In the present paper, turbulent flow in a composite porous-fluid system including a permeable surface-mounted bluff body immersed in a turbulent channel flow is investigated using pore-scale large eddy simulation. The effect of Reynolds number (Re) on the flow leakage from porous to non-porous regions, Kelvin-Helmholtz (K-H) instabilities, as well as coherent structures over the porous-fluid interface are elaborated. Results show that more than 52% of the fluid entering the porous blocks leaks from the first half of the porous region to the non-porous region through the porous-fluid interface. As the Re number increases, the flow leakage decreases by 24%. Flow visualization shows that the Re number affects the size of counter-rotating vortex pairs and coherent hairpin structures above the porous block. Moreover, turbulence statistics show that by reducing the Re number, turbulence production is delayed downstream; at the Re=14400, it begins from the leading edge of the porous block (X/D=0), while at the Re=3600, turbulence production is postponed and starts nearly at the middle of the porous block (X/D=4.6). Finally, the distribution of pressure gradient for the three Re numbers confirms the occurrence of the K-H instability vortices over the porous-fluid interface. For Re = 3600, the K-H instability vortices show a linear growth rate in the vertical and horizontal directions with the slope of 0.136 and 0.05, respectively. However, by increasing the Re from 3600 to 14400, the growth rate slop in the horizontal direction decreases by nearly 33.8%, while in the vertical direction, it increases by 201%.

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In this paper, a novel zonal machine learning (ML) approach for Reynolds-averaged Navier–Stokes (RANS) turbulence modeling based on the divide-and-conquer technique is introduced. This approach involves partitioning the flow domain into regions of flow physics called zones, training one ML model in each zone, then validating and testing them on their respective zones. The approach was demonstrated with the tensor basis neural network (TBNN) and another neural net called the turbulent kinetic energy neural network (TKENN). These were used to predict Reynolds stress anisotropy and turbulent kinetic energy, respectively, in test cases of flow over a solid block, which contain regions of different flow physics including separated flows. The results show that the combined predictions given by the zonal TBNNs and TKENNs were significantly more accurate than their corresponding standard non-zonal models. Most notably, shear anisotropy component in the test cases was predicted at least 20% and 55% more accurately on average by the zonal TBNNs compared to the non-zonal TBNN and RANS, respectively. The Reynolds stress constructed with the zonal predictions was also found to be at least 23% more accurate than those obtained with the non-zonal approach and 30% more accurate than the Reynolds stress predicted by RANS on average. These improvements were attributed to the shape of the zones enabling the zonal models to become highly locally optimized at predicting the output.