scholarly journals A Computational Fluid Dynamics Simulation of Oil–Air Flow Between the Cage and Inner Race of an Aero-engine Bearing

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Akinola A. Adeniyi ◽  
Hervé Morvan ◽  
Kathy Simmons
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Ball Bearing ◽  
Flow Behavior ◽  
Dynamics Simulation ◽  
Shaft Speed ◽  
Outer Race ◽  
Fine Mesh ◽  
Entry Points ◽  
Inner Race

In aero-engines, the shafts are supported on bearings that carry the radial and axial loads. A ball bearing is made up of an inner race, an outer race, and a cage, which contains the balls, these together comprise the bearing elements. The bearings require oil for lubrication and cooling. The design of the bearing studied in this work is such that the oil is fed to the bearing through holes/slots in the inner race. At each axial feed location, the oil is fed through a number of equispaced feedholes/slots but there are a different number of holes at each location. Once the oil has passed through the bearing, it sheds outward from both sides into compartments known as the bearing chambers. A number of studies have been carried out on the dynamics of bearings. Most of the analyses consider the contributions of fluid forces as small relative to the interaction of the bearing elements. One of the most sophisticated models for a cage–raceway analysis is based on the work of Ashmore et al. (2003, “Hydrodynamic Support and Dynamic Response for an Inner-Piloted Bearing Cage,” Proc. Inst. Mech. Eng. Part G, 217, pp. 19–28], where the cage–raceway is considered to be a short journal bearing divided into sectors by the oil feeds. It is further assumed that the oil exits from the holes and forms a continuous block of oil that exits outward on both sides of the cage–raceway. In the model, the Reynolds equation is used to estimate the oil dynamics. Of interest in this current work is the behavior of the oil and air within the space bounded by the cage and inner race. The aim is to determine whether oil feed to the bearing can be modeled as coming from a continuous slot or if the discrete entry points must be modeled. A volume of fluid (VOF) computational fluid dynamics (CFD) approach is applied. A sector of a ball bearing is modeled with a fine mesh, and the detailed simulations show the flow behavior for different oil splits to the three feed locations of the bearing, thus providing information useful to understanding oil shedding into the bearing chambers. This work shows that different flow behaviors are predicted by models where the oil inlets through a continuous slot are compared to discrete entry holes. The form and speed of oil shedding from the bearing are found to depend strongly on shaft speed with the shedding speed being slightly higher than the cage linear speed. The break-up pattern of oil on the cage inner surface suggests that smaller droplets will be shed at higher shaft speed.

Oil-Air Flow Between the Cage and Inner Race of an Aeroengine Bearing

2016 ◽  
Author(s):  
Akinola A. Adeniyi ◽  
Hervé Morvan ◽  
Kathy Simmons
Keyword(s):  
Ball Bearing ◽  
Flow Behaviour ◽  
Shaft Speed ◽  
Axial Loads ◽  
Outer Race ◽  
Fluid Forces ◽  
Fine Mesh ◽  
Entry Points ◽  
Continuous Block ◽  
Inner Race

In aeroengines the shafts are supported on bearings that carry the radial and axial loads. A ball bearing is made up of an inner-race, an outer-race and a cage which contains the balls, these together comprise the bearing elements. The bearings require oil for lubrication and cooling. The design of the bearing studied in this work is such that the oil is fed to the bearing through holes/slots in the inner race. At each axial feed location the oil is fed through a number of equispaced feedholes/slots but there is a different number of holes at each location. Once the oil has passed through the bearing it sheds outwards from both sides into compartments known as the bearing chambers. A number of studies have been carried out on the dynamics of bearings. Most of the analyses consider the contributions of fluid forces as small relative to the interaction of the bearing elements. One of the most sophisticated models for a cage-raceway analysis is based on the work of Ashmore et al. [1], where the cage-raceway is considered to be a short journal bearing divided into sectors by the oil feeds. It is further assumed that the oil exits from the holes and forms a continuous block of oil that exits outwards on both sides of the cage-raceway. In the model, the Reynolds equation is used to estimate the oil dynamics. Of interest in this current work is the behaviour of the oil and air within the space bounded by the cage and inner race. The aim is to determine whether oil feed to the bearing can be modelled as coming from a continuous slot or if the discrete entry points must be modelled. A Volume of Fluid Computational Fluid Dynamics approach is applied. A sector of a ball bearing is modelled with a fine mesh and the detailed simulations show the flow behaviour for different oil splits to the three feed locations of the bearing thus providing information useful to understanding oil shedding into the bearing chambers. The work shows that different flow behaviour is predicted by models where the oil inlets through a continuous slot compared to discrete entry holes. The form and speed of oil shedding from the bearing is found to depend strongly on shaft speed with the shedding speed being slightly higher than the cage linear speed. The break-up pattern of oil on the cage inner surface suggests smaller droplets will be shed at higher shaft speed.

The effect of actuator parameters on the performance of a liquid-jet hammer associated with its jet behavior

2016 ◽  
Vol 231 (14) ◽  
pp. 2610-2620 ◽  
Author(s):  
Xinxin Zhang ◽  
Jianming Peng ◽  
Jingqing Chen ◽  
Kun Bo ◽  
Kun Yin ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
High Performance ◽  
Flow Behavior ◽  
Experimental Tests ◽  
Simulation Method ◽  
Dynamics Simulation ◽  
Liquid Jet ◽  
Computational Fluid Dynamics Simulation ◽  
Jet Behavior

Bi-stable fluidic amplifier containing no moving parts was used for switching fluid flow passing through it into an actuator in a liquid-jet hammer. So far, there has been no design basis for developing a liquid-jet hammer with high performance. To provide a guidance, this paper elaborates on the computational fluid dynamics simulation method for investigating the effect of actuator parameters on the performance of a liquid-jet hammer associated with its jet behavior. Given that couple mechanism exists between the flow field in the bi-stable fluidic amplifier and the actuator, dynamic mesh technique and a user-defined function written in C programming language were used to update the mesh in the simulations. Two evaluation criteria—pressure recovery and flux utilization ratio—for a liquid-jet hammer were used in this study. Experimental tests were conducted to verify the simulation results, by which the accuracy and reliability of this computational fluid dynamics simulation method was proved. Besides, comprehensive analysis of the flow behavior in the fluidic amplifier of a liquid-jet hammer was performed by the use of computational fluid dynamics visualization method.

Fluid Mixing in Channels With Microridges

2007 ◽  
Author(s):  
Zheng Xia ◽  
Lou Cattafesta ◽  
Mark Sheplak ◽  
Renwei Mei ◽  
Z. Hugh Fan
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Fluid Flow ◽  
Cross Section ◽  
Fluorescence Intensity ◽  
Flow Behavior ◽  
Fluid Mixing ◽  
Dynamics Simulation ◽  
Uneven Surface ◽  
Computational Fluid Dynamics Simulation

Microflows in complex channels with uneven surface often display unusual flow behavior compared to their macroscale counterparts. Channels with micro ridges are created in a plastic device using photolithography and molding. Recirculation is reported in the flow in these ridged channels [1]. We use recirculation to enhance fluid mixing and the utility is demonstrated by homogenizing fluorescein and water in the microchannels. In addition, deconvolution microscopy is developed to visualize the fluid flow in the ridged channel. Flow twisting, as expected, results in enhanced fluid mixing; the fluorescence intensity at a cross section is calculated as an indicator of the degree of mixing. The preliminary results show rapid mixing in the ridged channels, verifying existence of circulation. We also compared the experimental results with that from computational fluid dynamics simulation.

scholarly journals Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David R. Rutkowski ◽  
Alejandro Roldán-Alzate ◽  
Kevin M. Johnson
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Velocity Fields ◽  
Dynamics Simulation ◽  
Patient Specific ◽  
4D Flow Mri ◽  
4D Flow ◽  
Flow Phase ◽  
Flow Mri ◽  
Trained Network

AbstractBlood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.

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