This paper presents the simulation of a two-rotor aircraft in different geometric configurations during hover flight. The analysis was performed using an implementation of the unsteady vortex-lattice method (UVLM). A description of the UVLM is presented as well as the techniques used to enhance the stability of results for rotors in hover flight. The model is validated for an isolated rotor in hover, comparing numerical results to experimental data (high-Reynolds, low-Mach conditions). Results show that an exclusion of the root vortex generates a more stable wake, without affecting results. Results for the two-rotor aircraft show an important influence of the number of blades on the vertical thrust. Furthermore, the geometric configuration has a considerable influence on the pitching moment.
Understanding the development of a turbulent mixing layer is essential for various aerospace applications. In particular, experiments found that the flow development is highly sensitive to inflow conditions, which are difficult to reproduce in the flow simulations. In previous direct numerical simulations (DNS) of temporarily and spatially developing turbulent mixing layers, idealized inflow conditions based on mathematical approximations of the mean velocity profile were used to facilitate turbulent flow conditions. The current paper presents results of DNS of a spatially developing turbulent mixing layer, where coflowing laminar boundary layers over a splitter plate are used as inflow conditions; no artificial perturbations are seeded into the flow. The goal is to closer replicate a naturally developing mixing layer. The flow conditions used in simulations closely match those in experiments by Bell & Mehta (1990). DNS were conducted using the spectral-element code Nek5000. Effects of the splitter plate thickness and the computational domain size in the spanwise direction on the flow development are analyzed. Profiles of the mean flow velocity and the Reynolds stresses are compared with the experimental data.
Statistical data obtained from direct numerical simulations (DNS) are often used as reference data for validating turbulence models. Thus, accuracy of the DNS data itself is of particular importance for understanding the potential error in Reynolds-averaged Navier- Stokes (RANS) simulations. Recent studies demonstrate that when the DNS data is used to represent budget terms in the RANS equations, simulations of wall-bounded turbulent flows conducted with such equations (herein referred to as RANS-DNS simulations) produce unphysical results. The current paper analyzes the contribution that convergence of DNS statistics makes to this discrepancy. The Reynolds stresses and budget terms in the RANSequations are collected in a fully developed channel flow ( = ) at increasing sample sizes and analyzed using the RANS-DNS simulation. The results demonstrate that statistical convergence is not the only contributing factor to the spurious RANS-DNS results, and further study is required. Nomenclature U = mean flow velocity in the streamwise direction U ∞ = free stream velocity + = / U u τ P = mean flow pressure u, v, w = turbulent velocity fluctuations in streamwise, normal-to-wall, and spanwise directions u i = turbulent velocity fluctuation in the i-direction u τ = friction velocity h = half-channel width t = time t n = averaging time of DNS data δ = boundary layer thickness = boundary layer momentum thickness ν = kinematic viscosity 1 Assistant Professor, Mechanical Engineering, MSC01 1104, 1 UNM Albuquerque, NM, 87131-00011, AIAA Associate Fellow. 2 AIAA Member.
Accurate prediction of wall-bounded flows with Reynolds-averaged Navier-Stokes turbulence models requires the improved modeling of physical processes that occur in turbulent flows such as the interaction of turbulent velocity and pressure fields, turbulent diffusion, and dissipative processes. Recently, novel models for the velocity/pressuregradient correlations through the fourth order were proposed by the authors and successfully validated against direct numerical simulation (DNS) data in wall-bounded flows without separation in a priori testing. In the current work, these models are implemented in transport equations for the Reynolds stresses to evaluate the model performance in a fullydeveloped planar channel. Other terms that need modeling in the Reynolds stress transport equations are represented in simulations by their DNS profiles to eliminate uncertainty due to modeling these terms. Problems with using DNS data as an input in simulations are highlighted. An approach to mitigate some of those problems is proposed. The computed results are compared against DNS data. NomenclatureU i = mean velocity component in the i-direction U = mean velocity in the streamwise direction ij Π = velocity -pressure-gradient tensor, ( ) , ,
The goal of this study is to determine whether an unsteady formulation of a RANS turbulence model leads to improved description of incompressible separated turbulent flows around two-dimensional bodies. Two geometries are considered: a NACA 4412 airfoil and a circular cylinder. Simulations are conducted with two-equation turbulent models: Menter's 1994 Shear Stress Transport model and Wilcox's 2006 -model, implemented in the opensource CFD OpenFOAM software. Comparison with experimental data and computational results obtained from Large and Detached Eddy Simulations as well as with URANS/RANS models by other research groups is also provided. Nomenclature Re = Reynolds number, U ∞ ρ/ v M = Mach number, U ∞ /a ρ = density P = pressure a = speed of sound U ∞ = free stream velocity u = streamwise velocity component = vertical velocity component u' = velocity fluctuation in the streamwise direction I = turbulence intensity, v = kinematic viscosity = dynamic viscosity c = chord C p = pressure coefficient, C pb = base pressure coefficient at C D = drag coefficient, C L = lift coefficient, C f = friction coefficient, A = frontal area F D = drag force 1 Graduate Student, Mechanical Engineering, MSC01 1105, 1 UNM Albuquerque, NM, 87131-00011, AIAA Student Member. 2 F L = lift force D = diameter = wall shear stress = friction velocity, x = streamwise flow direction y = normal-to-wall direction y + = dimensionless distance from the wall based on fluid properties, y w = distance between a wall and the first grid node in the y-direction t = time t* = dimensionless time = azimuth angle measured clockwise from the stagnation point = separation angle measured clockwise from the stagnation point α = angle of attack ω = turbulent specific dissipation, ε/ k ε = turbulent scalar dissipation k = turbulent kinetic energy = blending constant, L R = recirculation length h = characteristic mesh length for the constant domain size, N = total number of nodes in the computational domain
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