Search

In this paper, we report new insight into a symmetry-breaking phenomenon that occurs for turbulent flow in periodic porous media composed of cylindrical solid obstacles with circular cross-section. We have used Large Eddy Simulation to investigate the symmetry-breaking phenomenon by varying the porosity (0.57-0.99) and the pore scale Reynolds number (37-1,000). Asymmetrical flow distribution is observed in the intermediate porosity flow regime for values of porosities between 0.8 and 0.9, which is characterized by the formation of alternating low and high velocity flow channels above and below the solid obstacles. These channels are parallel to the direction of the flow. Correspondingly, the microscale vortices formed behind the solid obstacles exhibit a bias in the shedding direction. The transition from symmetric to asymmetric flow occurs in between the Reynolds numbers of 37 (laminar) and 100 (turbulent). A Hopf bifurcation resulting in unsteady oscillatory laminar flow marks the origin of a secondary flow instability arising from the interaction of the shear layers around the solid obstacle. When turbulence emerges, stochastic phase difference in the vortex wake oscillations caused by the secondary flow instability results in flow symmetry breaking. We note that symmetry breaking does not occur for cylindrical solid obstacles with square cross-section due to the presence of sharp vertices in the solid obstacle surface. At the macroscale level, symmetry-breaking results in residual transverse drag force components acting on the solid obstacle surfaces. Symmetry-breaking promotes attached flow on the solid obstacle surface, which is potentially beneficial for improving transport properties at the solid obstacle surface such as convection heat flux., Comment: 19 pages, 10 figures

Convolution Neural Networks (CNN) are well-suited to model the nonlinear relationship between the microscale geometry of porous media and the corresponding flow distribution, thereby accurately and efficiently coupling the flow behavior at the micro- and macro- scale levels. In this paper, we have identified the challenges involved in implementing CNNs for macroscale model closure in the turbulent flow regime, particularly in the prediction of the drag force components arising from the microscale level. We report that significant error is incurred in the crucial data preparation step when the Reynolds averaged pressure and velocity distributions are interpolated from unstructured stretched grids used for Large Eddy Simulation (LES) to the structured uniform grids used by the CNN model. We show that the range of the microscale velocity values is 10 times larger than the range of the pressure values. This invalidates the use of the mean squared error loss function to train the CNN model for multivariate prediction. We have developed a CNN model framework that addresses these challenges by proposing a conservative interpolation method and a normalized mean squared error loss function. We simulated a model dataset to train the CNN for turbulent flow prediction in periodic porous media composed of cylindrical solid obstacles with square cross-section by varying the porosity in the range 0.3 to 0.88. We demonstrate that the resulting CNN model predicts the pressure and viscous drag forces with less than 10% mean absolute error when compared to LES while offering a speedup of O(10^6)., Comment: 22 pages, 11 figures

Metal halide perovskite solar cells being one of the fastest emerging technologies for renewable energy still has to become more industry friendly in a way that will allow using it for thin film modules or tandems with conventional silicon devices. The simplest way to achieve this is to use chemical vapor deposition (CVD) technique for tandem production. In this work, we show a method for a single step production of MAPbI3 films in a simple two-zone CVD reactor from lead diacetate and methyl-ammonium iodide powders. Obtained films show highly ordered cubic MAPbI3 phase with a thickness of 400-500 um, good photoluminescence response and absorption band edge similar to spin-coated film. We used those films to produce p-i-n solar cells with ITO/NiO/MAPbI3/C60/Cu structure. The best cell showed negligible 0.23 % PCE right after the manufacturing but significantly improved to 5.5 % PCE after 8 hours of storage in the dark. The main limiting factor affecting the efficiency is low current density, which we attribute to non-optimized growth conditions; however, our approach is a first step to a single step CVD deposition of MAPbI3 on any type of substrates including texturized silicon subcells for better overall efficiency.

Hidden attractors are present in many nonlinear dynamical systems and are not associated with equilibria, making them difficult to locate. Recent studies have demonstrated methods of locating hidden attractors, but the route to these attractors is still not fully understood. In this letter, we present the route to hidden attractors in systems with stable equilibrium points and in systems without any equilibrium points. We show that hidden attractors emerge as a result of the saddle-node bifurcation of stable and unstable periodic orbits. Real-time hardware experiments were performed to demonstrate the existence of hidden attractors in these systems. Despite the difficulties in identifying the suitable initial conditions from the appropriate basin of attraction, we performed experiments to detect hidden attractors in nonlinear electronic circuits. Our results provide new insights into the generation of hidden attractors in nonlinear dynamical systems.

The focus of this paper is to systematically study the influence of solid obstacle surface roughness in porous media on the microscale flow physics and report its effect on macroscale drag and Nusselt number. The Reynolds averaged flow field is numerically simulated for a flow through a periodic porous medium consisting of an in-line arrangement of square cylinders with square roughness particles on the cylinder surface. Two flow regimes are identified with respect to the surface roughness particle height: fine and coarse roughness regimes. The effect of the roughness particles in the fine roughness regime is limited to the near-wall boundary layer around the solid obstacle surface. In the coarse roughness regime, the roughness particles modify the microscale flow field in the entire pore space of the porous medium. In the fine roughness regime, the heat transfer from the rough solid obstacles to the fluid inside the porous medium is less than that from a smooth solid obstacle. In the coarse roughness regime, there is an enhancement in the heat transfer from the rough solid obstacle to the fluid inside the porous medium. Total drag reduction is also observed in the fine roughness regime for the smallest roughness particle height. The surface roughness particle spacing determines the fractional area of the solid obstacle surface covered by recirculating, reattached, and stagnating flow. As the roughness particle spacing increases, there are two competing factors for the heat transfer rate: increase due to more surface area covered by reattached flow, and decrease due to the decrease in the number of roughness particles on the solid obstacle surface. Decreasing the porosity and increasing the Reynolds number amplify the effect of the surface roughness on the microscale flow.