论文标题

POD-Galerkin降低了钠的湍流钠在向后置的步骤的顺序模型

A POD-Galerkin reduced order model of a turbulent convective buoyant flow of sodium over a backward-facing step

论文作者

Star, Kelbij, Stabile, Giovanni, Rozza, Gianluigi, Degroote, Joris

论文摘要

基于有限体积的Pod-galerkin降低了稳态雷诺平均Navier的订单建模策略 - 用于低帕德尔数量流量的Stokes(RANS)模拟。还原的订单模型基于完整的模型,浮力对流量和热传递的影响的特征是改变了理查森数。雷诺应力是通过线性涡流粘度模型计算的。单个梯度扩散假说,以及用于评估湍流prandtl数量的局部相关性,用于对湍流通量进行建模。通过基于插值的数据驱动方法,在还原的订单模型中考虑了涡流粘度和湍流热扩散率场的贡献。还要测试降低的订单模型,用于浮力湍流的液体钠流,从垂直向后的步骤上进行,并在步骤下游的壁上施加了均匀的热通量。将壁热通量与Neumann边界条件合并到完整阶模型和还原订单模型中。使用降低的订单模型预测的速度和温度曲线在参数值范围内的相同和新的Richardson数字与RANS模拟非常吻合。同样,加热壁上的当地斯坦顿数量和皮肤摩擦分布在定性上被很好地捕获。最后,在单个核心上执行的减少订单模拟比在八个核心上执行的模拟的速度要快$ 10^5 $倍。

A Finite-Volume based POD-Galerkin reduced order modeling strategy for steady-state Reynolds averaged Navier--Stokes (RANS) simulation is extended for low-Prandtl number flow. The reduced order model is based on a full order model for which the effects of buoyancy on the flow and heat transfer are characterized by varying the Richardson number. The Reynolds stresses are computed with a linear eddy viscosity model. A single gradient diffusion hypothesis, together with a local correlation for the evaluation of the turbulent Prandtl number, is used to model the turbulent heat fluxes. The contribution of the eddy viscosity and turbulent thermal diffusivity fields are considered in the reduced order model with an interpolation based data-driven method. The reduced order model is tested for buoyancy-aided turbulent liquid sodium flow over a vertical backward-facing step with a uniform heat flux applied on the wall downstream of the step. The wall heat flux is incorporated with a Neumann boundary condition in both the full order model and the reduced order model. The velocity and temperature profiles predicted with the reduced order model for the same and new Richardson numbers inside the range of parameter values are in good agreement with the RANS simulations. Also, the local Stanton number and skin friction distribution at the heated wall are qualitatively well captured. Finally, the reduced order simulations, performed on a single core, are about $10^5$ times faster than the RANS simulations that are performed on eight cores.

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