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The original Table 2 includes incorrect data under the "Primary Energy" column for "multi-strike", "continuous" and "plasma" system types.

Autoignition delay times of two full blend gasoline fuels (high and low RON) were explored in a rapid compression machine. CO2 dilution by mass was introduced at 0%, 15%, and 30% levels with the O2:N2 mole ratio fixed at 1:3.76. This dilution strategy is used to represent exhaust gas recirculation (EGR) substitution in spark ignition (SI) engines by using CO2 as a surrogate for major EGR constituents(N2, CO2, H2O). Experiments were conducted over the temperature range of 650K-900K and at 10 bar and 20 bar compressed pressure conditions for equivalence ratios of (Φ =) 0.6-1.3. The full blend fuels were admitted directly into the combustion chamber for mixture preparation using the direct test chamber (DTC) approach. CO2 addition retarded the autoignition times for the fuels studied here. The retarding effect of the CO2 dilution was more pronounced in the NTC region when compared to the lower and higher temperature range.

Accurate prediction of in-vehicle powertrain bounce mode is necessary to ensure optimum responses are achieved at driver’s touch points during 4post shake or rough road shake events. But, during the early stages of vehicle development, building a detailed vehicle finite element (FE) model is not possible and often powertrain bounce modes are computed assuming the powertrain to be a stand-alone unit. Studies conducted on FE models of a large SUV with body on frame architecture showed that the stand-alone approach overestimates the powertrain bounce mode. Consequently, there is a need for a simplified version of vehicle model which can be built early on to compute powertrain modes. Previously, representing all the major components as rigid entities, simplified unibody vehicle models have been built to compute powertrain modes. But such an approach would be inaccurate here, for a vehicle with body on frame architecture due to the flexible nature of the frame (even at low frequencies).

Euro NCAP Pedestrian head impact protocol mandates the reduction of head injuries, measured using head injury criteria (HIC). Virtual tools driven design comprises of simulating the impact on the hood and post processing the results. Due to the high number of impact points, engineers spend a significant portion of their time in manual data management, processing, visualization and score calculation. Moreover, due to large volume of data transfer from these simulations, engineers face data bandwidth issues particularly when the data is in different geographical locations. This deters the focus of the engineer from engineering and also delays the product development process. This paper describes the development of an automated method using d3VIEW that significantly improves the efficiency and eliminates the data volume difficulties there by reducing the product development time while providing a higher level of simulation results visualization.

Computational cost plays a major role in the performance of scientific and engineering simulation. This in turn makes the virtual validation process complex and time consuming. In the simulation process, achievement of appropriate level of accurate models as close as physical testing is the root for increase in the computational cost. During preliminary phase of product development, it is difficult to identify the appropriate size, shape and other parameters of the component and they will undergo several modifications in concept and other stages. An approximation model called metamodel or surrogate model has developed for reducing these effects and minimizing the computational cost. Metamodel can be used in the place of actual simulation models. Metamodel can be an algorithm or a mathematical relation representing the relations between input and output parameters.

Determining coolant flow distribution in a topologically complex flow path for efficient heat rejection from the critical regions of the engine is a challenge. However, with the established computational methodology, thermal response of an engine (via conjugate heat transfer) can be accurately predicted [1, 2] and improved upon via Design of Experiment (DOE) study in a relatively short timeframe. This paper describes a method to effectively distribute the coolant flow in the engine coolant cavities and evenly remove the heat from various components using a novel technique of optimization based on an approximation model. The current methodology involves the usage of a sampling technique to screen the design space and generate the simulation matrix. Isight, a process automation and design exploration software, is used to set the framework of this study with the engine thermal simulation setup done in the CFD solver, STAR-CCM+.

Significant reduction in Nitrogen Oxide (NOx) emissions will be required to meet LEV III/Tier III Emissions Standards for Light Duty Diesel (LDD) passenger vehicles. As such, Original Equipment Manufacturers (OEMs) are exploring all possible aftertreatment options to find the best balance between performance, durability and cost. The primary technology adopted by OEMs in North America to achieve low NOx levels is Selective Catalytic Reduction (SCR). The critical parameters needed for SCR to work properly are: an appropriate reductant such as ammonia (NH3) provided as Diesel Exhaust Fluid (DEF), which is an aqueous urea solution 32.5% concentration in weight with water (CO(NH2)2 + H2O), optimum operating temperatures, and optimum nitrogen dioxide (NO2) to NOx ratios (NO2/NOx). The NO2/NOx ratio is most influenced by Precious Group Metals (PGM) containing catalysts upstream of the SCR catalyst.

As CAFE requirements increase, automotive OEMs are pursuing innovative methods to lightweight their Body In Whites (BIWs). Within FCA US, this lightweighting research and development activity often occurs through Decoupled Innovation projects. A Decoupled Innovation team comprised of engineers from the BIW Structures Group, in collaboration with Tier 1 supplier Magna Exteriors, sought to re-design a loadbearing component on the BIW that would offer significant weight savings when the current steel component was replaced with a carbon fiber composite. This paper describes the design, development, physical validation and partnership that resulted in a composite Rear Package Shelf Assembly solution for a high-volume production vehicle. As the CAFE requirements loom closer and closer, these innovation-driven engineering activities are imperative to the successful lightweighting of FCA US vehicles.

Several popular frequency-based fatigue damage models (Wirsching and Light, Ortiz and Chen, Larsen and Lutes, Benascuitti and Tovo, Benascuitti and Tovo with α.75, Dirlik, Zhao and Baker, and Lalanne) are reviewed and assessed. Seventy power spectrum densities with varied amplitude, shape, and irregularity factors from Dirlik’s dissertation are used to study the accuracies of these methods. Recommendations on how to set up the inverse fast Fourier transform to synthesize load data and obtain accurate rainflow cycle counts are given. Since Dirlik’s method is the most commonly used one in industry, a comprehensive investigation of parameter setups for Dirlik’s method is presented. The mean error and standard deviation of the error between the frequency-based model and the rainflow cycle counting method was computed for fatigue slope exponent m ranging from 3 to 12.

Automobiles have a high degree of mechanical and electrical complexity. However, product complexity has the accompanying effect of requiring high levels of design and process oversight. The net result is a product creation process which is prone to creating failures. These failures typically have their origin in an overall lack of complete understanding of the system in terms of materials, geometries and energy flows. Despite all of the engineering intentions, failures are inevitable, common, and must be dealt with accordingly. In the worst case, if a failure manifests itself into an observable failure the customer may have a negative experience. Therefore, it is imperative that design engineers, suppliers along with reliability professionals be able to assess the design risk. One approach to assess risk is the use of degradation analysis. Degradation analysis often provides more information than failure time data for assessing reliability and predicting the remnant life of a system.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

Every passing year automotive engineers are challenged to attain higher fuel economy and improved emission targets. One widely used approach is to use Cooled Exhaust Gas Recirculation (CEGR) to meet these objectives. Apart from reducing emissions and improving fuel economy, CEGR also plays a significant role in knock mitigation in spark ignited gasoline engines. Generally, CEGR is introduced into the intake manifold in SI gasoline engine. Even though the benefits of using CEGR are significant, they can be easily negated by the uneven CEGR flow distribution between the cylinders, which can result in combustion instability. This paper describes the application of co-simulation between one and three dimensional tools to accurately predict the distribution of CEGR to the cylinders and the effect of its distribution on engine performance.

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.

Threaded fasteners or bolted joints are used extensively in automotive assemblies. There are standard procedures to evaluate joint performance under block cycles or road loads. The deciding load case for such joint design is slippage analysis of the joint. There are studies done to evaluate the theoretical and experimental behavior of these joints. There are different ways of understanding the interaction between the bolt and the nut under different loading scenarios. However, none have provided a satisfactory method of quantifying bolt loosening or loss of clamp load under cyclic loading, where no slippage is observed. Under varying loads, initial relaxation of the joint is followed by a loss of clamping load. Below a critical value, complete loss of clamping load progresses very rapidly and this results in a loose joint.

This paper describes an experimental study on the performance of self-piercing riveted (SPR) joints and adhesively bonded SPR joints connecting steel and aluminum components under both quasi-static and cyclic loading. The joint configurations cover a wide range of material gauges, types and grades. Two and three thickness joints, with and without adhesive are also part of this study. Load versus deflection behavior, load carrying capacity, fatigue life and the failure modes for each type of joint are discussed. This study focuses on the influence of dissimilar material and adhesives to the joint performance.

The purpose of this paper is to provide a comparison of multiaxial fatigue limit models and their correlation to experimental data. This paper investigates equivalent stress, critical plane and invariant-based multiaxial fatigue models. Several methods are investigated and compared based on ability to predict multiaxial fatigue limits from data published in literature. The equivalent stress based model developed by Lee, Tjhung and Jordan (LTJ), provides very accurate predictions of the fatigue limit under multiaxial loading due to its ability to account for non-proportional loading. This accuracy comes from the model constant which is calculated based on multiaxial fatigue data. This is the only model investigated that requires multiaxial fatigue testing to generate the model parameters. All other models rely on uniaxial test results.

As automotive emissions become more stringent, accurate control of three-way catalyst temperature is increasingly important for maintaining high levels of conversion efficiency as well as preventing damage to the catalyst. A real-time catalyst temperature model provides critical information to the engine control system. In order to improve emissions and ensure regulatory compliance over a wide range of speed-load conditions, it is desirable to use modelled catalyst temperature as the primary input to catalyst efficiency control strategies. This requirement creates a challenge for traditional empirical models designed for component protection at high speed-load conditions. Simulation results show that a physics aligned model can estimate temperature in all operating conditions, including: cold-start, extended idle, engine shutdown, stop-start events, deceleration fuel shut-off, as well as traditional high load and part load points.

Increase in the atmospheric temperature across the globe during summer, increases the heat load in the vehicle cabin, creating a huge thermal discomfort for the passengers. There are two scenarios where these adverse conditions can be a problem during the summer. Firstly, while driving the vehicle in traffic conditions and secondly, when the vehicle is parked under the sun. When the vehicle is exposed to the radiation from the sun for a period, the cabin temperature can reach alarming levels, which may have serious discomfort and health effects on the people entering the vehicle. Although there are options of remote switching on of air conditioners, they are restricted to vehicles having an automatic transmission and availability of the mobile network. So, it is important to explore the possible options which can be used for restricting the external heat load to the cabin.

Warranty forecasting of repairable systems is very important for manufacturers of mass produced systems. It is desired to predict the Expected Number of Failures (ENF) after a censoring time using collected failure data before the censoring time. Moreover, systems may be produced with a defective component resulting in extensive warranty costs even after the defective component is detected and replaced with a new design. In this paper, we present a forecasting method to predict the ENF of a repairable system using observed data which is used to calibrate a Generalized Renewal Processes (GRP) model. Manufacturing of products may exhibit different production patterns with different failure statistics through time. For example, vehicles produced in different months may have different failure intensities because of supply chain differences or different skills of production workers, for example.

One approach of lighting vehicle weight is using composite materials. Fiber reinforced polypropylene is one of the most popular composite materials. To improve accuracy in the prediction of durability performance of structures made of this kind of composite material, static and fatigue properties of a short glass fiber reinforced polypropylene have been physically studied. This paper describes details of test coupon design, fabrication, test setup of both quasi-static and fatigue tests, test results and discussions. In this study, various loading orientations (0o, 20o, 90o and knit line), temperatures (22oC/23oC and 80oC/85oC), loading ratio (R = -1.0, -0.5, -0.2, 0.1 and 0.4) are considered.