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Time-averaged temperatures at critical locations on the piston of a direct-fuel injected, two-stroke, 388 cm3, research engine were measured using an infrared telemetry device. The piston temperatures were compared to data [7] of a carbureted version of the two-stroke engine, that was operated at comparable conditions. All temperatures were obtained at wide open throttle, and varying engine speeds (2000-4500 rpm, at 500 rpm intervals). The temperatures were measured in a configuration that allowed for axial heat flux to be determined through the piston. The heat flux was compared to carbureted data [8] obtained using measured piston temperatures as boundary conditions for a computer model, and solving for the heat flux. The direct-fuel-injected piston temperatures and heat fluxes were significantly higher than the carbureted piston. On the exhaust side of the piston, the direct-fuel injected piston temperatures ranged from 33-73 °C higher than the conventional carbureted piston.

The need for accurate virtual prototyping prediction is well documented in the literature. For welded body structures one notable shortcoming has been the ability for finite element analysis (FEA) to accurately predict the failure of welded joints due to cyclic loading. A new approach to representing spot-welds for durability evaluation in automotive sheet metal structures is presented here. Excellent correlation with spot-weld failures in actual tests have been observed through this modeling approach. We present a method of representing spot-welds using the finite element method. This method has shown to be able of predicting the behavior of spot-welds prior to the build of any prototypes or testing. Further, for spot-weld failures we present evidence that reveals which radial quadrant of the spot-weld will contain the failure. This method also allows engineers to determine the mechanism of failure. This paper describes in detail the spot-weld modeling method.

As automotive OEMs (Original Equipment Manufacturer) struggle to reach a balance between Design and Performance, environmental legislations continues to demand more rapid gains in vehicle efficiency. As a result, more attention is being given to the contributions of both tire and wheels. Not only tire rolling resistance, but also tire and wheel aerodynamics are being shown to be contributors to overall efficiency. To date, many studies have been done to correlate CFD simulations of rotating wheels both in open and closed wheeled environments to windtunnel results. Whereas this ensures proper predictive capabilities, little focus has been given to thoroughly explaining the physics that govern this complex environment. This study seeks to exhaustively investigate the complex interactions between the ground, body, and a rotating tire/wheel.

An interactive tool for predicting the performance of vehicle designs in the pedestrian leg impact test has been developed. This tool allows users to modify the design of a vehicle front structure through the use of a graphical interface, and then evaluates the performance of the design with a response surface. This performance is displayed in the graphical interface, providing the user with nearly instantaneous feedback to his design changes. An example is shown that demonstrates how the tool can be used to help guide the user towards vehicle designs that are likely to improve performance. As part of the development of this tool, a simplified, parametric finite element model of the front structure of the vehicle was created. This vehicle model included eleven parameters that could be adjusted to change the structural dimensions and structural behavior of the model.

In this work, an experimental and analysis methodology was developed to evaluate the preignition propensity of fuels and engine operating conditions in an SI engine. A heated glow plug was introduced into the combustion chamber to induce early propagating flames. As the temperature of the glowplug varied, both the fraction of cycles experiencing these early flames and the phasing of this combustion in the engine cycle varied. A statistical methodology for assigning a single-value to this complex behavior was developed and found to have very good repeatability. The effects of engine operating conditions and fuels were evaluated using this methodology. While this study is not directly studying the so-called stochastic preignition or low-speed preignition problem, it studies one aspect of that problem in a very controlled manner.

Fast-running metamodels (surrogates or response surfaces) that approximate multivariate input/output relationships of time-consuming CAE simulations facilitate effective design trade-offs and optimizations in the vehicle development process. While the cross-validated nonparametric metamodeling methods are capable of capturing the highly nonlinear input/output relationships, it is crucial to ensure the adequacy of the metamodel error estimates. Moreover, in order to circumvent the so-called curse-of-dimensionality in constructing any nonlinear multivariate metamodels from a realistic number of expensive simulations, it is necessary to reliably eliminate insignificant inputs and consequently reduce the metamodel prediction error by focusing on major contributors. This paper presents a robust data-adaptive nonparametric metamodeling procedure that combines a convergent variable screening process with a robust 2-level error assessment strategy to achieve better metamodel accuracy.

The present work aims to characterize the microstructure of valvetrain camshaft lobes that are currently applied in the automotive industry, obtained by different processing routes. The cam lobe microstructure has been assessed by microscopy, whereas the mechanical properties by hardness profile measurements on the surface region. Microconstituents type and form, composing the final microstructure at the cam lobe work region, are defined by the casting route and/or post-heat treatment process other than alloy chemical composition, so that knowledge and control of processing route is vital to assure suitable valvetrain system assembly performance and durability. Most of the mechanical solicitations on the part occur at the interface between cam and follower; the actual contact area is significantly smaller than the apparent area. As a result, the microstructure at and near the surface performs a direct role on the performance of the valvetrain, cam lobe and its counterpart.

Reductions of hardware costs as well as implementations of new innovative functions are the main drivers of today's automotive electronics. Indeed more and more resources are spent on adapting existing solutions to different environments. At the same time, due to the increasing number of networked components, a level of complexity has been reached which is difficult to handle using traditional development processes. The automotive industry addresses this problem through a paradigm shift from a hardware-, component-driven to a requirement- and function-driven development process, and a stringent standardization of infrastructure elements. One central standardization initiative is the AUTomotive Open System ARchitecture (AUTOSAR). AUTOSAR was founded in 2003 by major OEMs and Tier1 suppliers and now includes a large number of automotive, electronics, semiconductor, hard- and software companies.

This paper presents an overview of the aerodynamic development of the 2019 Chevrolet Corvette C7 ZR1. Extensive wind tunnel testing and computational fluid dynamics simulations were completed to engineer the ZR1’s aerodynamics to improve lift-to-drag efficiency and track capability over previous Corvette offerings. The ZR1 architecture changes posed many aerodynamic challenges including increased vehicle cooling, strict packaging demands, wider front track width, and aggressive exterior styling. Through motorsports-inspired aerodynamic development, the ZR1 was engineered to overcome these challenges through the creation of new devices such as a raised rear wing and front underwing. The resulting Standard ZR1 achieved a top speed of 212 mph making it the fastest Corvette ever [1]. Optionally, the ZR1 with the ZTK Performance Package provides the highest downforce of any Corvette, generating approximately 950 pounds at the ZTK’s top speed [1].

Many countries including India have aggressively aimed to implement electric vehicles (EVs) usage from 2030 onwards. Companies such as General Motors, Uber, Waymo and Nissan etc. are exploring the realm of autonomous vehicles (AV) for use as taxis as early as 2019. Above facts logically arrive at the solution of Autonomous EVs as taxis. With the commitment towards enabling an all-electric future, there exists a need to provide suitable infrastructure for recharging. Major urban cities located by the shoreline such as New York, Hong Kong, Mumbai, Los Angeles etc. have been facing the space crunch, with real estate prices sky-rocketing exponentially. With this premise, the operating company would need a large amount of space to store their EVs for charging which attributes to a longer downtime. This brings a need for an economical charging location that has a reduced usage of urban infrastructure and energy consumption.

An integrated stochastic design framework that facilitates practical applications involving time-consuming CAE simulations is described. The probabilistic performance measure that addresses stochastic uncertainties in CAE modeling and simulations is used to support design decision-making. Two enabling metamodeling methods using cross-validated radial basis functions (CVRBF) and a corresponding uniform sampling method are introduced to approximate highly nonlinear CAE model input/output relationships. A vehicle restraint system example is used to demonstrate the effectiveness of the proposed framework and enabling techniques.

As one of many pack-level battery simulation approaches developed within the General Motors-led Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project, the system approach treats the entire battery pack as a dynamic system which includes multiple engineering disciplines for simulation. It is the most efficient approach of all the CAEBAT battery pack-level approaches in terms of computational time and resources. This paper reports the application of the system approach for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The results using the system approach are found to have a very good agreement with the measurements.

In recent years several variants of lightweight multi-layered acoustic treatments have been used successfully in vehicles to replace conventional barrier-decoupler interior dash mats. The principle involved is to utilize increased acoustic absorption to offset the decrease in insertion loss from the reduced mass such that equivalent vehicle level performance can be achieved. Typical dual density fibrous constructions consist of a relatively dense cap layer on top of a lofted layer. The density and flow resistivity of these layers are tuned to optimize a balance of insertion loss and absorption performance. Generally these have been found to be very effective with the exception of dash mats with very high insertion loss requirements. This paper describes an alternative treatment which consists of a micro-perforated film top layer and fibrous decoupler layer.

The methodology of predicting analytical fatigue life of automotive body structures using two commercially available computer codes, NASTRAN and NCODE is described. Modal transient durability simulations are improved with use of residual vectors incorporating inertia relief basis functions. Simulations consisting of hundreds of thousand finite elements and hours of road loads are routine.

Internal combustion engines (ICEs) exhaust emissions, particularly nitrogen oxides (NOx), have become a growing environmental and health concern. The biggest challenge for contemporary ICE industry is the development of clean ICEs, and the use of advanced design tools like Computational Fluid Dynamics (CFD) simulation is paramount to achieve this goal. In particular, the development of aftertreatment systems like Selective Catalyst Reduction (SCR) is a key step to reduce NOx emissions, and accurate and efficient CFD models are essential for its design and optimization. In this work, we propose a novel 3D-CFD methodology, which uses a Machine Learning (ML) approach as a surrogate model for the SCR catalyst chemistry, which aims to enhance accuracy of the simulations with a moderate computational cost. The ML approach is trained on a dataset generated from a set of 1D-CFD simulations of a single channel of an SCR catalyst.

Economic market forces and increasing environmental awareness of gasoline have led to interest in developing alternatives to gasoline, and extending the current global supply for transportation fuels. One viable strategy is the use of alternative alcohol fuels for combustion engines, with ethanol and methanol in various concentration ranges proposed and in-use. Utilizing and citing data from this review, a comprehensive overview of the materials selection and engineering challenges facing metals, plastics and elastomers are presented. The engineering approach and solution-sets discussed will focus on production feasibility and implementation. The effects from the fuel chemistry and quality of fuel ethanol produced on the related vehicle components are discussed.

The introduction of new light-duty vehicle emission limits to comply under real driving conditions (RDE) is pushing the diesel engine manufacturers to identify and improve the technologies and strategies for further emission reduction. The latest technology advancements on the after-treatment systems have permitted to achieve very low emission conformity factors over the RDE, and therefore, the biggest challenge of the diesel engine development is maintaining its competitiveness in the trade-off “CO2-system cost” in comparison to other propulsion systems. In this regard, diesel engines can continue to play an important role, in the short-medium term, to enable cost-effective compliance of CO2-fleet emission targets, either in conventional or hybrid propulsion systems configuration. This is especially true for large-size cars, SUVs and light commercial vehicles.

Vehicle dynamics simulation with Hardware In the Loop (HIL) has been demonstrated to reduce development and validation time for dynamic control systems. For dynamic control systems such as Anti-lock Braking System (ABS) and Electronic Stability Control (ESC), an accurate vehicle dynamics performance simulation system requires the Electronic Brake Control Module (EBCM) coupled with the vehicles brake system hardware. This kind of HIL simulation-specific software tool can further increase efficiency by means of automation and optimization of the development and validation process. This paper presents a method for HIL vehicle dynamics simulator optimization through Brake Response Time (BRT) correlation. The paper discusses the differences between the physical vehicle and the HIL vehicle dynamics simulator. The differences between the physical and virtual systems are used as factors in the development of a Design Of Experiment (DOE) quantifying HIL simulator performance.

The friction material is arguably at the heart of any brake system, with its properties taking one of the most important roles in defining its performance characteristics. High performance applications, such as race track capable brake systems in high powered vehicles, exert considerable stress on the friction materials, in the form of very high heat flux loads, high clamp and brake torque loads, and high operating temperatures. It is important, for high performance applications, to select capable friction materials, and furthermore, it is important to understand fully what operating conditions the friction material will face in the considered application.

The objective of the paper is to outline a CFD based lumped parameter method that compares the thermal performance of brake rotors, predicts the transient temperatures and brake lining wear in vehicle. A two-pronged approach was developed for this purpose. A rotor stand-alone model was used to predict rotor performance curves. Simultaneously heat transfer coefficients of the brake rotor were computed corresponding to the rotor performance curves and the appropriate heat transfer correlations were established. The second part of this approach involved developing a brake model in a vehicle and solving for the air flow through rotors in different vehicles at various speeds. These rotor flows were cross-referenced with the rotor performance curves, generated earlier for that rotor, to compute the heat transfer coefficients in the vehicle.