•High-speed shadowgraphy visualization of cavitating flow.•Investigation of a transparent injector replica.•Vortex shedding detected at the attached cavity trailing edge.•Dominant frequency of the ...phenomenon obtained for various flow conditions.•Locations of the cavity cloud onset and collapse specified.
The development and collapse of cloud cavitation and its link to surface erosion within a transparent test single-orifice nozzle operating with a closed Diesel fuel hydraulic circuit, has been characterized using high-speed imaging. Data have been obtained for a range of cavitation and Reynolds numbers under fixed lift positions. Post processing of a large number of images acquired with short exposure time (1μs) allowed the elucidation of the distinct flow phenomena associated with the highly transient two-phase flow. At the inlet of the flow orifice, the vapour cloud was found to occupy the largest part of the nozzle hole cross-section. Coherent vortical structures of a hairpin shape have been detected to onset at the closure region of this vapour cloud and shed downstream in a fully transient manner. The effect of the operating parameters on the temporal and spatial characteristics with regards to the emergence and collapse of the hairpin vortices has been quantified. It has been established that the cavitation-vortex shedding was taking place in a periodical manner, characterized by a Strouhal number.
Spray flows containing droplets and particles are used in various industrial fields. In this study, we investigate an efficient and reliable way to predict the spray flow of droplets by combining the ...discrete droplet model (DDM) with ensemble data assimilation for application to such industrial problems. The aim is to augment cross-sectional measurements such as particle image velocimetry (PIV) with fast DDM simulations of droplets. In this paper, we focus on the numerical experiment of data assimilation, which is also known as twin experiments, and discuss how such cross-sectional measurements and DDM can be integrated by the ensemble Kalman filter. The results showed that the position and velocity of the droplet and the spray nozzle's state were estimated by assimilating the time-averaged velocity measurements on the cross-section using a carefully prepared ensemble of droplets. Furthermore, the droplet size distribution was estimated indirectly through DDM.
An annular venturi injector (AVI) was proposed to form an intermittent flow structure in airlift pump for a good pump performance. Experiments were conducted to investigate the performance of the ...airlift pump with this AVI by comparing with pump performance with traditional injectors, at a series of air flow rates . It was found that airlift pump with AVI had higher flow rates of output liquid and particle, than those with the traditional injectors. This AVI promoted the gas core to collapse and formed an intermittent flow structure in rising pipe. For this intermittent structure, its slug length, firstly increased to a maximal value with increasing gas flow rate and then remained stable even under a high gas flow rate, while its slug frequency decreased with gas flow rate and then remained to a minimal value under a high gas flow rate.
The inner fluid flow within the micro-sac injector is investigated numerically via AVL-Fire CFD code particularly in a microscale dimension of nozzle hole is carried out in the present work. The ...structural parameters of the injector are left unchanged, while the physical properties of the fluid such as pressure gradient and fluid temperature are taken into account to survey their influence on vapor volume fraction evolution, vapor mass flow rate, cavitation inception, and discharge coefficient. The reliability of modeling results is confirmed by comparing obtained data with experimental one and numerical results of Payri et al. and a close match served as an indication for the accuracy of the methodology. The multiphase modality is activated for the two-fluid model application in the nozzle segment, while the interfacial source term accounts for the momentum exchange of phases in the cavitation drag model. According to results, increasing the fuel temperature is a factor for increasing turbulent kinetic energy; meanwhile, vapor volume fraction at different times shows a different trend. Concerning the discharge coefficient, it seems that increasing the fluid temperature reduces this parameter. In contrast, increasing the pressure gradient leads to a considerable increase in the discharge coefficient.
Present paper outlines the simulation technique for dynamic analysis of cavitating Diesel injector flow, considering longitudinal and lateral needle displacements. The current research is performed ...with the goal to eventually enable direct coupling of 1D hydraulic tools, providing exact needle positions in three coordinates, with 3D CFD tool AVL FIRER. At this stage the needle displacements in longitudinal and lateral directions have been predefined. In reality the needle will be pushed aside by the flow for different reasons; different radii at the hole inlet and non-equi-angular hole positioning. Predicted flow shows visible non-uniformity resulting from non-symmetric nature of the injector domain.
•A deep learning methodology is proposed for fast calculation of real-fluid properties.•The method features a neural network with appropriate boundary information.•The method can be coupled to a flow ...solver in a robust manner.•The approach is demonstrated in primitive- and conservative-variable based solvers.•The approach significantly reduces the simulation time and memory usage.
A deep-learning based approach is developed for efficient evaluation of thermophysical properties in numerical simulation of complex real-fluid flows. The work enables a significant improvement of computational efficiency by replacing direct calculation of the equation of state with a deep feedforward neural network with appropriate boundary information (DFNN-BC). The proposed method can be coupled to a flow solver in a robust manner. Depending on the numerical formulation of the flow solver, the neural network takes in either the primitive or conservative variables, including the chemical composition of the system, and calculates all relevant fluid properties for the subsequent routines in the solver. Two test problems are employed to validate the proposed methodology. The first uses a preconditioning scheme with dual-time integration for the simulation of swirl rocket injector flow dynamics under supercritical conditions. The second uses a conservative-variable based formulation for the simulation of laminar counterflow diffusion flames for cryogenic combustion. A parametric analysis is performed to optimize the numbers of hidden layers and neurons per hidden layer. The computational accuracy, efficiency, and memory requirements of the neural network are examined. The DFNN-BC model accelerates the evaluation of real-fluid properties by a factor of 2.43 and 3.7 for the two test problems, respectively, and the overall flowfield simulation by 1.5 and 2.3, respectively. In addition, the memory usage is reduced by up to five orders of magnitude in comparison with the table look-up method.
A combined experimental and computational study is described, aimed at validation of LES predictions of the flow and mixing fields created by a fuel injector which incorporates a single, radially-fed ...annular swirl stream surrounding a central axial jet flow. The focus of this work was on turbulent mixing processes, so an isothermal (water flow) experiment was carried out, using a representative injector geometry operated at appropriate Reynolds and Swirl Numbers. Combined SPIV and PLIF instrumentation allowed simultaneous instantaneous measurements of velocity vector and a conserved scalar introduced into the fuel stream. For the first time in such a flow, all 3 components of the turbulent scalar flux were available for validation of LES-based CFD predictions. A careful assessment of experimental errors was carried out. Very close to the injector exit plane (x/D
S
< 0.1), the measured data were prone to larger errors (~20%). Further downstream (x/D
S
= 0.2 − 1.5), spatial filtering errors were much smaller (~5 − 10%). Comparison of LES predictions with experimental data showed very good agreement for both 1st and 2nd moment statistics, as well as spectra and scalar pdfs. It is particularly noteworthy that comparison between LES computed and measured scalar fluxes was very good; this represents successful validation of the simple (constant Schmidt No.) SGS model used for this complex and practically important flow.
PDF
