Modeling bypass transition within $k-\varepsilon$ approach

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This article is dedicated to investigation of the possibility to predict bypass transition by means of an unsophisticated low-Reynolds $k-\varepsilon$ turbulence model. Such a model has been developed at TESIS Ltd. The model is implemented in the FlowVision software suit. The ideas implemented in the model are discussed in the article. The capability of the model to predict bypass transition is demonstrated on well-known test cases T3B, T3A, T3A-.

Keywords: turbulent flow, bypass transition, low-Reynolds $k-\varepsilon$ turbulence model, grid convergence
Citation in English: Zhluktov S.V., Aksenov A.A., Karasev P.I. Modeling bypass transition within $k-\varepsilon$ approach // Computer Research and Modeling, 2014, vol. 6, no. 6, pp. 879-888
Citation in English: Zhluktov S.V., Aksenov A.A., Karasev P.I. Modeling bypass transition within $k-\varepsilon$ approach // Computer Research and Modeling, 2014, vol. 6, no. 6, pp. 879-888
DOI: 10.20537/2076-7633-2014-6-6-879-888
According to Crossref, this article is cited by:
  • Sergey V. Zhluktov, Andrey A. Aksenov. Wall functions for high-Reynolds calculations in FlowVision software. // Computer Research and Modeling. 2015. — V. 7, no. 6. — P. 1221. DOI: 10.20537/2076-7633-2015-7-6-1221-1239
  • Sergey V. Zhluktov, Andrey A. Aksenov, Dmitriy Vladimirovich Savitskiy. High-Reynolds number calculations of turbulent heat transfer in FlowVision software. // Computer Research and Modeling. 2018. — V. 10, no. 4. — P. 461. DOI: 10.20537/2076-7633-2018-10-4-461-481
  • Sergey V. Zhluktov, Andrey A. Aksenov, P. I. Karasev. Modeling separated flow with use of two-equation turbulence model. // Computer Research and Modeling. 2016. — V. 8, no. 1. — P. 79. DOI: 10.20537/2076-7633-2016-8-1-79-88
  • Sergei V. Kalashnikov, Aleksei A. Krivoschapov, Aleksandr L. Mitin, Nikolai V. Nikolaev. Computational investigation of aerodynamic performance of the generic flying-wing aircraft model using FlowVision computational code. // Computer Research and Modeling. 2017. — V. 9, no. 1. — P. 67. DOI: 10.20537/2076-7633-2017-9-67-74
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