The Tesla valve is a passive-type check valve used for flow control in micro- or minichannel systems for a variety of applications. Although the design and effectiveness of a singular Tesla valve is somewhat well understood, the effects of using multiple, identically shaped Tesla valves in series—forming a multistaged Tesla valve (MSTV)—have not been well documented in the open literature. Therefore, using high-performance computing (HPC) and three-dimensional (3D) computational fluid dynamics (CFD), the effectiveness of an MSTV using Tesla valves with preoptimized designs was quantified in terms of diodicity for laminar flow conditions. The number of Tesla valves/stages (up to 20), valve-to-valve distance (up to 3.375 hydraulic diameters), and Reynolds number (up to 200) was varied to determine their effect on MSTV diodicity. Results clearly indicate that the MSTV provides for a significantly higher diodicity than a single Tesla valve and that this difference increases with Reynolds number. Minimizing the distance between adjacent Tesla valves can significantly increase the MSTV diodicity, however, for very low Reynolds number (Re < 50), the MSTV diodicity is almost independent of valve-to-valve distance and number of valves used. In general, more Tesla valves are required to maximize the MSTV diodicity as the Reynolds number increases. Using data-fitting procedures, a correlation for predicting the MSTV diodicity was developed and shown to be in a power-law form. It is further concluded that 3D CFD more accurately simulates the flow within the Tesla valve over a wider range of Reynolds numbers than 2D simulations that are more commonly reported in the literature. This is supported by demonstrating secondary flow patterns in the Tesla valve outlet that become stronger as Reynolds number increases. Plots of the pressure and velocity fields in various MSTVs are provided to fully document the complex physics of the flow field.
Computational fluid dynamics (CFD) prediction of high Reynolds number flow over a 3D axisymmetric hill presents a unique set of challenges for turbulence models. The flow on the leeward side of the hill is characterized by the presence of complex vortical structures, unsteady wakes, and regions of boundary layer separation. As a result, traditional eddy-viscosity Reynolds-averaged Navier-Stokes (RANS) models have been found to perform poorly. Recent studies have focused on the use of Large Eddy Simulation (LES) and hybrid RANS-LES (HRL) methods to improve accuracy. In this study, the capability of a dynamic hybrid RANS-LES (DHRL) model to resolve the flow over a 3D axisymmetric hill is investigated and compared to numerical results using a traditional RANS model and a conventional hybrid RANS-LES model, and to experimental data. Results show that the RANS model fails to accurately predict the mean flow features in the wake region, which is in agreement with prior studies. The conventional HRL model provides better prediction of the flow characteristics but suffers from grid sensitivity and delayed transition to LES mode. The DHRL method provides the best agreement with experimental data overall and shows least sensitivity to grid resolution. Results also highlight the importance of using a low dissipation flux formulation for flow simulations in which a portion of the turbulence spectrum is resolved, including hybrid RANS-LES.
Simulation of turbulent boundary layers for flows characterized by unsteady driving conditions is important for solving complicated engineering problems such as combustion, blood flow in stenosed arteries, and flow over immersed structures. These flows are often dominated by complex vortical structures, regions of varying turbulence intensities, and fluctuating pressure fields. Pulsating channel flow is one such case that presents a unique set of challenges for newly developed and existing turbulence models used in computational fluid dynamics (CFD) solvers. In the present study, performance of the dynamic hybrid RANS-LES model (DHRL) with exponential time averaging (ETA) is evaluated against Monotonically Integrated Large Eddy Simulation (MILES) and a previously documented LES study for a fully developed channel flow with a time-periodic driving pressure gradient. Results indicate that MILES over predicts mean streamwise velocity for all forcing frequencies while the DHRL model with ETA provides a method for improved results, especially for the lower frequencies. It is concluded that a hybrid RANS-LES model with ETA is a useful alternative to simulate unsteady non-stationary flows but further work is needed to determine the appropriate filter width for ETA to significantly improve the predictive capabilities of the DHRL model.
A Tesla valve is a fluidic dioide that may be used in a variety of mini/micro channel applications for passive flow rectification and/or control. The valve’s effectiveness is quantified by the diodicity, which is primarily governed by the incoming flow speed, its design and direction-dependent minor losses throughout its structure during forward and reverse flows. It has been previously shown that the Reynolds number at the valve inlet is not representative of the entire flow regime throughout the Tesla structure. Therefore, pure-laminar solving methods are not necessarily accurate. Local flow instabilities exist and exhibit both transitional and turbulent characteristics. Therefore, the current investigation seeks to identify a suitable RANS-based flow modeling approach to predict Tesla valve diodicity via three-dimensional (3D) computational fluid dynamics (CFD) for inlet Reynolds numbers up to Re = 2,000. Using ANSYS FLUENT (v. 14), a variety of models were employed, including: the Realizable k-ε, k-kL-ω and SST k-ω models. All numerical simulations were validated against available experimental data obtained from an identically-shaped Tesla valve structure. It was found that the k-ε model drastically under-predicts experimental data for the entire range of Reynolds numbers investigated and cannot accurately model the Tesla valve flow. The k-kL-ω and SST k-ω models approach the experimentally-measured diodicity better than regular 2D CFD. The k-kL-ω demonstrates exceptional agreement with experimental data for Reynolds numbers up to approximately 1,500. However, both the k-kL-ω and k-ω SST models over-predict experimental data for Re = 2,000.
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