Search Results

Wet-running multi-plate clutches and brakes are important components of modern powershift gearboxes and industrial powertrains. In the open stage, drag losses occur due to fluid shear. The identification of drag losses is possible by experiment or CFD-simulation. For the calculation of the complex fluid flow of an open clutch, CFD-approaches such as the volume of fluid (vof) method or the Singhal cavitation model are applicable. Every method has its own specific characteristics. This contribution sets up CFD-calculation models for different clutches with diverse groove designs. We present results of calculations in various operating conditions obtained from the Singhal cavitation model and the vof method. The usage of modern commercial CFD-Tools (Simerics MP+) results in short calculation times.

Laser induced fluorescence (LIF) is used to investigate oil transport mechanisms under real engine conditions. The engine oil is mixed with a dye that can be induced by a laser. The emitted light intensity from the dye correlates with the residual oil at the sensor position and the resulting oil film thicknesses can be precisely determined for each crank angle. However, the general expectation is not always achieved, e.g. an exact representation of piston ring barrel shapes. In order to investigate the responsible lubrication effects of this behavior, a new cavitation algorithm for the Reynolds equation has been developed. The solution retains the mass conservation and does not use any switch function in its mathematical approach. In contrast to common approaches, no vapor-liquid ratio is used, but one or several bigger bubbles are approximated, as have been observed in other experiments already.

Development processes for modern combustion engines already make substantial use of more or less sophisticated simulation approaches. The enhancement of computational resources additionally allows the increasing use of simulation tools in terms of time-consuming three-dimensional CFD approaches. In particular, the preliminary estimation of feasible operating ranges and strategies requires a vast multitude of single simulations. Here, multi-zone simulation approaches incorporate the advantages of comparably short simulation durations. Nevertheless, the combination with more detailed sub-models allows these rather simple modeling approaches to offer considerable insight into relevant engine operation phenomena. In the context of combustion process development, this paper describes a phenomenological model approach for the prediction of operating point characteristics of a dual-fuel pilot injection combustion process.

Autonomous driving systems and connected mobility are the next big developments for the car manufacturers and their suppliers during the next decade. To achieve the high computing power needs and fulfill new upcoming requirements due to functional safety and security, heterogeneous processor architectures with a mixture of different core architectures and hardware accelerators are necessary. To tackle this new type of hardware complexity and nevertheless stay within monetary constraints, high performance computers, inspired by state of the art data center hardware, could be adapted in order to fulfill automotive quality requirements. The European Processor Initiative (EPI) research project tries to come along with that challenge for next generation semiconductors. To be as close as possible to series development needs for the next upcoming car generations, we present a hybrid semiconductor system-on-chip architecture for automotive.

The recent growth of available computational resources has enabled the automotive industry to utilize unsteady Computational Fluid Dynamics (CFD) for their product development on a regular basis. Over the past years, it has been confirmed that unsteady CFD can accurately simulate the transient flow field around complex geometries. Concerning the aerodynamic properties of road vehicles, the detailed analysis of the transient flow field can help to improve the driving stability. Until now, however, there haven’t been many investigations that successfully identified a specific transient phenomenon from a simulated flow field corresponding to driving stability. This is because the unsteady flow field around a vehicle consists of various time and length scales and is therefore too complex to be analyzed with the same strategies as for steady state results.

A key criterion for launching autonomous vehicles on real roads is the knowledge of their capability to ensure traffic safety. In contrast to ADAS, deriving this measure of safety is difficult to achieve as the functional scope of an autonomous driving function exceeds by far the one of ADAS. As a consequence, real-world testing solely is not sufficient enough to cover the required test volume. This assessment problem imposes new requirements on a valid test concept for automated driving. A possible solution represents simulation by enabling it to generate reliable test kilometers. As a first step, we discuss in this paper the feasibility of simulation frameworks to re-simulate a real-world test in certain scenarios. We will demonstrate that even with ground truth information of the vehicle odometry and corresponding environment model an acceptable accordance of functional behavior is not guaranteed.

In this paper a method for the identification of most significant vehicle parameters influencing the behavior of a lateral control system of autonomous car is presented. Requirements for the design stage of the controller need to consider many uncertainties in the plant. While most vehicle properties can be compensated by an appropriate tuning of the control parameters, other vehicle properties can change significantly during usage. The control system is evaluated based on performance measures. Analyzed parameters comprise functional tire characteristics, mass of the vehicle and position of its center of gravity. Since the parameters are correlated, but Sobol’ sensitivity analysis assumes decorrelated inputs, random variation yields no reasonable results. Furthermore, the variation of each parameter or set of parameters is not applicable since the numbers of required simulations is increased significantly according to input dimension.

The performance of environment perceiving sensors such as e.g. lidar, radar, camera and ultrasonic sensors is safety critical for automated driving vehicles. Therefore, one has to assess the sensors’ performance to assure the automated driving system’s safety. The performance of these sensors is however to some degree sensitive towards adverse weather conditions. A challenge is to quantify the effect of adverse weather conditions on the sensor’s performance early in the development of an automated driving system. This challenge is addressed in this work for lidar sensors. The lidar equation was previously employed in this context to derive estimates of a lidar’s maximum range in different weather conditions. In this work, we present a stochastic simulation framework based on a probabilistic extension of the lidar equation, to quantify the effect of adverse rainfall conditions on a lidar’s raw detection performance.

Vehicle thermal management (VTM) simulations constitute an important step in the early development phase of a vehicle. They help in predicting the temperature profiles of critical components over a drive cycle and identify components which are exceeding temperature design limits. Parts with the highest temperatures in a vehicle with an internal combustion engine are concentrated in the engine bay area. As packaging constraints grow tighter, the components in the engine bay are packed closer together. This makes the thermal protection in the engine bay even more crucial. The fan influences the airflow into the engine bay and plays an important role in deciding flow distribution in this region. This makes modelling of the fan an important aspect of VTM simulations. The challenge associated with modelling the fan is the accurate simulation of the rotation imparted by the fan to the incoming flow. Currently, two modelling approaches are prevalent in the industry.

Over the past years, the question as to what may be the powertrain of the future has become ever more apparent. Aiming to improve upon a given technology, the internal combustion engine still offers a number of development paths in order to maintain its position in public and private mobility. In this study, an innovative combustion process is investigated with the goal to further approximate the ideal Otto cycle. Thus far, similar approaches such as Homogeneous Charge Compression Ignition (HCCI) shared the same objective yet were unable to be operated under high load conditions. Highly increased control efforts and excessive mechanical stress on the components are but a few examples of the drawbacks associated with HCCI. The approach employed in this work is the so-called Spark Assisted Compression Ignition (SACI) in combination with a pre-chamber spark plug, enabling short combustion durations even at high dilution levels.

Thermoplastic fibre reinforced composites offer a wide range of adjusting the material behaviour by varying material selection, layup and fibre orientation. By default, damping and stiffness of composites are contradictory material properties related to the fibre orientation. Thus, finite element analysis (FEA) based composite design requires special modelling efforts implying anisotropic damping of the composite as well as fluid-structure-inter-action for the oil filling. In contrast, multi-dimensional optimisations for various layups require computationally fast numerical solutions. In this study, a complex but efficient vibro-acoustic modelling approach of a composite oil pan is presented. The FEA model includes a strain energy based modal damping approach for the layerwise accumulation of the anisotropic composite damping as well as a structural representation of the additional mass of the oil filling avoiding fluid modelling.

Measurement techniques for piston ring rotation, engine oil emission and blow by have been implemented on a single-cylinder petrol engine. A novel method of analysis allows continuous and fast real-time identification of the piston ring rotation of the two compression rings, while the mass-spectrometric analysis of the exhaust gas delivers the cylinder oil emission instantly and with a high temporal resolution. Only minor modifications to the piston rings were made for the insertion of the γ-emitters, the rings rotate freely around the circumference of the piston. The idea of this setup is that through online observation at the test bench, instant feedback of the measured variables is available, making it possible to purposefully select and compare measurement points. The high time resolution of the measurement methods enables the analysis of dynamic effects. In this article, the measurement setup and evaluation method is described.

The effectiveness of ADAS addressing property damage has an increasing impact on car manufacturers, insurers and customers, as accident avoidance or mitigation can lead to loss reduction. In order to obtain benefits, it is essential that ADAS primarily address monetarily relevant accident scenarios. Furthermore, sensor technologies and algorithms have to be configured in a way that relevant accident situations can be sufficiently avoided at reasonable system costs. A new methodology is developed to identify and configure monetarily effective parameters for ADAS during parking and maneuvering. ADAS parameters e.g. relevant accident scenarios, required crash avoidance speeds and different sensor layouts are analyzed and evaluated using a real-world in-depth accident database of insurance claims provided by Allianz Center for Technology and Allianz Automotive Innovation Center. For this purpose, a sensitivity analysis is conducted to identify most monetarily effective accident scenarios.

In times when automated driving is becoming increasingly relevant, dynamic simulators present an appropriate simulation environment to faithfully reproduce driving scenarios. A realistic replication of driving dynamics is an important criterion to immerse persons in the virtual environments provided by the simulator. Motion Cueing Algorithms (MCAs) compute the simulator’s control input, based on the motions of the simulated vehicle. The technical restrictions of the simulator’s actuators form the main limitation in the execution of these input commands. Typical dynamic simulators consist of a hexapod with six degrees of freedom (DoF) to reproduce the vehicle motion in all dimensions. Since its workspace dimensions are limited, significant improvements in motion capabilities can be achieved by expanding the simulator with redundant DoF by means of additional actuators.

Aging effects such as coking or erosive damage that occur in fuel injection nozzles are known to deteriorate the engine performance. This article proposes an optimization method to compensate for injector aging and to control the combustion behavior over engine lifetime by adapting the injection strategy. First, a control-oriented combustion model is presented, which takes the condition of the injection nozzle into account. In combination with a simulation model of the entire fuel injection system from a previous study, the model is capable of predicting the heat release rate (HRR) at different working conditions. Measurements with a single-cylinder diesel engine were performed, using injectors with modified and aged nozzles, to validate the proposed combustion model and particularly to analyze the influence of injector aging. Using the simulation model, optimal injection strategies were obtained by applying a line search optimization scheme to recover a reference HRR trajectory.

In order to fulfill future exhaust emission regulations, the variety of subsystems of internal combustion engines is progressively investigated and optimized in detail. The present article mainly focuses on studies of the flow field and the resulting discharge coefficients of the intake and exhaust valves and ports. In particular, the valves and ports influence the required work for the gas exchange process, as well as the cylinder charge and consequently highly impact the engine’s performance. For the evaluation of discharge coefficients of a modern combustion engine, a stationary flow test bench has been set up at the Chair of Internal Combustion Engines (LVK) of the Technical University of Munich (TUM). The setup is connected to the test bench’s charge air system, allowing the adjustment and control of the system pressure, as well as the pressure difference across the particular gas exchange valve.

Over the past decades, interior noise from wind noise or engine noise have been significantly reduced by leveraging improvements of both the overall vehicle design and of sound package. Consequently, noise sources originating from HVAC systems (Heat Ventilation and Air Conditioning), fans or exhaust systems are becoming more relevant for perceived quality and passenger comfort. This study focuses on HVAC systems and discusses a Flow-Induced Noise Detection Contributions (FIND Contributions) numerical method enabling the identification of the flow-induced noise sources inside and around HVAC systems. This methodology is based on the post-processing of unsteady flow results obtained using Lattice Boltzmann based Method (LBM) Computational Fluid Dynamics (CFD) simulations combined with LBM-simulated Acoustic Transfer Functions (ATF) between the position of the sources inside the system and the passenger’s ears.

Highly automated driving (HAD) is under rapid development and will be available for customers within the next years. However the evidence that HAD is at least as safe as human driving has still not been produced. The challenge is to drive hundreds of millions of test kilometers without incidents to show that statistically HAD is significantly safer. One approach is to let a HAD function run in parallel with human drivers in customer cars to utilize a fraction of the billions of kilometers driven every year. To guarantee safety, the function under test (FUT) has access to sensors but its output is not executed, which results in an open loop problem. To overcome this shortcoming, the proposed method consists of four steps to close the loop for the FUT. First, sensor data from real driving scenarios is fused in a world model and enhanced by incorporating future time steps into original measurements.

With increasing levels of driving automation, the perception provided by automotive environment sensors becomes highly safety relevant. A correct assessment of the sensors’ perception reliability is therefore crucial for ensuring the safety of the automated driving functionalities. There are currently no standardized procedures or guidelines for demonstrating the perception reliability of the sensors. Engineers therefore face the challenge of setting up test procedures and plan test drive efforts. Null Hypothesis Significance Testing has been employed previously to answer this question. In this contribution, we present an alternative method based on Bayesian parameter inference, which is easy to implement and whose interpretation is more intuitive for engineers without a profound statistical education. We show how to account for different environmental conditions with an influence on sensor performance and for statistical dependence among perception errors.

Improving fuel efficiency while meeting relevant emission limits set by emissions legislation is among the main objectives of engine development. Simultaneously the development costs and development time have to be steadily reduced. For these reasons, the high demands in terms of quality and validity of measurements at the engine test bench are continuously rising. This paper will present a new methodology for efficient testing of an industrial combustion engine in order to improve the process of decision making for combustion-relevant component setups. The methodology includes various modules for increasing measurement quality and validity. Modules like stationary point detection to determine steady state engine behavior, signal quality checks to monitor the signal quality of chosen measurement signals and plausibility checks to evaluate physical relations between several measurement signals ensure a high measurement quality over all measurements.