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引用次数: 5

摘要

给出了后面向台阶典型试验用例的计算流体力学(CFD)模拟结果。BFS的情况表现出复杂的物理特性,包括湍流分离、再附着和边界层重新启动。采用两种不同的湍流模型作为两类建模的代表性例子:reynolds -average Navier-Stokes (RANS)和hybrid ranss - les(大涡模拟)。具体使用的模型是k-ω海表温度和动态混合ranss - les (DHRL)。本研究的目的是比较在三种不同的流动求解器(flow Psi、Loci-CHEM和Ansys FLUENT)中实现的两种湍流模型的性能,并使用三种不同的方法对控制方程中的对流项进行数值离散化。为了验证目的,将结果与实验数据进行比较。结果表明,k-ω海表温度和DHRL模型均能较好地再现平均流动物理。由于求解器算法和对流离散化方案的不同,两种模型的差异都很明显,但DHRL模型的灵敏度更高,与预期的一样。总的来说,结果强调了考虑湍流CFD模拟的所有综合方面以确保采用模型和数值方法的最佳组合的重要性。
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Evaluation of Performance and Code-to-Code Variation of a Dynamic Hybrid RANS/LES Model for Simulation of Backward-Facing Step Flow
Computational fluid dynamics (CFD) simulation results are presented for the canonical test case of flow over a backward facing step (BFS). The BFS case exhibits complex physics including turbulent separation, reattachment, and boundary layer restart. Results are obtained using two different turbulence models as representative examples of two classes of modeling: Reynolds-averaged Navier-Stokes (RANS) and hybrid RANS-LES (large-eddy simulation). The specific models used are k-ω SST and dynamic hybrid RANS-LES (DHRL). The objective of the study is to compare the performance of both turbulence models as implemented in three different flow solvers (Flow Psi, Loci-CHEM, and Ansys FLUENT) and using three different methods for numerical discretization of the convective terms in the governing equations. Results are compared to experimental data for validation purposes. Results show that both k-ω SST and DHRL models are capable of reproducing the mean flow physics with reasonable accuracy. The differences due to solver algorithm and convective discretization scheme are apparent for both models, but the DHRL model shows more sensitivity, as expected. Overall the results highlight the importance of considering all integrated aspects of a turbulent CFD simulation to ensure that an optimum combination of model and numerical method are employed.
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