Sensitivities of Free-Air RANS and DDES Methods on the High-Lift NASA CRM

To reduce the time-to-market of future aircraft, it is crucial to predict the flight envelope accurately before building prototypes for flight tests. The High-Lift Prediction Workshop (HLPW) series aims to assess the numerical prediction capability of current CFD technology considering NASA's h...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Zauner, Markus, Sansica, Andrea, Matsuzaki, Tomoaki, Lusher, David James, Hashimoto, Atsushi
Format: Article
Language:English
Subjects:
Angle of attack
Computational efficiency
Convergence
Detached eddy simulation
Flight envelopes
Flight tests
High lift
Numerical prediction
Simulation
Stalling
Turbulence models
Wind tunnel testing
Wind tunnels
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To reduce the time-to-market of future aircraft, it is crucial to predict the flight envelope accurately before building prototypes for flight tests. The High-Lift Prediction Workshop (HLPW) series aims to assess the numerical prediction capability of current CFD technology considering NASA's high-lift version of the Common Research Model (CRM-HL). The present work contributes to these collaborative efforts, quantifying sensitivities for RANS-based steady, unsteady, and hybrid RANS/LES scale-resolving approaches. Uncertainties associated with the choice of turbulence model, initialization strategies, grid resolution, and iterative convergence at free-air conditions are covered. Near stall, a large spread of RANS results was observed for different turbulence models and initialization strategies, while iterative convergence appeared less crucial for the present simulations. Steady and unsteady RANS simulations were unable to predict the correct flow physics near CLmax, even for large grids. Delayed Detached Eddy Simulations (DDES), however, showed good accuracy compared with wind-tunnel experiments and predicted CLmax with an error of around 5%. Compared to steady RANS, the computational cost of DDES was a factor of ten higher. Lessons learned and potential best-practice strategies are shared to aid future studies. While warm-started RANS simulations using SA models are recommended at lower angles of attack, scale-resolving methods are required near stall.
ISSN:2331-8422