Inertial particles in turbulent flows are characterised by preferential concentration and segregation and, at sufficient mass loading, dense particle clusters may spontaneously arise due to momentum ...coupling between the phases. These clusters, in turn, can generate and sustain turbulence in the fluid phase, which we refer to as cluster-induced turbulence (CIT). In the present work, we tackle the problem of developing a framework for the stochastic modelling of moderately dense particle-laden flows, based on a Lagrangian probability-density-function formalism. This framework includes the Eulerian approach, and hence can be useful also for the development of two-fluid models. A rigorous formalism and a general model have been put forward focusing, in particular, on the two ingredients that are key in moderately dense flows, namely, two-way coupling in the carrier phase, and the decomposition of the particle-phase velocity into its spatially correlated and uncorrelated components. Specifically, this last contribution allows us to identify in the stochastic model the contributions due to the correlated fluctuating energy and to the granular temperature of the particle phase, which determine the time scale for particle–particle collisions. The model is then validated and assessed against direct-numerical-simulation data for homogeneous configurations of increasing difficulty: (i) homogeneous isotropic turbulence, (ii) decaying and shear turbulence and (iii) CIT.
We describe the results of a numerical and experimental investigation aimed at assessing the performance of a control method to delay boundary layer separation consisting of the introduction on the ...surface of contoured transverse grooves, i.e. of small cavities with an appropriate shape orientated transverse to the incoming flow. The shape of the grooves and their depth – which must be significantly smaller than the thickness of the incoming boundary layer – are chosen so that the flow recirculations present within the grooves are steady and stable. This passive control strategy is applied to an axisymmetric bluff body with various rear boat tails, which are characterized by different degrees of flow separation. Variational multiscale large eddy simulations and wind tunnel tests are carried out. The Reynolds number, for both experiments and simulations, is
$Re=u_{\infty }D/\unicodeSTIX{x1D708}=9.6\times 10^{4}$
; due to the different incoming flow turbulence level, the boundary layer conditions before the boat tails are fully developed turbulent in the experiments and transitional in the simulations. In all cases, the introduction of one single axisymmetric groove in the lateral surface of the boat tails produces significant delay of the boundary layer separation, with consequent reduction of the pressure drag. Nonetheless, the wake dynamical structure remains qualitatively similar to the one typical of a blunt-based axisymmetric body, with quantitative variations that are consistent with the reduction in wake width caused by boat tailing and by the grooves. A few supplementary simulations show that the effect of the grooves is also robust to the variation of the geometrical parameters defining their shape. All the obtained data support the interpretation that the relaxation of the no-slip boundary condition for the flow surrounding the recirculation regions, with an appreciable velocity along their borders, is the physical mechanism responsible for the effectiveness of the present separation-control method.
The sensitivity of turbulent dynamics in spatially evolving mixing layers to small skew angles $\unicodeSTIX{x1D703}$ is investigated via direct numerical simulation. Angle $\unicodeSTIX{x1D703}$ is ...a measure of the lack of parallelism between the two asymptotic flows, whose interaction creates the turbulent mixing region. The analysis is performed considering a large range of values of the shear intensity parameter $\unicodeSTIX{x1D6FC}$. This two-dimensional parameter space is explored using the results of a database of 18 direct numerical simulations. Instantaneous fields as well as time-averaged quantities are investigated, highlighting important mechanisms in the emergence of turbulence and its characteristics for this class of flows. In addition, a stochastic approach is used in which $\unicodeSTIX{x1D703}$ and $\unicodeSTIX{x1D6FC}$ are considered as random variables with a given probability distribution. The response surfaces of flow statistics in the parameter space are built through non-intrusive generalized polynomial chaos. It is found that variations of the parameter $\unicodeSTIX{x1D6FC}$ have a primary effect on the growth of the mixing region. A secondary effect associated with $\unicodeSTIX{x1D703}$ is observed as well. Higher values for the skew angle are responsible for a rapid increase in growth of the inlet structures, enhancing the development of the mixing region. The impact on the turbulence features and, in particular, on the Reynolds stress tensor is also significant. A modification of the normalized diagonal components of the Reynolds stress tensor due to $\unicodeSTIX{x1D703}$ is observed. In addition, the interaction between the parameters $\unicodeSTIX{x1D703}$ and $\unicodeSTIX{x1D6FC}$ is here the governing element.
•Numerical simulation of 3 phase flows in injectors: liquid in air with cavitation•Calibration of cavitation model parameters using gPC•The calibrated set-up robust to change in the injector geometry ...and conditions•The calibrated set-up improves the predictions compared to default set-up
The work focuses on the calibration of cavitation model parameters for the numerical simulation of three-phase injector flows. Cavitation is modeled through a transport equation for the void fraction closed by the Schnerr-Sauer relation. The vaporization and condensation factors contained in this model are considered for calibration against experimental data available for a test-case characterized by fuel injection in a reservoir filled of air through an axisymmetric channel. In spite of the simplified geometry, this flow configuration is representative of a real injector and contains most of the complex physical phenomena which may be encountered in injector flows, as turbulence, cavitation and hydraulic flip, i.e. a back flow of air from outside the injector along the whole length of the channel replacing the cavitating regions. Since a direct calibration would imply huge computational costs, not affordable in practical applications, response surfaces of the quantities of interest are built through generalized Polynomial Chaos. These response surfaces, which can be obtained starting from only a few deterministic simulations, have been then used to carry out the parameter calibration. The quantities of interest taken into consideration are the critical cavitation point (CCP), i.e. the value of the outlet pressure at which the flow inside the injector can be considered choked (for a fixed inlet pressure), and the mass-flow-rate (MFR) at the CCP. To further reduce the computational costs, calibration is carried out by using axisymmetric simulations. It has been then checked that the obtained cavitation model gives accurate results also in three-dimensional simulations of the actual geometry. Moreover, this set-up has been applied to two different complex one-hole injector geometries, i.e. a sector of real injector geometries, and the results have been compared against available experimental data. The calibrated cavitation model set-up appears to be robust, giving good predictions also in conditions significantly different from those in which it has been obtained.
In the light of an increment of the safety of CFRP components and systems subjected to low-velocity impacts, the identification of a damage onset threshold is desired. Hence the suitability of an ...analytical model for the estimation of the critical load of delamination onset, as well as, an approximation of the load-displacement curve, has been investigated. Starting from the Olsson's analytical model, available in the literature, a new model has been developed for the prediction of the mechanical behaviour of composite laminates subjected to low-velocity impacts, following a dynamic approach and taking into account stiffness degradation. The performances of the new model have been compared with results of an experimental impact testing program, showing good agreement with the data and significant improvements when compared with the Olsson's model.
Changes of intestinal permeability (IP) have been extensively investigated in inflammatory bowel diseases (IBD) and celiac disease (CD), underpinned by a known unbalance between microbiota, IP and ...immune responses in the gut. Recently the influence of IP on brain function has greatly been appreciated. Previous works showed an increased IP that preceded experimental autoimmune encephalomyelitis development and worsened during disease with disruption of TJ. Moreover, studying co-morbidity between Crohn's disease and MS, a report described increased IP in a minority of cases with MS. In a recent work we found that an alteration of IP is a relatively frequent event in relapsing-remitting MS, with a possible genetic influence on the determinants of IP changes (as inferable from data on twins); IP changes included a deficit of the active mechanism of absorption from intestinal lumen. The results led us to hypothesize that gut may contribute to the development of MS, as suggested by another previous work of our group: a population of CD8+CD161high T cells, belonging to the mucosal-associated invariant T (MAIT) cells, a gut- and liver-homing subset, proved to be of relevance for MS pathogenesis. We eventually suggest future lines of research on IP in MS: studies on IP changes in patients under first-line oral drugs may result useful to improve their therapeutic index; correlating IP and microbiota changes, or IP and blood-brain barrier changes may help clarify disease pathogenesis; exploiting the IP data to disclose co-morbidities in MS, especially with CD and IBD, may be important for patient care.
•Numerical simulations of cavitating internal flow in injectors.•Stochastic sensitivity analysis and calibration of cavitation model parameters.•The calibrated set-up gives accurate predictions of ...the main quantities of interest.•Improvements in predicting hydraulic flip in 3-phase flow conditions.
A stochastic analysis of the cavitation model parameter sensitivity is carried out for internal flows relevant to injector configurations. Stochastic methodologies, namely generalized polynomial chaos and stochastic collocation, are used to obtain continuous response surfaces of the quantities of interest in the parameter space starting from a limited number of simulations. Cavitation is modeled through a transport equation for the void fraction closed by the Schnerr-Sauer relation, containing four free parameters. As for turbulence, the URANS equations are considered, together with two different closure models. The sensitivity to the cavitation model parameters is investigated, first, for a throttle geometry, for which experimental and LES data are available. First, two out of the four parameters are identified as the most important through a preliminary analysis based on 2D simulations, namely the vaporization and condensation factors. Then, the sensitivity of 3D simulation results to the previously identified most important parameters is investigated. The stochastic range of variability of the results contains the reference data. Thus, a parameter optimization is carried out in order to obtain the values giving the best agreement with the LES data. It is then shown that the cavitation parameter sensitivity is practically independent of the working fluid. Finally, it is shown that the calibrated cavitation model can be successfully applied to a different configuration, characterized by the hydraulic flip phenomenon, namely a 3-phase case in which liquid N-heptane flows from an inlet reservoir through a circular channel in an outlet reservoir where air is present.
The variation of the base drag of an axisymmetric bluff body caused by modifications of the boundary-layer separating at the sharp-edged contour of its base is analysed through different numerical ...simulations, and the results are compared with those of a previous experimental investigation. Variational MultiScale Large-Eddy Simulations (VMS-LES) are first carried out on the same nominal geometry and at the same Reynolds number of the experiments. Subsequently, Direct Numerical Simulations (DNS) are performed at Reynolds numbers that are roughly two orders of magnitude lower, in order to investigate on the sensitivity of the main findings to the Reynolds number. The results of experiments, VMS-LES and DNS simulations show that an increase of the base pressure – and thus a decrease of the base drag – may be obtained by increasing the boundary layer thickness before separation, which causes a proportional increase of the length of the mean recirculation region behind the body. In spite of the different setups, Reynolds numbers and turbulence levels in the experiments and numerical simulations, in all cases the base pressure is found to be directly proportional to the length of the mean recirculation region, which is thus a key index of the base drag value. In turn, the recirculation length seems to be connected with the location of the incipient instability of the detaching shear layers, which can be moved downstream by an increase of the thickness of the separating boundary layer and upstream by an increase of the turbulence level.
•Effect of boundary layer thickness on the base drag of an axisymmetric bluff body.•The base drag decreases with increasing separating boundary layer thickness.•The quantitative effect depends on the turbulence level and on the Reynolds number.•A direct correlation exists between the mean recirculation length and the base drag.
•Realistic uncertainty quantification of two popular RANS turbulence models.•The sensitivity of the RANS results to the epistemic uncertainties is model dependent.•The k-omega SST model proved to be ...the more robust.•DA calibration leads to optimal coefficients different from the literature values.
The epistemic uncertainty in the free coefficients of two popular Reynolds-averaged Navier–Stokes (RANS) eddy-viscosity turbulence models is quantified. In particular, the Launder–Sharma low-Reynolds K-ε and the Menter K-ω SST models are considered. The free coefficients present in turbulence models are retrieved from some properties of benchmark turbulent flows, viz. the energy power law exponent for decaying homogeneous isotropic turbulence, the value of the Von Karman constant, the turbulence production over dissipation rate estimated in the asymptotic regime of a homogeneous shear flow and the dimensionless turbulent kinetic energy in the logarithmic layer. The values presented in literature for these quantities, obtained from experiments or direct numerical simulations (DNS), show a significant dispersion, indicating the presence of an epistemic uncertainty. Starting from the data collected in literature, realistic continuous probability density functions of the basic flow properties, and hence of the RANS model coefficients, are obtained through generalized Polynomial Chaos (gPC). The impact of this uncertainty on the results of RANS simulations of the turbulent channel flow is then investigated for different Reynolds numbers through comparison with DNS data. The solution over the continuous multi-dimensional uncertainty space of the considered random variables is reconstructed through the application of a surrogate model (response surface) obtained by means of gPC. In general, the predictions of the K-ω SST model are less sensitive to the uncertainty in the model parameters than those of the low-Reynolds K-ε model. For both models and for any combination of the coefficients, the predictions of the turbulent kinetic energy profile are not satisfactory, while low errors can be obtained for friction and mean velocity. Evaluation of optimal values of free parameters is a Data Assimilation (DA) problem. An example of the use of the gPC response surface for an efficient calibration of the model coefficients in order to minimize the error in the prediction of these two last variables is provided. The result accuracy estimated through the gPC surrogate model well agrees with that obtained in deterministic simulations carried out with the calibrated values of the model constants.
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