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The automotive trends of vehicles with lower aerodynamic drag and more powerful drivetrains have caused increasing concern regarding stability issues at high speeds, since more streamlined bodies show greater sensitivity to crosswinds. This is especially pronounced for high vehicles, such as sports utility vehicles. Besides, the competitiveness in the automotive industry requires faster development times and, thus, a need to evaluate the high speed stability performance in an early design phase, preferable using simulation tools. The usefulness of these simulation tools partly relies on realistic boundary conditions for the wind and quantitative measures for assessing stability without the subjective evaluation of experienced drivers. This study employs an on-road experimental measurements setup to define relevant wind conditions and to find an objective methodology to evaluate high speed stability.

Soot formation and oxidation are complex and competing processes during diesel combustion. The balance between the two processes and their history determines engine-out soot values. Besides the efforts to lower soot formation with measures to influence the flame lift-off distance for example or to use HCCI-combustion, enhancement of late soot oxidation is of equal importance for low-λ diffusion-controlled low emissions combustion with EGR. The purpose of this study is to investigate soot oxidation in a heavy duty diesel engine by statistical analysis of engine data and in-cylinder endoscopic high speed photography together with CFD simulations with a main focus on large scale in-cylinder gas motion. Results from CFD simulations using a detailed soot model were used to reveal details about the soot oxidation.

Flow visualization techniques are widely used in aerodynamics to investigate the surface trace pattern. In this experimental investigation, the surface flow pattern over the rear end of a full-scale passenger car is studied using tufts. The movement of the tufts is recorded with a DSLR still camera, which continuously takes pictures. A novel and efficient tuft image processing algorithm has been developed to extract the tuft orientations in each image. This allows the extraction of the mean tuft angle and other such statistics. From the extracted tuft angles, streamline plots are created to identify points of interest, such as saddle points as well as separation and reattachment lines. Furthermore, the information about the tuft orientation in each time step allows studying steady and unsteady flow phenomena. Hence, the tuft image processing algorithm provides more detailed information about the surface flow than the traditional tuft method.

Due to an ongoing trend of high injection pressures in the realm of internal combustion engines, the role of cavitation that typically happens inside the injector nozzle has become increasingly important. In this work, a large Eddy Simulation (LES) with cavitation modeled on the basis of an Eulerian Stochastic Field (ESF) method and a homogeneous mixture model is performed to investigate the role of cavitation on the Engine Combustion Network (ECN) spray G2. The Eulerian stochastic field cavitation model is coupled to a pressure based solver for the flow, which lowers the computational cost, thereby making the methodology highly applicable to realistic injector geometries. Moreover, the nature of the Eulerian stochastic field method makes it more convenient to achieve a high scalability when applied to parallel cases, which gives the method the edge over cavitation models that are based on Lagrangian tracking.

The new corner-based architecture of electrified road vehicles requires a redesign of vehicle suspension components. The design protocol must satisfy the target parameters derived from dynamics requirements. The roll stiffness of the anti-roll bar is a crucial parameter for the handling performance of a vehicle. During the development of a new suspension, the design of the anti-roll bar needs to be modified. To this aim, two-dimensional beam theory models can quickly provide a preliminary design of this component. However, the simplified models might be inaccurate due to the three-dimensional and complex shapes of the bars. The present study aims to overcome this limitation. An analytical beam model based on the spline description of the bar has been developed, which is accurate even for complex geometries of the bars. Assuming a hollow and closed circular cross-section, the model returns the average diameter and the radial thickness needed to achieve the stiffness performance.

Increasing demand for simulation accuracy often leads to increased finite element model complexity, which in turn, results in higher computational costs. As a provision, component mode synthesis approaches are employed to approximate the system response by using dynamic substructuring and model reduction techniques in linear systems. However, the use of available model reduction techniques in nonlinear problems containing the contact type of nonlinearities remains an interesting topic. In this paper, the application of a component mode synthesis method in squeak and rattle nonlinear simulation has been investigated. Critical regions for squeak and rattle of the side door model of a passenger car were modelled by nonlinear contact definition in finite element simulation. Craig-Bampton model reduction method was employed to substructure the finite element model while keeping the nonlinear contacts in the model.

Approximately 25 % of a passenger vehicle’s aerodynamic drag comes directly or indirectly from its wheels, indicating that the rim geometry is highly relevant for increasing the vehicle’s overall energy efficiency. An extensive experimental study is presented where a parametric model of the rim design was developed, and statistical methods were employed to isolate the aerodynamic effects of certain geometric rim parameters. In addition to wind tunnel force measurements, this study employed the flowfield measurement techniques of wake surveys, wheelhouse pressure measurements, and base pressure measurements to investigate and explain the most important parameters’ effects on the flowfield. In addition, a numerical model of the vehicle with various rim geometries was developed and used to further elucidate the effects of certain geometric parameters on the flow field.

Wind tunnels are an essential tool in vehicle development. To simulate the relative velocity between the vehicle and the ground, wind tunnels are typically equipped with moving ground and boundary layer control systems. For passenger vehicles, wind tunnels with five-belt systems are commonly used as a trade-off between accurate replication of the road conditions and uncertainty of the force measurements. To allow different tyre sizes, the wheel drive units (WDUs) can often be fitted with belts of various widths. Using wider belts, the moving ground simulation area increases at the negative cost of larger parasitic lift forces, caused by the connection between the WDUs and the balance. In this work, a crossover SUV was tested with 280 and 360mm wide belts, capturing forces, surface pressures and flow fields. For further insights, numerical simulations were also used.

Engineering design-decisions often involve many attributes which can differ in the levels of their importance to the decision maker (DM), while also exhibiting complex statistical relationships. Learning a decision-making policy which accurately represents the DM’s actions has long been the goal of decision analysts. To circumvent elicitation and modeling issues, this process is often oversimplified in how many factors are considered and how complicated the relationships considered between them are. Without these simplifications, the classical lottery-based preference elicitation is overly expensive, and the responses degrade rapidly in quality as the number of attributes increase. In this paper, we investigate the ability of deep preference machine learning to model high-dimensional decision-making policies utilizing rankings elicited from decision makers.