Der Vortrag behandelt die praktische Umsetzung einer Rast in Rast Kurvenscheibensynthese (Übertragungsfunktion: modifizierte Sinoide) bis zum Ableiten einer ersten Grundform der Kurvenscheibe. Aufgezeigt werden dabei die notwendigen Schritte zur Parametrisierung der Eingabedaten wie Kurvenscheiben- und Übertragungswinkel sowie das spätere Ausleiten einer Spurkurve (Bewegungsaufzeichnung des Kontaktpunktes Kurvenscheibe/Koppel) in das Modell der Kurvenscheibe. Die folgenden notwendigen Optimierungsschritte zur Reduzierung des Krafteingriffswinkels, des Normalkraftverlaufs und der Kurvenscheiben - Mittelpunktslage werden theoretisch behandelt. Das Umsetzen der Optimierung könnte mit Hilfe des Behavioral Modeling Extension - Moduls aus Creo Elements/Pro erfolgen.

With ever-increasing numbers of engine actuators to calibrate within increasingly stringent emissions legislation, the engine mapping and calibration task of identifying optimal actuator settings is much more difficult. The aim of this research is to evaluate the feasibility and effectiveness of the Multidisciplinary Design Optimisation (MDO) frameworks to optimise the multi-attribute steady state engine calibration optimisation problems. Accordingly, this research is concentrated on two aspects of the steady state engine calibration optimisation: 1) development of a sequential Design of Experiment (DoE) strategy to enhance the steady state engine mapping process, and 2) application of different MDO architectures to optimally calibrate the complex engine applications. The validation of this research is based on two case studies, the mapping and calibration optimisation of a JLR AJ133 Jaguar GDI engine; and calibration optimisation of an EU6 Jaguar passenger car diesel engine. These case studies illustrated that: -The proposed sequential DoE strategy offers a coherent framework for the engine mapping process including Screening, Model Building, and Model Validation sequences. Applying the DoE strategy for the GDI engine case study, the number of required engine test points was reduced by 30 – 50 %. - The MDO optimisation frameworks offer an effective approach for the steady state engine calibration, delivering a considerable fuel economy benefits. For instance, the MDO/ATC calibration solution reduced the fuel consumption over NEDC drive cycle for the GDI engine case study (i.e. with single injection strategy) by 7.11%, and for the diesel engine case study by 2.5%, compared to the benchmark solutions.

The use of parametric CAD models in engineering, analysis, and optimization has greatly enhanced the effectiveness and efficiency of the product development process. Parametric models provide an attractive avenue for expansive design exploration. There still exists, however, a great challenge for products requiring design input from multiple disciplines. The collaboration of engineering disciplines can be hampered by many factors including: competing design objectives, communication of design changes, the use of different design and analysis software, and different geometry definitions. These obstacles become compounded when developing products at the assembly level. The use of solid parametric assembly models is not readily employed for products developed by groups from differing engineering disciplines. This is due to the huge cooperative effort required to create, analyze, and iterate on the geometry of the assembly model. The objective of this thesis is to present a method for separate disciplines to be able to analyze multiple parameterizations of the same CAD assembly to help develop a master parametric assembly, and to define the design space to be explored during analysis and optimization. This is done through a custom application developed using the Application Programming Interface of Siemens' NX CAD software. The custom application allows the user to monitor the affects of manipulating the driving parameters of an assembly by observing user specified geometry, features, or parametric expressions. The application also allows switching from one set of parametric design rules controlling the assembly to another in a matter of seconds. Manipulating and observing key geometry from different parameterizations allows engineering teams to discover the impact of each discipline's driving equations and geometry on another discipline. This will have a profound impact on multidisciplinary design teams in developing a robust parametric assembly, while still taking consideration of the requirements of each discipline. The collaborative efforts in the development of parametric assembly models used by multidisciplinary design teams are vastly improved through the method and application developed herein. This research will also show both the enhancements that could be made to existing CAD software, as well as the benefits of custom design tool development within the CAD environment.

No / This paper presents the development of an Analytical Target Cascading (ATC) Multidisciplinary Design Optimization (MDO) framework for a steady-state engine calibration optimization problem. The implementation novelty of this research is the use of the ATC framework to formulate the complex multi-objective engine calibration problem, delivering a considerable enhancement compared to the conventional 2-stage calibration optimization approach [1]. A case study of a steady-state calibration optimization of a Gasoline Direct Injection (GDI) engine was used for the calibration problem analysis as ATC. The case study results provided useful insight on the efficiency of the ATC approach in delivering superior calibration solutions, in terms of “global” system level objectives (e.g. improved fuel economy and reduced particulate emissions), while meeting “local” subsystem level requirements (such as combustion stability and exhaust gas temperature constraints). The ATC structure facilitated the articulation of engineering preference for smooth calibration maps via the ATC linking variables, with the potential to deliver important time saving for the overall calibration development process.

With ever-increasing numbers of engine actuators to calibrate within increasingly stringent emissions legislation, the engine mapping and calibration task of identifying optimal actuator settings is much more difficult. The aim of this research is to evaluate the feasibility and effectiveness of the Multidisciplinary Design Optimisation (MDO) frameworks to optimise the multi-attribute steady state engine calibration optimisation problems. Accordingly, this research is concentrated on two aspects of the steady state engine calibration optimisation: 1) development of a sequential Design of Experiment (DoE) strategy to enhance the steady state engine mapping process, and 2) application of different MDO architectures to optimally calibrate the complex engine applications. The validation of this research is based on two case studies, the mapping and calibration optimisation of a JLR AJ133 Jaguar GDI engine; and calibration optimisation of an EU6 Jaguar passenger car diesel engine. These case studies illustrated that: -The proposed sequential DoE strategy offers a coherent framework for the engine mapping process including Screening, Model Building, and Model Validation sequences. Applying the DoE strategy for the GDI engine case study, the number of required engine test points was reduced by 30 – 50 %. - The MDO optimisation frameworks offer an effective approach for the steady state engine calibration, delivering a considerable fuel economy benefits. For instance, the MDO/ATC calibration solution reduced the fuel consumption over NEDC drive cycle for the GDI engine case study (i.e. with single injection strategy) by 7.11%, and for the diesel engine case study by 2.5%, compared to the benchmark solutions. / UK Technology Strategy Board (TSB)

Automotive companies are exposed to tough competition and therefore strive to design better products in a cheaper and faster manner. This challenge requires continuous improvements of methods and tools, and simulation models are therefore used to evaluate every possible aspect of the product. Optimization has become increasingly popular, but its full potential is not yet utilized. The increased demand for accurate simulation results has led to detailed simulation models that often are computationally expensive to evaluate. Metamodel-based design optimization (MBDO) is an attractive approach to relieve the computational burden during optimization studies. Metamodels are approximations of the detailed simulation models that take little time to evaluate and they are therefore especially attractive when many evaluations are needed, as e.g. in multidisciplinary design optimization (MDO). In this thesis, state-of-the-art methods for metamodel-based design optimization are covered and different multidisciplinary design optimization methods are presented. An efficient MDO process for large-scale automotive structural applications is developed where aspects related to its implementation is considered. The process is described and demonstrated in a simple application example. It is found that the process is efficient, flexible, and suitable for common structural MDO applications within the automotive industry. Furthermore, it fits easily into an existing organization and product development process and improved designs can be obtained even when using metamodels with limited accuracy. It is therefore concluded that by incorporating the described metamodel-based MDO process into the product development, there is a potential for designing better products in a shorter time.

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A decomposition methodology based on the concept of "thermoeconomic isolation" applied to the synthesis/design and operational optimization of an advanced tactical fighter aircraft is the focus of this research. Conceptual, time, and physical decomposition were used to solve the system-level as well as unit-level optimization problems. The total system was decomposed into five sub-systems as follows: propulsion sub-system (PS), environmental control sub-system (ECS), fuel loop sub-system (FLS), vapor compressor and PAO loops sub-system (VC/PAOS), and airframe sub-system (AFS) of which the AFS is a non-energy based sub-system. Configurational optimization was applied. Thus, a number of different configurations for each sub-system were considered. The most promising set of candidate configurations, based on both an energy integration analysis and aerodynamic performance, were developed and detailed thermodynamic, geometric, physical, and aerodynamic models at both design and off-design were formulated and implemented. A decomposition strategy called Iterative Local-Global Optimization (ILGO) developed by Muñoz and von Spakovsky was then applied to the synthesis/design and operational optimization of the advanced tactical fighter aircraft. This decomposition strategy is the first to successfully closely approach the theoretical condition of "thermoeconomic isolation" when applied to highly complex, highly dynamic non-linear systems. This contrasts with past attempts to approach this condition, all of which were applied to very simple systems under very special and restricted conditions such as those requiring linearity in the models and strictly local decision variables. This is a major advance in decomposition and has now been successfully applied to a number of highly complex and dynamic transportation and stationary systems. This thesis work presents the detailed results from one such application, which additionally considers a non-energy based sub-system (AFS). / Master of Science