity increases. Although traditional MHD flows depend on thermal ionization to enhance the electrical conductivity, ionization due to nuclear interactions may achieve a comparable or improved conductivity enhancement while avoiding many of the limitations inherent to thermal ionization. Calculations suggest that nuclear-enhanced electrical conductivity increases as the neutron flux increases; conductivity of pure He-3 greater than 10 mho/m may be achievable if exposed to a flux greater than 10(exp 12) neutrons/cm2/s.) However, this remains to be demonstrated experimentally. An experimental facility has been constructed at the Propulsion Research Center at the NASA Marshall Space Flight Center, using helium as the test fluid. High energy electrons will be used to simulate the effects of neutron-induced ionization of helium gas to produce a plasma. These experiments will be focused on diagnosis of the plasma in a virtually static system; results will be applied to future tests with a MHD system. Initial experiments will utilize a 50 keV electron gun that can operate at up to a current of 200 micro A. Spreading the electron beam over a four inch diameter window results in an electron flux of 1.5x 10(exp 13) e/sq cm/s. The equivalent neutron flux that would produce the same ionization fraction in helium is 1x10(exp 12) n/sq cm/s. Experiments will simulate the neutron generated plasma modeled by Bitteker, which takes into account the products of thermal neutron absorption in He-3, and includes various ion species in estimating the conductivity of the resulting plasma. Several different probes will be designed and implemented to verify the plasma kinetics model. System parameters and estimated operating ranges are summarized. The predicted ionization fraction, electron density, and conductivity levels are provided in for an equivalent neutron flux of 1x10(exp 12) n/cm2/s. Understanding the complex plasma kinetics throughout a MHD channel is necessary to design an optimal power conversion system for space propulsion applications. The proposed experiments seek to fully characterize the helium plasma and to determine the reliability of each measurement technique, such that they may be applied to more advanced MHD studies. The expected value of each plasma parameter determined from theoretical models will be verified experimentally by several independent techniques to determine the most reliable method of obtaining each parameter. The results of these experiments will be presented in the final paper.

Bragg-Sitton, Shannon M.↗
DOE OSTI2016

Uncertainty as a Function of Time for Subcritical Experiment Parameters Using the Hage-Cifarelli Formalism

When designing critical or subcritical experiments, it is desirable to have an estimate of the measurement uncertainties prior to performing an experiment. In addition, an important goal is to have the smallest measurement uncertainties possible given measurement time constraints. Having well-designed experiments with small uncertainties helps improve nuclear data and therefore has direct impacts on criticality safety. This work shows how the uncertainties in various measurement parameters vary as a function of counting time and provides an approach to estimate measured uncertainties and guides in optimizing the available counting time. The uncertainty in six parameters used in subcritical experiments was investigated as a function of time: singles count rate (R{sub 1}), doubles count rate (R{sub 2}), lifetime (1/λ), leakage multiplication (M{sub L}), spontaneous fission rate (FS), and detector efficiency (ε). A comparison to measured data will be presented at the meeting. The approach and results in this work will be used to optimize measurement time in future subcritical experiments and will be used to help estimate measurement uncertainties during the design phase. Having well-designed experiments with the smallest uncertainties possible can help the criticality safety community through data validation. (authors)

42 ENGINEERING↗
DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Analyze the real-time performance of the optimized fiber-PTR instrument

An instrument to perform in-reactor, real-time thermal property measurements of nuclear fuels and materials was recently designed, optimized, and tested at Massachusetts Institute of Technology Research Reactor. It was the second insertion experiments of a total of two rounds. Here we report the collection and analysis of the real-time captured thermal diffusivity data on the preloaded SiC. With the optimized design, the instrument temperature had been successfully raised as we expected. Only one probe (the top one) returned real-time thermal diffusivity in a time span of approximately two days. Measured thermal diffusivity of the preloaded SiC was significantly lower than the literature value at the same temperature, indicating a significant influence from the irradiation induced microstructure damage. Meanwhile, the real-time thermal diffusivity value continuously reduced with time, indicating the dynamic microstructure evolution. The possible reasons of the probes that did not return data were hypothesized. The further optimization of the instrument with the objective to improve the measurement accuracy and instrument survivability is also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quanti cation, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

Selection of an optimal plan for construction of a spacecraft antenna system

An attempt is made to summarize the experience of the design of antennas for various types of spacecraft: fully oriented, partially oriented, and with arbitrary orientation. Several examples of the construction of antenna systems and their characteristics are given. The optimal relationship between weight, dimension and power characteristics of on-board devices allows selection of the most acceptable versions. Questions are presented for consideration in design of optimal spacecraft antenna-feeder systems.

Prigoda, B. A.↗
DOE OSTI2019

Stiff isotropic lattices beyond the Maxwell criterion

Materials with a stochastic microstructure, like foams, typically exhibit low mechanical stiffness, whereas lattices with a designed microarchitecture often show notably improved stiffness. These periodic architected materials have previously been designed by rule, using the Maxwell criterion to ensure that their deformation is dominated by the stretching of their struts. Classical designs following this rule tend to be anisotropic, with stiffness depending on the load orientation, but recently, isotropic designs have been reported by superimposing complementary anisotropic lattices. We have designed stiff isotropic lattices de novo with topology optimization, an approach based on continuum finite element analysis. Here, we present results of experiments on these lattices, fabricated by additive manufacturing, that validate predictions of their performance and demonstrate that they are as efficient as those designed by rule, despite appearing to violate the Maxwell criterion. These results highlight the enhanced potential of topology optimization to design materials with unprecedented properties.

42 ENGINEERING↗
DOE OSTI2021

Beneficiation of Bastnaesite Ore with New Flotation Collector Ligands

Interest in Rare Earth Elements (REEs) has increased due to their distinct properties and new applications. China currently has a monopoly on rare earth production due to the lack of an economically viable process used outside of China. Bastnaesite, a rare earth fluorocarbonate, is one of the most common minerals containing REEs, which is mostly comprised of cerium and other light REEs. The objective of this research is to find a novel collector for the beneficiation of bastnaesite through froth flotation experiments to increase the grade and recovery of rare earth oxides while rejecting the major gangue minerals. Microflotation and rougher bench flotation studies were evaluated to find optimal collector(s) from 19 possible candidates. Collectors 2, 5, and 8 were the top performing collectors chosen from microflotation experiments. A design of experiment matrix was set up to evaluate the variables, variable interactions, and provide a reference matrix for future optimization studies. Collector 2 was assessed to be the optimal collector. A rougher flotation experiment produced a rare earth oxide grade and recovery of 41% and 78%, respectively with a 91.5% rejection of calcite. Collector 8 and 14 also showed promising results to replace fatty acid. As such, this patent pending technology can serve to provide a pathway for efficient domestic REE production.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Flotation of Bastnaesite Ore Using Novel Collectors

Interest in rare earth elements (REEs) has increased due to their distinct properties and new applications. China currently has a monopoly on rare earth production due to the lack of an economically viable process used outside of China. Bastnaesite, a rare earth fluorocarbonate, is one of the most common minerals containing REEs, which is mostly comprised of cerium and other light REEs. The objective of this research is to find a novel collector for the beneficiation of bastnaesite through froth flotation experiments to increase the grade and recovery of rare earth oxides while rejecting the major gangue minerals. Microflotation and rougher bench flotation studies were evaluated to find optimal collector(s) from 19 possible candidates. Collector 2, 5, and 8 were the top performing collectors chosen from microflotation experiments. A design of experiment matrix was set up to evaluate the variables, variable interactions and provide a reverence matrix for future optimization studies. Collector 2 was assessed to be the optimal collector. A rougher flotation experiment produced a rare earth oxide grade and recovery of 41% and 78% respectively and a 91.5% rejection of calcite. Collector 8 and 14 also showed promising results in replace fatty acid.

Everly, Dylan↗
DOE OSTI2015

Nuclear Energy Knowledge and Validation Center (NEKVaC) Needs Workshop Summary Report

The Department of Energy (DOE) has made significant progress developing simulation tools to predict the behavior of nuclear systems with greater accuracy and of increasing our capability to predict the behavior of these systems outside of the standard range of applications. These analytical tools require a more complex array of validation tests to accurately simulate the physics and multiple length and time scales. Results from modern simulations will allow experiment designers to narrow the range of conditions needed to bound system behavior and to optimize the deployment of instrumentation to limit the breadth and cost of the campaign. Modern validation, verification and uncertainty quantification (VVUQ) techniques enable analysts to extract information from experiments in a systematic manner and provide the users with a quantified uncertainty estimate. Unfortunately, the capability to perform experiments that would enable taking full advantage of the formalisms of these modern codes has progressed relatively little (with some notable exceptions in fuels and thermal-hydraulics); the majority of the experimental data available today is the "historic" data accumulated over the last decades of nuclear systems R&D. A validated code-model is a tool for users. An unvalidated code-model is useful for code developers to gain understanding, publish research results, attract funding, etc. As nuclear analysis codes have become more sophisticated, so have the measurement and validation methods and the challenges that confront them. A successful yet cost-effective validation effort requires expertise possessed only by a few, resources possessed only by the well-capitalized (or a willing collective), and a clear, well-defined objective (validating a code that is developed to satisfy the need(s) of an actual user). To that end, the Idaho National Laboratory established the Nuclear Energy Knowledge and Validation Center to address the challenges of modern code validation and to manage the knowledge from past, current, and future experimental campaigns. By pulling together the best minds involved in code development, experiment design, and validation to establish and disseminate best practices and new techniques, the Nuclear Energy Knowledge and Validation Center (NEKVaC or the ‘Center’) will be a resource for industry, DOE Programs, and academia validation efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Input-response space-filling designs

Traditional space-filling designs are a convenient way to explore throughout an input space of flexible dimension and have design points close to any region where future predictions might be of interest. In some applications, there may be a model connecting the input factors to the response(s), which provides an opportunity to consider the spacing not only in the input space but also in the response space. In this paper, we present an approach for leveraging current understanding of the relationship between inputs and responses to generate designs that allow the experimenter to flexibly balance the spacing in these two regions to find an appropriate design for the experimental goals. Applications where good spacing of the observed response values include calibration problems where the goal is to demonstrate the adequacy of the model across the range of the responses, sensitivity studies where the outputs from a submodel may be used as inputs for subsequent models, and inverse problems where the outputs of a process will be used in the inverse prediction for the unknown inputs. Here, we use the multi-objective optimization method of Pareto fronts to generate multiple non-dominated designs with different emphases on the input and response space-filling criteria from which the experimenter can choose. The methods are illustrated through several examples and a chemical engineering case study.

42 ENGINEERING↗
DOE OSTI2023

Contact pressure analysis to allow improved design for the clearing plate in a biomass comminution system

For biomass processing systems, feedstocks with small but equal sizes can improve flowability and thermochemical outputs. The Crumbler rotary shear system was designed to achieve such ideal feedstocks to reduce costs and energy throughout the biomass supply chain. Improving the wear resistance of major components in this system is important to decrease the cost. Accelerating the feedstock flow through the rotary shear machine plays an equivalent role of improving its efficiency. An analytical analysis and a finite-element analysis of the stationary clearing plate are presented in this work. Here, these tools are used to investigate the effect of contact surface curvature on the impact pressure. The optimized new clearing plate design is expected to improve the component’s lifetime and wood chips flowability. The field tests confirm that the trend of the new clearing plate design guided by FEA. The optimization method, model verification, and validation experience applied in this work can also be applied to other static components with similar simple contact wear problems.

09 BIOMASS FUELS↗
DOE OSTI2022

Learning high-dimensional parametric maps via reduced basis adaptive residual networks

We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs and outputs. When just few training data are available, it is beneficial to have a compact parametrization in order to ameliorate the ill-posedness of the neural network training problem. By linearly restricting high-dimensional maps to informed reduced bases of the inputs, one can compress high-dimensional maps in a constructive way that can be used to detect appropriate basis ranks, equipped with rigorous error estimates. A scalable neural network learning framework is thus to learn the nonlinear compressed reduced basis mapping. Unlike the reduced basis construction, however, neural network constructions are not guaranteed to reduce errors by adding representation power, making it difficult to achieve good practical performance. Inspired by recent approximation theory that connects ResNets to sequential minimizing flows, we present an adaptive ResNet construction algorithm. This algorithm allows for depth-wise enrichment of the neural network approximation, in a manner that can achieve good practical performance by first training a shallow network and then adapting. We prove universal approximation of the associated neural network class for $L^2_v$ functions on compact sets. Our overall framework allows for constructive means to detect appropriate breadth and depth, and related compact parametrizations of neural networks, significantly reducing the need for architectural hyperparameter tuning. Numerical experiments for parametric PDE problems and a 3D CFD wing design optimization parametric map demonstrate that the proposed methodology can achieve remarkably high accuracy for limited training data, and outperformed other neural network strategies we compared against.

42 ENGINEERING↗