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These IMechE conference transactions examine how major improvements have been made in product delivery processes by the effective use of both statistical and analytical methods, as well as examining the problems that can occur as a result of under utilization of information. This volume will be of great interest to managers, engineers, and statisticians at all levels, engaged in project management or the design and development of motor vehicles, their subsystems, and components. CONTENTS INCLUDE Applications of advanced modelling methods in engine development Application of adaptive online DoE techniques for engine ECU calibration Radial basis functions for engine modelling Designing for Six Sigma reliability Dimensional variation analysis for automotive hybrid aluminium body structures Reliability-based multidisciplinary design optimization of vehicle structures
Diesel Engine System Design links everything diesel engineers need to know about engine performance and system design in order for them to master all the essential topics quickly and to solve practical design problems. Based on the author's unique experience in the field, it enables engineers to come up with an appropriate specification at an early stage in the product development cycle. - Links everything diesel engineers need to know about engine performance and system design featuring essential topics and techniques to solve practical design problems - Focuses on engine performance and system integration including important approaches for modelling and analysis - Explores fundamental concepts and generic techniques in diesel engine system design incorporating durability, reliability and optimization theories
The book includes contributions on the latest model-based methods for the development of personal and commercial vehicle control devices. The main topics treated are: application of simulation and model design to development of driver assistance systems; physical and database model design for engines, motors, powertrain, undercarriage and the whole vehicle; new simulation tools, methods and optimization processes; applications of simulation in function and software development; function and software testing using HiL, MiL and SiL simulation; application of simulation and optimization in application of control devices; automation approaches at all stages of the development process.
This thesis deals with the development of a model-based adaptive test design strategy with a focus on steady-state combustion engine calibration. The first research topic investigates the question how to handle limits in the input domain during an adaptive test design procedure. The second area of scope aims at identifying the test design method providing the best model quality improvement in terms of overall model prediction error. To consider restricted areas in the input domain, a convex hull-based solution involving a convex cone algorithm is developed, the outcome of which serves as a boundary model for a test point search. A solution is derived to enable the application of the boundary model to high-dimensional problems without calculating the exact convex hull and cones. Furthermore, different data-driven engine modeling methods are compared, resulting in the Gaussian process model as the most suitable one for a model-based calibration. To determine an appropriate test design method for a Gaussian process model application, two new strategies are developed and compared to state-of-the-art methods. A simulation-based study shows the most benefit applying a modified mutual information test design, followed by a newly developed relevance-based test design with less computational effort. The boundary model and the relevance-based test design are integrated into a multicriterial test design strategy that is tailored to match the requirements of combustion engine test bench measurements. A simulation-based study with seven and nine input parameters and four outputs each offered an average model quality improvement of 36 % and an average measured input area volume increase of 65 % compared to a non-adaptive space-filling test design. The multicriterial test design was applied to a test bench measurement with seven inputs for verification. Compared to a space-filling test design measurement, the improvement could be confirmed with an average model quality increase of 17 % over eight outputs and a 34 % larger measured input area. Diese Arbeit befasst sich mit der Entwicklung einer modellbasierten adaptiven Versuchsplanungsstrategie für die Anwendung in der Applikation des Stationärverhaltens von Verbrennungsmotoren. Der erste Forschungsteil untersucht, wie sich Grenzen im Eingangsraum in die Versuchsplanung eines adaptiven Prozesses einbinden lassen. Ein weiterer Fokus liegt auf der Identifikation einer modellbasierten Versuchsplanung, die eine bestmögliche Verbesserung der globalen Modellqualität hinsichtlich des Prädiktionsfehlers ermöglicht. Es wird ein Grenzraummodell auf Basis der konvexen Hülle unter Zuhilfenahme eines Algorithmus zur Bestimmung eines konvexen Konus entwickelt, das als Grundlage für eine Versuchsplanung in beschränkten Eingangsräumen verwendet wird. Um die Anwendbarkeit bei hochdimensionalen Problemstellungen zu gewährleisten, wird ein Verfahren vorgestellt, das eine Berechnung auch ohne die Bestimmung der exakten konvexen Hülle und konvexen Konen ermöglicht. Des Weiteren werden verschiedene Methoden zur datengetriebenen Modellbildung des Verbrennungsmotors verglichen, wobei das Gauß-Prozess Modell als die geeignetste Modellierungsmethode hervorgeht. Um die bestmögliche Versuchsplanungsmethode bei der Anwendung des Gauß-Prozess Modells zu ermitteln, werden zwei neue Strategien entwickelt und mit verfügbaren Methoden aus der Literatur verglichen. Eine simulationsbasierte Studie zeigt, dass eine angepasste Mutual Information Methode die besten Ergebnisse liefert. Ein neu entwickeltes relevanzbasiertes Verfahren erreicht die zweitbesten Ergebnisse, bietet aber einen geringeren Berechnungsaufwand als das Mutual Information Verfahren. Das Grenzmodell und das relevanzbasierte Verfahren werden in einem multikriteriellen Versuchsplanungsverfahren zusammengeführt, das an die Anforderungen von Messungen an einem Verbrennungsmotorenprüfstand angepasst ist. In einer simulationsbasierten Studie mit sieben bzw. neun Eingangsparametern und jeweils vier Ausgängen konnte eine durchschnittliche Modellqualitätsverbesserung von 36 % und eine mittlere Vergrößerung des vermessenen Eingangsraumvolumens von 65 % im Vergleich zu einer nichtadaptiven raumfüllenden Versuchsplanung gezeigt werden. Das multikriterielle Versuchsplanungsverfahren wurde anhand von Prüfstandsmessungen mit sieben Eingangsparametern verifiziert. Im Vergleich zu einer raumfüllenden Versuchsplanung konnte eine mittlere Modellqualitätsverbesserung über alle acht Ausgänge von 17 % und ein um 34 % vergrößertes vermessenes Eingangsraumvolumen erreicht werden, wodurch die Ergebnisse der Simulationen bestätigt werden konnten.
The holistic view of powertrain development that includes engine, transmission and driveline is now well accepted. Current trends indicate an increasing range of engines and transmissions in the future with, consequently, a greater diversity of combinations. Coupled with the increasing introduction of hybrid vehicles, the scope for research, novel developments and new products is clear. This volume presents a collection of papers from the Institution of Mechanical Engineers Conference Integrated Powertrain and Driveline Systems 2006 (IPDS 2006) organised by the IMechE Automobile Division. Main themes include transmissions; concept to market evolution; powertrain integration; and engine integration. Novel concepts relating, for example, to continuously variable transmissions (CVTs) and hybridization are discussed, as well as approaches to modelling and simulation. - The main themes include transmissions, concept to market evolution and powertrain evolution - Diiscusses concepts relating to continuously variable transmissions and hybridization
More than 120 authors from science and industry have documented this essential resource for students, practitioners, and professionals. Comprehensively covering the development of the internal combustion engine (ICE), the information presented captures expert knowledge and serves as an essential resource that illustrates the latest level of knowledge about engine development. Particular attention is paid toward the most up-to-date theory and practice addressing thermodynamic principles, engine components, fuels, and emissions. Details and data cover classification and characteristics of reciprocating engines, along with fundamentals about diesel and spark ignition internal combustion engines, including insightful perspectives about the history, components, and complexities of the present-day and future IC engines. Chapter highlights include: • Classification of reciprocating engines • Friction and Lubrication • Power, efficiency, fuel consumption • Sensors, actuators, and electronics • Cooling and emissions • Hybrid drive systems Nearly 1,800 illustrations and more than 1,300 bibliographic references provide added value to this extensive study. “Although a large number of technical books deal with certain aspects of the internal combustion engine, there has been no publication until now that covers all of the major aspects of diesel and SI engines.” Dr.-Ing. E. h. Richard van Basshuysen and Professor Dr.-Ing. Fred Schäfer, the editors, “Internal Combustion Engines Handbook: Basics, Components, Systems, and Perpsectives”
Volume 2 of the two-volume set Advanced direct injection combustion engine technologies and development investigates diesel DI combustion engines, which despite their commercial success are facing ever more stringent emission legislation worldwide. Direct injection diesel engines are generally more efficient and cleaner than indirect injection engines and as fuel prices continue to rise DI engines are expected to gain in popularity for automotive applications. Two exclusive sections examine light-duty and heavy-duty diesel engines. Fuel injection systems and after treatment systems for DI diesel engines are discussed. The final section addresses exhaust emission control strategies, including combustion diagnostics and modelling, drawing on reputable diesel combustion system research and development. - Investigates how HSDI and DI engines can meet ever more stringent emission legislation - Examines technologies for both light-duty and heavy-duty diesel engines - Discusses exhaust emission control strategies, combustion diagnostics and modelling
This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential difficulties that may arise in practice. Moreover, the book is self-contained, requiring only a basic grasp of matrix algebra, signals and systems, and statistics. Accordingly, it can also serve as an introduction to linear system identification, and provides a practical overview of the major optimization methods used in engineering. The focus is on gaining an intuitive understanding of the subject and the practical application of the techniques discussed. The book is not written in a theorem/proof style; instead, the mathematics is kept to a minimum, and the ideas covered are illustrated with numerous figures, examples, and real-world applications. In the past, nonlinear system identification was a field characterized by a variety of ad-hoc approaches, each applicable only to a very limited class of systems. With the advent of neural networks, fuzzy models, Gaussian process models, and modern structure optimization techniques, a much broader class of systems can now be handled. Although one major aspect of nonlinear systems is that virtually every one is unique, tools have since been developed that allow each approach to be applied to a wide variety of systems.
This book highlights the latest research in computational intelligence and its applications. It covers both conventional and trending approaches in individual chapters on Fuzzy Systems, Intelligence in Robotics, Deep Learning Approaches, Optimization and Classification, Detection, Inference and Prediction, Hybrid Methods, Emerging Intelligence, Intelligent Health Care, and Engineering Data- and Model-Driven Applications. All chapters are based on peer-reviewed contributions presented at the 19th Annual UK Workshop on Computational Intelligence, held in Portsmouth, UK, on 4–6 September 2019. The book offers a valuable reference guide for readers with expertise in computational intelligence or who are seeking a comprehensive and timely review of the latest trends in computational intelligence. Special emphasis is placed on novel methods and their use in a wide range of application areas, updating both academics and professionals on the state of the art.