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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.

Standard CFD methods require a mesh that fits the boundaries of the computational domain. For a complex geometry the generation of such a grid is time-consuming and often requires modifications to the model geometry. This paper evaluates the Immersed Boundary (IB) approach which does not require a boundary-conforming mesh and thus would speed up the process of the grid generation. In the IB approach the CAD surfaces (in Stereo Lithography -STL- format) are used directly and this eliminates the surface meshing phase and also mitigates the process of the CAD cleanup. A volume mesh, consisting of regular, locally refined, hexahedrals is generated in the computational domain, including inside the body. The cells are then classified as fluid, solid and interface cells using a simple ray-tracing scheme. Interface cells, correspond to regions that are partially fluid and are intersected by the boundary surfaces.

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.

The GM Aerodynamics Laboratory (GMAL) was modified in 2001 to reduce the background noise level and provide a semi-anechoic test section for wind noise testing. The walls and ceiling of the test section were lined with acoustic foam and foam-filled turning vanes were installed in the corners. Portions of the wind tunnel circuit were also treated with fiberglass material covered by perforated sheet metal panels. High skin drag due to roughness of the foam surfaces, along with high blockage due to the large turning vanes, increased the wind tunnel circuit losses so that the maximum wind speed in the test section was reduced. The present study calculates the averaged total pressure losses at three locations to evaluate the reductions in skin drag and blockage from proposed modifications to the circuit, which were intended to increase the test section wind speed without compromising noise levels.

With the current increase in concern and awareness regarding sustainability and energy, a new focus has been placed on the field of engineering. In this realm of focus, how to educate engineers, more specifically how to continually educate engineers to keep up with technology and the changing workforce has become a very important topic of interest. There exists a gap between graduate studies and professional implementation of technology which the Energy Systems Engineering [ESE] program currently in deployment and development between the University of Michigan and General Motors seeks to address. This work outlines current efforts in encouraging new engineers to enter the field, but focuses primarily on continuing and re-educating the workforce to meet the needs of new technologies. Examples of academic-industry cooperation will be discussed, with some focus on the benefit and experience of the student.

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.

This study investigates a methodology in which the general public's subjective interpretation of vehicle handling and performance can be predicted. Several vehicle handling measurements were acquired, and associated metrics calculated, in a controlled setting. Human evaluators were then asked to drive and evaluate each vehicle in a winter driving school setting. Using the acquired data, multiple linear regression and artificial neural network (ANN) techniques were used to create and refine mathematical models of human subjective responses. It is shown that artificial neural networks, which have been trained with the sets of objective and subjective data, are both more accurate and more robust than multiple linear regression models created from the same data.

The objective of this paper is to develop an analytical approach to compute cooling airflow at any particular fan and vehicle speed condition in a vehicle from a minimum number of CFD (Computational Fluid Dynamic) simulations or test runs using fan performance data. The vehicle system resistance is used with fan performance curves to find the cooling airflows of the vehicle. Fan performance curves at any fan speed are computed using fan laws and the CFD simulations are used in computing the system resistances at a particular vehicle speed. The paper outlines the prediction of system resistances at other vehicle speeds and its use in computing the cooling flows at those speeds. The approach is validated using CFD for different combinations of vehicle and fan speeds.

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.

It is well understood that conditions encountered during racetrack driving are amongst the most severe to which vehicle braking systems can be subjected. High braking pressure is combined with enormous energy input and high temperatures for multiple braking events. Brake fade, degradation of brake pedal feel, and brake lining taper/overall wear are common results of racetrack usage. This paper focuses on how racetrack and high energy driving-type conditioning affects the performance of the brake caliper - in particular, its ability to maintain an even pressure distribution at all of its interfaces (pad to rotor, piston to pad backing plate, and housing to pad backing plate).

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].

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.

This study evaluated the effectiveness of alternative workload-based interventions intended to restore driver alertness following drowsy episodes. Unlike traditional drowsy driving studies, this experiment did not target sleep-deprived individuals, but rather studied normally rested drivers under the assumption that low-workload environments could trigger drowsy driving episodes. The study served as a proof of concept for varying the nature and onset of countermeasure interventions intended to disrupt the drowsiness cycle. Interventions to combat drowsiness attempted to target driver workload, either physical or cognitive, and included two primary treatment conditions: 1) physical workload to increase driver steering demands and 2) trivia-based interactive games to mentally challenge drivers. A benchmark comparison condition using music was also investigated to contrast the relative influence of workload-based interventions with passive listening to musical arrangements.

After-treatment sensors are used in the ECU feedback control to calibrate the engine operating parameters. Due to their contact with exhaust gases, especially NOx sensors are prone to soot deposition with a consequent decay of their performance. Several phenomena occur at the same time leading to sensor contamination: thermophoresis, unburnt hydrocarbons condensation and eddy diffusion of submicron particles. Conversely, soot combustion and shear forces may act in reducing soot deposition. This study proposes a predictive 3D-CFD model for the analysis of the development of soot deposition layer on the sensor surfaces. Alongside with the implementation of deposit and removal mechanisms, the effects on both thermal properties and shape of the surfaces are taken in account. The latter leads to obtain a more accurate and complete modelling of the phenomenon influencing the sensor overall performance.

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.