Song Fangying, Karniadakis George Em
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China.
Division of Applied Mathematics, School of Engineering, Brown University, Providence, RI 02912, USA.
Entropy (Basel). 2021 Jun 20;23(6):782. doi: 10.3390/e23060782.
Modeling of wall-bounded turbulent flows is still an open problem in classical physics, with relatively slow progress in the last few decades beyond the log law, which only describes the intermediate region in wall-bounded turbulence, i.e., 30-50 y+ to 0.1-0.2 R+ in a pipe of radius R. Here, we propose a fundamentally new approach based on fractional calculus to model the entire mean velocity profile from the wall to the centerline of the pipe. Specifically, we represent the Reynolds stresses with a non-local fractional derivative of variable-order that decays with the distance from the wall. Surprisingly, we find that this variable fractional order has a universal form for all Reynolds numbers and for three different flow types, i.e., channel flow, Couette flow, and pipe flow. We first use existing databases from direct numerical simulations (DNSs) to lean the variable-order function and subsequently we test it against other DNS data and experimental measurements, including the Princeton superpipe experiments. Taken together, our findings reveal the continuous change in rate of turbulent diffusion from the wall as well as the strong nonlocality of turbulent interactions that intensify away from the wall. Moreover, we propose alternative formulations, including a divergence variable fractional (two-sided) model for turbulent flows. The total shear stress is represented by a two-sided symmetric variable fractional derivative. The numerical results show that this formulation can lead to smooth fractional-order profiles in the whole domain. This new model improves the one-sided model, which is considered in the half domain (wall to centerline) only. We use a finite difference method for solving the inverse problem, but we also introduce the fractional physics-informed neural network (fPINN) for solving the inverse and forward problems much more efficiently. In addition to the aforementioned fully-developed flows, we model turbulent boundary layers and discuss how the streamwise variation affects the universal curve.
壁面约束湍流的建模在经典物理学中仍然是一个未解决的问题,在过去几十年里,除了对数定律之外,进展相对缓慢,对数定律仅描述了壁面约束湍流的中间区域,即在半径为R的管道中,从壁面到中心线的距离为30 - 50 y+到0.1 - 0.2 R+的区域。在此,我们提出一种基于分数阶微积分的全新方法,用于对从壁面到管道中心线的整个平均速度剖面进行建模。具体而言,我们用一个随离壁面距离衰减的变阶非局部分数阶导数来表示雷诺应力。令人惊讶的是,我们发现这个变分数阶对于所有雷诺数以及三种不同的流动类型,即槽道流、库埃特流和管道流,都具有通用形式。我们首先使用来自直接数值模拟(DNS)的现有数据库来学习变阶函数,随后将其与其他DNS数据和实验测量结果进行对比测试,包括普林斯顿超管道实验。综合来看,我们的研究结果揭示了从壁面开始的湍流扩散速率的连续变化,以及远离壁面时增强的湍流相互作用的强非局部性。此外,我们还提出了替代公式,包括一种用于湍流的散度变分数阶(双边)模型。总剪切应力由双边对称变分数阶导数表示。数值结果表明,这种公式可以在整个域中产生平滑的分数阶剖面。这个新模型改进了仅在半域(从壁面到中心线)考虑的单边模型。我们使用有限差分法来解决反问题,但也引入了分数阶物理信息神经网络(fPINN),以便更高效地解决反问题和正问题。除了上述充分发展的流动之外,我们还对湍流边界层进行建模,并讨论流向变化如何影响通用曲线。