A framework for assessing the key statistical parameters of complex flows in
choosing appropriate turbulence prediction methods on a quantitative basis is
developed. These parameters characterise flow/modelling matching
conditions quantified in this work. Matching conditions are important in
classifying complex turbulent flows in order to frame best practice for model
predictions to inform computational aerodynamics design optimisation in the
context of virtual test beds. In the incompressible low Reynolds number shear
flows considered here, the boundaries of the 'conforming domain' within
which turbulence models are valid need to be defined, based on basic
mechanisms of turbulence, and the statistical parameters. This has led to a
new guideline ‘localness map’ for standard model applications. Since the
choice of turbulence model depends on the complexity of the flows
considered, it is useful if systematic sets of the parameters indicate the type of
flow. They are that of residence time, the degree of spatial non-locality, the
straining, and the non-Gaussianity, each of which is appropriately normalised.
It can be demonstrated that the quantified map, in particular that of localness
for the shear flows, provides a firm foundation for evaluating a wider range of
Underlying Flow Regimes, including locating the Underlying Flow Regimes
on the generalised localness modeling map as a framework for best practice
guidelines. This work produces 7 sets of quantitative localness-structural
parameters, which are used as baseline sets for grouping the Underlying Flow
Regimes, and hence it opens the possibility of having complete modelling
maps for Application Challenges to assess the need for zonal modelling.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/7454 |
| Date | 11 1900 |
| Creators | Abdullah, Aslam |
| Contributors | Savill, A. M. |
| Publisher | Cranfield University |
| Source Sets | CRANFIELD1 |
| Language | English |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
| Rights | © Cranfield University 2011. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner. |
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