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The system response of many engineering systems depends on time. A random process approach is therefore, needed to quantify variation or uncertainty. The system input may consist of a combination of random variables and random processes. In this case, a time-dependent reliability analysis must be performed to calculate the probability of failure within a specified time interval. This is known as cumulative probability of failure which is in general, different from the instantaneous probability of failure. Failure occurs if the limit state function becomes negative at least at one instance within a specified time interval. Time-dependent reliability problems appear if for example, the material properties deteriorate in time or if random loading is involved which is modeled by a random process. Existing methods to calculate the cumulative probability of failure provide an upper bound which may grossly overestimate the true value.

Styrene-Butadiene Rubber (SBR) is a copolymer of butadiene and styrene. It has a wide range of applications in the automotive industry due to its high durability, resistance to abrasion, oils and oxidation. SBR applications vary from tires to vibration isolators and gaskets. SBR is also used in tuned dampers which aim to reduce and control the angular vibrations of crankshafts, acting as an isolator and energy absorber between the tune damper's hub and the inertia ring. The dynamic properties of this polymer are therefore, very important in developing an appropriate analytical model. This paper presents the results of a series of experiments performed to determine the dynamic stiffness and damping properties of SBR. The frequency, temperature and displacement dependent properties are determined in a low frequency range from 0.4 to 150 Hz, and in a mid frequency range from 150 to 550 Hz. The most interesting property of SBR is its frequency dependent behavior.

A Reliability-Based Design Optimization (RBDO) method for multiple failure regions is presented. The method uses a Probabilistic Re-Analysis (PRRA) approach in conjunction with an approximate global metamodel with local refinements. The latter serves as an indicator to determine the failure and safe regions. PRRA calculates very efficiently the system reliability of a design by performing a single Monte Carlo (MC) simulation. Although PRRA is based on MC simulation, it calculates “smooth” sensitivity derivatives, allowing therefore, the use of a gradient-based optimizer. An “accurate-on-demand” metamodel is used in the PRRA that allows us to handle problems with multiple disjoint failure regions and potentially multiple most-probable points (MPP). The multiple failure regions are identified by using a clustering technique. A maximin “space-filling” sampling technique is used to construct the metamodel. A vibration absorber example highlights the potential of the proposed method.

The door closing effort is a quality issue concerning both automobile designers and customers. This paper describes an Excel based mathematical model for predicting the side door closing effort in terms of the required minimum energy or velocity, to close the door from a small open position when the check-link ceases to function. A simplified but comprehensive model is developed which includes the cabin pressure (air bind), seal compression, door weight, latch effort, and hinge friction effects. The flexibility of the door and car body is ignored. Because the model simplification introduces errors, we calibrate it using measured data. Calibration is also necessary because some input parameters are difficult to obtain directly. In this work, we provide the option to calibrate the hinge model, the latch model, the seal compression model, and the air bind model. The door weight effect is geometrically exact, and does not need calibration.

An approach for Probabilistic Reanalysis (PRA) of a system is presented. PRA calculates very efficiently the system reliability or the average value of an attribute of a design for many probability distributions of the input variables, by performing a single Monte Carlo simulation. In addition, PRA calculates the sensitivity derivatives of the reliability to the parameters of the probability distributions. The approach is useful for analysis problems where reliability bounds need to be calculated because the probability distribution of the input variables is uncertain or for design problems where the design variables are random. The accuracy and efficiency of PRA is demonstrated on vibration analysis of a car and on system reliability-based optimization (RBDO) of an internal combustion engine.

When the finite element model of a complex structure is partitioned into substructures in order to enable component mode synthesis, the reduced order model obtained from the Craig-Bampton method often features a large number of interface degrees of freedom (DOF). The authors have recently developed a method to reduce the interface DOF by using a set of so-called characteristic constraint (CC) modes. The resultant, highly compact CC-mode-based reduced order model provides a good platform to calculate the power flow between substructures. In this paper, the CC-mode method is applied to the finite element model of a vehicle structure with about 1.5 million DOF. A convergence study is conducted to find optimal mode selection criteria, and a 2124 DOF reduced order model is obtained for the 0-200 Hz range by using the CC-mode method.

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.

In this paper, an efficient method for the reliability analysis of systems with nonlinear limit states is described. It combines optimization-based and simulation-based approaches and is particularly applicable for problems with highly nonlinear and implicit limit state functions, which are difficult to solve by conventional reliability methods. The proposed method consists of two major parts. In the first part, an optimization-based method is used to search for the most probable point (MPP) on the limit state. This is achieved by using adaptive response surface approximations. In the second part, a multi-modal adaptive importance sampling method is proposed using the MPP information from the first part as the starting point. The proposed method is applied to the reliability estimation of a vehicle body-door subsystem with respect to one of the important quality issues -- the door closing effort.

High vibration levels at the rear bearing cap and oil pump were observed in dyno tests for a particular design of a V6 engine at a rated speed of 4800 r/min. It was found experimentally that the crankshaft-flywheel assembly had a bending resonance at 240 Hz which was excited at around 4800 r/min by 3rd order forces on the crankshaft. A newly developed crankshaft system model (CRANKSYM) was used to analytically verify the above finding and propose possible solutions to the problem. CRANKSYM can perform a coupled analysis among the crankshaft structural dynamics, main bearing hydrodynamics and engine block flexibility. It considers the flywheel dynamics (including the gyroscopic effect), belt loads, crankshaft “bent” and block misboring, and the anisotropy of the block flexibility as seen from a rotating crankshaft. It can also calculate the dynamic stresses on the crankshaft throughout the whole engine cycle. A brief description of CRANKSYM is given in the paper.