Aircraft Drag Prediction and Reduction. Addendum 1

The accurate prediction of aircraft aerodynamic drag is a generally recognized and respected problem. So is the accurate measurement of drag in the wind tunnel that, eventually, forms the basis for full scale drag prediction. In the past 15 years Computational Fluid Dynamics (CFD) has emerged as an...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Slooff,J W
Format: Report
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The accurate prediction of aircraft aerodynamic drag is a generally recognized and respected problem. So is the accurate measurement of drag in the wind tunnel that, eventually, forms the basis for full scale drag prediction. In the past 15 years Computational Fluid Dynamics (CFD) has emerged as an additional and complementary tool for aerodynamic design and analysis. The purpose of this lecture is to review and comment on its role as a drag prediction and analysis tool. The aerodynamic design process of aircraft is characterized by a sequence of design and analysis cycles. In each cycle a (further) reduction of drag will, generally, be one, but not the only objective. Identification of the source of an unacceptably or undesirably high drag level or drag variation with lift or Mach number is a prerequisite for a successful drag reduction program. Identification of drag sources may follow different approaches. The classical or phenomenological one is based on the availability of overall force (wind tunnel) data only, in combination with simple, semi-empirical theory. CFD, as we shall see later, offers possibilities for a more physically/analytically oriented approach in which the various contributions to drag are distinguished by the underlying physical mechanisms rather than by the observed aerodynamic force variation phenomena. It will also be demonstrated that, in spite of its current shortcomings, CFD is a powerful tool for drag diagnostics. The final part of the lecture contains a discussion on computational drag minimization. Addendum 1 to report dated Jul 85, AD-A160 718.