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An Innovative 3D-CFD-Approach towards Virtual Development of Internal Combustion Engines
In the engine development process, simulation and predictive programs have continuously gained in reliance. Due to the complexity of future internal combustion engines the application of simulation programs towards a reliable “virtual engine development” is a need that represents one of the greatest challenges. Marco Chiodi presents an innovative 3D-CFD-tool, exclusively dedicated and optimized for the simulation of internal combustion engines. Thanks to improved or newly developed 3D-CFD-models for the description of engine processes, this tool ensures an efficient and reliable calculation also by using coarse 3D-CFD-meshes. Based on this approach the CPU-time can be reduced up to a factor 100 in comparison to traditional 3D-CFD-simulations. In addition an integrated and automatic “evaluation tool” establishes a comprehensive analysis of the relevant engine parameters. Due to the capability of a reliable “virtual development” of full-engines, this fast response 3D-CFD-tool makes a major contribution to the engine development process. Südwestmetall-Förderpreis 2010
A Quasi-dimensional Charge Motion and Turbulence Model for Combustion and Emissions Prediction in Diesel Engines with a fully Variable Valve Train
Qirui Yang develops a model chain for the simulation of combustion and emissions of diesel engine with fully variable valve train (VVT) based on extensive 3D-CFD simulations, and experimental measurements on the engine test bench. The focus of the work is the development of a quasi-dimensional (QDM) flow model, which sets up a series of sub-models to describe phenomenologically the swirl, squish and axial charge motions as well as the shear-related turbulence production and dissipation. The QDM flow model is coupled with a QDM combustion model and a nitrogen oxides (NOx) / soot emission model. With the established model chain, VVT operating strategies of diesel engine can be developed and optimized as part of the simulation for specific engine performance parameters and the lowest NOx and soot emissions.
Potential of Water Injection for Gasoline Engines by Means of a 3D-CFD Virtual Test Bench
Water injection is one of the most promising technologies to improve the engine combustion efficiency, by mitigating knock occurrences and controlling exhaust gas temperature before turbine. As result, the engine can operate at stoichiometric conditions over the whole engine map, even during the more power-demanding RDE cycles. Antonino Vacca presents a methodology to study and optimize the effect of water injection for gasoline engines by investigating different engine layouts and injection strategies through the set-up of a 3D-CFD virtual test bench. He investigates indirect and direct water injection strategies to increase the engine knock limit and to reduce exhaust gas temperature for several operating points.
Knocking Combustion of Methane-Based and Highly Knock Resistant Liquid Fuels
Marcel Eberbach provides insight into the investigations of the knocking behavior of methane-based fuels and compares them with the knocking behavior of very high knock resistant liquid fuels during engine combustion. With pressure-based knock detection algorithms and thermodynamic evaluation, the atypical knocking combustion phenomena are evaluated with respect to the abnormalities on the heat release curve. Based on the investigated fuels an engine specific relation between the fuel index numbers (RON and MN) and the actual knock resistance of the fuels by means of the motor methane number was established and applied to the investigated gaseous and liquid fuels during knocking combustion.
A Phenomenological Knock Model for the Development of Future Engine Concepts
The majority of 0D/1D knock models available today are known for their poor accuracy and the great effort needed for their calibration. Alexander Fandakov presents a novel, extensively validated phenomenological knock model for the development of future engine concepts within a 0D/1D simulation environment that has one engine-specific calibration parameter. Benchmarks against the models commonly used in the automotive industry reveal the huge gain in knock boundary prediction accuracy achieved with the approach proposed in this work. Thus, the new knock model contributes substantially to the efficient design of spark ignition engines employing technologies such as full-load exhaust gas recirculation, water injection, variable compression ratio or lean combustion. About the Author Alexander Fandakov holds a PhD in automotive powertrain engineering from the Institute of Internal Combustion Engines and Automotive Engineering (IVK) at the University of Stuttgart, Germany. Currently, he is working as an advanced powertrain development engineer in the automotive industry.
Leading in Hyper-Complexity
Today's leaders and managers have to operate in an environment characterized by complexity. What does coping with this ever- and faster-increasing degree of complexity require of them? It means that if they are to deal successfully with all the challenges they need to reconsider their leadership approaches. Leading in Hyper-Complexity is dedicated to all experienced managers and leaders who lead across space and time while simultaneously operating in 'conventional' structures. It takes a closer look at how to lead, and how to navigate in today's complex and sometimes unpredictable business environment. Anyone experienced in tackling today's complexity will know that you cannot expect to find easy-to-follow recipes for success. But we are not powerless, as is shown by the ten in-depth interviews with top managers from different cultures featured in this book.
Transient Effects in Simulations of Hybrid Electric Drivetrains
This work presents an investigation of the influence of different modeling approaches on the quality of fuel economy simulations of hybrid electric powertrains. The main focus is on the challenge to accurately include transient effects and reduce the computation time of complex models. Methods for the composition of entire powertrain models are analyzed as well as the modeling of the individual components internal combustion engine and battery. The results shall help with the selection of suitable models for specific simulation tasks and provide a deeper understanding of the dynamic processes within simulations of hybrid electric vehicles. About the Author Florian Winke was research associate at the Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (FKFS), where he worked on modeling and simulation of hybrid electric powertrains. After finishing his doctorate, he joined a German automotive manufacturer, where he is working in software development in the field of hybrid operation strategies.
Fail-operational Safety Architecture for ADAS/AD Systems and a Model-driven Approach for Dependent Failure Analysis
Bülent Sari deals with the various fail-operational safety architecture methods developed with consideration of domain ECUs containing multicore processors and describes the model-driven approaches for the development of the safety lifecycle and the automated DFA. The methods presented in this study provide fail-operational system architecture and safety architecture for both conventional domains such as powertrains and for ADAS/AD systems in relation to the processing chain from sensors to actuators. About the Author: Bülent Sari works as a functional safety expert for autonomous driving projects. His doctoral thesis was supervised at the Institute of Internal Combustion Engines and Automotive Engineering, University of Stuttgart, Germany. He is a technical lead for not only functional safety in vehicles, but also for SOTIF, embracing the ISO 26262 standard as well as ISO PAS 21448. In this role, he coordinates and organizes the safety case execution of several product groups within different divisions of ZF.
Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data
Philipp Bergmeir works on the development and enhancement of data mining and machine learning methods with the aim of analysing automatically huge amounts of load spectrum data that are recorded for large hybrid electric vehicle fleets. In particular, he presents new approaches for uncovering and describing stress and usage patterns that are related to failures of selected components of the hybrid power-train.
