Omidreza Ebrat

Calculation of Journal Bearing Dynamic Characteristics Including Journal Misalignment and Bearing Structural Deformation

A detailed journal bearing analysis for accurate evaluation of film dynamic characteristics is presented. The new formulation is based on a local perturbation of the oil film at each computational node that captures the important effects of journal misalignment and bearing structural deformation in rotor dynamics and engine NVH applications. The new algorithm is an extension to the classical approach of evaluating film dynamic characteristics based on journal eccentricity perturbation. The governing equations for the oil film pressure, stiffness, and damping are solved using a finite difference approach and their output is validated with numerical results from the literature.

An Elastohydrodynamic Coupling of a Rotating Crankshaft and a Flexible Engine Block

A comprehensive formulation is presented for the dynamics of a rotating flexible crankshaft coupled with the dynamics of an engine block through a finite difference elastohydrodynamic main bearing lubrication algorithm. The coupling is based on detailed equilibrium conditions at the bearings. The component mode synthesis is employed for modeling the crankshaft and block dynamic behavior. A specialized algorithm for coupling the rigid and flexible body dynamics of the crankshaft within the framework of the component mode synthesis has been developed. A finite difference lubrication algorithm is used for computing the oil film elastohydrodynamic characteristics. A computationally accurate and efficient mapping algorithm has been developed for transferring information between a high-density computational grid for the elastohydrodynamic bearing solver and a low-density structural grid utilized in computing the crankshaft and block structural dynamic response. The new computational capability is used to compute the vibratory response of an automotive V6 engine due to combustion and inertia loading.

Probabilistic Main Bearing Performance for an Internal Combustion Engine

A probabilistic analysis is presented for studying the variation effects on the main bearing performance of an I.C. engine system, under structural dynamic conditions. For computational efficiency, the probabilistic analysis is based on surrogate models (metamodels), which are developed using the kriging method. An optimum symmetric Latin hypercube algorithm is used for efficient “space-filling” sampling of the design space. The metamodels provide an efficient and accurate substitute to the actual engine bearing simulation models. The bearing performance is based on a comprehensive engine system dynamic analysis which couples the flexible crankshaft and block dynamics with a detailed main bearing elastohydrodynamic analysis. The clearance of all main bearings and the oil viscosity comprise the random variables in the probabilistic analysis. The maximum oil pressure and the percentage of time within each cycle that a bearing operates with oil film thickness below a threshold value of 0.27μm at each main bearing constitute the system performance measures. Probabilistic analyses are first performed to calculate the mean, standard deviation and probability density function of the bearing performance measures. Subsequently, a probabilistic sensitivity analysis is described for identifying the important random variables. Finally, a reliability-based design optimization study is conducted for optimizing the main bearing performance under uncertainty. Results from a V6 engine are presented.

Probabilistic and sensitivity analyses for the performance characteristics of the main bearings in an operating engine due to variability in bearing properties

Surrogate models (metamodels) are developed and used to evaluate an engine bearing performance and perform probabilistic sensitivity analyses. The metamodels are developed based on results from a simulation solver computed at a limited number of sample points. An integrated system-level engine simulation model, consisting of flexible crankshaft and block dynamic models, connected by a detailed hydrodynamic lubrication model, is employed for constructing the metamodels. An optimal symmetric Latin hypercube sampling algorithm is utilised. The metamodels are employed for performing probabilistic and sensitivity analyses. The initial clearance between the crankshaft and each main bearing and the oil viscosity comprise the random variables. The maximum oil pressure and the percentage of time (time ratio) within each cycle that a bearing operates with oil film thickness less than a user defined threshold value at each main bearing constitute the system performance variables.

Probabilistic Computations for the Main Bearings of an Operating Engine Due to Variability in Bearing Properties

This paper presents the development of surrogate models (metamodels) for evaluating the bearing performance in an internal combustion engine. The metamodels are employed for performing probabilistic analyses for the engine bearings. The metamodels are developed based on results from a simulation solver computed at a limited number of sample points, which sample the design space. An integrated system-level engine simulation model, consisting of a flexible crankshaft dynamics model and a flexible engine block model connected by a detailed hydrodynamic lubrication model, is employed in this paper for generating information necessary to construct the metamodels. An optimal symmetric latin hypercube algorithm is utilized for identifying the sampling points based on the number and the range of the variables that are considered to vary in the design space. The development of the metamodels is validated by comparing results from the metamodels with results from the actual simulation models over a large number of evaluation points. Once the metamodels are established they are employed for performing probabilistic analyses. The initial clearance between the crankshaft and the bearing at each main bearing and the oil viscosity comprise the random variables in the probabilistic analyses. The maximum oil pressure and the percentage of time (the time ratio) within each cycle that a bearing operates with oil film thickness less than a user defined threshold value at each main bearing constitute the performance variables of the system. The availability of the metamodels allows comparing the performance of several probabilistic methods in terms of accuracy and computational efficiency. A useful insight is gained by the probabilistic analysis on how variability in the bearing characteristics affects the performance of the bearings.

Probabilistic Analyses for the Performance Characteristics of the Main Bearings in an Operating Engine Due to Variability in Bearing Properties

This paper presents the development of surrogate models (metamodels) for evaluating the bearing performance in an internal combustion engine. The metamodels are employed for performing probabilistic analyses for the engine bearings. The metamodels are developed based on results from a simulation solver computed at a limited number of sample points, which sample the design space. An integrated system-level engine simulation model, consisting of a flexible crankshaft dynamics model and a flexible engine block model connected by a detailed hydrodynamic lubrication model, is employed in this paper for generating information necessary to construct the metamodels. An optimal symmetric Latin hypercube algorithm is utilized for identifying the sampling points based on the number and the range of the variables that are considered to vary in the design space. The development of the metamodels is validated by comparing results from the metamodels with results from the actual simulation models over a large number of evaluation points. Once the metamodels are established they are employed for performing probabilistic analyses. The initial clearance between the crankshaft and the bearing at each main bearing and the oil viscosity comprise the random variables in the probabilistic analyses. The maximum oil pressure and the percentage of time (the time ratio) within each cycle that a bearing operates with oil film thickness less than a user defined threshold value at each main bearing constitute the performance variables of the system. The availability of the metamodels allows comparing the performance of several probabilistic methods in terms of accuracy and computational efficiency. A useful insight is gained by the probabilistic analysis on how variability in the bearing characteristics affects its performance.© 2003 ASME