Entrained air in oil can cause malfunctions and damages within hydraulic systems. In this paper, we extend existing approaches to reduce the amount of entrained air by separating air bubbles from oil using filter elements. The aim of this study was to investigate the ability of different untreated and surface modified woven and nonwoven fabrics (NWF) to separate air bubbles from oil when directly integrated into an intake socket of an oil pump. An experimental setup was constructed to generate entrained air in oil and to characterize changes in oil aeration and pressure drop induced by the filters. Measurements were conducted at volume flow rates of 2.2 and 5.4 l/min with an inflow angle normal to the filter elements. The developed setup and aeration measurement method proved to be suitable to generate entrained air in oil in a reproducible manner and to accurately characterize aerated oil up to air contents of about 5%. Significant influences on the aeration characteristics were found only for the NWF. Whereas the number of air bubbles decreased by up to 33% relative to the values in the oil reservoir for a flow rate of 2.2 l/min, a significant reduction of the volumetric air ratio could not be achieved as resulting bubble distributions comprised a higher number of large bubbles. We suggest that the lack of effective bubble separation was a result of the flow-induced pressure drop by the filters, which increased with the flow rate.
Physics-informed neural networks (PINN) can be used to predict flow fields with a minimum of simulated or measured training data. As most technical flows are turbulent, PINNs based on the Reynolds-averaged Navier–Stokes (RANS) equations incorporating a turbulence model are needed. Several studies demonstrated the capability of PINNs to solve the Naver–Stokes equations for laminar flows. However, little work has been published concerning the application of PINNs to solve the RANS equations for turbulent flows. This study applied a RANS-based PINN approach to a backward-facing step flow at a Reynolds number of 5100. The standard k-ω model, the mixing length model, an equation-free νt and an equation-free pseudo-Reynolds stress model were applied. The results compared favorably to DNS data when provided with three vertical lines of labeled training data. For five lines of training data, all models predicted the separated shear layer and the associated vortex more accurately.
Passive flow control techniques are needed to reduce flow separation and enhance aerodynamic performance. In this work, the effect of a knitted wire mesh on the flow separation of a backward-facing ramp was numerically investigated for a Reynolds number of 3000. A grid independence study and a RANS turbulence model sensitivity analysis were conducted. The CFD simulations exhibited counter-rotating streamwise vortices emerging from the knitted wire mesh, and the reattachment length was significantly reduced. A variation of the knitted wire rows revealed a maximum reduction of the reattachment length of 25.7% for the case of four rows. A comparison with a different knitted wire mesh geometry yielded a decreased reattachment length reduction.
Physics-informed neural networks are a promising method to yield surrogate models of flow fields. We present a metamodeling technique for variable geometries based on physics-informed neural networks. The method was applied to the DU99W350 airfoil at a Reynolds number of 1×105. The model predicted the Reynolds-averaged velocity and pressure field around the airfoil for arbitrary angles of attack between 10.0° and 17.5°. The model was trained with data from CFD simulations for a limited set of angles of attack. Additionally, satisfaction of the a priori known boundary conditions as well as the Reynolds-averaged Navier-Stokes equations were trained. A sensitivity analysis concerning the Reynolds number, the amount and distribution of training data, and the turbulence model was conducted showing the superiority of the pseudo-Reynolds stress method and the demand of labeled training data in the domain. The trained network was capable of predicting the developing flow separation on the suction surface and exhibited excellent agreement with CFD results even in the proximity to the wall for interpolations as well as extrapolations from the labeled data set.
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