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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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Debiased Uncertainty Quantification Approach for Probabilistic Transient Stability Assessment
Not provided.
A General Framework of Rotational Sparse Approximation in Uncertainty Quantification
Not provided.
Uncertainty Quantification for Electronic Hamiltonian
This program will generate random points for electrons within the dimensions given by a parameter input file. Based on these randomly generated electron positions and the nuclear positions given by a position input file it will generate a value for the total electronic energy of an isolated system. This total electronic energy is calculated using the electronic Hamiltonian for a monoatomic system with atoms having the same number of protons and neutrons. The size of the system is defined by the parameter input file. The program will do this many times to generate a distribution of theoretically possible electronic total energies of the system. A user can then compare the total electronic energy given by their electronic structure method to make sure it falls within the distribution of theoretically possible values.
Fast Computational Algorithms for Partial Differential Equations and Uncertainty Quantifications
This project concerned the construction, testing and analysis of computational algorithms for solving parameterized and stochastic partial differential equations. The study and understanding of equations of this type is of fundamental importance in numerous engineering and scientific applications. Examples include simulation of plasma dynamics in models of electric propulsion and nuclear fusion, simulation of multiphase flows, such as the flow of water, gas and oil in reservoirs, and structural analysis of the dependence of structures on materials. Parametrization is used in such settings when properties of the models such as viscosity of fluids or electric resistivity of materials are not precisely understood and instead are treated as random variables. The resulting solutions are themselves random, and having such solutions will enable engineers to use probabilistic methods to assess the likelihood of events, for example, whether a pollutant in a liquid will exceed a limit, and to use such analyses to develop ways to ensure positive outcomes. Construction of accurate (high resolution) computational solutions is expensive, requiring significant computer time and computational resources, and there is need to reduce computational cost to make simulation useful and effective. The aim of the project was to construct algorithms to efficiently compute surrogate solutions to parameterized problems to allow for efficient and accurate simulation. The technical approach used focused on two related strategies, based on rank-reduction methods and reduced-order models. These methods construct surrogate solutions of parameter-dependent models by projection or interpolation into low-dimensional approximation spaces. Cost savings are achieved if the low-dimensional spaces can be identified and constructed efficiently and if the resulting low-dimensional algebraic systems can be solved cheaply. Accomplishments include: Theoretical and empirical demonstration of the effectiveness of fast multigrid solution strategies for computing low-rank representations of parameter-dependent solutions to discrete partial differential equations, including the first proof establishing so-called textbook convergence properties for low-rank methods. Development of efficient solution algorithms for solving nonlinear parameter-dependent partial differential equations used in models of fluid dynamics. Developent of efficient algorithms for low-rank representation of solutions of time-dependent simulations of fluid dynamics using multi-dimensional tensor representations of solutions.
Uncertainty Quantification for EoS and Porous Hugoniots
Abstract not provided.
Impact of nuclear data validation with uncertainty quantification and diverse benchmarks on criticality safety [Slides]
Interview for Scientist 2 position in XCP-7: Radiation Transport Applications
Machine Learning to Enable Efficient Uncertainty Quantification, Data Assimilation, and Informed Data Acquisition
This whitepaper addresses the following focal area: (1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing)
Uncertainty Quantification in High Explosives Equations of State [Slides]
In order to fit models to data, we need a way to simulate the data given a model. Our projects involved simulators for the Manganin gauge experiment and for cylinder tests, which we use to calibrate models to experiments. We call the parameters we seek to optimize the degrees of freedom (DOF). We use a Bayesian approach with its associated UQ benefits.
Science & Engineering of Cyber Security by Uncertainty Quantification and Rigorous Experimentation (SECURE) HANDBOOK
Abstract not provided.
Dakota Optimization and Uncertainty Quantification Software:CASL Capabilities and Impact.
Abstract not provided.
Comprehensive Uncertainty Quantification by Solving PDF Equations using Lagrangian Methods.
Abstract not provided.
Multifidelity Uncertainty Quantification of Wind Turbine Interaction Using Enhanced Nalu-Wind Actuator Simulations.
Abstract not provided.
Multifidelity information fusion via network models for uncertainty quantification in aerospace dynamical systems.
Abstract not provided.
Uncertainty quantification and effectiveness of cantilevered fluid with constraints.
Abstract not provided.
Uncertainty Quantification of a Cantilevered Pipeline Conveying Fluid with Motion Limiting Constraints.
Abstract not provided.