Search Results

Car manufacturers put large efforts into reducing wind noise to improve the comfort level of their cars. Each component of the vehicle is designed to meet its individual noise target to ensure the wind noise passenger comfort level inside the vehicle is met. Sunroof designs are tested to meet low-frequency buffeting (also known as boom) targets and broadband noise targets for the fully open sunroof with deflector and for the sunroof in vent position. Experimentally testing designs and making changes to meet these design targets typically involves high cost prototypes, expensive wind tunnel sessions, and potentially late design changes. To reduce the associated costs as well as development times, there is strong motivation for the use of a reliable numerical prediction capability early in the vehicle design process.

Automotive aerodynamics measurements and simulations now routinely use a moving ground and rotating wheels (MVG&RW), which is more representative of on-road conditions than the fixed ground-fixed wheel (FG&FW) alternative. This can be understood as a combination of three elements: (a) moving ground (MVG), (b) rotating front wheels (RWF) and (c) rotating rear wheels (RWR). The interaction of these elements with the flow field has been explored to date by mainly experimental means. This paper presents a mainly computational (CFD) investigation of the effect of RWF and RWR, in combination with MVG, on the flow field around a saloon vehicle. The influence of MVG&RW is presented both in terms of a combined change from a FG&FW baseline and the incremental effects seen by the addition of each element separately. For this vehicle, noticeable decrease in both drag and rear lift is shown when adding MVG&RW, whereas front lift shows little change.

In a real-world environment, a vehicle on the road is subjected to a range of flow yaw angles, the most severe of which can impact handling and stability. A fully coupled, six degrees-of-freedom CFD and vehicle handling simulation has modelled the complete closed loop system. Varying flow yaw angles are introduced via time dependent boundary conditions and aerodynamic loads predicted, whilst a handling model running simultaneously calculates the resulting vehicle response. Updates to the vehicle position and orientation within the CFD simulation are achieved using the overset grid method. Using this approach, a crosswind simulation that follows the parameters of ISO 12021:2010 (Sensitivity to lateral wind - Open-loop test method using wind generator input), was performed using the fastback variant of the DrivAer model. Fully coupled aerodynamic and vehicle response was compared to that obtained using the simplified quasi-steady and unsteady, one way coupled method.

Engines equipped with pressure charging systems are more prone to knock partly due the increased intake temperature. Meanwhile, turbocharged engines when operating at high engine speeds and loads cannot fully utilize the exhaust energy as the wastegate is opened to prevent overboost. The turboexpansion concept thus is conceived to reduce the intake temperature by utilizing some otherwise unexploited exhaust energy. This concept can be applied to any turbocharged engines equipped with both a compressor and a turbine-like expander on the intake loop. The turbocharging system is designed to achieve maximum utilization of the exhaust energy, from which the intake charge is over-boosted. After the intercooler, the turbine-like expander expands the over-compressed intake charge to the required plenum pressure and reduces its temperature whilst recovering some energy through the connection to the crankshaft.

Vehicle efficiency and range, along with the DC charging speed, are deemed as the most important criteria for an electric vehicle currently. The electric vehicle energy consumption is impacted by the change in temperature along with the driving style and average speed of a customer, all other factors being constant. Hence understanding the patterns and impact of different aspects of an EV range & charging speed is crucial in delivering an electric vehicle with robust efficiency across all weather conditions. In this paper we have analysed vehicle parameters of global Jaguar I-PACE customer data. We present and analyse the collated big data of around 50,000+ unique vehicles with a data aggregate of well over 482 million km. In moderate ambient conditions the analysis indicated a good correlation with 50th to 75th percentile drivers’ energy consumption to the EPA label figure.

Complexity of electronics and embedded software systems in automobiles has been increasing over the years. This necessitates the need for an effective and exhaustive development and validation process in order to deliver fault free vehicles at reduced time to market. Model-based Product Engineering (MBPE) is a new process for development and validation of embedded control software. The process is generic and defines the engineering activities to plan and assess the progress and quality of the software developed for automotive applications. The MBPE process is comprised of six levels (one design level and five verification and validation levels) ranging from the vehicle requirements phase to the start of production. The process describes the work products to be delivered during the course of product development and also aligns the delivery plan to overall vehicle development milestones.

Whilst the primary function of the active grille shutters is to reduce the aerodynamic drag of the car, there are some secondary benefits like improving the warm up time of engine and also retaining engine heat when parked. In turbocharged IC engines the air is compressed (heated) in the turbo and then cooled by a low temperature cooling system before going into the engine. When the air intake temperature exceeds a threshold value, the engine efficiency falls - this drives the need for the cooling airflow across the radiator in normal operation. Airflow is also required to manage the convective heat transfer across various components in the engine bay for its lifetime thermal durability. Grill shutters can also influence the aerodynamic lift balance thus impacting the vehicle dynamics at high speed. The vehicle HVAC system also relies on the condenser in the front heat exchanger pack disposing the waste heat off in the most efficient way.

The advent of 3D displays offers Human-Machine Interface (HMI) designers and engineers new opportunities to shape the user's experience of information within the vehicle. However, the application of 3D displays to the in-vehicle environment introduces a number of new parameters that must be carefully considered in order to optimise the user experience. In addition, there is potential for 3D displays to increase driver inattention, either through diverting the driver's attention away from the road or by increasing the time taken to assimilate information. Manufacturers must therefore take great care in establishing the ‘do’s and ‘don’t's of 3D interface design for the automotive context, providing a sound basis upon which HMI designers can innovate. This paper describes the approach and findings of a three-part investigation into the use of 3D displays in the instrument cluster of a road car, the overall aim of which was to define the boundaries of the 3D HMI design space.

The field of vision of the driver during wet road conditions is essential for safety at all times. Additionally, the safe use of the increasing number of sensors integrated in modern cars for autonomous driving and intelligent driver assistant systems has to be ensured even under challenging weather conditions. To fulfil these requirements during the development process of new cars, experimental and numerical investigations of vehicle soiling are performed. This paper presents the surface contamination of self- and foreign-soiling tested in the wind tunnel. For these type of tests, the fluorescence method is state-of-the-art and widely used for visualizing critical areas. In the last years, the importance of parameters like the contact angle have been identified when designing the experimental setup. In addition, new visualization techniques have been introduced.

Many electric (EV) and hybrid-electric (HEV) vehicles are designed to operate using only electric propulsion at low road speeds. This has resulted in significantly reduced vehicle noise levels in urban situations. Although this may be viewed by many as a benefit, a risk to safety exists for those who rely on the engine noise to help detect the presence, location and behaviour of a vehicle in their vicinity. In recognition of this, legislation is being introduced globally which will require automotive manufacturers to implement external warning sound systems. A key challenge for premium vehicle manufacturers is the development of a suitable warning sound signature which also conveys the appropriate brand aspirations for the product. A further major difficulty exists when trying to robustly evaluate potential exterior sounds by running large-scale trials in the real world.

Brake disc materials are being utilised that have low noise/dust properties, but are sensitive to contamination by surface water. This drives large dust shields, making brake cooling increasingly difficult. However, brake cooling must be delivered without compromising aerodynamic drag and hence CO2 emissions targets. Given that front brake discs sit in a region of geometric, packaging and flow complexity optimization of their performance requires the analysis of thermal, aerodynamic and multi-phase flows. Some of the difficulties inherent in this task would be alleviated if the complete analysis could be performed in the same CAE environment: utilizing common models and the same solver technology. Hence the project described in this paper has sought to develop a CFD method that predicts the amount of contamination (water) that reaches the front brake discs, using a standard commercial code already exploited for both brake disc thermal and aerodynamics analysis.

For electric vehicles (EVs), driving range is one of the major concerns for wider customer acceptance and the cabin climate system represents the most significant auxiliary load for battery consumption. Unlike internally combustion engine (ICE) vehicles, EVs cannot utilize the waste heat from an engine to heat the cabin through the heating, ventilation and air conditioning (HVAC) system. Instead, EVs use battery energy for cabin heating, this reduces the driving range. To mitigate this situation, one of the most promising solutions is to optimize the recirculation of cabin air, to minimize the energy consumed by heating the cold ambient air through the HVAC system, whilst maintaining the same level of cabin comfort. However, the development of this controller is challenging, due to the coupled, nonlinear and multi-input multi-output nature of the HVAC and thermal systems.

For the automotive industry, the quality and level of the wind noise contribution has a growing importance and therefore should be addressed as early as possible in the development process. Each component of the vehicle is designed to meet its individual noise target to ensure the wind noise passenger comfort level inside the vehicle is met. Sunroof broadband noise is generated by the turbulent flow developed over the roof opening. A strong shear layer and vortices impacting on the trailing edge of the sunroof are typical mechanisms related to the noise production. Sunroof designs are tested to meet broadband noise targets. Experimentally testing designs and making changes to meet these design targets typically involves high cost prototypes, expensive wind tunnel sessions and potentially late design changes.

A vehicle on the road encounters an unsteady flow due to turbulence in the natural wind, due to the unsteady wakes of other vehicles and as a result of traversing through the stationary wakes of roadside obstacles. There is increasing concern about potential differences between the steady flow conditions used for development and the transient conditions that occur on the road. This paper seeks to determine if measurements made under steady state conditions can be used to predict the aerodynamic behaviour of a vehicle on road in a gusty environment. The project has included measurements in two full size wind tunnels, including using the Pininfarina TGS, steady-state and transient inlet simulations in Exa Powerflow, and a campaign of testing on-road and on-track. The particular focus of this paper is on steady wind tunnel measurements and on-road tests, representing the most established development environment and the environment experienced by the customer, respectively.

The pressure on the base of a vehicle is a major contributor to the aerodynamic drag of all practical vehicle geometries, and for some vehicles, such as an SUV, it is particularly important because it can account for up to 50% of the overall drag. Understanding the mechanisms that influence the base pressure and developing our simulation tools to ensure that base pressure is accurately predicted are essential requirements for the vehicle design and engineering process. This paper reports an experimental study to investigate the base pressure on a specifically designed generic SUV model. The results from ¼ scale wind tunnel tests include force and moment data, surface pressures over the base region and particle image velocimetry (PIV) in the wake. Results are presented for the vehicle in different ride height, underfloor roughness and wheel configurations and the paper includes some description of the experimental errors. Some initial CFD simulations are also reported.

Simulation of local flow structures in the A-pillar/side glass region of bluff SUV geometries, typical of Land Rover vehicles, presents a considerable challenge. Features such as relatively tight A-pillar radii and upright windscreens produce flows that are difficult to simulate. However, the usefulness of aerodynamics simulations in the early assessment of wind noise depends particularly on the local accuracy obtained in this region. This paper extends work previously published by the author(1) with additional data and analysis. An extended review of the relevant published literature is also provided. Then the degree to which a commercial Lattice-Boltzman solver (Exa PowerFLOW™) is currently able to capture both the local flow structure and surface pressure distribution (both time averaged and unsteady) is evaluated. Influential factors in the simulation are shown to be spatial resolution, turbulence and boundary layer modelling.

This paper presents a methodology for simulating Fluid Structure Interaction (FSI) for a typical vehicle bonnet (hood) under a range of onset flow conditions. The hood was chosen for this study, as it is one of the panels most prone to vibration; particularly given the trend to make vehicle panels lighter. Among the worst-case scenarios for inducing vibration is a panel being subjected to turbulent flow from vehicle wakes, and the sudden peak loads caused by emerging from a vehicle wake. This last case is typical of a passing manoeuvre, with the vehicle suddenly transitioning from being immersed in the wake of the leading vehicle, to being fully exposed to the free-stream flow. The transient flowfield was simulated for a range of onset flow conditions that could potentially be experienced on the open road, which may cause substantial vibration of susceptible vehicle panels.

Vehicle development involves the design and integration of subsystems of different domains to meet performance, efficiency, and emissions targets set during the initial developmental stages. Before a physical prototype of a vehicle or vehicle powertrain is tested, engineers build and test virtual prototypes of the design(s) on multiple stages throughout the development cycle. In addition, controllers and physical prototypes of subsystems are tested under simulated signals before a physical prototype of the vehicle is available. Different departments within an automotive company tend to use different modelling and simulation tools specific to the needs of their specific engineering discipline. While this makes sense considering the development of the said system, subsystem, or component, modern holistic vehicle engineering requires the constituent parts to operate in synergy with one-another in order to ensure vehicle-level optimal performance.

Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach.

Cooling drag, typically known as the difference in drag coefficient between open and closed cooling configurations, has traditionally proven to be a difficult flow phenomenon to predict using computational fluid dynamics. It was seen as an academic yardstick before the advent of grille shutter systems. However, their introduction has increased the need to accurately predict the drag of a vehicle in a variety of different cooling configurations during vehicle development. This currently represents one of the greatest predictive challenges to the automotive industry due to being the net effect of many flow field changes around the vehicle. A comprehensive study is presented in the paper to discuss the notion of defining cooling drag as a number and to explore its effect on three automotive models with different cooling drag deltas using the commercial CFD solvers; STARCCM+ and Exa PowerFLOW.