Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA numerical model to predict unsteady cavitating flow behaviour in inducer blade cascades
87
0
0.0
(
0
)
Added by
Legi Correspondant Hal 2
Publication date
2020
fields
Physics
and research's language is
English
Authors
Olivier Coutier-Delghosa
Ask ChatGPT about the research
No Arabic abstract
The cavitation behaviour of a four-blade rocket engine turbopump inducer is simulated. A 2D numerical model of unsteady cavitation was applied to a blade cascade drawn fromthe inducer geometry. The physical model is based on a homogeneous approach of cavitation, coupled with a barotropic state law for the liquid/vapour mixture. The numericalresolution uses a pressure-correction method derived from the SIMPLE algorithm and a finite volume discretization. Unsteadybehaviour of sheet cavities attached to the blade suction side depends on the flow rate and cavitation number. Two differentunstable configurations of rotating cavitation, respectively sub-synchronous and super-synchronous, are identified. The mechanisms that are responsible for these unstable behaviours are discussed, and the stress fluctuations induced on the blade by the rotating cavitation are estimated.
rate research
Read More
We present results of large eddy simulations of a cavitating nozzle flow and injection into gas, investigating the interactions of cavitation in the nozzle, primary jet breakup, mass-flow rates, and gas entrainment. During strong cavitation, detached vapor structures can reach the nozzle outlet, leading to partial entrainment of gas from the outflow region into the nozzle. The gas entrainment can affect cavitation dynamics, mass-flow rates, and jet breakup. Moreover, the implosion of detached vapor structures induces pressure peaks that on the one hand amplify turbulent fluctuations and subsequently can enhance jet breakup and on the other hand can damage walls in the proximity and thus lead to cavitation erosion. Our numerical setup is based on a reference experiment, in which liquid water is discharged into ambient air through a step nozzle. The cavitating liquid and the non-condensable gas phase are modeled with a barotropic homogeneous mixture model while for the numerical model a high-order implicit large eddy approach is employed. Full compressibility of all components is taken into account, enabling us to capture the effects of collapsing vapor structures. Two operating points covering different cavitation regimes and jet characteristics are investigated. Special emphasis is placed on studying the effects of cavitation on the mass flow and the jet as well as the impact of partial gas entrainment. Therefore, frequency analyses of the recorded time-resolved signals are performed. Furthermore, the dynamics and intensities of imploding vapor structures are assessed.
The need to develop models to predict the motion of microrobots, or robots of a much smaller scale, moving in fluids in a low Reynolds number regime, and in particular, in non Newtonian fluids, cannot be understated. The article develops a Lagrangian based model for one such mechanism - a two-link mechanism termed a microscallop, moving in a low Reynolds number environment in a non Newtonian fluid. The modelling proceeds through the conventional Lagrangian construction for a two-link mechanism and then goes on to model the external fluid forces using empirically based models for viscosity to complete the dynamic model. The derived model is then simulated for different initial conditions and key parameters of the non Newtonian fluid, and the results are corroborated with a few existing experimental results on a similar mechanism under identical conditions. Lastly, with a view to implementing control algorithms we explore accessibility of the system at certain configurations.
A rapidly growing bubble close to a free surface induces jetting: a central jet protruding outwards and a crown surrounding it at later stages. While the formation mechanism of the central jet is known and documented, that of the crown remains unsettled. We perform axisymmetric simulations of the problem using the free software program basilisk, where a finite-volume compressible solver has been implemented, that uses a geometric Volume-of-Fluid method (VoF) for the tracking of the interface. We show that the mechanism of crown formation is a combination of a pressure distortion over the curved interface, inducing flow focusing, and of a flow reversal, caused by the second expansion of the toroidal bubble that drives the crown. The work culminates in a parametric study with the Weber number, the Reynolds number, the pressure ratio and the dimensionless bubble distance to the free surface as control parameters. Their effects on both the central jet and the crown are explored. For high Weber numbers, we observe the formation of weaker secondary crowns, highly correlated with the third oscillation cycle of the bubble.
Cloud cavitation is related to an intrinsic instability where clouds are shed periodically. The shedding process is initiated either by the motion of a liquid re-entrant jet or a condensation shock. Cloud cavitation in nozzles interacts with the flow field in the nozzle, the mass flow and the spray break-up, and causes erosion damage. For nozzle geometries cloud shedding and the associated processes have not yet been studied in detail. In this paper, we investigate the process of cloud cavitation shedding, the re-entrant jet and condensation shocks in a scaled-up generic step nozzle with injection into gas using implicit Large-Eddy Simulations (LES). For modeling of the cavitating liquid we employ a barotropic equilibrium cavitation model, embedded in a homogeneous multi-component mixture model. Full compressibility of all components is taken into account to resolve the effects of collapsing vapor structures. We carry out simulations of two operating points exhibiting different cavitation regimes. The time-resolved, three-dimensional simulation results cover several shedding cycles and provide deeper insight into the flow field. Our results show that at lower cavitation numbers, shedding is initiated by condensation shocks, which has not yet been reported for nozzle flows with a constant cross-section. We analyze the cavitation dynamics and the shedding cycles of both operating points. Based on our observations we propose modifications to established schematics of the cloud shedding process. Additionally, we analyze the near-wall upstream flow in and underneath the vapor sheet and possible driving mechanism behind the formation of the re-entrant jet.
Convolutional neural networks (CNNs) have recently been applied to predict or model fluid dynamics. However, mechanisms of CNNs for learning fluid dynamics are still not well understood, while such understanding is highly necessary to optimize the network or to reduce trial-and-errors during the network optmization. In the present study, a CNN to predict future three-dimensional unsteady wake flow using flow fields in the past occasions is developed. Mechanisms of the developed CNN for prediction of wake flow behind a circular cylinder are investigated in two flow regimes: the three-dimensional wake transition regime and the shear-layer transition regime. Feature maps in the CNN are visualized to compare flow structures which are extracted by the CNN from flow at the two flow regimes. In both flow regimes, feature maps are found to extract similar sets of flow structures such as braid shear-layers and shedding vortices. A Fourier analysis is conducted to investigate mechanisms of the CNN for predicting wake flow in flow regimes with different wave number characteristics. It is found that a convolution layer in the CNN integrates and transports wave number information from flow to predict the dynamics. Characteristics of the CNN for transporting input information including time histories of flow variables is analyzed by assessing contributions of each flow variable and time history to feature maps in the CNN. Structural similarities between feature maps in the CNN are calculated to reveal the number of feature maps that contain similar flow structures. By reducing the number of feature maps that contain similar flow structures, it is also able to successfully reduce the number of parameters to learn in the CNN by 85% without affecting prediction performances.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
