Accession Number:

AD1096260

Title:

Scalable Environment for Quantification of Uncertainty and Optimization in Industrial Applications (SEQUOIA)

Descriptive Note:

Technical Report,11 Sep 2015,10 Mar 2019

Corporate Author:

Stanford University Stanford United States

Personal Author(s):

Report Date:

2019-06-10

Pagination or Media Count:

63.0

Abstract:

Major Goals The Scalable Environment for Quantification of Uncertainty and Optimization in Industrial Applications SE-QUOIA project provides an integrated plan for performing uncertainty quantification UQ and design under uncertainty DUU that aggressively pursues new frontiers in scale and complexity. In particular, the coordinated investments in this effort will create advancements in scalable forward and inverse UQ algorithms and the rigorous quantification of model inadequacy, providing the primary foundation for the development ofgeneralized stochastic design approaches that address the robustness and reliability of complex multi-disciplinary systems. This project will demonstrate new UQ methods on high-performance aircraft nozzle analysis and design problems that are simultaneously designed for aerodynamic performance, thermal and pressure loads, and fatigue, while subject to geometric constraints to be fully integrated with complex vehicle shapes. The ability to handle all kinds of uncertainty at very large scale will enable the design of future components and vehicles that can have a substantial impact on DARPAs mission. Within Thrust Area 1, we will combine scalable polynomial chaos expansions with explicit dimension reduction in order to accurately resolve the most influential subspace within large-scale parameter domains. These approaches will be tailored and applied within both forward and inverse UQ contexts, as we seek to discover low-dimensional structure within high-dimensional domains, and will enable the use of very large numbers of uncertain parameters in our UQ and DUU efforts. In Thrust Area 2, we will address the critical challenge of model inadequacies and develop methodologies for determining the source and extent of model-form discrepancies in simulation models by using i observationalhigh-fidelity simulation data, and ii Physics constraints.

Subject Categories:

  • Operations Research
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE