AI 中文总结
研究基于修正的纳维-斯托克斯方程对充分发展湍流槽道流做直接数值模拟，通过摒弃斯托克斯各向同性假设等方法，能修正近壁动量传输、捕捉涡旋结构、调节能量级联，更精细感知壁面湍流内在动力学，提高预测能力。
AI 中文摘要
进行了雷诺数Re_tau = 550时充分发展湍流槽道流的直接数值模拟，以研究修正的纳维-斯托克斯（CNS）方程。基于涡旋流体运动学，CNS摒弃了斯托克斯各向同性假设，通过明确消除应力张量中有争议的拉伸项应用仅剪切本构关系。与传统纳维-斯托克斯（TNS）的DNS数据比较表明，CNS固有地修正了近壁动量传输。拉伸诱导耗散的去除使内区和缓冲层边界向壁面移动，有效抑制了TNS中平均速度剖面的过冲。湍流统计显示了多尺度动能重新分布，强化了近壁产生-耗散循环。此外，通过速度梯度张量判别（Delta = 0）对瞬时相干结构进行拓扑描绘，即发现和定义旋转/非旋转界面（RNRI），证实CNS能够捕捉高度复杂且交织的涡旋结构。最终，谱适当正交分解（SPOD）揭示并阐明仅剪切机制本质上调节时空能量级联，促进更密集、更倾斜的涡旋，同时通过分割相干包增强湍流间歇性。总体而言，基于CNS的DNS通过以物理纯度隔离和检测仅剪切机制，更精细地感知壁面湍流的内在动力学，提供更符合物理的模型来捕捉多尺度相干结构间的相互作用，并提高预测壁面湍流的能力。
英文摘要
Direct numerical simulations (DNS) of fully developed turbulent channel flows at Re_tau = 550 were performed to investigate the corrected Navier-Stokes (CNS) equations. Grounded in the fluid kinematics of Rortex, the CNS abandons the Stokes' isotropic hypothesis and applies the shearing-only constitutive relation by explicitly eliminating the controversial stretching terms in the stress tensor. Comparisons with the DNS data from the traditional Navier-Stokes (TNS) suggest that the CNS inherently rectifies the near-wall momentum transport. The removal of stretching-induced dissipation shifts the inner- and buffer-layer boundaries towards the wall and effectively suppresses the overshoot in the mean velocity profile in TNS. The turbulence statistics demonstrate a multiscale kinetic energy redistribution that intensifies the near-wall production-dissipation cycle. Furthermore, the topological delineation of instantaneous coherent structures, namely the discovery and definition of rotational/non-rotational interface (RNRI) via the velocity gradient tensor (VGT) discriminant (Delta = 0), confirms that the CNS is capable of capturing the highly complex and interwoven vortical structures. Ultimately, the spectral proper orthogonal decomposition (SPOD) unveils and elucidates that the shearing-only mechanisms intrinsically modulate the spatiotemporal energy cascade, promoting denser and more inclined vortices while enhancing turbulence intermittency by fragmenting the coherent packets. Overall, by isolating and detecting the shearing-only mechanism with physics purity, the DNS based on the CNS provides a more refined perception of the intrinsic dynamics of wall-bounded turbulence, offering a more physical soundness model to capture the interactions among the multiscale coherent structures and the improved capability in predicting the wall-bounded turbulence.
Comments57 pages, 15 figures
