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ACTIVITY_TITLE

Uncertainty Quantification in Computational Fluid Dynamics

ACTIVITY_NUMBER

AVT-235

CLASSIFICATION

UU

ACTIVITY_STATUS_LABEL

2

ACTIVITY_LABEL

RLS

START_DATE

01/03/2013

END_DATE

01/12/2014

ACTIVITY_OPEN_TO_PARTNERS

1

KEYWORDS

Computational Fluid Dynamics; Uncertainty Quantification; Stochastic propagation methods; Data assimilation; Validation and Verification; Calibration; Robust design and optimization; Model-form uncertainties 

BACKGROUND

The availability of powerful computational resources and general‐purpose numerical algorithms creates increasing opportunities to attempt flow simulations in complex systems such as hypersonic cruise vehicles. How accurate are the resulting predictions? Are the mathematical and physical models correct? Do we have sufficient information to define relevant operating conditions? In general, how can we establish “error bars” on the results? At the interface between physics, mathematics, probability and optimization, and although quite mature in the experimental community, Uncertainty Quantification (UQ) efforts are in their infancy in computational science. The 2007 AVT-147 Symposium on “Computational Uncertainty in Military Vehicle Design” was previously designed to evaluate current methods of assessing simulation uncertainty, to identify future research and development needs associated with these methods, and to present examples of how these needs are being addressed and how the methods are being applied. The 2011 AVT-193 RTO-VKI LS on theory, applications and numerical tools for Uncertainty Quantification was the opportunity to introduce UQ to the larger computational fluid dynamics community and focused in particular on the difficulties stemming from the strong non‐linearity and multiscale nature of flow dynamics.

OBJECTIVES

Uncertainty Quantification aims at developing rigorous methods to characterize the impact of “limited knowledge” on quantities of interest. After demonstrating the importance of uncertainty quantification for improving the predictive capabilities for overarching applications, the present course will review the new trends in uncertainty quantification in computational fluid dynamics focusing on model-form uncertainties, data assimilation, robust design and optimization.

TOPICS

Probabilistic analyses are at the core of current UQ approaches, and therefore, the challenges offered, for example by high‐fidelity turbulence flow simulations are multiplied when uncertainty characterization is required. This poses a tremendous burden o

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Attachments

Created at 01/10/2014 10:15 by System Account
Last modified at 02/11/2014 16:26 by System Account