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In the development of an FAW SUV, one of the goals is to achieve a state of the art drag level. In order to achieve such an aggressive target, feedback from aerodynamics has to be included in the early stage of the design decision process. The aerodynamic performance evaluation and improvement is mostly based on CFD simulation in combination with some wind tunnel testing for verification of the simulation results. As a first step in this process, a fully detailed simulation model is built. The styling surface is combined with engine room and underbody detailed geometry from a similar size existing vehicle. From a detailed analysis of the flow field potential areas for improvement are identified and five design parameters for modifying overall shape features of the upper body are derived. In a second step, a response surface method involving design of experiments and adaptive sampling techniques are applied for characterizing the effects of the design changes.

Measuring deformation under dynamic loading is still a key problem in the automobile industry. The first spatial phase-shift shearography system for relative deformation measurement is reported. Traditional temporal phase-shift technique-based shearography systems are capable of measuring relative deformation by using a reference object. However, due to its low acquisition rate, the existing temporal phase-shift shearography system can be only used under static loading situations. This paper introduces a digital shearography system which utilizes the spatial phase-shift technique to obtain an extremely high acquisition rate. The newly developed spatial phase-shift shearography system uses a Michelson-Interferometer as the shearing device. A high power laser at 532nm wavelength is used as the light source. A one mega pixels high speed CCD camera is used to record the speckle pattern interference.

Coatings have the potential to improve bearing tribological performance. However, every coating application process and material combination may create different residual stresses and coating microstructures, and their effect on bearing fatigue and wear performance is unclear. The aim of this work is to investigate coating induced residual stress effects on bearing failure indicators using a microstructural contact mechanics (MSCM) finite element (FE) model. The MSCM FE model consists of a two-dimensional FE model of a coated bearing surface under sliding contact where individual grains are represented by FE domains. Interactions between FE domains are represented using contact element pairs. Unique to this layered rolling contact FE model is the use of polycrystalline material models to represent realistic bearing and coating microstructural behavior. The MSCM FE model was compared to a second non-microstructural contact mechanics (non-MSCM) model.

Using the total probability theorem, we propose a method to calculate the failure rate of a linear vibratory system with random parameters excited by stationary Gaussian processes. The response of such a system is non-stationary because of the randomness of the input parameters. A space-filling design, such as optimal symmetric Latin hypercube sampling or maximin, is first used to sample the input parameter space. For each design point, the output process is stationary and Gaussian. We present two approaches to calculate the corresponding conditional probability of failure. A Kriging metamodel is then created between the input parameters and the output conditional probabilities allowing us to estimate the conditional probabilities for any set of input parameters. The total probability theorem is finally applied to calculate the time-dependent probability of failure and the failure rate of the dynamic system. The proposed method is demonstrated using a vibratory system.

There is randomness in both the applied loads and the strength of systems. Therefore, to account for the uncertainty, the safety of the system must be quantified using its reliability. Monte Carlo Simulation (MCS) is widely used for probabilistic analysis because of its robustness. However, the high computational cost limits the accuracy of MCS. Smarslok et al. [2010] developed an improved sampling technique for reliability assessment called Separable Monte Carlo (SMC) that can significantly increase the accuracy of estimation without increasing the cost of sampling. However, this method was applied to time-invariant problems involving two random variables. This paper extends SMC to problems with multiple random variables and develops a novel method for estimation of the standard deviation of the probability of failure of a structure. The method is demonstrated and validated on reliability assessment of an offshore wind turbine under turbulent wind loads.

An experimental study was conducted to evaluate the effect of water absorption on tensile and fatigue behaviors of an impact-modified short glass fiber polyamide-6 and a short glass fiber polybutylene terephthalate. Specimens were prepared in the longitudinal and transverse directions with respect to the injection mold flow direction and immersed in water. Kinetics of water absorption was studied and found to follow the Fick's law. Tensile tests were performed at room temperature with specimens in the longitudinal and transverse directions and with various degrees of water absorption. Mathematical relations were developed to represent tensile properties as a function of water content. Load-controlled tension-tension fatigue tests were conducted in both longitudinal and transverse directions and correlations between tensile and fatigue strengths were obtained. Specimen fracture surfaces were also microscopically studied and mechanisms of tensile and fatigue failures were identified.

A twist beam rear suspension system is modeled, analyzed and optimized in this paper. An ADAMS model is established based on the REC (Rigid-Elastic Coupling) Theory, which is verified by FEM (Finite Element Method) approach, the effects of the geometric parameters on the twist beam suspension performance are investigated. In order to increase the calculation efficiency and improve the simulation accuracy, a neural network model and NSGA II (Non-dominated Sorting Genetic Algorithm II) are adopted to conduct a multi-objective optimization on a twist beam rear suspension system.

Deep charge and discharge cycling of 24 Volt battery strings composed of two 12 Volt VRLA batteries wired in series affects reliability and life expectancy. This is a matter of interest in vehicle power source applications. These cycles include those specific operational cases requiring the delivery of the full storage capacity during discharge. The concern here is related to applications where batteries serve as a primary power source and the energy content is an issue. It is a common practice for deep cycling a 24 volt battery string to simply add the specified limit voltages during charge and discharge for the individual 12 Volt batteries. In reality, the 12 Volt batteries have an inherent capacity variability and are not identical in their performance characteristics. The actual voltages of the individual 12 Volt batteries are not identical.

An experimental study was conducted to evaluate the variable amplitude fatigue behavior of a neat polymer (polypropylene impact co-polymer) and a polymer composite made of polybutylene terephthalate (PBT) with 30 wt% short glass fibers. Fatigue tests were conducted on un-notched and notched specimens at room temperatures. Plate-type specimens were prepared in the transverse direction with respect to the injection mold flow direction and a circular hole was drilled in the center of notched specimens. Two-step loadings (high-low and low-high) tests at two damage ratio of 0.2 and 0.5 at stress ratios of R = 0.1 and -1 were conducted to investigate load sequence effects and prediction accuracy of the linear damage rule. Different behaviors were observed for unreinforced and short glass fiber reinforced polymers under the two-step loading tests.

Global surrogate modeling aims to build surrogate model with high accuracy in the whole design domain. A major challenge to achieve this objective is how to reduce the number of function evaluations to the original computer simulation model. To date, the most widely used approach for global surrogate modeling is the adaptive surrogate modeling method. It starts with an initial surrogate model, which is then refined adaptively using the mean square error (MSE) or maximizing the minimum distance criteria. It is observed that current methods may not be able to effectively construct a global surrogate model when the underlying black box function is highly nonlinear in only certain regions. A new surrogate modeling method which can allocate more training points in regions with high nonlinearity is needed to overcome this challenge. This article proposes an efficient global surrogate modeling method based on a multi-layer sampling scheme.

Optimizing the trade-off between reliability and cost of operating a microgrid, including vehicles as both loads and sources, can be a challenge. Optimal energy management is crucial to develop strategies to improve the efficiency and reliability of microgrids, as well as new communication networks to support optimal and reliable operation. Prior approaches modeled the grid using MATLAB, but did not include the detailed physics of loads and sources, and therefore missed the transient effects that are present in real-time operation of a microgrid. This article discusses the implementation of a physics-based detailed microgrid model including a diesel generator, wind turbine, photovoltaic array, and utility. All elements are modeled as sources in Simulink. Various loads are also implemented including an asynchronous motor. We show how a central control algorithm optimizes the microgrid by trying to maximize reliability while reducing operational cost.

This article presents an approach for comparing alternative repairable systems and calculating the value of information obtained by testing a specified number of such systems. More specifically, an approach is presented to determine the value of information that comes from field testing a specified number of systems in order to appropriately estimate the reliability metric associated with each of the respective repairable systems. Here the reliability of a repairable system will be measured by its failure rate. In support of the decision-making effort, the failure rate is translated into an expected utility based on a utility curve that represents the risk tolerance of the decision-maker. The algorithm calculates the change of the expected value of the decision with the sample size. The change in the value of the decision represents the value of information obtained from testing.

Computer-aided engineering (CAE) is an important tool routinely used to simulate complex engineering systems. Virtual simulations enhance engineering insight into prospective designs and potential design issues and can limit the need for expensive engineering prototypes. For complex engineering systems, however, the effectiveness of virtual simulations is often hindered by excessive computational cost. To minimize the cost of running expensive computer simulations, approximate models of the original model (often called surrogate models or metamodels) can provide sufficient accuracy at a lower computing overhead compared to repeated runs of a full simulation. Metamodel accuracy improves if constructed using space-filling designs of experiments (DOEs). The latter provide a collection of sample points in the design space preferably covering the entire space.

Two models are presented for the long life (107 cycles) axial fatigue strength of four ferrous powder metal (PM) material series: sintered and heat-treated iron-carbon steel, iron-copper and copper steel, iron-nickel and nickel steel, and pre-alloyed steel. The materials are defined at ranges of carbon content and densities using the broad data available in the Metal Powder Industries Federation (MPIF) Standard 35 for PM structural parts. The first model evaluates 107 cycles axial fatigue strength as a function of ultimate strength and the second model as a function of hardness. For all 118 studied materials, both models are found to have a good correlation between calculated and 107 cycles axial fatigue strength with a high Pearson correlation coefficient of 0.97. The article provides details on the model development and the reasoning for selecting the ultimate strength and hardness as the best predictors for 107 cycles axial fatigue strength.

Lithium-Ion batteries are the standard portable power solution to many consumers and industrial applications. These batteries are commonly used in laptop computers, heavy duty devices, unmanned vehicles, electric and hybrid vehicles, cell phones, and many other applications. Charging these batteries is a delicate process because it depends on numerous factors such as temperature, cell capacity, and, most importantly, the power and energy limits of the battery cells. Charging capacity, charging time and battery pack temperature variations are highly dependent on the charging method used. These three factors can be of special importance in applications with strict charging time requirements or with limited thermal management capabilities. In this paper, three common charging methods are experimentally studied and analyzed. Constant-current constant-voltage, the time pulsed charging method, and the multistage constant current charging methods were considered.

Weight reduction is very important in automotive design because of stringent demand on fuel economy. Structural optimization of dynamic systems using finite element (FE) analysis plays an important role in reducing weight while simultaneously delivering a product that meets all functional requirements for durability, crash and NVH. With advancing computer technology, the demand for solving large FE models has grown. Optimization is however costly due to repeated full-order analyses. Reanalysis methods can be used in structural vibrations to reduce the analysis cost from repeated eigenvalue analyses for both deterministic and probabilistic problems. Several reanalysis techniques have been introduced over the years including Parametric Reduced Order Modeling (PROM), Combined Approximations (CA) and the Epsilon algorithm, among others.

To improve fuel economy, there is a trend in automotive industry to use light weight, high strength materials. Automotive body structures are composed of several panels which must be downsized to reduce weight. Because this affects NVH (Noise, Vibration and Harshness) performance, engineers are challenged to recover the lost panel stiffness from down-gaging in order to improve the structure borne noise transmitted through the lightweight panels in the frequency range of 100-300 Hz where most of the booming and low medium frequency noise occurs. The loss in performance can be recovered by optimized panel geometry using beading or damping treatment. Topography optimization is a special class of shape optimization for changing sheet metal shapes by introducing beads. A large number of design variables can be handled and the process is easy to setup in commercial codes. However, optimization methods are computationally intensive because of repeated full-order analyses.

This work presents a method for estimating human body orientation using a combination of convolutional neural network (CNN) and stereo camera in real time. The approach uses the CNN model to predict certain human body keypoints then transforms these points into a 3D space using the stereo vision system to estimate the body orientations. The CNN module is trained to estimate the shoulders, the neck and the nose positions, detecting of three points is required to confirm human detection and provided enough data to translate the points into 3D space.

As vacuum suction cups are widely used in stamping plants, it becomes urgent and important to understand their performance and failure mode. Vacuum suction cups are employed to lift, move, and place sheet metal instead of human hands. Occasionally the vacuum cups would fail and drop parts, even it would cause expensive delays in the production line. In this research, several types of vacuum cups have been studies and compared experimentally. A new tensile device and test method was developed to measure the pulling force and deformation of vacuum cups. The digital image correlation technique has been adopted to capture and analyze the contour, deformation and strain of the cups under different working conditions. The experimental results revealed that the relevant influential parameters include cup type, pulling force angles, vacuum levels, sheet metal curvatures, etc.

An objective evaluation of ground vehicle performance is a challenging task. This is further exacerbated by the increasing level of autonomy, dynamically changing the roles and capabilities of these vehicles. In the context of decision making involving these vehicles, as the capabilities of the vehicles improve, there is a concurrent change in the preferences of the decision makers operating the vehicles that must be accounted for. Decision based methods are a natural choice when multiple conflicting attributes are present, however, most of the literature focuses on static preferences. In this paper, we provide a sequential Bayesian framework to accommodate time varying preferences. The utility function is considered a stochastic function with the shape parameters themselves being random variables. In the proposed approach, initially the shape parameters model either uncertain preferences or variation in the preferences because of the presence of multiple decision makers.