Identiffcation of Momentum Forcing Required to Reduce Base-Model Errors Using Full-Field Inversion

Master thesis (2017)

Authors

W. Yoon Aerospace Engineering

Contributors

M.I. Gerritsma Aerodynamics - Aerospace Engineering (mentor)
R.P. Dwight Aerodynamics - Aerospace Engineering (mentor)
D. Ragni Wind Energy - Aerospace Engineering (mentor)
M. Schmelzer Aerodynamics - Aerospace Engineering (mentor)
Faculty
Aerospace Engineering, Aerospace Engineering
Copyright: © 2017 Woosik Yoon
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Published Date

20-02-2017

Language

English

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Faculty

Aerospace Engineering

Abstract

Reynolds Averaged Navier-Stokes turbulence models have been widely used in many industrial applications because of their lower computational cost compared to other simulation approaches such as DNS and LES. This is a consequence of considering mean-ow quantities achieved by the time-averaging process. However, the time-averaging introduces the Reynolds stresses, which make the system of the RANS equations underdetermined, thus requiring modelling approaches. As a consequence of modelling the Reynolds stresses based on the Boussinesq hypothesis, RANS simulations contain model errors. To improve the RANS predictive capability, this thesis aims to identify momentum forcing f that is required to reduce base-model errors with respect to any given data, and determine its characteristics.

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