Preface 1.General Considerations 2.Basic Processes in Atomization 3.Drop Size Distributions of Sprays 4.Atomizers 5.Flow in Atomizers 6.Atomizer Performance 7.External Spray Charcteristics 8.Drop Evaporation 9.Drop Sizing Methods Index

Atomization and Sprays

In recent years, the data-driven turbulence model has attracted widespread concern in fluid mechanics. The existing approaches modify or supplement the original turbulence model by machine learning based on the experimental/numerical data, in order to augment the capability of the present turbulence models. Different from the previous researches, this paper directly reconstructs a mapping function between the turbulent eddy viscosity and the mean flow variables by neural networks and completely replaces the original partial differential equation model. On the other hand, compared with the machine learning models for the low Reynolds (Re) number flows based on direct numerical simulation data, high Reynolds number flows around airfoils present the apparent scaling effects and strong anisotropy, which induce large challenges in accuracy and generalization capability for the machine learning algorithm. We mainly concentrate on the high Reynolds number turbulent flows around the airfoils and take the results calculated by the computational fluid dynamics solver with the Spallart-Allmaras (SA) model as training data to construct a high-dimensional data-driven network model based on machine learning. The radial basis function neural network and the auxiliary optimization methods are adopted, and the individual models are built separately for the flow fields of the near-wall region, wake region, and far-field region. The training data in this paper is extracted from only three subsonic flow fields of NACA0012 airfoil. The data-driven turbulence model can be applied to various airfoils and flow states, and the predicted eddy viscosity, lift/drag coefficients, and skin friction distributions are all in good agreement with the results of the original SA model. This research demonstrates the promising prospect of machine learning methods in future studies about turbulence modeling.

Machine learning methods for turbulence modeling in subsonic flows around airfoils

https://tspace.library.utoronto.ca/bitstream/1807/71473/1/2015_Oberleithner_Stoehr_CF_author_final%20Tspace.pdf

Formation and flame-induced suppression of the precessing vortex core in a swirl combustor: Experiments and linear stability analysis

乱流予混合火炎のDirect Numerical Simulation

Temperature measurement techniques for gas and liquid flows using thermographic phosphor tracer particles

The drag reduction induced by superhydrophobic surfaces is investigated in a turbulent pipe flow. Wetted superhydrophobic surfaces are shown to trap gas bubbles in their asperities. This stops the liquid from coming in direct contact with the wall in that location, allowing the flow to slip over the air bubbles. We consider a well-defined texture with streamwise grooves at the walls in which the gas is expected to be entrapped. This configuration is modeled with alternating no-slip and shear-free boundary conditions at the wall. With respect to the classical turbulent pipe flow, a substantial drag reduction is observed which strongly depends on the grooves’ dimension and on the solid fraction, i.e., the ratio between the solid wall surface and the total surface of the pipe’s circumference. The drag reduction is due to the mean slip velocity at the wall which increases the flow rate at a fixed pressure drop. The enforced boundary conditions also produce peculiar turbulent structures which on the contrary d...

Drag reduction induced by superhydrophobic surfaces in turbulent pipe flow

Particle image velocimetry (PIV) estimates the fluid velocity field measuring the displacement of small dispersed particles between two successive instants separated by a small time interval. The accuracy of the measurements depends on the ability of the particles to accommodate their velocity to the fluid fluctuations. When the fluid is subjected to extreme accelerations, the small but finite inertia prevents the particles from following the fluid, originating a substantial relative velocity. This effect is shown to be crucial for applications of PIV to turbulent premixed combustion, particularly in the product region at locations just behind the instantaneous flame front. The issuing inaccuracy may easily spoil the estimate of certain statistical observables which are of crucial importance in the theory of turbulent premixed combustion. By exploiting the direct numerical simulation of a model air/methane flame, a suitable criterion for proper particle seeding is validated and compared with the corresponding experiments with a combined PIV/OH-LIF (laser-induced fluorescence) system. The proposed parameter, the flamelet Stokes number, depends on particle properties and thermochemical conditions of the flame and substantially restricts the particle dimensions required for a reliable estimate of the relevant flow statistics.

Dynamics of PIV seeding particles in turbulent premixed flames

The generalised Kolmogorov equation is used to describe the scale-by-scale turbulence dynamics in the shear layer and in the separation bubble generated by a bulge at one of the walls in a turbulent channel flow. The second-order structure function, which is the basis of such an equation, is used as a proxy to define a scale-energy content, that is an interpretation of the energy associated with a given scale. Production and dissipation regions and the flux interchange between them, in both physical and separation space, are identified. Results show how the generalised Kolmogorov equation, a five-dimensional equation in our anisotropic and strongly inhomogeneous flow, can describe the turbulent flow behaviour and related energy mechanisms. Such complex statistical observables are linked to a visual inspection of instantaneous turbulent structures detected by means of the Q-criterion. Part of these turbulent structures are trapped in the recirculation where they undergo a pseudo-cyclic process of disruption and reformation. The rest are convected downstream, grow and tend to larger streamwise scales in an inverse cascade. The classical picture of homogeneous isotropic turbulence in which energy is fed at large scales and transferred to dissipate at small scales does not simply apply to this flow where the energy dynamics strongly depends on position, orientation and length scale.

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Turbulence dynamics in separated flows: the generalised Kolmogorov equation for inhomogeneous anisotropic conditions

Turbulent flow separation induced by a protuberance on one of the walls of an otherwise planar channel is investigated using direct numerical simulations. Different bulge geometries and Reynolds numbers – with the highest friction Reynolds number simulation reaching a peak of – are addressed to understand the effect of the wall curvature and of the Reynolds number on the dynamics of the recirculating bubble behind the bump. Global quantities reveal that most of the drag is due to the form contribution, whilst the friction contribution does not change appreciably with respect to an equivalent planar channel flow. The size and position of the separation bubble strongly depends on the bump shape and the Reynolds number. The most bluff geometry has a larger recirculation region, whilst the Reynolds number increase results in a smaller recirculation bubble and a shear layer more attached to the bump. The position of the reattachment point only depends on the Reynolds number, in agreement with experimental data available in the literature. Both the mean and the turbulent kinetic energy equations are addressed in such non-homogeneous conditions revealing a non-trivial behaviour of the energy fluxes. The energy introduced by the pressure drop follows two routes: part of it is transferred towards the walls to be dissipated and part feeds the turbulent production hence the velocity fluctuations in the separating shear layer. Spatial energy fluxes transfer the kinetic energy into the recirculation bubble and downstream near the wall where it is ultimately dissipated. Consistently, anisotropy concentrates at small scales near the walls irrespective of the value of the Reynolds number. In the bulk flow and in the recirculation bubble, isotropy is restored at small scales and the isotropy recovery rate is controlled by the Reynolds number. Anisotropy invariant maps are presented, showing the difficulty in developing suitable turbulence models to predict separated turbulent flow dynamics. Results shed light on the processes of production, transfer and dissipation of energy in this relatively complex turbulent flow where non-homogeneous effects overwhelm the classical picture of wall-bounded turbulent flows which typically exploits streamwise homogeneity.

/pdf/effect-of-geometry-and-reynolds-number-on-the-turbulent-4hh43b7ucc.pdf

F. Battista

Papers

Drag reduction induced by superhydrophobic surfaces in turbulent pipe flow

Dynamics of PIV seeding particles in turbulent premixed flames

Turbulence dynamics in separated flows: the generalised Kolmogorov equation for inhomogeneous anisotropic conditions

Effect of geometry and Reynolds number on the turbulent separated flow behind a bulge in a channel

Application of the Exact Regularized Point Particle method (ERPP) to particle laden turbulent shear flows in the two-way coupling regime