Improved Transition Prediction in FLOWer

Kurzfassung

The work reported in this technical report has been carried out in the work package ‘WP3 – Methods & Tools’ of the European specific targeted research project EUROLIFT II (European High Lift Program II) of the 6th European framework program. This work belongs to ‘Task 3.1 – Transition Prediction’. In Task 3.1, based on EUROLIFT (I) findings and the effects of prescribed laminar areas investigated in WP 1.2 of EUROLIFT (II), taking into account transition prediction (location and type) directly within CFD computations was investigated. An extension to 3D of the different prediction tools was carried out, and different coupling strategies with RANS solvers were compared. Objectives of Task 3.1 are: A)Extension of EUROLIFT (I) methods for transition prediction from 2.5D to 3D basic flow, based on databases or on local theory B)Validation of internal transition prediction in RANS. Extension of FLOWer by internal coupling with exact stability code LILO C)Improvement of the physical understanding of specific 3D transition mechanisms, transfer from 2.5D to full 3D mean flow D)Effect of transition in laminar separation bubbles The DLR work is located in part B) of this task and is defined as follows: The DLR FLOWer code will be extended using empirical criteria to allow the detection of transition inside separation bubbles, bypass transition and attachment line contamination. These extensions will be added to the transition module, that consists of a laminar boundary layer method for swept, tapered wings (Horton/Stock), an eN database method for TS waves (Stock) and an eN database method for CF instabilities (Casalis&Arnal). The resulting code will be applied to the 3D high lift system of KH3Y high lift model (DLR F11 model) from task 1.2 A further improvement of this strategy will consist in replacing the approximate database methods with a fast, exact stability computation, relying on the Airbus Deutschland code LILO. For this purpose, the stability code needs to be highly automated.