Transport of Turbulence Across Permeable Interface in a Turbulent Channel Flow: Interface-Resolved Direct Numerical Simulation

Abstract

Turbulence transportation across permeable interfaces is investigated using direct numerical simulation, and the connection between the turbulent surface flow and the pore flow is explored. The porous media domain is constructed with an in-line arranged circular cylinder array. The effects of Reynolds number and porosity are also investigated by comparing cases with two Reynolds numbers ($$Re\approx 3000,6000$$ R e ≈ 3000 , 6000 ) and two porosities ($$\varphi =0.5,0.8$$ φ = 0.5 , 0.8 ). It was found that the change of porosity leads to the variation of flow motions near the interface region, which further affect turbulence transportation below the interface. The turbulent kinetic energy (TKE) budget shows that turbulent diffusion and pressure transportation work as energy sink and source alternatively, which suggests a possible route for turbulence transferring into porous region. Further analysis on the spectral TKE budget reveals the role of modes of different wavelengths. A major finding is that mean convection not only affects the distribution of TKE in spatial space, but also in scale space. The permeability of the wall also have an major impact on the occurrence ratio between blow and suction events as well as their corresponding flow structures, which can be related to the change of the Kármán constant of the mean velocity profile.

Spatial and spectral characteristics of information flux between turbulent boundary layers and porous media

“…Meanwhile, the characteristics of the near-wall cycle are substantially weakened. This scenario is supported by both experimental (Kim et al 2018) and numerical results (Manes et al 2009;Kuwata & Suga 2016;Motlagh & Taghizadeh 2016;Chu et al 2021).…”

“…In particular, the fundamental cause-and-effect relations between the surface and subsurface flows are not systematically inspected. In many cases, a causal relationship between flow quantities is inferred using linear correlation (Breugem et al 2006;Chu et al 2021;Wang, Pan & Wang 2021b), which is closely related to the linear stochastic estimation (LSE) introduced by Adrian & Moin (1988). Recently, nonlinear modelling of dynamics based on the neural network has also found popularity in the fluid mechanics community.…”

Information transfer between turbulent boundary layers and porous media

“…Breugem et al (2006) reported the correlation between the sign of the wall-normal flow transporting fluid across the permeable interface and the sign of the fluctuating streamwise velocity in the surface flow. This linkage was confirmed by the experiments of Kim et al (2018) and the DNS of Chu et al (2021). Manes et al (2009) uncovered two types of turbulence within the porous bed using spectral analysis.…”

“…(2018) and the DNS of Chu et al. (2021). Manes et al.…”

Coupling staggered-grid and MPFA finite volume methods for free flow/porous-medium flow problems

“…Applications that rely on a detailed description of this exchange are abundant, ranging from large scale environmental applications such as soil-water evaporation and salt precipitation [37,23], to the small scale exchange processes within a PEM fuel cell [16]. Evaluating these applications via direct numerical simulation of the flow fields, like those shown in [8], is usually infeasible due to the immense computational cost and the effort involved in procuring pore geometries. For the analysis of most applications, evaluations using the concept of a representative elementary volume (REV) scale [2] can be used, where averaging techniques create upscaled descriptions of flow in a porous domain [39].…”