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An operational scheme with fuel-lean and exhaust gas dilution in spark-ignited engines increases thermal efficiency and decreases NOx emission, while these operations inherently induce combustion instability and thus large cycle-to-cycle variation in engine. In order to stabilize combustion variations, the development of an advanced ignition system is becoming critical. To quantify the impact of spark-ignition discharge, ignitability tests were conducted in an optically accessible combustion vessel to characterize the flame kernel development of lean methane-air mixture with CO₂ simulating exhaust diluent. A shrouded fan was used to generate turbulence in the vicinity of J-gap spark plug and a Variable Output Ignition System (VOIS) capable of producing a varied set of spark discharge patterns was developed and used as an ignition source. The main feature of the VOIS is to vary the secondary current during glow discharge including naturally decaying and truncated with multiple strikes.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

Ultrasonic fatigue tests (testing frequency around 20 kHz) have been conducted on four different cast aluminum alloys each with a distinct composition, heat treatment, and microstructure. Tests were performed in dry air, laboratory air and submerged in water. For some alloys, the ultrasonic fatigue lives were dramatically affected by the environment humidity. The effects of different factors like material composition, yield strength, secondary dendrite arm spacing and porosity were investigated; it was concluded that the material strength may be the key factor influencing the environmental humidity effect in ultrasonic fatigue testing. Further investigation on the effect of chemical composition, especially copper content, is needed.

This study demonstrates the use of pressure sensing technology to predict the crash severity of frontal impacts. It presents an investigation of the pressure change in the front structural elements (bumper, crush cans, rails) during crash events. A series of subsystem tests were conducted in the laboratory that represent a typical frontal crash development series and provided empirical data to support the analysis of the concept. The pressure signal energy at different sensor mounting locations was studied and design concepts were developed for amplifying the pressure signal. In addition, a pressure signal processing methodology was developed that relies on the analysis of the air flow behavior by normalizing and integrating the pressure changes. The processed signal from the pressure sensor is combined with the restraint control module (RCM) signals to define the crash severity, discriminate between the frontal crash modes and deploy the required restraint devices.

Gas Metal Arc Welding (GMAW) is widely employed for joining relatively thick sheet steels in automotive body-in-white structures and frames. The GMAW process is very flexible for various joint geometries and has relatively high welding speed. However, fatigue failures can occur at welded joints subjected to various types of loads. Thus, vehicle design engineers need to understand the fatigue characteristics of welded joints produced by GMAW. Currently, automotive structures employ various advanced high strength steels (AHSS) such as dual-phase (DP) and transformation-induced plasticity (TRIP) steels to produce lighter vehicle structures with improved safety performance and fuel economy, and reduced harmful emissions. Relatively thick gages of AHSS are commonly joined to conventional high strength steels and/or mild steels using GMAW in current body-in-white structures and frames.

The r-value is a very important parameter in the forming simulations of high strength steels, especially for steels with prominent anisotropy. R-values for sheet steels conventionally measured by extensometers were found neither consistent nor accurate due to difficulties in measuring the width strain. In this study, the Digital Image Correlation (DIC) technique was applied to determine r-values in Longitudinal (L), Transverse (T) and Diagonal (D) directions for cold rolled DP980 GI, DP780 GI, DP600 GI and BH250 GI sheet steels. The r-values measured from DIC were validated by finite element analysis (FEA) of a uniaxial tensile test for BH250. The simulation results of the load-displacement for two plasticity models were compared to experimental data, with one being the isotropic yield (von-Mises) and the other being an anisotropic model (Hill-48) using the r-value measured from DIC.

High cycle fatigue tests at a constant positive mean stress have been performed on a Al-Si-Cu cast aluminum alloy. The Random Fatigue Limit (RFL) model was employed to fit the probabilistic S-N curves based on Maximum Likelihood Estimate (MLE). Fractographic studies indicated that fatigue cracks in most specimens initiate from oxide films located at or very close to specimen surface. The RFL model was proved to be able to accurately capture the scatter in fatigue life. The cumulative density function (CDF) of fatigue life determined by RFL fit is found to be approximately equal to the complementary value of the CDF of the near-surface fatigue initiator size.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

Experimental studies have been undertaken on a single-cylinder HPCR diesel engine with a compression ratio of 15.5:1 to explore the effect of fuel injection strategy on cycle by cycle stability. The influence of the number, separation and quantity of pilot injections on the coefficient of variation of IMEP has been investigated at -20°C, 1000 rev/min, post-start idling conditions. Injection strategy and glow plug temperature trade-off has also been investigated at a range of soak temperatures. Up to four pilot injections have been used. For timing of the main injection near to the optimum, CoVIMEP values of 10% or better can be achieved. Closer spacing of injections improved stability and extended the range of timings to meet target stability. The best combinations of pilot number and pilot quantity varied with total fuel delivered.

Advanced high strength steels (AHSS) have been widely accepted as a material of choice in the automotive industry to balance overall vehicle weight and stringent vehicle crash test performance targets. Combined with efficient use of geometry and load paths through shape and topology optimization, AHSS has enabled vehicle manufacturers to obtain the highest possible ratings in safety evaluations by the Insurance Institute for Highway Safety (IIHS) and the National Highway Traffic Safety Administration (NHTSA). In this study, vehicle CAE side impact models were used to evaluate three side impact crash test conditions (IIHS side impact, NHTSA LINCAP and FMVSS 214 side pole) and the IIHS roof strength test condition and to identify several key components affecting the side impact test performance. HyperStudy® optimization software and LS-DYNA® nonlinear finite element software were utilized for shape and gauge optimization.

A Ford 2.4-liter 115PS light-duty diesel engine was modified to allow solenoid control of the oil feed to the piston cooling jets, enabling these to be switched on or off on demand. The influence of the jets on piston temperatures, engine thermal state, gaseous emissions and fuel economy has been investigated. With the jets switched off, piston temperatures were measured to be between 23 and 88°C higher. Across a range of speed-load points, switching off the jets increased engine-out emissions of NOx typically by 3%, and reduced emissions of CO by 5-10%. Changes in HC were of the same order and were reductions at most conditions. Fuel consumption increased at low-speed, high-load conditions and decreased at high-speed, low-load conditions. Applying the results to the NEDC drive cycle suggests active on/off control of the jets could reduce engine-out emissions of CO by 6%, at the expense of a 1% increase in NOx, compared to the case when the jets are on continuously.

Reducing friction in crankshaft bearings during cold engine operation by heating the oil supply to the main gallery has been investigated through experimental investigations and computational modelling. The experimental work was undertaken on a 2.4l DI diesel engine set up with an external heat source to supply hot oil to the gallery. The aim was to raise the film temperature in the main bearings early in the warm up, producing a reduction in oil viscosity and through this, a reduction in friction losses. The effectiveness of this approach depends on the management of heat losses from the oil. Heat transfer along the oil pathway to the bearings, and within the bearings to the journals and shells, reduces the benefit of the upstream heating.

As vehicle fuel economy continues to grow in importance, the ability to accurately measure the level of efficiency on all driveline components is required. A standardized test procedure enables manufacturers and suppliers to measure component losses consistently and provides data to make comparisons. In addition, the procedure offers a reliable process to assess enablers for efficiency improvements. Previous published studies have outlined the development of a comprehensive test procedure to measure transfer case speed-dependent parasitic losses at key speed, load, and environmental conditions. This paper will take the same basic approach for the Power Transfer Units (PTUs) used on Front Wheel Drive (FWD) based All Wheel Drive (AWD) vehicles. Factors included in the assessment include single and multi-stage PTUs, fluid levels, break-in process, and temperature effects.

Model validation is a process of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model. In reliability based design, the intended use of the model is to identify an optimal design with the minimum cost function while satisfying all reliability constraints. It is pivotal that computational models should be validated before conducting the reliability based design. This paper presents an ensemble approach for model bias prediction in order to correct predictions of computational models. The basic idea is to first characterize the model bias of computational models, then correct the model prediction by adding the characterized model bias. The ensemble approach is composed of two prediction mechanisms: 1) response surface of model bias, and 2) Copula modeling of a series of relationships between design variables and the model bias, between model prediction and the model bias.

Finite Element (FE) models are widely used in automotive for vehicle design. Even with increasing speed of computers, the simulation of high fidelity FE models is still too time-consuming to perform direct design optimization. As a result, response surface models (RSMs) are commonly used as surrogates of the FE models to reduce the turn-around time. However, RSM may introduce additional sources of uncertainty, such as model bias, and so on. The uncertainty and model bias will affect the trustworthiness of design decisions in design processes. This calls for the development of stochastic model interpolation and extrapolation methods that can address the discrepancy between the RSM and the FE results, and provide prediction intervals of model responses under uncertainty.