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The conventional forming limit curve (FLC) has been widely and successfully used as a failure criterion to detect localized necking in stamping. However, in stamping advanced high strength steels (AHSS), under certain circumstances such as stretching-bending over a small die radius, the sheet metal fails much earlier than predicted by the FLC. This type of failure on the die radius is commonly called “shear fracture.” In this paper, the laboratory Stretch-Forming Simulator (SFS) and the Bending under Tension (BUT) tester are used to study shear fracture occurring during both early and later stages of stamping. Results demonstrate that the occurrence of shear fracture depends on the combination of the radius-to-thickness (R/T) ratio and the tension/stretch level applied to the sheet during stretching or drawing. Based on numerous experimental results, an empirical shear fracture limit curve or criterion is obtained.

This paper presents the extended Kalman filter-based sideslip angle estimator design using a nonlinear 5DoF single-track vehicle dynamics model with stochastic modeling of tire forces. Lumped front and rear tire forces have been modeled as first-order random walk state variables. The proposed estimator is primarily designed for vehicle sideslip angle estimation; however it can also be used for estimation of tire forces and cornering stiffness. This estimator design does not rely on linearization of the tire force characteristics, it is robust against the variations of the tire parameters, and does not require the information on coefficient of friction. The estimator performance has been first analyzed by means of computer simulations using the 10DoF two-track vehicle dynamics model and underlying magic formula tire model, and then experimentally validated by using data sets recorded on a test vehicle.

In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affect the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. This paper further investigates the Bayesian inference based model extrapolation method which is previously proposed by the authors, and provides a systematic and integrated stochastic bias corrected model extrapolation and robustness design process under uncertainty. A real world vehicle design example is used to demonstrate the validity of the proposed method.

Motivated by a combination of increasing consumer demand for fuel efficient vehicles, more stringent greenhouse gas, and anticipated future Corporate Average Fuel Economy (CAFE) standards, automotive manufacturers are working to innovate in all areas of vehicle design to improve fuel efficiency. In addition to improving aerodynamics, enhancing internal combustion engines and transmission technologies, and developing alternative fuel vehicles, reducing vehicle weight by using lighter materials and/or higher strength materials has been identified as one of the strategies in future vehicle development. Weight reduction in vehicle components, subsystems and systems not only reduces the energy needed to overcome inertia forces but also triggers additional mass reduction elsewhere and enables mass reduction in full vehicle levels.

Flow bench and engine testing can be used to detect flow induced noise, but understanding the fundamental mechanisms of such noise generation is necessary for developing an effective design. This paper describes Computational Aero-Acoustic (CAA) analyses performed to obtain the broad-band and BPF noise sources A computational aero-acoustics simulation on the aerodynamic noise generation of an automotive radiator fan assembly is carried out. Three-dimensional Computational Fluid Dynamics (CFD) simulation of the unsteady flow field was performed including the entire impeller and shroud to obtain the source of an audible broad-band flow noise between 2 to 4 kHz. Static pressure probes placed around the outer-periphery and at the center of the impeller inlet side and, at the shroud cavities to capture the noise sources. The static pressure at all probe locations were FFT (Fast Fourier Transform) processed and sound pressure level (SPL) was calculated.

The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

In an attempt to predict the responses of side crash pressure sensors, the Corpuscular Particle Method (CPM) was adopted and enhanced in this research. Acceleration-based crash sensors have traditionally been used extensively in automotive industry to determine the air bag firing time in the event of a vehicle accident. The prediction of crash pulses obtained from the acceleration-based crash sensors by using computer simulations has been very challenging due to the high frequency and noisy responses obtained from the sensors, especially those installed in crash zones. As a result, the sensor algorithm developments for acceleration-based sensors are largely based on prototype testing. With the latest advancement in the crash sensor technology, side crash pressure sensors have emerged recently and are gradually replacing acceleration-based sensor for side impact applications.

An Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this study to predict the responses of side crash pressure sensors in an attempt to assist pressure sensor algorithm development by using computer simulations. Acceleration-based crash sensors have traditionally been used to deploy restraint devises (e.g., airbags, air curtains, and seat belts) in vehicle crashes. The crash pulses recorded by acceleration-based crash sensors usually exhibit high frequency and noisy responses depending on the vehicle's structural design. As a result, it is very challenging to predict the responses of acceleration-based crash sensors by using computer simulations, especially those installed in crush zones. Therefore, the sensor algorithm developments for acceleration-based sensors are mostly based on physical testing.

Model validation is a process to assess the validity and predictive capabilities of a computer model by comparing simulation results with test data for its intended use of the model. One of the key difficulties for model validation is to evaluate the quality of a computer model at different test configurations in design space, and interpolate or extrapolate the evaluation results to untested new design configurations. In this paper, an integrated model interpolation and extrapolation framework based on Bayesian inference and Response Surface Models (RSM) is proposed to validate the designs both within and outside of the original design space. Bayesian inference is first applied to quantify the distributions' hyper-parameters of the bias between test and CAE data in the validation domain. Then, the hyper-parameters are extrapolated from the design configurations to untested new design. They are then followed by the prediction interval of responses at the new design points.

With a goal to help develop pressure sensor calibration and deployment algorithms using computer simulations, an Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this research to predict the responses of side crash pressure sensors for unitized vehicles. For occupant protection, acceleration-based crash sensors have been used in the automotive industry to deploy restraint devices when vehicle crashes occur. With improvements in the crash sensor technology, pressure sensors that detect pressure changes in door cavities have been developed recently for vehicle crash safety applications. Instead of using acceleration (or deceleration) in the acceleration-based crash sensors, the pressure sensors utilize pressure change in a door structure to determine the deployment of restraint devices. The crash pulses recorded by the acceleration-based crash sensors usually exhibit high frequency and noisy responses.

In an attempt to assist pressure sensor algorithm and calibration development using computer simulations, an Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this study to predict the responses of side crash pressure sensors for body-on-frame vehicles. Acceleration based, also called G-based, crash sensors have been used extensively to deploy restraint devices, such as airbags, curtain airbags, seatbelt pre-tensioners, and inflatable seatbelts, in vehicle crashes. With advancements in crash sensor technologies, pressure sensors that measure pressure changes in vehicle side doors have been developed recently and their applications in vehicle crash safety are increasing. The pressure sensors are able to detect and record the dynamic pressure change when the volume of a vehicle door changes as a result of a crash.

Modern thermal propulsion systems (TPS) as part of hybrid powertrains are becoming increasingly complex. They have an increased number of components in comparison to traditionally powered vehicles leading to increased demand in packaging requirements. Many of the components in these systems relate to achieving efficiency gains, weight saving and pollutant reduction. This includes turbochargers and diesel or gasoline particulate filters for example and these are known to be very sensitive to inlet boundary conditions. When overcoming packaging requirements, sub-optimal flow distributions throughout the TPS can easily occur. Moreover, the individual components are often designed in isolation assuming relatively flat and artificially quiescent inlet flow conditions in comparison to those they are actually presented with. Thus, some of the efficiency benefits are lost through reduced component aerodynamic efficiency.

In the engineering design attempt to minimize customer complaints related to belt misalignment, a maximum belt pulley entry and pulley exit angle was chosen as a critical target for avoiding excessive belt wear and chirp noise. Calculating belt misalignment due to pulley misalignment from the perfect plane between two grooved pulleys is done using statistical stackup calculations in three dimensional space. This is usually done using available computer statistical simulation models and the most current component detail drawings. For backside pulleys (flat pulleys), such calculations can be performed only if the position of the belt as it contacts a backside pulley is known. Since the shape of the grooves no longer fixes the position of a belt at pulley entry point, the position of the belt at the backside pulley can not be determined by the position of the pulley groove.

Dual Phase (DP) high strength steel is widely used in Europe and Japan for automotive component applications, and has recently drawn greater attention in the North American automotive industry for improving crash performance and reducing weight. In comparison with high-strength low-alloy (HSLA) steel grades with similar initial yield strength, DP steel has the following advantages: higher strain hardening, higher energy absorption, higher fatigue strength, higher bake hardenablility, and no yield point elongation. This paper compares the performance of DP and HSLA steel grades before, during, and after hydroforming. Computer simulation results show that DP steel demonstrates more uniform material flow during hydroforming, better crash performance and less wrinkling tendency.

Computer simulations, dummy experiments with a new enhanced upper extremity, and small female cadaver experiments were used to analyze the small female upper extremity response under side air bag loading. After establishing the initial position, three tests were performed with the 5th percentile female hybrid III dummy, and six experiments with small female cadaver subjects. A new 5th percentile female enhanced upper extremity was developed for the dummy experiments that included a two-axis wrist load cell in addition to the existing six-axis load cells in both the forearm and humerus. Forearm pronation was also included in the new dummy upper extremity to increase the biofidelity of the interaction with the handgrip. Instrumentation for both the cadaver and dummy tests included accelerometers and magnetohydrodynamic angular rate sensors on the forearm, humerus, upper and lower spine.

A Variable Displacement Engine (VDE) improves fuel economy by deactivating half the cylinders at light load. The actual fuel economy benefit attained in the vehicle depends on how often cylinders can be deactivated, which is a function of test cycle, engine size, and vehicle weight. In practice, cylinder deactivation will also be constrained by NVH (noise, vibration, and harshness). This paper presents fuel economy projections for VDE in several different engine and vehicle applications. Sensitivity to NVH considerations is quantified by calculating fuel economy with and without cylinder deactivation in various operating modes: idle, low engine speed, 1st and 2nd gear, and warm-up after cold start. The effects of lug limits and calibration hysteresis are also presented.

The continued increase in complexity of automotive electronics has further necessitated for systems to be designed for optimal testing and servicing in the field. Locating failures within complex, interactive electronic systems can create conditions of both high warranty costs and customer dissatisfaction. Decisions about levels of system integration and diagnostics early in the design phases are fundamental parts of these considerations and are addressed in this paper. The approach focuses on projecting field repair rates and maintenance costs using a Monte Carlo computer simulation method given various component parameters such as part costs, reliability, labor time, and field diagnostic strategies. Results of the projection technique were shown to be quite accurate in projecting field warranty costs of a complex electronic system.

The pump-pipe-injector-injection system is the most commonly used type of injection equipment for Diesel engines. In order to be compatible with digital engine control, this system needs to be modified. The resulting fuel injection system should have the following characteristics: mechanical simplicity, direct control capability and low cost. Based on these requirements, the direct digital control of the pump-pipe-injector injection system has been investigated. A new solenoid control valve has been designed to simultaneously control the injection timing, fuel quantity and hydraulic performance. The conventional jerk-pump is very much simplified. A research type control unit based on a PC has been developed. The system has the possible configuration of electronic pump-pipe-valve-injector and electronic pump-valve-pipe-injector. The system was designed and analyzed on the basis of a comprehensive mechanical - magnetic - electrical - hydraulic computer simulation of the system.

The new generation of electronic Diesel fuel injection systems with special solenoid valves presents a complicated mechanical/electrical system. It involves a combination of mechanical motion, hydraulic pressure wave propagation, and the transient magnetic and electrical processes which interact with other. In this paper, the coupled dynamic behavior of the new system is studied based on a research type electronic pump-pipe-injector system developed by authors. A general physical model is established, which includes other structure types such as the electronic unit injector and the electronic distributor pump system. Traditional mathematical models for conventional mechanical injection system or conventional solenoid valves, alone or simply connected, are not suitable for the new type of injection system. Therefore, a new comprehensive mathematical model is formulated.

The advent of Eco-Boost technology in gasoline engines creates new challenges that need to be addressed with innovative designs. One of them is flow induced noise caused by flow, entering the turbocharger, at off design operation. At certain vehicle operation conditions, the mass flow rate and pressure ratio are such that compressor wheel generates a broad band frequency noise caused by flow separation from blade surfaces, which is called ‘whoosh’ noise. Flow bench and engine testing can be used to detect flow induced noise, but understanding the fundamental mechanisms of such noise generation is necessary for developing an effective design. This paper describes Computational Aero-Acoustic (CAA) analyses performed to study the effects of inlet condition on the whoosh noise. A 3D Computational Fluid Dynamics (CFD) simulation performed including the entire compressor wheel and volute. The wheel consisted of six main and six splitter blades.