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This paper presents a calibration optimization study for a Gasoline Direct Injection engine based on a multidisciplinary design optimization (MDO) framework. The paper presents the experimental framework used for the GDI engine mapping, followed by an analysis of the calibration optimization problem. The merits of the MDO approach to calibration optimization are discussed in comparison with a conventional two-stage approach based on local trade-off optimization analysis, focused on a representative emissions drive cycle (NEDC) and limited part load engine operation. The benefits from using the MDO optimisation framework are further illustrated with a study of relative effectiveness of different camshaft timing control strategies (twin independent Versus fixed timing, exhaust only, inlet only and fixed overlap / dual equal) for the reference GDI engine based on the part load test data.

The complexity of powertrain calibration has increased significantly with the development and introduction of new technologies to improve fuel economy and performance while meeting increasingly stringent emissions legislation with given time and cost constraints. This paper presents research to improve the model-based engine calibration optimization using an integrated sequential Design of Experiments (DoE) strategy for engine mapping experiments. This DoE strategy is based on a coherent framework for a model building - model validation sequence underpinned by Optimal Latin Hypercube (OLH) space filling DoEs. The paper describes the algorithm development and implementation for generating the OLH space filling DoEs based on a Permutation Genetic Algorithm (PermGA), subsequently modified to support optimal infill strategies for the model building - model validation sequence and to deal with constrained non-orthogonal variables space.

The teaching of engine mapping and calibration provides a unique challenge to Universities and Technical institutes the world over. The engine test cell facilities required for such tuition is prohibitively expensive for many organizations, for those fortunate enough to have the facilities their use is often already oversubscribed. In any case it is not desirable to have untrained operatives experimenting with expensive and potentially dangerous equipment without very close time intensive supervision. In the School of Engineering Design and Technology at the University of Bradford, although fortunate enough to have a number of state-of-the-art transient engine test cells, these safety concerns are contrasted against students' frustrations at a lack of practical experience in this area for the above reasons.

The quality of the output generated by a team is directly influenced by how well the team works together. Despite the complexity of the team system, within a typical Design for Six Sigma (DFSS) project the consideration given to the team process is often disproportionately small in comparison to that paid to the technical aspects of the project. This paper presents an efficient approach to teamwork within an engineering design context such as a DFSS project, in which team skills are modelled on DFSS technical processes allowing team members to learn both technical and teamwork skills within the common context of the technical process. DFSS engineering tools used within the framework of Failure Mode Avoidance are used to identify key potential failure modes in the team process and their effects and causes. A series of effective and efficient countermeasures to the team process failure modes are introduced as straight forward and easy to use interlinking teamwork tools.

This paper presents an approach to product design and development based on function failure avoidance, using of series of well known engineering tools including Function Fault Tree Analysis, P-Diagram and Design Verification. A 4-step function failure mode avoidance process is presented. The use of the engineering tools in an integrated and synergistic manner to achieve robust and reliable product design is illustrated by considering information flow within an automotive case study. The central role of FMEA within the process is described. The authors’ experience of using the process is discussed.

This paper presents an investigation into the functional robustness of an in-tank fuel delivery system (FDS) used in a saddle type fuel tank application for a high performance petrol engine. Robust design tools were used to identify the noise factors that affect the performance of the in-tank FDS. A transfer function relating the system's response to key control and noise factors was developed using a combination of theoretical modeling based on fluid mechanics and component level experimentation. The transfer function was validated with data from system level testing. A sensitivity study using the transfer function was conducted to validate the performance of the system against key noise factors.

This paper introduces a function failure approach to Fault Tree Analysis (FFTA) and illustrates its application through an automotive case study. The methodology is structured and straightforward to use. It is argued that the FFTA methodology integrates and interconnects well with other failure mode avoidance tools in common use in the automotive engineering design, such as FMEA and P-Diagram. FFTA shares the same platform for function based system analysis as other analysis tools and delivers complementary information