Hybrid RANS/LES of flow in a rib-roughened channel with rotation

The aim of the present study is to verify the reliability of a k-ω based hybrid RANS/LES model in reproducing the flow in a rib-roughened rotating channel. The numerical results obtained with the hybrid RANS/LES model are compared to experimental data by Coletti and Arts (2011) and to the results obtained with the RANS k-ω model of Wilcox (2008). We demonstrate that the hybrid RANS/LES model gives realistic results for simulation of the rotating ribbed duct flow, without the necessity to add ad hoc corrections for system rotation to the underlying RANS mode

Hybrid RANS/LES of plane jets impinging on a flat plate at small nozzle-plate distances

A k-omega BASED HYBRID RANS/LES (Reynolds-averaged Navier-Stokes/large eddy simulation) model is tested for simulation of plane impinging jets at various nozzle-plate distances (H/B), where H is the distance and B is the slot's width) and various Reynolds numbers (based on the slot's width and the velocity in the symmetry plane). The studied combinations are H/B = 2 for Re = 10000, H/B = 4 for Re = 18 000 and H/B = 9.2 for Re = 20000. The focus is on small distance of the nozzle exit to the plate. In LES mode, the hybrid RANS/LES model uses two definitions of the local grid size, one based on the maximum distance between the cell faces in the destruction term of the turbulent kinetic energy equation and one based on the cube root of the cell volume in the eddy-viscosity formula. This allows accounting for flow inhomogeneity on anisotropic grids. In RANS mode, the hybrid model turns into the newest version of the k-omega model by Wilcox

Ergodic Capacity Analysis of Remote Radio Head Associations in Cloud Radio Access Networks

Characterizing user to Remote Radio Head (RRH) association strategies in cloud radio access networks (C-RANs) is critical for performance optimization. In this letter, the single nearest and N--nearest RRH association strategies are presented, and the corresponding impact on the ergodic capacity of C-RANs is analyzed, where RRHs are distributed according to a stationary point process. Closed-form expressions for the ergodic capacity of the proposed RRH association strategies are derived. Simulation results demonstrate that the derived uplink closed-form capacity expressions are accurate. Furthermore, the analysis and simulation results show that the ergodic capacity gain is not linear with either the RRH density or the number of antenna per RRH. The ergodic capacity gain from the RRH density is larger than that from the number of antennas per RRH,which indicates that the association number of the RRH should not be bigger than 4 to balance the performance gain and the implementation cost.Comment: 4 pages, 2 figures, accepted by IEEE Wireless Communication Letter

Sketch-To-Solution: An Exploration of Viscous CFD with Automatic Grids

Numerical simulation of the Reynolds-averaged NavierStokes (RANS) equations has become a critical tool for the design of aerospace vehicles. However, the issues that affect the grid convergence of three dimensional RANS solutions are not completely understood, as documented in the AIAA Drag Prediction Workshop series. Grid adaption methods have the potential for increasing the automation and discretization error control of RANS solutions to impact the aerospace design and certification process. The realization of the CFD Vision 2030 Study includes automated management of errors and uncertainties of physics-based, predictive modeling that can set the stage for ensuring a vehicle is in compliance with a regulation or specification by using analysis without demonstration in flight test (i.e., certification or qualification by analysis). For example, the Cart3D inviscid analysis package has automated Cartesian cut-cell gridding with output-based error control. Fueled by recent advances in the fields of anisotropic grid adaptation, error estimation, and geometry modeling, a similar work flow is explored for viscous CFD simulations; where a CFD application engineer provides geometry, boundary conditions, and flow parameters, and the sketch-to-solution process yields a CFD simulation through automatic, error-based, grid adaptation

RANS Equations with Explicit Data-Driven Reynolds Stress Closure Can Be Ill-Conditioned

Reynolds-averaged Navier--Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linear eddy viscosity models are intrinsically unable to handle flows with non-equilibrium turbulence. Reynolds stress models, on the other hand, are plagued by their lack of robustness. Recent studies in plane channel flows found that even substituting Reynolds stresses with errors below 0.5% from direct numerical simulation (DNS) databases into RANS equations leads to velocities with large errors (up to 35%). While such an observation may have only marginal relevance to traditional Reynolds stress models, it is disturbing for the recently emerging data-driven models that treat the Reynolds stress as an explicit source term in the RANS equations, as it suggests that the RANS equations with such models can be ill-conditioned. So far, a rigorous analysis of the condition of such models is still lacking. As such, in this work we propose a metric based on local condition number function for a priori evaluation of the conditioning of the RANS equations. We further show that the ill-conditioning cannot be explained by the global matrix condition number of the discretized RANS equations. Comprehensive numerical tests are performed on turbulent channel flows at various Reynolds numbers and additionally on two complex flows, i.e., flow over periodic hills and flow in a square duct. Results suggest that the proposed metric can adequately explain observations in previous studies, i.e., deteriorated model conditioning with increasing Reynolds number and better conditioning of the implicit treatment of Reynolds stress compared to the explicit treatment. This metric can play critical roles in the future development of data-driven turbulence models by enforcing the conditioning as a requirement on these models.Comment: 35 pages, 18 figure