Abstract:
Computational Fluid Dynamics (CFD) computer codes have
proven to be a powerful tool in the analysis of all kinds of fluid
systems. However, there is still a lack of practical methods for
determining the uncertainty of their results, as most current techniques
require performing too many simulations to be affordable
in industrial-scale situations. One of the most promising methods
for uncertainty quantification in computational fluid dynamics
is Polynomial Chaos Expansion, a name that includes a variety
of techniques, all based on the same mathematical background:
projecting the system’s response into a basis of orthogonal polynomials.
This paper discusses the main advantages and drawbacks
of three of these techniques, namely random sampling,
Gaussian quadrature and linear regression, in terms of reliability,
ease of use and computational costs. All three techniques were
applied to simulations of the turbulent mixing of two streams of
water inside a Y-shaped channel, and the results compared with
experimental data. Results show that, in this test case, quadrature
method provides more reliable results than the other two
techniques, with a lower computational cost. Due to its robustness
and low number of simulations required, Polynomial Chaos
Expansion via quadrature methods might be suitable for most industrial
CFD simulations.
Description:
Papers presented to the 12th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Costa de Sol, Spain on 11-13 July 2016.
