F
Florian R. Menter
Researcher at Ansys
Publications - 43
Citations - 5743
Florian R. Menter is an academic researcher from Ansys. The author has contributed to research in topics: Computational fluid dynamics & Turbulence. The author has an hindex of 20, co-authored 43 publications receiving 4674 citations.
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A Correlation-Based Transition Model Using Local Variables—Part I: Model Formulation
TL;DR: Langtry et al. as discussed by the authors developed a new correlation-based transition model based strictly on local variables, which is compatible with modern computational fluid dynamics (CFD) approaches, such as unstructured grids and massive parallel execution.
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Correlation-Based Transition Modeling for Unstructured Parallelized Computational Fluid Dynamics Codes
Robin Langtry,Florian R. Menter +1 more
TL;DR: The main goal of the present paper is to publish the full model and release it to the research community so that it can continue to be further validated and possibly extended or improved.
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Transition Modelling for General Purpose CFD Codes
TL;DR: An incremental approach was used to validate the model, first on 2D flat plates and airfoils and then on to progressively more complicated test cases such as a three-element flap, a 3D transonic wing and a full helicopter configuration, and good agreement with the available experimental data was observed.
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Development of DDES and IDDES Formulations for the k-ω Shear Stress Transport Model
TL;DR: In this paper, the authors proposed modifications of two recently developed hybrid CFD strategies, Delayed Detached Eddy Simulation (DDES) and DDES with Improved wall-modeling capability (IDDES), aimed at fine-tuning of these approaches to the k-ω SST background RANS model.
Journal ArticleDOI
A Correlation-Based Transition Model Using Local Variables—Part II: Test Cases and Industrial Applications
TL;DR: Menter et al. as mentioned in this paper proposed a new correlation-based transition model based on local variables, which is compatible with modern computational fluid dynamics (CFD) methods using unstructured grids and massive parallel execution.