Journal bearings are used to support rotors in a wide range of applications. In order to ensure reliable operation, accurate analyses of these rotor-bearing systems are crucial. Coupled analysis of the rotor and the journal bearing is essential in the case that the rotor is flexible. The accuracy of prediction of the model at hand depends on its comprehensiveness. In this study, we construct three bearing models of increasing modeling comprehensiveness and use these to predict the response of two different rotor-bearing systems. The main goal is to evaluate the correlation with measurement data as a function of modeling comprehensiveness: 1D versus 2D pressure prediction, distributed versus lumped thermal model, Newtonian versus non-Newtonian fluid description and non-mass-conservative versus mass-conservative cavitation description. We conclude that all three models predict the existence of critical speeds and whirl for both rotor-bearing systems. However, the two more comprehensive models in general show better correlation with measurement data in terms of frequency and amplitude. Furthermore, we conclude that a thermal network model comprising temperature predictions of the bearing surroundings is essential to obtain accurate predictions. The results of this study aid in developing accurate and computationally-efficient models of flexible rotors supported by plain journal bearings.@en

Floating ring bearings are the commonly used type of bearing for automotive turbochargers. The automotive industry continuously investigates how to reduce the bearing friction losses and how to create silent turbochargers. Many of these studies involve creating a numerical model of the rotor-bearing system and performing validation on a test bench on which a turbocharger is driven by hot gases. This approach, however, involves many uncertainties which diminish the validity of the measurement results. In this study, we present a test setup in which these uncertainties are minimized. The measurement results show the behavior of the floating ring bearing as a function of oil feed pressure, oil feed temperature, rotor unbalance and bearing clearances. Next to an increased validity, the test setup provides measurement data with good repeatability and can therefore represent a case study which can be used for validation of rotor-bearing models@en

In the competitive automotive market, the performance of turbochargers is constantly being pushed towards their theoretical optimum. One of the key components of the turbocharger is the rotor-bearing system, which determines the friction losses and noise output and furthermore affects the overall turbocharger efficiency, reliability and cost. In order to fulfil the demands of the automotive market, developing methods to optimize the rotor-bearing system is the focus of this study, where particular attention is paid to taking into account the product-to-product variations that are inevitable in cost-effective mass-produced parts, as well as the variations in turbocharger operating conditions. First, a model of the rotor-bearing system was developed to predict the rotordynamic response over the operating range. The model is constructed in a step-by-step fashion, starting with a simple test case: a Laval rotor supported by plain journal bearings. As the behavior of the rotor-bearing system varies over its rotation speed range, run-up simulations were performed by a time-transient multi-physical model. In this model, several sub-models are coupled: a rotordynamic sub-model, a thermo-hydrodynamic submodel and a thermal network model. Once a satisfactory correlation was found between numerical simulation results and measurement results, the test case progressed to a Laval rotor with floating ring bearings instead of plain journal bearings. Correspondingly, the bearing model was extended to include the dynamics of the floating ring and its two oil films. The resulting run-ups showed a response consisting of a critical speed, an oil whirl and an oil whip. Analysis of a turbocharger rotor-bearing system was subsequently performed, showing a more complex response, consisting of multiple critical speeds and the co-existence of sub-synchronous whirling modes. The effect of the rotor-bearing operating conditions, unbalance configuration, the thrust bearing and the bearing cylindricity were investigated. Most of the trends are correctly predicted by the model, however the correlation between measurement results and simulation results was clearly inferior to the case of the Laval rotor, most likely due to the uncertainties in the actual turbocharger geometry and the actual unbalance distribution. Lastly, an optimization of a Laval rotor-bearing system was performed. The resulting robust optimum design ensures optimum rotor-bearing performance, even at the most severe operating conditions and even if all manufacturing tolerances represent the worst case scenario. Particularly the uncertainties in rotor unbalance and oil supply temperature were found to have a significant influence on the optimum design. @en

Accurate prediction of cavitation is an important feature in hydrodynamic bearing modeling. Especially for thermo-hydrodynamic modeling, it is crucial to use a mass-conservative cavitation algorithm. This paper introduces a new mass-conserving Reynolds cavitation algorithm, which provides fast convergence and easy implementation in finite element models. The proposed algorithm is based on a variable transformation for both the pressure and mass fraction, which is presented in the form of a complementary condition. Stabilization in the streamline and crosswind direction is provided by artificial diffusion. The model is completed by including a simple and efficient thermal model and is validated using the numerical values of a reference plain journal bearing experiment under steady-state conditions. In addition, a transient analysis is performed of a journal bearing subjected to a harmonic load. It is shown that the proposed cavitation algorithm results are in good agreement with the reference measurement results. Moreover, the algorithm proves to be stable and requires only a small number of iterations to convergence in the Reynolds-based finite element model.@en