Scalar and its dissipation in the near field of turbulent lifted jet flame

In this study various scalar dissipation rates and their modelling in the context of partially premixed flame are investigated. A DNS dataset of the near field of a turbulent hydrogen lifted jet flame is processed to analyse the mixture fraction and progress variable dissipation rates and their cross dissipation rate at several axial positions. It is found that the classical model for the passive scalar dissipation rate ε{lunate}̃ZZ gives good agreement with the DNS, while models developed based on premixed flames for the reactive scalar dissipation rate ε{lunate}̃cc only qualitatively capture the correct trend. The cross dissipation rate ε{lunate}̃cZ is mostly negative and can be reasonably approximated at downstream positions once ε{lunate}̃ZZ and ε{lunate}̃cc are known, although the sign cannot be determined. This approach gives better results than one employing a constant ratio of turbulent timescale and the scalar covariance c'Z'̃. The statistics of scalar gradients are further examined and lognormal distributions are shown to be very good approximations for the passive scalar and acceptable for the reactive scalar. The correlation between the two gradients increases downstream as the partially premixed flame in the near field evolves ultimately to a diffusion flame in the far field. A bivariate lognormal distribution is tested and found to be a reasonable approximation for the joint PDF of the two scalar gradients. © 2011 The Combustion Institute.

Assessment of computational fluid dynamics and experimental data for shock boundary-layer interactions

A workshop on the computational fluid dynamics (CFD) prediction of shock boundary-layer interactions (SBLIs) was held at the 48th AIAA Aerospace Sciences Meeting. As part of the workshop, numerous CFD analysts submitted solutions to four experimentally measured SBLIs. This paper describes the assessment of the CFD predictions. The assessment includes an uncertainty analysis of the experimental data, the definition of an error metric, and the application of that metric to the CFD solutions. The CFD solutions provided very similar levels of error and, in general, it was difficult to discern clear trends in the data. For the Reynolds-averaged Navier-Stokes (RANS) methods, the choice of turbulence model appeared to be the largest factor in solution accuracy. Scale-resolving methods, such as large-eddy simulation (LES), hybrid RANS/LES, and direct numerical simulation, produced error levels similar to RANS methods but provided superior predictions of normal stresses. Copyright © 2012 by Daniella E. Raveh and Michael Iovnovich.

A priori assessment of algebraic flame surface density models in the context of large eddy simulation for nonunity lewis number flames in the thin reaction zones regime

The performance of algebraic flame surface density (FSD) models has been assessed for flames with nonunity Lewis number (Le) in the thin reaction zones regime, using a direct numerical simulation (DNS) database of freely propagating turbulent premixed flames with Le ranging from 0.34 to 1.2. The focus is on algebraic FSD models based on a power-law approach, and the effects of Lewis number on the fractal dimension D and inner cut-off scale η i have been studied in detail. It has been found that D is strongly affected by Lewis number and increases significantly with decreasing Le. By contrast, η i remains close to the laminar flame thermal thickness for all values of Le considered here. A parameterisation of D is proposed such that the effects of Lewis number are explicitly accounted for. The new parameterisation is used to propose a new algebraic model for FSD. The performance of the new model is assessed with respect to results for the generalised FSD obtained from explicitly LES-filtered DNS data. It has been found that the performance of the most existing models deteriorates with decreasing Lewis number, while the newly proposed model is found to perform as well or better than the most existing algebraic models for FSD. © 2012 Mohit Katragadda et al.

Bayesian non-parametrics and the probabilistic approach to modelling.

Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman's coalescent, Dirichlet diffusion trees and Wishart processes.

Effects of turbulent reynolds number on the performance of algebraic flame surface density models for large eddy simulation in the thin reaction zones regime: A direct numerical simulation analysis

A direct numerical simulation (DNS) database of freely propagating statistically planar turbulent premixed flames with a range of different turbulent Reynolds numbers has been used to assess the performance of algebraic flame surface density (FSD) models based on a fractal representation of the flame wrinkling factor. The turbulent Reynolds number Ret has been varied by modifying the Karlovitz number Ka and the Damköhler number Da independently of each other in such a way that the flames remain within the thin reaction zones regime. It has been found that the turbulent Reynolds number and the Karlovitz number both have a significant influence on the fractal dimension, which is found to increase with increasing Ret and Ka before reaching an asymptotic value for large values of Ret and Ka. A parameterisation of the fractal dimension is presented in which the effects of the Reynolds and the Karlovitz numbers are explicitly taken into account. By contrast, the inner cut-off scale normalised by the Zel'dovich flame thickness ηi/δz does not exhibit any significant dependence on Ret for the cases considered here. The performance of several algebraic FSD models has been assessed based on various criteria. Most of the algebraic models show a deterioration in performance with increasing the LES filter width. © 2012 Mohit Katragadda et al.

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.

Comparison of les to LDA and PIV measurements of a small scale high speed coflowing jet

Measurements and predictions are made of a short cowl co-flowing jet with a bypass ratio of 8:1. The Reynolds number for computations and measurements are matched at 300,000 and the Mach numbers representative of realistic jet conditions with core and co flow velocities of 240m/s and 216m/s respectively. The low Reynolds number of the measurements makes the case well suited to the assessment of large eddy resolving computational strategies. Also, the nozzle concentricity was carefully controlled to deal with the emerging metastability issues of jets with coflow. Measurements of mean quantities and turbulence statistics are made using both two dimensional coincident LDA and PIV systems. The computational simulations are completed on a modest 12×106 mesh. The simulation is now being run on a 50×106 mesh using hybrid RANSNLES (Numerical Large Eddy Simulation). Close to the nozzle wall a k-l RANS model is used. For an axisymmetric jet, comparison is made between simulations which use NLES, RANSNLES and also a simple imposed velocity profile where the nozzle is not modeled. The use of a near wall RANS model is shown to be beneficial. When compared with the measurements the NLES results are encouraging. Copyright © 2008 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

High-order DES and zonal LES of a labyrinth seal

Hybrid numerical large eddy simulation (NLES) and detached eddy simulation (DES) methods are assessed on a labyrinth seal geometry. A high sixth order discretization scheme is used and is validated using a test case of a two dimensional vortex. The hybrid approach adopts a new blending function and along with DES is initially validated using a simple cavity flow. The NLES method is also validated outside of RANS zones. It is found that there is very little resolved turbulence in the cavity for the DES simulation. For the labyrinth seal calculations the DES approach is problematic giving virtually no resolved turbulence content. It is seen that over the tooth tips the extent of the LES region is small and is likely to be a strong contributor to excessive flow damping in these regions. On the other hand the zonal Hamilton-Jacobi approach did not suffer from this trait. In both cases the meshes used are considered to be hybrid RANS-LES adequate. Fortunately (or perhaps unfortunately) the DES profiles are in agreement with the time mean experimental measurements. It is concluded that for an inexperienced CFD practitioner this could have wider implications particularly if transient results such as unsteady loading are desired. Copyright © 2012 by the American Institute of Aeronautics and Astronautics, Inc.

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.