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CFD for Prediction of Flow Separation from Aircraft Tail Surfaces

  • Andrea MasiEmail author
  • Jeremy Benton
  • Paul G. Tucker
Conference paper
Part of the Notes on Numerical Fluid Mechanics and Multidisciplinary Design book series (NNFM, volume 131)

Abstract

The correct prediction of flow separation in aerospace industry is important to generate benefits in aircraft performance. In this study the attention is focused on one component of the aircraft that is usually large and requires a heavy assembly: the vertical tailplane. For common multi-engine commercial airliners, the size of this component is driven by a particular flight condition: loss of an engine during take-off and low speed climb. In this condition, the tailplane has to be sufficient in size to control the aircraft. The vertical tailplane is also crucial during crosswind take-off and landing, so it is important to study the behavior of the flow around it when a sideslip angle is present. Due to uncertainties in prediction of vertical tailplane (VTP) effectiveness, aircraft designers keep to a conservative approach, risking to specify a larger size for the tail than it is probably necessary. Currently CFD studies are performed using Reynolds Averaged Navier-Stokes (RANS) solvers with the use of eddy-viscosity models. However, the behavior of the flow computed with these models does not always match experimental observations when separation occurs, so it is interesting to evaluate RANS techniques that implement a more advanced approach than eddy-viscosity models, in the form of second moment Reynolds Stress Models (RSM). However, results from steady RANS simulations suggest that, when massive flow separation occurs, steady simulations do not perform well, leading to the decision of investigating time dependent approaches. In the first stage of this research, URANS simulations are performed, and a comparison of steady and unsteady results is provided in this paper.

Keywords

Flow Separation Suction Side Sideslip Angle Density Residual Reynolds Stress Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Engineering DepartmentAirbus/University of CambridgeCambridgeUK
  2. 2.AirbusFilton, BristolUK
  3. 3.Engineering DepartmentUniversity of CambridgeCambridgeUK

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