The γ-$\stackrel{⌢}{R{e}_{\theta t}}$ transition model has been applied within the framework of high order discontinuous Galerkin (DG) discretization of Reynolds-averaged Navier-Stokes (RANS) equations. A hybrid discretization strategy that takes advantage of all available information from DG method is suggested for the implementation of the transition model. Some techniques for the spatial and temporal discretization are utilized to enhance the effectiveness of the method. The resulting approaches achieve high accuracy of transition prediction and meanwhile do not increase notable computational challenge when compared to DG discretization of fully coupled RANS/transition system. We carry out both steady and unsteady transitional simulations to examine the performance of the designed method. Numerical results have demonstrated that the present approaches are capable of capturing the transition flow characteristics as well as improving transition prediction accuracy with high order DG schemes.

γ-$\stackrel{⌢}{\mathrm{[R}{Ë}_{\theta Ť}}$过渡模型已在雷诺平均Navier-Stokes（RANS）方程的高阶不连续Galerkin（DG）离散化框架内应用。提出了一种混合离散化策略，该策略利用DG方法的所有可用信息来实现过渡模型。利用一些用于空间和时间离散化的技术来增强该方法的有效性。与完全耦合的RANS /过渡系统的DG离散化相比，所生成的方法实现了过渡预测的高精度，并且同时不会增加显着的计算挑战。我们进行稳态和非稳态过渡仿真，以检验设计方法的性能。