Transition model based on the laminar kinetic energy concept for the prediction of all transition modes

被引:0
|
作者
Dellacasagrande, M. [2 ]
Ghidoni, A. [1 ]
Noventa, G. [1 ]
Simoni, D. [2 ]
机构
[1] Univ Brescia, Dipartimento Ingn Meccan & Ind, Via Branze 38, I-25123 Brescia, BS, Italy
[2] Univ Genoa, Dipartimento Ingn Meccan Energet Gestionale & Tras, Via Montallegro 1, I-16145 Genoa, GE, Italy
关键词
RANS equations; Discontinuous Galerkin; Laminar kinetic energy; Transition; Flat plates; PRESSURE-GRADIENT; BOUNDARY-LAYERS; BYPASS TRANSITION; TURBULENCE MODEL; LOCAL VARIABLES;
D O I
10.1016/j.ijheatfluidflow.2024.109680
中图分类号
O414.1 [热力学];
学科分类号
摘要
Separation-induced transition showed to be the weakness of the transition models based on the laminar kinetic energy concept. In fact, these models contemplate only the Tolmien-Schlichting waves, for the natural mode, and the Klebanoff streaks, for the bypass mode. Literature is very poor about the use of these models to cases with the separation-induced mode and in all these works no proofs of the phenomenological agreement between the models and the physics of the flow are spotlighted. A further improvement for the Reynolds-Averaged Navier-Stokes equations in separated and transitional shear layers needs more accurate models to describe the physics behind the phenomena, e.g., the introduction of ad-hoc designed terms for the Kelvin-Helmholtz instability in the transport equations. The objective of this work is to assess a phenomenological and local transition model based on the laminar kinetic energy concept, implemented in a high-order discontinuous Galerkin solver, for the simulation of transitional flows. The prediction capabilities of the model are proved with the simulations of the flow over the ERCOFTAC and UNIGE flat plates, characterized by the bypass and separation-induced mode of transition. The education of the model is not only based on integral coefficients and first-order statistics, but also on the turbulence intensity, laminar and turbulent kinetic energy distributions extracted from finely processed experimental data.
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页数:17
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