Quantification and Management of Uncertainties in an Engineering System

November 1, 2017, 1:00 PM - 2:00 PM CDT
Presented by Mark Andrews, SmartUQ UQ Technology Steward
He is responsible for advising SmartUQ on the industry’s uncertainty quantification needs and challenges. Dr. Andrews is SmartUQ’s principal investigator for the Probabilistic Secondary Flow and Heat Transfer Model project as part of the Probabilistic Analysis Consortium for Engines (PACE). Prior to working at SmartUQ, Dr. Andrews spent 15 years at Caterpillar where he worked as a Senior Research Engineer, Engineering Specialist in Corporate Reliability, and Senior Engineering Specialist in Virtual Product Development. He has a Ph.D. and M.S. in Mechanical Engineering from the New Mexico State University, as well as a BS in Mechanical Engineering from the University of New Mexico.

Uncertainty is an inescapable reality that can be found in nearly all types of engineering analyses. It arises from sources like measurement inaccuracies, material properties, boundary and initial conditions, and modeling approximations. To validate and account for uncertainty in their simulation and modeling results, many companies have started Verification, Validation, and Uncertainty Quantification (VVUQ) initiatives. This webinar will first introduce probabilistic methods and UQ tools, and then use case studies to demonstrate the benefits of VVUQ processes.

Modern UQ techniques use powerful statistical models to map the input-output relationships of the system, significantly reducing the number of simulations or tests required to get meaningful results. Probabilistic methods can inform the user on current design robustness as well as avenues for improvement. Using UQ tools, it is also possible to evaluate the degree of uncertainty contributed by the statistical model, quantify confidence in analysis results, and assess how well the model represents reality.

The attendees for this webinar would be engineers, program managers, and data scientists who want to further investigate how Uncertainty Quantification can maximize insight, improve design robustness, and increase time and resource efficiency.

Learning Outcomes:

  • Knowledge of common UQ and probabilistic methods
  • How to apply UQ methods to an engineering system
  • How to use UQ techniques to drastically save design time
  • How to develop a robust and reliable design with UQ techniques
  • How to interpret UQ results when making decisions