DESider : a European effort on hybrid RANS-LES modelling : results of the European-Union funded project, 2004-2007
著者
書誌事項
DESider : a European effort on hybrid RANS-LES modelling : results of the European-Union funded project, 2004-2007
（Notes on numerical fluid mechanics and multidisciplinary design, v. 103）
Springer, c2010
- : [pbk.]
注記
Bibliography: p. [437]-452
"European Commission Research Directorate General " -- cover
内容説明・目次
内容説明
Preface "In aircraft design, efficiency is determined by the ability to accurately and rel- bly predict the occurrence of, and to model the development of, turbulent flows. Hence, the main objective in industrial computational fluid dynamics (CFD) is to increase the capabilities for an improved predictive accuracy for both complex flows and complex geometries". This text part taken from Haase et al (2006), - scribing the results of the DESider predecessor project "FLOMANIA" is still - and will be in future valid. With an ever-increasing demand for faster, more reliable and cleaner aircraft, flight envelopes are necessarily shifted into areas of the flow regimes exhibiting highly unsteady and, for military aircraft, unstable flow behaviour. This undou- edly poses major new challenges in CFD; generally stated as an increased pred- tive accuracy whist retaining "affordable" computation times. Together with highly resolved meshes employing millions of nodes, numerical methods must have the inherent capability to predict unsteady flows. Although at present, (U)RANS methods are likely to remain as the workhorses in industry, the DESider project focussed on the development and combination of these approaches with LES methods in order to "bridge" the gap between the much more expensive (due to high Reynolds numbers in flight), but more accurate (full) LES.
目次
I The DESider Project.- II Presentation of Modelling Approaches.- III DESider Measurements.- IV Applications - Test Cases.- V Technical, Partner-Related Reports - Methods, Models and Applications Performed.- VI Summary of Experience with Hybrid RANS-LES Methods and a Look Ahead.
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