Search Results

Aerodynamic CFD simulations in the automotive industry, which are based on the steady-state RANS (Reynolds-averaged Navier-Stokes) approach typically utilize approximate numerical methods to account for rotating wheels. In these methods, the computational mesh representing the rim geometry remains stationary, and the influence of the wheel rotation on the air flow is modelled. As the rims are considered only in one fixed rotational position (chosen arbitrarily in most cases), the effects of the rim orientation on the aerodynamic simulation results are disregarded and remain unquantified. This paper presents a numerical sensitivity study to examine the impact of the rim orientation position on the aerodynamic parameters of a detailed production vehicle. The simulations are based on the steady-state RANS approach.

Due to current progresses in the field of driver assistance systems and the continuously growing electrification of vehicle drive trains, the evaluation of driver behavior has become an important part in the development process of modern cars. Findings from driver analyses are used for the creation of individual profiles, which can be permanently adapted due to ongoing data processing. A benefit of data-based dynamic control systems lies in the possibility to individually configure the vehicle behavior for a specific driver, which can contribute to increasing customer acceptance and satisfaction. In this way, an optimization of the control behavior between driver and vehicle and the resulting mutual system learning and -adjustment hold great potential for improvements in driving behavior, safety and energy consumption.

There are several different possibilities to analyze a squealing brake system. The present paper introduces a complex measuring system which is mounted on a complete vehicle axle at a test rig. This system was developed because the previously performed state-of-the-art tests did not allow any insights in the squeal triggering mechanisms. First of all, a frequency analysis was performed. Thereby the main vibrating parts and the directions of the oscillation could be determined during a squeal event. The second was a modal analysis of the vehicle axle, which was necessary to get further insights into the system as well as to verify an existing Finite Element Method model. Through these tests, however, it was not possible to get any insight into the contact area, and therefore it was impossible to determine the squeal triggering mechanism. Because of this limitation, special guide pins were developed, which are able to measure the vibrating friction force.

Drivetrain electrification is increasing in the kart racing sector since noise emissions are an important factor in urban areas. To improve range, it has become necessary to optimize the rolling resistance of kart racing tires. This paper introduces a parameter study for small bias-ply tires which are used in kart racing and investigates the effect of these parameters on rolling resistance. In recent literature, rolling resistance is mostly examined in radial passenger car tires. Most testing devices are limited to rim sizes from ten inches upwards. In this study, a test rig was developed with focus on low cost and small rim sizes. This self-developed test rig was validated through a comparison with an approved test rig according to ISO 18164 standard. A parameter study was conducted to investigate the effect of changes in the construction of the tire. These changes affect the warp count of the carcass fabric and the crown angle of the different plies.

Virtual sensing refers to the processing of desired physical data based on measured values. Virtual sensors can be applied not only to obtain physical quantities which cannot be measured or can only be measured at an unreasonable expense but also to reduce the number of physical sensors and thus lower costs. In the field of spark ignited internal combustion engines, the virtual sensing approach may be used to predict the cylinder pressure signal (or characteristic pressure values) based on the acceleration signal of a knock sensor. This paper presents a method for obtaining the cylinder pressure signal in the high-pressure phase of an internal combustion engine based on the measured acceleration signal of a knock sensor. The approach employs a partial differential equation to represent the physical transfer function between the measured signal and the desired pressure. A procedure to fit the modeling constants is described using the example of a large gas engine.

Precise prediction of combustion parameters such as peak firing pressure (PFP) or crank angle of 50% burned mass fraction (MFB50) is essential for optimal engine control. These quantities are commonly determined from in-cylinder pressure sensor signals and are crucial to reach high efficiencies and low emissions. Highly accurate in-cylinder pressure sensors are only applied to test rig engines due to their high cost, limited durability and special installation conditions. Therefore, alternative approaches which employ virtual sensing based on signals from non-intrusive sensors retrieved from common knock sensors are of great interest. This paper presents a comprehensive comparison of selected approaches from literature, as well as adjusted or further developed methods to determine engine combustion parameters based on knock sensor signals. All methods are evaluated on three different engines and two different sensor positions.