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An efficient approach for series system reliability-based design optimization (RBDO) is presented. The key idea is to apportion optimally the system reliability among the failure modes by considering the target values of the failure probabilities of the modes as design variables. Critical failure modes that contribute the most to the overall system reliability are identified. This paper proposes a computationally efficient, system RBDO approach using a single-loop method where the searches for the optimum design and for the most probable failure points proceed simultaneously. Specifically, at each iteration the optimizer uses approximated most probable failure points from the previous iteration to search for the optimum. A second-order Ditlevsen upper bound is used for the joint failure probability of failure modes. Also, an easy to implement active strategy set is employed to improve algorithmic stability.

Reliability-based design optimization accounts for variation. However, it assumes that statistical information is available in the form of fully defined probabilistic distributions. This is not true for a variety of engineering problems where uncertainty is usually given in terms of interval ranges. In this case, interval analysis or possibility theory can be used instead of probability theory. This paper shows how possibility theory can be used in design and presents a computationally efficient sequential optimization algorithm. The algorithm handles problems with only uncertain or a combination of random and uncertain design variables and parameters. It consists of a sequence of cycles composed of a deterministic design optimization followed by a set of worst-case reliability evaluation loops. A crank-slider mechanism example demonstrates the accuracy and efficiency of the proposed sequential algorithm.

Recently the use of digital 3D profilometry in the automotive industries has become increasingly popular. The effective techniques for 3D shape measurement, especially for the measurement of complicated structures, have become more and more significant. Different optical inspective methods, such as 3D profilometry, laser scanning and Coordinate-Measuring Machine (CMM), have been applied for 3D shape measurement. Among these methods, 3D profilometry seems to be the fastest and inexpensive method with considerably accurate result, and it has simple setup and full field measuring ability compared with other techniques. In this paper, a novel calibration method for 3D-profilometry will be introduced. In this method, a multiple-step calibration procedure is utilized and best-fit calibration curves are obtained to improve measurement accuracy. A recursive algorithm is used for data evaluation, along with calibration data.

Low vibration and noise level in internal combustion engines has become an essential part of the design process. It is well known that the piston assembly can be a major source of engine mechanical friction and cold start noise, if not designed properly. The piston secondary motion and piston-bore contact pattern are critical in piston design because they affect the skirt-to-bore impact force and therefore, how the piston impact excitation energy is damped, transmitted and eventually radiated from the engine structure as noise. An analytical method is presented in this paper for simulating piston secondary dynamics and piston-bore contact for an asymmetric half piston model. The method includes several important physical attributes such as bore distortion effects due to mechanical and thermal deformation, inertia loading, piston barrelity and ovality, piston flexibility and skirt-to-bore clearance. The method accounts for piston kinematics, rigid-body dynamics and flexibility.

An analytical method is presented in this paper for simulating piston secondary dynamics and piston-bore contact for an asymmetric half piston model including elastohydrodynamic (EHD) lubrication at the bore-skirt interface. A piston EHD analysis is used based on a finite-difference formulation. The oil film is discretized using a two-dimensional mesh. For improved computational efficiency without loss of accuracy, the Reynolds’ equation is solved using a perturbation approach which utilizes an “influence zone” concept, and a successive over-relaxation solver. The analysis includes several important physical attributes such as bore distortion effects due to mechanical and thermal deformation, inertia loading and piston barrelity and ovality. A Newmark-Beta time integration scheme combined with a Newton-Raphson linearization, calculates the piston secondary motion.

The quality of engine lubrication depends upon how much oil is supplied and how the lubricant is pressurized to the lubricated components. These variables strongly affect the safe operation and lifespan of an engine. During the conceptual design stage of an engine, its lubrication system cannot be verified experimentally. It is highly desirable for design engineers to utilize computer simulations and robust design methodology in order to achieve their goal of optimizing the engine lubrication system. The heuristic design principle is a relatively routine resource for design engineers to pursue although it is time consuming and sacrifices valuable developing time. This paper introduces an unusual design methodology in which design engineers were involved in analyzing their own designs along with lubrication system analyst to establish a link between two sophisticated software packages.

High-fidelity finite element (FE) tire-snow interaction models have the advantage of better understanding the physics of the tire-snow system. They can be used to develop semi-analytical models for vehicle design as well as to design and interpret field test results. For off-terrain conditions, there is a high level of uncertainties inherent in the system. The FE models are computationally intensive even when uncertainties of the system are not taken into account. On the other hand, field tests of tire-snow interaction are very costly. In this paper, dynamic metamodels are established to interpret interaction forces from FE simulation and to predict those forces by using part of the FE data as training data and part as validation data. Two metamodels are built based upon the Krieging principle: one has principal component analysis (PCA) taken into account and the other does not.

Full nonlinear finite element radial-ply and bias-ply tire models are developed to investigate different structured tires' transmissibility phenomena. The reaction forces of the tire axles in time domain are recorded first when the tires encounter a bump (cleat), and then the FFT algorithm is applied to examine the dynamic response information in frequency domain. The results of the radial-ply vs. bias-ply tires' transmissibility are validated against previous studies and show reasonable agreement.

In this paper, we develop a methodology to enable the isolation of the heat release contribution of knocking combustion from flame-propagation combustion. We first address the empirical modeling of individual non-autoigniting combustion history using the Wiebe function, and subsequently apply this methodology to investigate the effect of autoignition on the heat release history of knocking cycles in a spark ignition (SI) engine. We start by re-visiting the Wiebe function, which is widely used to model empirically mass burned histories in SI engines. We propose a method to tune the parameters of the Wiebe function on a cycle-by-cycle basis, i.e., generating a different Wiebe to suitably fit the heat release history of each cycle. Using non-autoigniting cycles, we show that the Wiebe function can reliably simulate the heat release history of an entire cycle, if only data from the first portion of the cycle is used in the tuning process.

An analytical study of spray from an outwardly opening pressure swirl injector has been presented in this paper. A number of model injectors with varying design configurations have been used in this study. The outwardly opening injection process has been modeled using a modified spray breakup model presented in an earlier study. It has been observed that simulation results from the study clearly capture the mechanism by which an outwardly opening conical spray interacts with the downstream flow field. Velocity field near the tip of the injector shows that the conical streams emanating from an outwardly opening injector have the tendency to entrap air into the flow stream which is responsible for finer spray. A deviation from the optimum set of physical parameters showed a high propensity to produce large spray droplets. This study also emphasizes the importance of computational fluid dynamics (CFD) as an engineering tool to understand the complex physical processes.

This study is concerned with computational fluid dynamics (CFD) simulations of flow in an automotive reverse flow type cyclone particle separator using the Reynolds Stress Model (RSM) turbulence model. Steady simulations were found to never fully converge, with pressure, velocity and vorticity results exhibiting small oscillations as the solution was iterated further. Transient simulations showed the presence of a main vortex precession that resulted in periodic fluctuations of the flow parameters. Fourier analysis was used to characterize this semi-periodic flow feature and to assess its effect on the two main performance measures of the cyclone: overall pressure drop and particle separation efficiency.

During the production controller and software development process, one critical step is the controller and software verification. There are various ways to perform this verification. One of the commonly used methods is to utilize an HIL (hardware-in-the-loop) test bench to emulate powertrain hardware for development and validation of powertrain controllers and software. A key piece of an HIL bench is the plant dynamics model used to emulate the external environment of a modern controller, such as engine (ECM), transmission (TCM) or powertrain controller (PCM), so that the algorithms and their software implementation can be exercised to confirm the desired results. This paper presents a 6-speed automatic transmission plant dynamics model development for hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software. The modeling method, model validation, and application in an HIL test environment are described in details.

Predicting the optimum performance parameters of an automotive cyclone particle separator (separation efficiency and pressure drop) using computational fluid dynamics by varying its geometrical parameters is challenging and a time consuming process due to the highly swirling nature of the flow. This study presents results of three investigations of the performance and design of a cyclone separator: a sensitivity analysis, deterministic optimization and a reliability based design optimization. All three cases involved variation of four geometric parameters that characterize the design of the cyclone.

This paper presents a hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software, with a focus on a closed-loop vehicle drive-train model incorporating a detailed automatic transmission plant dynamics model developed for certain applications. Specifically, this paper presents the closed-loop integration of a 6-speed automatic transmission model developed for our HIL transmission controller and algorithm test bench (Opal-RT TestDrive based). The model validation, integration and its application in an HIL test environment are described in details.

In recent years, gasoline direct injection (GDi) engines have been popular due to their inherent potential for reduction of exhaust emissions and fuel consumption to meet stringent EPA standards. These engines require high-pressure fuel injection in order to improve the atomization process and accelerate mixture preparation. The high-pressure fuel pump is an essential component in the GDi system. Therefore, understanding the flow characteristics of this device and its associated behavior is critical for improving the performance of this category of engines. In this paper, the fluid flow characteristics in a high-pressure single-piston pump for use in GDi engines are analyzed using 1-D LMS Imagine.Lab AMESim system and 3-D Ansys Fluent computational fluid dynamics (CFD) models. The flow rate of the fuel pump under various cam speeds has been examined along with characteristics of the pump's control valve.

This paper describes a correlation study on fuel spray pattern recognition of multi-hole injectors for gasoline direct injection (GDi) engines. Spray pattern is characterized by patternation length, which represents the distance of maximum droplet concentration from the axis of the injector. Five fuel injectors with different numbers and sizes of nozzle holes were considered in this study. Experimental data and CFD modeling results were used separately to develop regression models for spray patternation. These regressions predicted the influence of a number of injector operating and design parameters, including injection system operating pressure, valve lift, injector hole length-to-diameter ratio (L/d) and the orientation of the injector hole. The regression correlations provided a good fit with both experimental and CFD spray simulation results. Thus CFD offers a good complement to experimental validation during development efforts to meet a desired injector spray pattern.

The advantages of having higher stiffness to weight ratio and strength to weigh ratio that composite materials have resulted in an increased interest in them. In automotive engineering, the weight savings has positive impacts on other attributes like fuel economy and possible noise, vibration and harshness (NVH). The driveline of an automotive system can be a target for possible use of composite materials. The design of the driveshaft of an automotive system is primarily driven by its natural frequency. This paper presents an exact solution for the vibration of a composite driveshaft with intermediate joints. The joint is modeled as a frictionless internal hinge. The Euler-Bernoulli beam theory is used. Lumped masses are placed on each side of the joint to represent the joint mass. Equations of motion are developed using the appropriate boundary conditions and then solved exactly.

Probabilistic studies can be prohibitively expensive because they require repeated finite element analyses of large models. Re-analysis methods have been proposed with the premise to estimate accurately the dynamic response of a structure after a baseline design has been modified, without recalculating the new response. Although these methods increase computational efficiency, they are still not efficient enough for probabilistic analysis of large-scale dynamic systems with low failure probabilities (less or equal to 10-3). This paper presents a methodology that uses deterministic and probabilistic re-analysis methods to generate sample points of the response. Subsequently, tail modeling is used to estimate the right tail of the response PDF and the probability of failure a highly reliable system. The methodology is demonstrated on probabilistic vibration analysis of a realistic vehicle FE model.

In reliability design, often, there is scarce data for constructing probabilistic models. It is particularly challenging to model uncertainty in variables when the type of their probability distribution is unknown. Moreover, it is expensive to estimate the upper and lower bounds of the reliability of a system involving such variables. A method for modeling uncertainty by using Polynomial Chaos Expansion is presented. The method requires specifying bounds for statistical summaries such as the first four moments and credible intervals. A constrained optimization problem, in which decision variables are the coefficients of the Polynomial Chaos Expansion approximation, is formulated and solved in order to estimate the minimum and maximum values of a system’s reliability. This problem is solved efficiently by employing a probabilistic re-analysis approach to approximate the system reliability as a function of the moments of the random variables.

In a CAS system, the distance and relative velocity between front and host vehicles are estimated to calculate time-to-collision (TTC). The distance estimates by different methods will certainly include noise which should be removed to ensure the accuracy of TTC calculations. Kalman filter is a good tool to filter such type of noise. Nevertheless, Kalman filter is a model based filter, which means a correct model is important to get the good filtering results. Usually, a vehicle is either moving with a constant velocity (CV) or constant acceleration (CA) maneuvers. This means the distance data between front and host vehicles can be described by either constant velocity or constant acceleration model. In this paper, first, CV and CA models are used to design two Kalman filters and an interacting multiple model (IMM) is used to dynamically combine the outputs from two filters.