Prediction of high lift: review of present CFD capability

By C. Rumsey, S. Ying. 2002

258 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/26673a99b1143beb523911f32f10ed91be61a18b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Donghyun You
2. David L. Darmofal
3. Parviz Moin
4. D. Venditti
5. Sanjiva K. Lele
6. M. Roger
7. Stéphane Moreau
8. Michael S. Howe

In Computational Mechanics

1. David L. Darmofal
2. Donghyun You
3. D. Venditti
4. Parviz Moin
5. Krzysztof J. Fidkowski
6. Sanjiva K. Lele
7. Sergio Pirozzoli
8. Jonathan B. Freund

Citations in the corpus

• Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils (2016, unknown): “resulting in delayed separation and stall [46].”
• Turbulence Model Behavior in Low Reynolds Number Regions of Aerodynamic Flowfields (2008, unknown): “See [33] for a summary.”
• Active Control of Flow Separation Over an Airfoil Using Synthetic Jets (2008, methodology): “The complex unsteady flow is difficult to compute by traditional computational fluid dynamics (CFD) techniques based on Reynolds-averaged Navier–Stokes (RANS) equations (Rumsey and Ying, 2002).”
• Anisotropic grid adaptation for functional outputs: application to two-dimensional viscous flows (2003, methodology): “Rumsey and Ying [44] have conducted a survey of recent CFD methods applied to high-lift configurations and have concluded that numerical error often supersedes modeling error as the primary reason for inconsistent lift predictions.”