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This paper investigates the surface pressures found on the sides of a Davis model under steady state conditions and during yawed oscillations at a reduced frequency which would generally be assumed to give a quasi-static response. The surface pressures are used to investigate the flow field and integrated to infer aerodynamic loads. The results show hysteresis in the oscillating model's results, most strongly in the A-pillar flows. The changes to the oscillating model's flow field reduces the intensity of the surface pressures around the rear pillars, reduce the strength and extent of the A-pillar vortex and cause the surface pressures to couple with the oscillating motion. This work shows the flows around the front of a vehicle may be more important to a vehicle's unsteady aerodynamics than is generally accepted and also leads to the conclusions that the reduced frequency parameter may not fully describe the onset unsteadiness.

This paper presents the implementation of a vehicle and powertrain model of the parallel hybrid electric vehicle which can be used for several purposes: as a model for estimating fuel consumption, as a model for estimating performance, and as a control model for the hybrid powertrain optimisation. The model is specified as a multi-domain physical model in MATLAB Simscape, which captures the key electrical, mechanical and thermal energy flows in the vehicles. By applying hand crafted boundary conditions, this model can be simulated either in the forwards or backwards direction, and it can easily be simplified as required to address specific control problems. Modelling in the forwards direction, the driver inputs are specified, and the vehicle response is the model output. In the backwards direction, the vehicle velocity as a function of time is the specified input, and the engine torque, and fuel consumption are the model outputs.

Significant exhaust enthalpy is wasted in gasoline turbocharged direct injection (GTDI) engines; even at moderate loads the WG (Wastegate) starts to open. This action is required to reduce EBP (Exhaust Back Pressure). Another factor is catalyst protection, placed downstream turbine. Lambda enrichment is used to perform this. However, the conventional turbine has a temperature drop across it when used for energy recovery. Catalyst performance is critical for emissions, therefore the only location for any additional device is downstream of it. This is a challenge for any additional energy recovery, but a smaller turbine is a design requirement, optimised to work at lower operating pressure ratios. A WAVE model of the 2.0L GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated with steady state dynamometer data to estimate drive cycle benefit.

The promise of thermo-electric (TE) technology in vehicles is a low maintenance solid state device for power generation. The Thermo-Electric Generator (TEG) will be located in the exhaust system and will make use of an energy flow between the warmer exhaust gas and the external environment. The potential to make use of an otherwise wasted flow of energy means that the overall system efficiency can be improved substantially. One of the barriers to a successful application of the technology is the device efficiency. The TE properties of even the most advanced materials are still not sufficient for a practical, cost effective device. However the rate of development is such that practical devices are likely to be available within the next fifteen years. In a previous paper [ 1 ], the potential for such a device was shown through an integrated vehicle simulation and TEG model.

A large contribution to the aerodynamic drag of a vehicle is the loss of pressure in the wake region, especially on square-back configurations. Wake pressure recovery can be achieved by a variety of physical shape changes, but with vehicle shapes becoming ever more aerodynamically efficient research into active technologies for flow manipulation is becoming more prominent. The aim of the current paper is to generate an understanding of how an optimized roof trailing edge, in the form of a chamfer, can reduce wake size, increase base pressures and reduce drag. A comprehensive study using PIV (Particle Image Velocimetry), balance measurements and static pressure measurements was performed in order to investigate the flow and wake structure behind a simplified car model. Significant reductions in C d are demonstrated and directly related to the measured base and slant pressures.

The dynamics of automotive differentials have been studied extensively to improve their efficiency and additionally, in recent years, generated noise and vibration. Various mathematical models have been proposed to describe the contact/impact of gear teeth pairs. However, the influence of vehicular cruising speed on the resisting torque has not been considered in sufficient detail. This can lead to unrealistic predictions with regards to loss of contact of teeth pair, a phenomenon which leads to NVH issues. The current work presents a torsional model of a hypoid gear pair. The resisting torque is a function of the traction force and aerodynamic drag, whilst the vehicle is cruising at nominally constant speed. The pinion input torque is derived through assumed instantaneous equilibrium conditions. In this approach, realistic excitation capturing the vehicle's driving conditions is imposed on the dynamics of the hypoid gear pair.

Particle Image Velocimetry has developed over the last decade into a relatively mature flow-field measurement technique, capable of providing insight into time averaged and instantaneous flows that in the past have not been readily accessible. The application of the method in the measurement and analysis of flows around road vehicles has so far been limited to a relatively small number of specialist applications, but its use is expanding. This paper reviews the modern digital PIV technique placing emphasis on the important considerations required to obtain reliable and accurate data. This includes comments on each aspect of the PIV process, including initial setup and image acquisition, processing, validation and analysis. A number of automotive case studies are presented covering different aspects of the method, including a diffuser exit flow, edge radius optimization, ‘A’ pillar flow and aerial wake flows.

In this paper the effect of in-cylinder crevices formed by the piston cylinder clearance, above the first ring, and the spark plug cavity, on the entrapment of unburned fuel air mixture during the late compression, expansion and exhaust phases of a spark ignition engine cycle, have been simulated using the Computational Fluid Dynamic (CFD) code KIVA II. Two methods of fuelling the engine have been considered, the first involving the carburetion of a homogeneous fuel air mixture, and the second an attempt to simulate the effects of manifold injection of fuel droplets into the cylinder. The simulation is operative over the whole four stroke engine cycle, and shows the efflux of trapped hydrocarbon from crevices during the late expansion and exhaust phases of the engine cycle.

The energy consumption of a vehicle is typically determined either by testing or in simulation. While both approaches are valid, they only work for a specific drive cycle, they are time intensive, and they do not directly result in a closed-form relationship between key parameters and consumption. This paper presents an alternative approach that determines the consumption based on a simple analytical model of the vehicle and statistical parameters of the drive cycle, specifically the moments of the velocity. This results in a closed-form solution that can be used for analysis or synthesis. The drive cycle is quantified via its moments, specifically the average speed, the standard deviation of the speed as well as the higher order moments skewness, and the kurtosis. A mixed quadratic term is added to account for acceleration or aggressiveness, but it is noticeably distinct from the conventional metric of positive kinetic energy (PKE).

Recent changes to the rules regarding aerodynamics within Formula SAE, combined with faster circuits at the European FSAE events, have made the implementation of aerodynamic devices, to add down-force, a more relevant topic. As with any race series it is essential that a detailed analysis is completed to establish the costs and benefits of including an aerodynamic package on the vehicle. The aim of the work reported here was to create a methodology that would fully evaluate all aspects of the package and conclude with an estimate of the likely gain in points at a typical FSAE event. The paper limits the analysis to a front and rear wing combination, but the approach taken can be applied to more complex aerodynamic packages.

Coastdown testing is a proven method for determining the drag coefficients for road cars whilst the vehicle is in its normal operating environment. An accurate method of achieving this has been successfully developed at Loughborough University. This paper describes the adaptation and application of these techniques to the special case of a contemporary Formula One racing car. The work was undertaken in conjunction with the Benetton Formula One racing team. The paper outlines the development and application of a suitable mathematical model for this particular type of vehicle. The model includes the aerodynamic, tyre, drivetrain and the un-driven wheel drags and accounts for the change in aerodynamic drag due to ambient wind and changes in vehicle ride height during the coastdown. The test and analysis methods are described.

The recent development of diesel engine fuel injection systems has been dominated by how to manage the degrees of freedom that common rail multi-pulse systems now offer. A number of production engines already use four injection events while in research, work based on up to eight injection events has been reported. It is the degrees of freedom that lead to a novel experimental requirements. There is a potentially complex experimental program needed to simply understand how injection parameters influence the combustion process in steady state. Combustion behavior is not a continuum and as both injection and EGR rates are adjusted, distinct combustion modes emerge. Conventional calibration processes are severely challenged in the face of large number of degrees of freedom and as a consequence new development approaches are needed.

Vehicle handling is heavily influenced by the torque distribution to the driving wheels. This work presents a newly developed differential, designed to actively control the driving torque distribution to the wheels. The new device incorporates an electric machine, which can operate either as a motor or generator. A control unit monitors signals from various sources in the vehicle, such as steering angle, yaw acceleration and wheel rotational speed. Then, a control algorithm takes into account the steering angle rate and the vehicle speed in order to determine the suitable difference between output torque values. The handling improvement capabilities are evaluated by simulating in ADAMS/Car the driving behavior of a vehicle equipped with the new differential. The model that has been used to simulate vehicle handling is that of a Formula SAE type racing car.

The use of simulation tools by vehicle manufacturers to design, optimize and validate their vehicles is essential if they are to respond to the demands of their customers, to meet legislative requirements and deliver new vehicles ever more quickly. The use of such tools in the aerodynamics community is already widespread, but they remain some way from replacing physical testing completely. Further advances in simulation capabilities depend on the availability of high quality validation data so that simulation code developers can ensure that they are capturing the physics of the problems in all the important areas of the flow-field. This paper reports on an experimental program to generate such high quality validation data for a SAE 20 degree backlight angle notchback reference model.

This paper presents an investigation into Elastohydrodynamic (EHL) modeling of differential hypoid gears that can be used in coupling with Newtonian (or multibody) dynamics to study Noise, Vibration and Harshness (NVH) phenomena, such as axle whine. The latter is a noise of a tonal nature, emitted from differential axles, characterised by the gear meshing frequency and its multiples. It appears at a variety of operating conditions; during drive and coasting, high and low torque loading. Key design targets for differential hypoid gears are improved efficiency and reduced vibration, which depend critically on the formation of an EHL lubricant film. The stiffness and damping of the oil film and friction generated in the contact can have important effects and cannot be neglected when examining the NVH behaviour of hypoid gears.

The process of autoignition in an internal combustion engine cylinder produces large amplitude high frequency gas pressure waves accompanied by significant increases in gas temperature and velocity, and as a consequence large convective heat fluxes to piston and cylinder surfaces. Extended exposure of these surfaces to autoignition, results in their damage through thermal fatigue, particularly in regions where small clearances between the piston and cylinder or cylinder head, lie in the path of the oscillatory gas pressure waves. The ability to predict spatial and temporal' variations in cylinder gas pressure, temperature and velocity during autoignition and hence obtain reasonable estimates of surface heat flux, makes it possible to assess levels of surface fatigue at critical zones of the piston and cylinder head, and hence improve their tolerance to autoignition.

One of the most critical challenges currently facing the diesel engine industry is how to improve fuel economy under emission regulations. Improvement in fuel economy can be achieved by precisely controlling Air/Fuel ratio and by monitoring fuel consumption in real time. Accurate and repeatable measurements of fuel rate play a critical role in successfully controlling air/fuel ratio and in monitoring fuel consumption. Volumetric and gravimetric measurements are well-known methods for measuring fuel consumption of internal combustion engines. However, these methods are not suitable for obtaining fuel flow rate data used in real-time control/measurement. In this paper, neural networks are used to solve the problem concerning discontinuous data of fuel flow rate measured by using an AVL 733 s fuel meter. The continuous parts of discontinuous fuel flow rate are used to train and validate a neural network, which can then be used to predict the discontinuous parts of the fuel flow rate.

Aerodynamic aids, such as spoilers, applied to the rear of cars can provide drag reduction to improve performance, or can enhance high speed stability by reducing lift at the rear axle. In some cases these can be conflicting demands. It has been noted, however, that when rear axle lift is reduced there is often a reduction in yawing moment which has a beneficial effect on crosswind sensitivity. Wind tunnel results from real road vehicles are presented to illustrate this effect. This beneficial relationship is further explored in a wind tunnel experiment using simple models to represent road vehicles. Force and moment coefficients as a function of yaw angle are measured for a range of vehicle geometries which generate a substantial variation in lift. It is shown that as lift is reduced, yawing moment is also reduced, while side force and rolling moment are increased.

In order to identify predictive models for a diesel engine combustion process, combustion cylinder pressure together with other fuel path variables such as rail pressure, injector current and sleeve pressure of 1000 continuous cycles were sampled and collected at high resolution. Using these engine steady state test data, three types of modeling approach have been studied. The first is the Auto-Regressive-Moving-Average (ARMA) model which had limited prediction ability for both peak combustion pressure (Pmax) and Indicated Mean Effective Pressure (IMEP). By applying correlation analysis, proper inputs were found for a linear predictive model of Pmax and IMEP respectively. The prediction performance of this linear model is excellent with a 30% fit number for both Pmax and IMEP. Further nonlinear modeling work shows that even a nonlinear Neural Network (NN) model does not have improved prediction performance compared to the linear predictive model.