Antonio Attili

Statistics and scaling of turbulence in a spatially developing mixing layer at Reλ = 250

The turbulent flow originating from the interaction between two parallel streams with different velocities is studied by means of direct numerical simulation. Rather than the more common temporal evolving layer, a spatially evolving configuration, with perturbed laminar inlet conditions is considered. The streamwise evolution and the self-similar state of turbulence statistics are reported and compared to results available in the literature. The characteristics of the transitional region agree with those observed in other simulations and experiments of mixing layers originating from laminar inlets. The present results indicate that the transitional region depends strongly on the inlet flow. Conversely, the self-similar state of turbulent kinetic energy and dissipation agrees quantitatively with those in a temporal mixing layer developing from turbulent initial conditions [M. M. Rogers and R. D. Moser, “Direct simulation of a self-similar turbulent mixing layer,” Phys. Fluids 6, 903 (1994)]. The statistica...

Statistics of the turbulent/non-turbulent interface in a spatially developing mixing layer

The thin interface separating the inner turbulent region from the outer irrotational fluid is analysed in a direct numerical simulation of a spatially developing turbulent mixing layer. A vorticity threshold is defined to detect the interface separating the turbulent from the non-turbulent regions of the flow, and to calculate statistics conditioned on the distance from this interface. The conditional statistics for velocity are in remarkable agreement with the results for other free shear flows available in the literature, such as turbulent jets and wakes. In addition, an analysis of the passive scalar field in the vicinity of the interface is presented. It is shown that the scalar has a jump at the interface, even stronger than that observed for velocity. The strong jump for the scalar has been observed before in the case of high Schmidt number (Sc). In the present study, such a strong jump is observed for a scalar with Sc ≈ 1. Conditional statistics of kinetic energy and scalar dissipation are presented. While the kinetic energy dissipation has its maximum far from the interface, the scalar dissipation is characterised by a strong peak very close to the interface. Finally, it is shown that the geometric features of the interfaces correlate with relatively large scale structures as visualised by low-pressure isosurfaces.

Advancing predictive models for particulate formation in turbulent flames via massively parallel direct numerical simulations

Combustion of fossil fuels is likely to continue for the near future due to the growing trends in energy consumption worldwide. The increase in efficiency and the reduction of pollutant emissions from combustion devices are pivotal to achieving meaningful levels of carbon abatement as part of the ongoing climate change efforts. Computational fluid dynamics featuring adequate combustion models will play an increasingly important role in the design of more efficient and cleaner industrial burners, internal combustion engines, and combustors for stationary power generation and aircraft propulsion. Today, turbulent combustion modelling is hindered severely by the lack of data that are accurate and sufficiently complete to assess and remedy model deficiencies effectively. In particular, the formation of pollutants is a complex, nonlinear and multi-scale process characterized by the interaction of molecular and turbulent mixing with a multitude of chemical reactions with disparate time scales. The use of direct numerical simulation (DNS) featuring a state of the art description of the underlying chemistry and physical processes has contributed greatly to combustion model development in recent years. In this paper, the analysis of the intricate evolution of soot formation in turbulent flames demonstrates how DNS databases are used to illuminate relevant physico-chemical mechanisms and to identify modelling needs.

Simulation of aerosol nucleation and growth in a turbulent mixing layer

A large-scale simulation of aerosol nucleation and growth in a turbulent mixing layer is performed and analyzed with the aim of elucidating the key processes involved. A cold gaseous stream is mixed with a hot stream of vapor, nanometer sized droplets nucleate as the vapor becomes supersaturated, and subsequently grow as more vapor condenses on their surface. All length and time scales of fluid motion and mixing are resolved and the quadrature method of moments is used to describe the dynamics of the condensing, non-inertial droplets. The results show that a region of high nucleation rate is located near the cold, dry stream, while particles undergo intense growth via condensation on the hot, humid vapor side. Supersaturation and residence times are such that number densities are low and neither coagulation nor vapor scavenging due to condensation are significant. The difference in Schmidt numbers of aerosol particles (approximated as infinity) and temperature and vapor (near unity) causes a drift of the ...

Structure function scaling in a Reλ = 250 turbulent mixing layer

A highly resolved Direct Numerical Simulation of a spatially developing turbulent mixing layer is presented. In the fully developed region, the flow achieves a turbulent Reynolds number Reλ = 250, high enough for a clear separation between large and dissipative scales, so for the presence of an inertial range. Structure functions have been calculated in the self-similar region using velocity time series and Taylors frozen turbulence hypothesis. The Extended Self-Similarity (ESS) concept has been employed to evaluate relative scaling exponents. A wide range of scales with scaling exponents and intermittency levels equal to homogeneous isotropic turbulence has been identified. Moreover an additional scaling range exists for larger scales; it is characterized by smaller exponents, similar to the values reported in the literature for flows with strong shear.

Comprehensive Validation of Skeletal Mechanism for Turbulent Premixed Methane–Air Flame Simulations

A new skeletal mechanism, consisting of 16 species and 72 reactions, has been developed for lean methane–air premixed combustion from the GRI-Mech 3.0. The skeletal mechanism is validated for eleva...

Lagrangian Analysis of Mixing and Soot Transport in a Turbulent Jet Flame

Soot particles are formed during rich combustion of fossil fuels in technical devices such as internal combustion

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

Abstract Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.