Large-eddy simulation of complex geometry jets

Computations are made for chevron and coflowing jet nozzles. The latter has a bypass ratio of 6:1. Also, unlike the chevron nozzle, the core flow is heated, making the inlet conditions reminiscent of those for a real engine. A large-eddy resolving approach is used with circa 12 × 10 6 cell meshes. Because the codes being used tend toward being dissipative the subgrid scale model is abandoned, giving what can be termed numerical large-eddy simulation. To overcome near-wall modeling problems a hybrid numerical large-eddy simulation-Reynolds-averaged Navier-Stokes related method is used. For y + ≤ 60 a Reynolds-averaged Navier-Stokes model is used. Blending between the two regions makes use of the differential Hamilton-Jabobi equation, an extension of the eikonal equation. For both nozzles, results show encouraging agreement with measurements of other workers. The eikonal equation is also used for ray tracing to explore the effect of the mean flow on acoustic ray trajectories, thus yielding a coherent solution strategy. © 2011 by Cambridge University.

Large eddy simulation of a hot, high speed coflowing jet

Computations are made of a short cowl coflowing jet nozzle with a bypass ratio 8 : 1. The core flow is heated, making the inlet conditions reminiscent of those for a real engine. A large eddy resolving approach is used with a 12 × 106 cell mesh. Since the code being used tends towards being dissipative the sub-grid scale (SGS) model is abandoned giving what can be termed Numerical Large Eddy Simulation (NLES). To overcome near wall modelling problems a hybrid NLES-RANS (Reynolds Averaged Navier-Stokes) related method is used. For y+ ≤ 60 a κ-l model is used. Blending between the two regions makes use of the differential Hamilton-Jabobi (HJ) equation, an extension of the eikonal equation. Results show encouraging agreement with existing measurements of other workers. The eikonal equation is also used for acoustic ray tracing to explore the effect of the mean flow on acoustic ray trajectories, thus yielding a coherent solution strategy. Copyright © 2011 by ASME.

Large-eddy simulations and measurements of a small-scale high-speed coflowing jet

Measurements and predictions are made of a short-cowl coflowing jet with a bypass ratio of 8:1. The Reynolds number is 300,000, and the inlet Mach numbers are representative of aeroengine conditions. The low Reynolds number of the measurements makes the case well suited to the assessment of large-eddy-simulation-related strategies. The nozzle concentricity is carefully controlled to deal with the emerging metastability issues of jets with coflow. Measurements of mean quantities and turbulence statistics are made using both laser Doppler anemometry and particle image velocimetry. The simulations are completed on 6× 106, 12× 106, and 50 × 106 cell meshes. To overcome near-wall modeling problems, a hybrid large-eddy-simulation-Reynolds-averaged-Navier-Stokesrelated method is used. The near-wall Reynolds-averaged-Navier-Stokes layer is helpful in preventing nonphysical separation from the nozzle wall.Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc.

Linear and nonlinear model large-eddy simulations of a plane jet

The nonlinear Kosovic, and mixed Leray and α subgrid scale models are contrasted with linear Smagorinsky and Yoshizawa Large Eddy Simulations for a Re = 4000 plane jet simulation. Comparisons are made with Direct Numerical Simulation data and measurements. Global properties of the jet such as the spreading and centreline velocity decay rates are investigated. The mean-flow and turbulence parameters in the self-similar region are also studied. All models generally give encouraging agreement with the Direct Numerical Simulation data and reliable measurements. Solution differences for the models are relatively minor, none giving clear improvements for all data comparisons.

Large Eddy Simulation for Turbines: Methodologies, Cost and Future Outlooks

Flows throughout different zones of turbines have been investigated using large eddy simulation (LES) and hybrid Reynolds-averaged Navier–Stokes-LES (RANS-LES) methods and contrasted with RANS modeling, which is more typically used in the design environment. The studied cases include low and high-pressure turbine cascades, real surface roughness effects, internal cooling ducts, trailing edge cut-backs, and labyrinth and rim seals. Evidence is presented that shows that LES and hybrid RANS-LES produces higher quality data than RANS/URANS for a wide range of flows. The higher level of physics that is resolved allows for greater flow physics insight, which is valuable for improving designs and refining lower order models. Turbine zones are categorized by flow type to assist in choosing the appropriate eddy resolving method and to estimate the computational cost.

Airfoil flow and noise computation using monotonically integrated large eddy simulation and acoustic analogy: Effect of the grid resolution.

A new scalable Monotonically Integrated Large Eddy Simulation (MILES) method based on the Compact Accurately Boundary-Adjusting high-REsolution Technique (CABARET) has been applied for the simulation of unsteady flow around NACA0012 airfoil at Re = 400,000 and M = 0.058. The flow solution is coupled with the Ffowcs Williams-Hawkings formulation for far-field noise prediction. The computational modeling results are presented for several computational grid resolutions: 8, 16, and 32 million grid cells and compared with the experimental data available.

Dynamic Covariance Models for Multivariate Financial Time Series

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.