This report documents the results of an FY22 ASC V&V level 2 milestone demonstrating new algorithms for multifidelity uncertainty quantification. Part I of the report describes the algorithms, studies their performance on a simple model problem, and then deploys the methods to a thermal battery example from the open literature. Part II (restricted distribution) applies the multifidelity UQ methods to specific thermal batteries of interest to the NNSA/ASC program.
Adams, Brian M.; Bohnhoff, William B.; Dalbey, Keith D.; Ebeida, Mohamed S.; Eddy, John E.; Eldred, Michael E.; Hooper, Russell H.; Hough, Patricia H.; Hu, Kenneth H.; Jakeman, John J.; Khalil, Mohammad K.; Maupin, Kathryn M.; Monschke, Jason A.; Ridgway, Elliott R.; Rushdi, Ahmad A.; Seidl, Daniel S.; Stephens, John A.; Winokur, Justin W.
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.
Adams, Brian M.; Bohnhoff, William B.; Dalbey, Keith D.; Ebeida, Mohamed S.; Eddy, John E.; Eldred, Michael E.; Hooper, Russell H.; Hough, Patricia H.; Hu, Kenneth H.; Jakeman, John J.; Khalil, Mohammad K.; Maupin, Kathryn M.; Monschke, Jason A.; Ridgway, Elliott R.; Rushdi, Ahmad A.; Seidl, Daniel S.; Stephens, John A.; Winokur, Justin W.
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 users manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
Adams, Brian M.; Bohnhoff, William B.; Dalbey, Keith D.; Ebeida, Mohamed S.; Eddy, John E.; Eldred, Michael E.; Hooper, Russell H.; Hough, Patricia H.; Hu, Kenneth H.; Jakeman, John J.; Khalil, Mohammad K.; Maupin, Kathryn M.; Monschke, Jason A.; Ridgway, Elliott R.; Rushdi, Ahmad A.; Seidl, Daniel S.; Stephens, John A.; Winokur, Justin W.
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.
The design of satellites usually includes the objective of minimizing mass due to high launch costs, which is challenging due to the need to protect sensitive electronics from the space radiation environment by means of radiation shielding. This is further complicated by the need to account for uncertainties, e.g. in manufacturing. There is growing interest in automated design optimization and uncertainty quantification (UQ) techniques to help achieve that objective. Traditional optimization and UQ approaches that rely exclusively on response functions (e.g. dose calculations) can be quite expensive when applied to transport problems. Previously we showed how adjoint-based transport sensitivities used in conjunction with gradient-based optimization algorithms can be quite effective in designing mass-efficient electron and/or proton shields in one- or two-dimensional Cartesian geometries. In this paper we extend that work to UQ and to robust design (i.e. optimization that considers uncertainties) in 2D. This consists primarily of using the sensitivities to geometric changes, originally derived for optimization, within relevant algorithms for UQ and robust design. We perform UQ analyses on previous optimized designs given some assumed manufacturing uncertainties. We also conduct a new optimization exercise that accounts for the same uncertainties. Our results show much improved computational efficiencies over previous approaches.
This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simiution) project focused on developing a sampling capability that can handle the challenges of generating samples from nuclear cross-section data. The covariance information between energy groups tends to be very ill-conditioned and thus poses a problem using traditional methods for generated correlated samples. This report outlines a method that addresses the sample generation from cross-section matrices. The treatment allows one to assume the cross sections are distributed with a multivariate normal distribution, lognormal distribution, or truncated normal distribution.
This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on developing a sampling capability that can handle the challenges of generating samples from nuclear cross-section data. The covariance information between energy groups tends to be very ill-conditioned and thus poses a problem using traditional methods for generated correlated samples. This report outlines a method that addresses the sample generation from cross-section matrices.