Aero-engine Bearing Research Articles

Analysis of Bubble-Flow Characteristics in Scavenge Pipe and Establishment of a Flow-Prediction Model

In the modern aerospace industry, the importance of a lubrication system is self-evident for aero-engines, and the aero-engine bearing chamber return line is an even more challenging environment, as it involves a complex two-phase flow. The designer of the scavenge pipe needs to have an accurate understanding of the flow conditions in the scavenge pipe. This paper establishes a visual scavenge pipe test system. The flow direction was vertical flow, the test temperature was 370 k, and a high-speed camera was used to take high-definition flow photographs, which can observe the three main flow types: bubble flow, slug flow, and annular flow. Code program was created to analyze many pictures taken to obtain the apparent flow rate and perimeter of bubbles in the pipe under different flow conditions and to explore the gas–liquid two-phase flow in the scavenge pipe. A support vector machine (SVM) was used for data regression prediction, and the converted velocities of the gas–oil phases were inputted as eigenvalues to obtain the predicted values of bubble-flow velocity. The bubble-flow analysis prediction model established in this paper has a good prediction effect with root mean square error RMSE = 0.0172, which can more objectively and accurately describe the bubble-flow characteristics in the scavenge pipe.

Research on Temperature Rise Characteristics Prediction of Main Shaft Dual-Rotor Rolling Bearings in Aircraft Engines

Traditional aero-engine bearings rotate simultaneously with their inner and outer rings, which makes the temperature rise prediction model computationally large with low accuracy, and it cannot be accurately verified due to the means of testing. This paper presents a method for predicting the temperature rise characteristics of aero-engine bearings under composite load conditions. Firstly, the local method is used to calculate the heat generation from heat sources such as bearing spin, lubricant drag, and the differential sliding of steel ball and collar, respectively, then finite element modelling and steady-state thermal analysis are carried out for aero-engine bearings under the simultaneous action of axial and radial external loads, a double-rotor test setup is designed and the predictive model is validated, and finally, the influences of rotational speed and load on the temperature rise characteristics of the bearings are investigated. The study shows that the aero-engine bearing prediction model proposed in this paper has high accuracy; with the increase in the rotational speed of the inner ring of the bearing, the temperatures of both the inner and outer rings of the bearing increase significantly; the temperatures of the inner and outer rings of the bearing increase with the increase in the axial load, and the effect of the radial load on the temperature of the bearing is not obvious.

A high-accuracy intelligent fault diagnosis method for aero-engine bearings with limited samples

As a crucial component supporting aero-engine functionality, effective fault diagnosis of bearings is essential to ensure the engine's reliability and sustained airworthiness. However, practical limitations prevail due to the scarcity of aero-engine bearing fault data, hampering the implementation of intelligent diagnosis techniques. This paper presents a specialized method for aero-engine bearing fault diagnosis under conditions of limited sample availability. Initially, the proposed method employs the refined composite multiscale phase entropy (RCMPhE) to extract entropy features capable of characterizing the transient signal dynamics of aero-engine bearings. Based on the signal amplitude information, the composite multiscale decomposition sequence is formulated, followed by the creation of scatter diagrams for each sub-sequence. These diagrams are partitioned into segments, enabling individualized probability distribution computation within each sector, culminating in refined entropy value operations. Thus, the RCMPhE addresses issues prevalent in existing entropy theories such as deviation and instability. Subsequently, the bonobo optimization support vector machine is introduced to establish a mapping correlation between entropy domain features and fault types, enhancing its fault identification capabilities in aero-engine bearings. Experimental validation conducted on drivetrain system bearing data, actual aero-engine bearing data, and actual aerospace bearing data demonstrate remarkable fault diagnosis accuracy rates of 99.83%, 100%, and 100%, respectively, with merely 5 training samples per state. Additionally, when compared to the existing eight fault diagnosis methods, the proposed method demonstrates an enhanced recognition accuracy by up to 28.97%. This substantiates its effectiveness and potential in addressing small sample limitations in aero-engine bearing fault diagnosis.

Adaptive thresholding and coordinate attention-based tree-inspired network for aero-engine bearing health monitoring under strong noise

In engineering, due to strong noise interference, it is a challenging issue for decision-making of the neural network to accurately detect healthy conditions of aero-engine bearings. As such, a hierarchical health monitoring model, termed adaptive thresholding and coordinate attention-based tree-inspired network (ATCATN) is developed for the health monitoring of aero-engine bearings under strong background noise. The ATCATN integrates the advantages of the adaptive thresholding and coordinate attention-based network (ATCAN) as well as a tree structure-based method, and it is not merely a straightforward integration of two models. First, the ATCAN is constructed as the backbone network, where the coordinate attention module is developed to process one-dimensional vibration signals; meanwhile, the designed deep residual adaptive thresholding module is introduced to extract significant features from vibration signals with strong noise. Second, a two-layer tree-inspired decision layer, consisting of seed and leaf nodes, is developed to reconstruct the output layer of the ATCAN. Finally, the trained ATCATN is more consistent with maintenance cognition involving multilevel decision information by the progressive determination of fault locations and fault sizes. The experimental results and comparative analysis with seven advanced methods on two aero-engine bearing datasets show that the developed model has the following advantages: (a) model robustness under strong noise; (b) more accurate diagnosis result and (c) hierarchical diagnosis with fault locations and fault sizes. Based on the experimental results, the ATCATN model demonstrates high accuracy in progressively identifying fault locations and sizes of aero-engine bearings even under strong noise interference, which is beneficial for formulating maintenance strategies and aligns with the basic understanding of maintenance.

Parameter optimization design of an aero-engine bearing chamber based on active learning Kriging

The air-oil two-phase flow in the aero-engine bearing chamber is highly complex and unsteady. To obtain the mapping relationship between the oil volume fraction in the chamber and the oil inlet flow rate and rotational speed for further design optimization, the air-oil two-phase flows are simulated using the level set method in COMSOL Multiphysics software. To avoid frequent calls to the simulation model during the optimization process, which consumes huge computational costs, the Kriging model is applied to approximate the true response of the computational fluid dynamics (CFD) simulations. The point addition process is assisted by introducing the active learning function EI to enhance the efficiency of constructing the Kriging model. Particle swarm optimization (PSO) is applied to optimize the Kriging-based optimization objective function. With the optimal parameters’ combination, the performance of the oil return in the aero-engine bearing chamber is improved. This study has guiding significance for the lubrication design of the aero-engine bearing chamber.

Multitask Learning for Aero-Engine Bearing Fault Diagnosis With Limited Data

Multitask Learning for Aero-Engine Bearing Fault Diagnosis With Limited Data

Research on Oil–Gas Two-Phase Flow Characteristics and Improvement of Aero-Engine Bearing Chamber

In order to study the oil–gas two-phase flow characteristics of an aero-engine bearing chamber and improve the scavenge effect of lubricating oil, the two-phase flow solution model of a bearing chamber based on the Euler–Euler method was established. Three improvement schemes were proposed for the ventilation structure and scavenge structure of the bearing chamber. The flow characteristics and scavenge characteristics of a conventional bearing chamber and three improvement schemes under different working conditions were analyzed in depth. The results show that after the conventional bearing chamber ventilation structure is embedded (Embed) and improved, with the increase in the embedding depth, the oil in the cavity is further blocked in the cavity, the amount of oil flowing out from the vent is further reduced, and the scavenge efficiency is further improved. After the slope improvement of the scavenge structure of the conventional bearing chamber, due to the increase in the depth of the oil return groove, the drag effect of the air shear force in the cavity on the oil in the oil return groove is further weakened, and the oil accumulation area on the right side of the scavenge port is further suppressed. The volume fraction of the oil in the cavity is further reduced, and the scavenge efficiency is further improved. The combined improvement scheme (ES) can take into account the advantages of embedding and slope improvement schemes, and further improve the scavenge efficiency. Compared to the conventional bearing chamber, when the oil flow rate is 200 L/h and the speed is 15,000 r/min, the oil return efficiency of the embedded (h = 12 mm), slope (l = 56 mm) and combined improvement schemes are increased by 20.19%, 13.43%, and 37.94%, respectively.

Effect of clearance on the dynamic characteristics of high-speed rolling bearings with multiple defects

Clearance is a pivotal factor in determining the fatigue life of high-speed rolling bearings (HSRBs). As HSRBs naturally experience various defects, their clearance levels tend to rise. However, a clear understanding of the vibration caused by clearance is still lacking. To address this gap, a five-degree-of-freedom dynamics model for HSRBs is proposed. This model accounts for centrifugal force, gyroscopic moment, and time-varying contact angle. In addition, the proposed model incorporates asymptotic theory to describe the defect process and the time-varying contact force derived from the Hertzian point contact theory between rolling elements and raceways. Furthermore, the model considers the variations in load zone and rolling element velocity that arise due to clearance. Applying this model, the dynamic response of HSRBs with different level clearances is calculated. The model's correctness is verified by comparing the dynamic response with the finite element model results and data measured using a test rig built for aero-engine spindle bearing. The results demonstrate that the extent of the load zone shrinks as the clearance increases, although the reduction rate gradually slows down. Since the bearing is loaded only in the load zone, aggravated clearance causes rolling elements to slip in the non-load zone. The effect of clearance on contact force is significantly weaker pronounced than that of defects. These research findings provide a theoretical foundation for the analysis and performance prediction of HSRBs and have important reference values for their optimal design.

Probabilistic fatigue life prediction for CSS-42L bearing in jet strengthen modification grinding using an improved WTP network

Considering the high quality requirement of advanced aeroengine equipment, accurate prediction of probabilistic fatigue life (PFL) for CSS-42 L bearing in jet strengthen modification grinding, is an important issue for aeroengine manufacturing. Here an improved robust prediction algorithm of Wavelet Transform Package (WTP) Network is designed, therefore the decisive influence of jet strengthen modification grinding on the probabilistic fatigue life of aeroengine bearing, could be predicted and analyzed. A complete set of comparative experiments demonstrate that, when Pw is 23.5 MPa, Mc is 225 g, Mb is 342 g, Ma is 304 g, Fa is 0.26 kg/min, and Ts is 40.7 mm/min, and Bf is 69.442%, Cs is 70.336%, Nb is 69.002%, Sb is 54.334%, Os is 67.334%, Fp is 71.254%, At is 33.557%, W is 56.375%, Tr is 62.445%, an optimal data set of FLC is 60.5 × 105, PFLS is 0.337, FLGV is 83.74 × 102, CMFD is 8.42, FDP is 79.43, and NPFD is 4.445 (error tolerance = ±5%), could be obtained. This research is novel and merited in implementing novel PFL prediction for CSS-42 L bearing, proposing PFL indexes to evaluate the fatigue performance of bearing material in jet strengthen modification grinding, and designing an improved WTP network to achieve accurate PFL prediction as well. Eventually it could be summarized that this research realizes extraordinary predictive capability and facilitates the productive PFL management for aeroengine equipment.

Failure analysis of an aero-engine inter-shaft bearing due to clearance between the outer ring and its housing

The inter-shaft bearing is a critical but vulnerable component of dual rotors, because of the severe operational conditions and loads. This paper investigates the failure mechanism of a fractured inter-shaft bearing belonging to a high thrust-weight ratio turbofan engine. Fractographic and microscopic analyses of the fracture surfaces were conducted, followed by vibration signal processing. Finite element analyses of the outer ring were also performed considering the centrifugal, thermal, and roller-raceway loads. What’s more, using the linear elastic fracture mechanics theory, the crack growth path simulation was obtained, whose result showed good agreement with the real fracture morphologies. The results indicated that due to the clearance between the outer ring and its housing during the operation, the anti-rotation tabs of the outer ring were subjected to non-uniform load, thus several tabs might suffer large stress concentrations at the root fillets, which could lead to the crack initiation. During the following operation of the aero-engine, the crack propagated through the outer ring and cause the fatigue fracture of tabs. The investigation provided herein suggests that the outer ring and its housing should be redesigned to eliminate the clearance during the operation, an outer ring formed integrally with the bearing housing flange is one of the failure preventions of this failure mode.

Identification of Oil and Gas Two-Phase Flow Patterns in Aero-Engine Bearing Chambers Based on Kriging Method

The identification of the flow pattern within the bearing chamber's oil and gas two-phase flow is crucial for its lubrication design. Aiming at the lack of accuracy and universality of the current bearing chamber oil and gas two-phase flow pattern recognition, this paper introduces a new method for recognising oil and gas two-phase flow patterns based on the Kriging model. In this paper, the oil-gas two-phase flow field in the bearing chamber is modelled in Comsol simulation software using the level set-k- ε model. Subsequently, the finite element analysis results were used as samples to fit the Kriging model, and the effects of different variables on the flow pattern were analysed and compared with the actual results. The results demonstrate that the flow pattern discrimination results fitted by the Kriging model exhibit higher accuracy and greater generalisability. This holds significant implications for future lubrication and structural design of bearing chambers.

Multiscale Noise Reduction Attention Network for Aeroengine Bearing Fault Diagnosis

Aero-engine has a complex mechanical structure and its working condition are varying widely. Its fault signals are thus modulated through complex nonlinear transfer paths and influenced by non-Gaussian noises. However, popular data-driven multi-scale diagnostic model does not sufficiently consider the embedded noises, and consequently keeps the noise content in the advanced discriminative features, which decreases the diagnostic accuracy. Therefore, a multi-scale attention network with adaptive noise reduction (MANANR) is proposed in this paper for aero-engine bearing fault diagnosis. The MANANR firstly divides the original vibration signal into different scales via the average method of adjacent points. Then, two stacked multi-scale noise reduction modules B1 and B2 is designed for noise reduction. The core strategy behind B1 and B2 is the threshold noise reduction (TNR) which removes the noises from multi-scale convolution features adaptively. Based on the physical principles that global average pooling (GAP) and max pooling (MAP) can extract periodic and impulsive characteristics of the fault signals respectively, the threshold value of TRN is thus constructed through fusing GAP and MAP outputs. Furthermore, attention mechanism is employed to enhance the discriminative capability of multi-scale features by globally capturing the relations among different scales and channels. Finally, one two-layer classifier is introduced to confirm bearing fault patterns. Experimental results demonstrate that the proposed method has the feature-level noise reduction property, and more importantly achieves satisfying intelligent diagnosis precision for aero-engine bearing with the minimum peeling area of 0.5 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> under the continuous acceleration condition from 12000 rpm to 12550 rpm, which outperforms state-of-the-art intelligent diagnostic methods.

Time-varying Noise Structure Inspired Adaptive Weighted Sparse Model for Aero-engine Bearing Fault Diagnosis

Time-varying Noise Structure Inspired Adaptive Weighted Sparse Model for Aero-engine Bearing Fault Diagnosis

Siamese multiscale residual feature fusion network for aero-engine bearing fault diagnosis under small-sample condition

Implementing condition monitoring and fault diagnosis of aero-engine bearings is crucial to ensure that aircraft operate safely and reliably. In engineering practice, the fault data for aero-engine bearings are extremely limited. However, the traditional fault diagnosis methods have two shortcomings under extremely small sample conditions: (1) they have limited diagnostic performance and generalization ability, and (2) they do not mine fault information sufficiently or efficiently. This article proposes a Siamese multiscale residual feature fusion network (SMSRFFN) for aero-engine bearing fault diagnosis under small-sample conditions to overcome the weaknesses above. In the proposed SMSRFFN, the training samples are first paired according to the matching rules to realize the expansion of the sample size. Second, a multiscale residual feature extraction network (MSRFEN) is constructed to excavate the fault features of different scales and speed up the convergence speed of the network. Then, a multiscale attention mechanism feature fusion module (MSAMFFM) is designed to achieve efficient fusion of fault features at different scales. Finally, the distance of the input sample is measured based on the fused deep feature representation to identify the fault state of the aero-engine bearing. The proposed SMSRFFN is evaluated using three bearing fault data and also compared with some state-of-the-art small-sample diagnostic methods. The experimental results demonstrate the effectiveness and superiority of the proposed SMSRFFN in mining fault information and improving diagnosis accuracy under extremely small sample conditions.

How Do Interfacial Shear and Gravity Affect the Oil Film Characteristics Near an Aero-Engine Bearing?

Abstract Recent demand for reducing carbon emission and for increasing engine efficiency has led aero-engine manufacturers to strive for a better oil flow system. Aero-engine bearing chambers that house the shaft-support bearings are among the most challenging parts of the engine systems and it is imperative to have a proper understanding of the oil flow characteristics inside a bearing chamber to increase the engine efficiency. The present work is focused on experimentally investigating the oil film characteristics near a ball bearing static slot in the co- and counter- current regions at various rotational Reynolds number (Reω), loads and liquid flow rates. The experimental investigation has been carried out over a wide range of engine relevant Reω up to 1.7×106 using high-speed imaging and a long distance microscope. The results show that formation of the oil film on the static elements of the bearing is governed by both gravity and interfacial forces at low Reω but only governed by interfacial forces at high Reω. The nondimensional film thickness ranged from 0.71 to 0.18 and decreases with increasing Reω. A regime map was obtained based on the waviness of the film interface showing three different types of wave. At all conditions investigated all waves were capillary waves. Reω, oil flowrate and gravity were found to have a significant effect on the film thickness (δ) with transitions matching the wave regime map.

SMoCo: A Powerful and Efficient Method Based on Self-Supervised Learning for Fault Diagnosis of Aero-Engine Bearing under Limited Data

Vibration signals collected in real industrial environments are usually limited and unlabeled. In this case, fault diagnosis methods based on deep learning tend to perform poorly. Previous work mainly used the unlabeled data of the same diagnostic object to improve the diagnostic accuracy, but it did not make full use of the easily available unlabeled signals from different sources. In this study, a signal momentum contrast for unsupervised representation learning (SMoCo) based on the contrastive learning algorithm—momentum contrast for unsupervised visual representation Learning (MoCo)—is proposed. It can learn how to automatically extract fault features from unlabeled data collected from different diagnostic objects and then transfer this ability to target diagnostic tasks. On the structure, SMoCo increases the stability by adding batch normalization to the multilayer perceptron (MLP) layer of MoCo and increases the flexibility by adding a predictor to the query network. Using the data augmentation method, SMoCo performs feature extraction on vibration signals from both time and frequency domains, which is called signal multimodal learning (SML). It has been proved by experiments that after pre-training with artificially injected fault bearing data, SMoCo can learn a powerful and robust feature extractor, which can greatly improve the accuracy no matter the target diagnostic data with different working conditions, different failure modes, or even different types of equipment from the pre-training dataset. When faced with the target diagnosis task, SMoCo can achieve accuracy far better than other representative methods in only a very short time, and its excellent robustness regarding the amount of data in both the unlabeled pre-training dataset and the target diagnosis dataset as well as the strong noise demonstrates its great potential and superiority in fault diagnosis.

Simulation of Unsteady Flows of Oil/Gas in the Ventless Bearing Chamber of an Aero-Engine

The unsteady motion behavior of oil/gas two-phase flow in a novel ventless bearing chamber has significant impacts on the lubrication and heat transfer efficiency of bearings due to the various advantages of lower pressure levels and weaker rotating airflow effects. In this paper, the unsteady motion behavior of oil/gas two-phase flow in a ventless aero-engine bearing chamber is investigated by three-dimensional numerical simulation through the volume of fluid (VOF) method, and the numerical method is verified using published experimental data. The flow characteristics of oil/gas two-phase in the secondary flow and three-dimensional flow are investigated. The results show the evolution of vortices and the transition of the driving mode in the unsteady motion of oil/gas two-phase flow, and a criterion for the shift of the driving mode at different rotor speeds is proposed. As the rotation speed increases, the variation trend of the velocity field and pressure field of oil/gas two-phase flow is consistent, and the accumulation region of oil becomes inconspicuous. The results indicate a reference for enhancing the performance of lubrication systems for aero-engines.

Mechanism of void healing in cold rolled aeroengine M50 bearing steel under electroshocking treatment: A combined experimental and simulation study

M50 bearing steel, as the important material used in aeroengine bearing, exhibits low cold working capacity with high susceptibility to produce voids. This would lead to the deterioration of mechanical property, which critically limits the industrial application of cold ring rolling (CRR) in M50 bearing rings. In this work, the mechanism of void healing in cold rolled aeroengine M50 bearing steel subjected to electroshocking treatment (EST) was investigated by combining experimental and simulation studies. The experimental results obtained using the three-dimensional X-ray microscopy (3D-XRM) technique show that the porosity of the CRR samples decreases from 0.26% to 0.09% after EST. The healing effect of larger voids is not obvious, while small voids are likely to be healed under EST. The simulation results indicate that due to the inhomogeneous distribution of the physical field, the local thermal compressive stress of as high as 2565.9 MPa is generated around the ellipsoidal void and thus drives void healing process. The healing process of ellipsoidal voids under EST can be divided into elastic and plastic deformation stages considering von Mises yield criterion. In the initial stage, only a small elastic deformation of 53.53 nm occurs in the minor axis direction of the void. In the plastic deformation stage, the material flows along the normal direction of the void with a reduction in the aspect ratio, thereby compressing the void surfaces and promoting their healing. Additionally, the degree of void healing depends on the shape characteristics (curvature and aspect ratio) of the void rather than the current direction. This research elucidates a new elastoplastic mechanism of void healing under EST, which also provides a promising prospect for the application of CRR in M50 bearing steel. • In situ characterization of voids before and after EST was realized using 3D-XRM. • A new elastoplastic mechanism of void healing under EST was elucidated. • The driving force of void healing was revealed by analysing the physical field distribution. • The ellipsoidal void is healed with the reduction in the aspect ratio. • The degree of void healing depends on the shape of the void rather than the current direction.

Simulation of a Simplified Aeroengine Bearing Chamber Using a Fully Coupled Two-Way Eulerian Thin Film/Discrete Phase Approach Part I: Film Behavior Near the Bearing

Abstract Previous work at the Gas Turbine and Transmissions Research Center (G2TRC) has highlighted the need for an adequate computational model, which can appropriately model the oil shedding behavior from bearings. Oil can break up forming droplets and ligaments, subsequently forming thin and thick films driven by both gravity and shear. Our previously published work using openfoam successfully coupled the Eulerian thin film model (ETFM) with the discrete phase model (DPM) (Nicoli et al., 2019, “A New OpenFOAM Solver Capable of Modelling Oil Jet-Breakup and Subsequent Film Formation for Bearing Chamber Applications,” ASME Paper No. GT2019-90264.). In this paper, the previously developed ETFM-DPM capability is, for the first time, extended to an aeroengine representative bearing chamber configuration. The configuration matches that of a simplified aeroengine bearing chamber that has been investigated by researchers at the Gas Turbine and Transmissions Research Center (G2TRC). Numerical investigations are conducted for three different shaft speeds, namely, 5000, 7000, and 12,000 rpm, at two different oil flow rates: 7.3 liters/minute and 5.2 liters/minute. CFD results are validated against existing experimental data for the two lower shaft speeds. Evaluation of computed mean film thickness shows excellent agreement with the experimental data. Results show that there is a diminishing reduction of film thickness with an increasing shaft speed. The computational study allows investigation of oil residence time in the annulus near the bearing. Residence time is seen to reduce with increasing shaft speed and with increasing oil flow rate. This CFD investigation represents the first successful fully coupled two-way ETFM-DPM investigation into the droplet generation process within a bearing chamber application, establishing a firm foundation for future aeroengine bearing chamber modeling.

Simulation of a Simplified Aeroengine Bearing Chamber Using a Fully Coupled Two-Way Eulerian Thin Film/Discrete Phase Approach Part II: Droplet Behavior in the Chamber

Abstract Within aeroengines, bearing chambers exhibit a highly complex two-phase environment as a result of the complex air/oil interactions. The desire to operate at both higher temperatures and shaft speeds requires a sufficient understanding of these systems for design optimization. Typically, bearings are used to support the radial and axial loads transmitted by the shafts and require oil for lubrication and cooling. These bearings are housed in bearing chambers that are sealed using airblown seals. Efficient scavenging systems ensure that the oil is collected and returned to the tank avoiding any unnecessary working of the oil. Previous work at the Gas Turbine and Transmissions Research Center (G2TRC) has highlighted the need for an adequate computational model which can appropriately model the oil shedding behavior from such bearings. Oil can breakup forming droplets and ligaments, subsequently forming thin and thick films driven by both gravity and shear. The objective of this paper is to explore the modeling capability of fully two-way coupled Eulerian thin film/discrete phase models (ETFM-DPM) applied to our simplified bearing chamber configuration. The models are created using openfoam and two-way coupling is employed, enabling Lagrangian droplets to either impinge on the film surface or be removed through effects such as film stripping, splashing, or edge separation. This paper focuses on the droplets, presenting statistics relating to size, velocity, impingement, and residence time, and provides insight into solution sensitivity to operational parameters including shaft speed and oil flow rate. This extends upon our previously published work and improves bearing chamber modeling capability.