The prediction of the two-phase flow in an aero-engine bearing chamber using the meshless Lagrangian Smoothed Particle Hydrodynamics (SPH) method is presented in this paper. The prediction of the prevailing flow types, like shear-driven wallfilms, droplet-wall- and droplet-film-interactions require an accurate numerical method, which is robust and efficient. Therefore, a code based on the SPH method was developed and validated to numerically predict such technical relevant multi-phase flows in gas turbines.
The simulations to be presented in this paper are focused on an aero-engine bearing chamber configuration, which was experimentally investigated previously. For time saving reasons, the bearing chamber is modeled as two-dimensional problem. This requires special boundary conditions for the oil- and sealing-air flow inlet and outlet, which must physically reflect those of the experiments. In the experiments different operating regimes at different boundary conditions could be identified.
The major objective of the simulations is to investigate if those different flow regimes can be captured by the numerical method. The simulations do reproduce the different flow regimes highly accurate and demonstrate the ability of this new approach.
The quantitative investigation of droplet laden turbulent flows at high temperature conditions is of great importance for numerous applications. In this study, an experiment was set up for investigation of evaporating urea-water sprays, which are relevant for the effective reduction of nitrogen oxide emissions of diesel engines using Selective Catalytic Reduction. A shadowgraphy setup is pushed to its limits in order to detect droplet diameters as small as 4 µm and droplet velocities up to 250 m s −1 . In addition, the operating conditions of the gaseous flow of up to 873 K and 0.6 MPa are an additional challenge. Due to the high temperature environment, image quality is prone to be compromised by Schlieren effects and astigmatism phenomena. A water-cooled window and an astigmatism correction device are installed in order to correct these problems. The results to be presented include characteristics of the turbulent gas flow as well as detailed spray characteristics at different positions downstream of the atomiser. It is demonstrated that the velocity of the gas can be approximated by the velocity of the smallest detectable droplets with sufficient accuracy. Furthermore, the statistical analysis of velocity fluctuations provides data for predicting the turbulent dispersion of the droplets.
This technical report investigates the potential of Convolutional Neural Networks to post-process images from primary atomization. Three tasks are investigated. First, the detection and segmentation of liquid droplets in degraded optical conditions. Second, the detection of overlapping ellipses and the prediction of their geometrical characteristics. This task corresponds to extrapolate the hidden contour of an ellipse with reduced visual information. Third, several features of the liquid surface during primary breakup (ligaments, bags, rims) are manually annotated on 15 experimental images. The detector is trained on this minimal database using simple data augmentation and then applied to other images from numerical simulation and from other experiment. In these three tasks, models from the literature based on Convolutional Neural Networks showed very promising results, thus demonstrating the high potential of Deep Learning to post-process liquid atomization. The next step is to embed these models into a unified framework DeepSpray.
Ammonia preparation from urea-water solutions is a key feature to ensure an effective reduction of nitrogen oxides in selective catalytic reduction (SCR) systems. Thereby, air-assisted nozzles provide fine sprays, which enhance ammonia homogenization. In the present study, a methodology was developed to model the spray formation by means of computational fluid dynamics (CFD) for this type of atomizer. Experimental validation data was generated in an optically accessible hot gas test bench using a shadowgraphy setup providing droplet velocities and size distributions at designated positions inside the duct. An adaption of the turbulence model was performed in order to correct the dispersion of the turbulent gas jet. The spray modeling in the near nozzle region is based on an experimentally determined droplet spectrum in combination with the WAVE breakup model. This methodology was applied due to the fact that the emerging two-phase flow will immediately disintegrate into a fine spray downstream the nozzle exit, which is also known from cavitating diesel nozzles. The suitability of this approach was validated against the radial velocity and droplet size distributions at the first measurement position downstream the nozzle. In addition, the simulation results serve as a basis for the investigation of turbulent dispersion phenomena and evaporation inside the spray.
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