摘要
可压缩壁湍流中平均流动剖面的准确预测对工程应用至关重要。然而,高保真数值模拟成本高昂,而传统的Reynolds平均Navier-Stokes(RANS)方法精度往往受限。提出了一种基于逆速度变换的全新模块化迭代框架,旨在准确且高效地预测可压缩槽道湍流(compressible turbulent channel flows,CTCFs)中的平均剖面。该框架的核心创新在于采用最近提出的平均温度-速度(TV)积分关系代替传统的基于Reynolds比拟的代数关系,从而消除了对槽道中心线平均温度经验公式的强烈依赖。这一改进不仅规避了以往方法重复施加对称性约束导致的不适定问题,还显著地提升了框架的通用性和稳健性。系统性的数值验证表明,所提出的方法在广泛的Mach数和Reynolds数范围内均表现优异。与作为基准的直接数值模拟数据相比,其预测的平均速度和温度剖面的局部相对误差的绝对值在绝大多数情况下分别低于3%和2%以内。同时,该方法能够可靠地预测壁面摩阻和壁面热流,它们在中等和高Reynolds数条件下的相对误差绝对值分别低于2%和1%。与现有方法相比,所提出的框架在保持高预测精度的同时,显著降低了迭代过程对先验经验知识的依赖,从而为可压缩壁湍流的快速预测和工程导向的建模提供了有力工具。
The accurate prediction of mean flow profiles in compressible wall-bounded turbulence is crucial for engineering applications.However,high-fidelity simulations remain computationally prohibitive,whereas traditional Reynolds-averaged Navier-Stokes(RANS)methods often suffer from limited accuracy.A novel modular iterative framework based on inverse velocity transformation for accurate and efficient prediction of mean profiles in compressible turbulent channel flows(CTCFs)was presented.The key innovation lies in replacing the conventional algebraic mean temperature-velocity(TV)relations,which are based on Reynolds analogy,with a recently proposed integral relation,thereby removing the strong dependence on empirical scalings for centerline mean temperature.This advancement not only avoids the ill-posedness arising from repeatedly imposing symmetry constraints in prior approaches but also significantly enhances the generality and robustness of the framework.Comprehensive numerical evaluations demonstrate that the proposed method performs excellently across a wide range of Mach and Reynolds numbers.It predicted mean velocity and temperature profiles with maximum relative errors below 3% and 2%,respectively,when compared with benchmark direct numerical simulation(DNS)data.Meanwhile,it reliably predicted wall skin friction and wall heat flux,achieving absolute relative errors below 2% and 1% in moderate-to high-Reynolds-number regimes.Compared with existing approaches,the present framework preserves high predictive accuracy while markedly reducing the reliance on prior empirical knowledge during the iteration,offering a powerful tool for rapid prediction and engineering-oriented modeling of compressible wall-bounded turbulence.
作者
朱栩柯
姬永超
衷洪杰
宋余滨
杨潇朔
夏振华
ZHU Xuke;JI Yongchao;ZHONG Hongjie;SONG Yubin;YANG Xiaoshuo;XIA Zhenhua(School of Aeronautics and Astronautics,Zhejiang University,Hangzhou 310027,China;AVIC Aerodynamics Research Institute,Shenyang 110034,China)
出处
《气体物理》
2026年第1期25-39,共15页
Physics of Gases
基金
国家自然科学基金(U25A6006,92152101,92152301)。
关键词
可压缩槽道湍流
平均流动剖面预测
速度变换
温度-速度关系
compressible turbulent channel flows
prediction of mean flow profiles
velocity transformation
mean temperature-velocity relation
