A generalized computational fluid dynamics approach for journal bearing performance prediction

1996 ◽  
Vol 22 ◽  
pp. 144 ◽  
Author(s):  
P Tucker
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Performance Prediction ◽  
Journal Bearing

scholarly journals Journal Bearing: An Integrated CFD-Analytical Approach for the Estimation of the Trajectory and Equilibrium Position

2020 ◽  
Vol 10 (23) ◽  
pp. 8573
Author(s):  
Franco Concli
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Pressure Distribution ◽  
Equilibrium Position ◽  
Analytical Approach ◽  
Journal Bearing ◽  
Cfd Model ◽  
Cavitation Model ◽  
Computational Fluid Dynamics Cfd ◽  
The Mathematical Model

For decades, journal bearings have been designed based on the half-Sommerfeld equations. The semi-analytical solution of the conservation equations for mass and momentum leads to the pressure distribution along the journal. However, this approach admits negative values for the pressure, phenomenon without experimental evidence. To overcome this, negative values of the pressure are artificially substituted with the vaporization pressure. This hypothesis leads to reasonable results, even if for a deeper understanding of the physics behind the lubrication and the supporting effects, cavitation should be considered and included in the mathematical model. In a previous paper, the author has already shown the capability of computational fluid dynamics to accurately reproduce the experimental evidences including the Kunz cavitation model in the calculations. The computational fluid dynamics (CFD) results were compared in terms of pressure distribution with experimental data coming from different configurations. The CFD model was coupled with an analytical approach in order to calculate the equilibrium position and the trajectory of the journal. Specifically, the approach was used to study a bearing that was designed to operate within tight tolerances and speeds up to almost 30,000 rpm for operation in a gearbox.

Three-Dimensional Thermo-Elasto-Hydrodynamic Computational Fluid Dynamics Model of a Tilting Pad Journal Bearing—Part II: Dynamic Response

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Jongin Yang ◽  
Alan Palazzolo
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Dynamic Response ◽  
Journal Bearing ◽  
Three Dimensional ◽  
Static Response ◽  
Damping Coefficients ◽  
Tilting Pad ◽  
Stiffness And Damping ◽  
Tilting Pad Journal Bearing

Part II presents a novel approach for predicting dynamic coefficients for a tilting pad journal bearing (TPJB) using computational fluid dynamics (CFD) and finite element method (FEM), including fully coupled elastic deflection, heat transfer, and fluid dynamics. Part I presented a similarly novel, high fidelity approach for TPJB static response prediction which is a prerequisite for the dynamic characteristic determination. The static response establishes the equilibrium operating point values for eccentricity, attitude angle, deflections, temperatures, pressures, etc. The stiffness and damping coefficients are obtained by perturbing the pad and journal motions about this operating point to determine changes in forces and moments. The stiffness and damping coefficients are presented in “synchronously reduced form” as required by American Petroleum Institute (API) vibration standards. Similar to Part I, an advanced three-dimensional thermal—Reynolds equation code validates the CFD code for the special case when flow Between Pad (BP) regions is ignored, and the CFD and Reynolds pad boundary conditions are made identical. The results show excellent agreement for this validation case. Similar to the static response case, the dynamic characteristics from the Reynolds model show large discrepancies compared with the CFD results, depending on the Reynolds mixing coefficient (MC). The discrepancies are a concern given the key role that stiffness and damping coefficients serve instability and response predictions in rotordynamics software. The uncertainty of the MC and its significant influence on static and dynamic response predictions emphasizes a need to utilize the CFD approach for TPJB simulation in critical machines.

Computational Fluid Dynamics Analysis of an Aerostatic Journal Bearing with Slot-Entry Restrictors

2011 ◽  
Vol 4 (8) ◽  
pp. 2817-2821 ◽  
Author(s):  
Yingchun Liang ◽  
Jingshi Liu ◽  
Yazhou Sun ◽  
Lihua Lu ◽  
Zhen Tong
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Journal Bearing ◽  
Dynamics Analysis ◽  
Computational Fluid Dynamics Analysis

Assessment of Loss Correlations for Performance Prediction of Low Reaction Gas Turbine Stages

2008 ◽  
Author(s):  
Ernesto Benini ◽  
Giovanni Boscolo ◽  
Andrea Garavello
Keyword(s):  
Fluid Dynamics ◽  
Experimental Data ◽  
Computational Fluid Dynamics ◽  
Gas Turbine ◽  
Performance Prediction ◽  
Reasonable Accuracy ◽  
Algebraic Models ◽  
Two Stage ◽  
Accurate Model ◽  
Multi Stage

In spite of the remarkable advances in the field of the Computational Fluid Dynamics, algebraic models built upon empirical loss and deviation correlations are still one of the most reliable and effective tools to predict the performance of gas turbine stages with reasonable accuracy, especially when low-reaction, multi-stage architectures are considered. This paper deals with a comparison among some of the most popular loss correlations used by gas turbine manufacturers; such comparison is performed on a two-stage low-reaction turbine for which detailed experimental data are available. An overall assessment on the validity of loss correlations is carried out to help the designer/analyst using the most accurate model when both on- and off-design are to be carried out.

A Generalized Computational Fluid Dynamics Approach for Journal Bearing Performance Prediction

1995 ◽  
Vol 209 (2) ◽  
pp. 99-108 ◽  
Author(s):  
P G Tucker ◽  
P S Keogh
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Surface Temperature ◽  
Performance Prediction ◽  
Reynolds Equation ◽  
Three Dimensional ◽  
Rotation Effect ◽  
Performance Predictions ◽  
Computational Fluid Dynamics Cfd ◽  
Shaft Surface

The use of computational fluid dynamics (CFD) techniques enables performance predictions of bearing designs to be made when the usual operating assumptions of the Reynolds equation Jail to hold. This paper addresses the application of a full three-dimensional thermohydrodynamic CFD approach to journal bearings. The journal/shaft may extend beyond the bearing length and the rotation effect is accounted for in the thermal transport process. A circumferentially uniform shaft surface temperature is not assumed. Cavitation modelling is based on averaged lubricant/vapour properties and does not set pressures directly, allowing sub-ambient pressures to be predicted. Lubricant inlet grooves are incorporated with conservation of mass and the possibility of backflow. The modelling is validated against published experimental work on fully circumferential, single inlet and two-inlet circular bore bearings. The predicted and experimental results are in general agreement, although the predicted cyclic variation of journal surface temperature is less than the experimental value. However, an assumption in the predictions was of a non-orbiting journal. The techniques developed may, in principle, be extended to the orbiting journal case providing a dynamic cavitation model can be formulated.

Computational Fluid Dynamics Based Mixing Prediction for Tilt Pad Journal Bearing TEHD Modeling—Part I: TEHD-CFD Model Validation and Improvements

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Jongin Yang ◽  
Alan Palazzolo
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Journal Bearing ◽  
Dynamic Performance ◽  
Three Dimensional ◽  
Prediction Method ◽  
Conventional Approach ◽  
Machine Learning Techniques ◽  
Prediction Errors ◽  
Cfd Model

Abstract The core contributions of Part I (1) present a computational fluid dynamics (CFD)-based approach for tilting pad journal bearing (TPJB) modeling including thermo-elasto hydrodynamic (TEHD) effects with multi-mode pad flexibility, (2) validate the model by comparison with experimental work, and (3) investigate the limitations of the conventional approach by contrasting it with the new approach. The modeling technique is advanced from the author’s previous work by including pad flexibility. The results demonstrate that the conventional approach of disregarding the three-dimensional flow physics between pads (BP) can generate significantly different pressure, temperature, heat flux, dynamic viscosity, and film thickness distributions, relative to the high-fidelity CFD model. The uncertainty of the assumed mixing coefficient (MC) may be a serious weakness when using a conventional, TPJB Reynolds model, leading to prediction errors in static and dynamic performance. The advanced mixing prediction method for “BP” thermal flow developed in Part I will be implemented with machine learning techniques in Part II to provide a means to enhance the accuracy of conventional Reynolds based TPJB models.

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