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Car Industry The fuel demand of a car depends among other factors also on the car weight. This can be reduced drastically by using lightweight materials such as plastics. An investigation by Fraunhofer Institute Freising in 1999 has given the following results: An average German medium-sized car uses ca. 165 kg of different plastic materials (Fig. 13.5). Out of this only 75 kg substitute alternative materials and thus save car weight. The remaining part has functions that cannot be ful lled otherwise (airbag, seat construction, etc.). The amount of fuel saved by reducing car weight certainly depends on the mileage of the car over its lifetime and on the ef ciency of the fuel mileage. For a low car mileage of 100,000 km over the lifetime of the car there are:

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The main barrier preventing CFD becoming an everyday development tool has traditionally been the time it takes to generate computational grids. When considering the specific application to internal combustion (IC) engines, specific issues arise relating to mesh motion, including the piston and valve motion, small clearances (low valve lifts and valve/piston clearances), and re-entrant geometry. Numerous workers within the literature, e.g. (1), have constantly referred to target times for mesh generation and analysis to achieve acceptability within the design and development environment. This chapter describes the industry-proven Ricardo mesh generation philosophy, the advanced mesh motion capabilities, typical applications, and elapsed times for mesh generation and analysis, and demonstrates that CFD can be very effectively integrated into design and development programmes.

Table 14.5 summarizes the rst 3 N 1 bucket algorithms for 5,6, and 8 frames, respectively. Table 14.6 gives an overview of all the families discussed in this chapter, including the required phase-steps, their name and the major reference introducing the family of algorithms.

Ef cient Process Development While the current de nition of design space does not preclude the incorporation of knowledge from other products and processes, model-based knowledge representation offers a more robust framework for incorporation of external or a priori information. Even though the level of quality expected by a particular combination of input and process parameters from another product is not likely to transfer to a new product or process (in absolute terms), the functional relationships which predict quality may be quite similar. Furthermore, model-based design space development enables direct incorporation of rst principles and mechanistic knowledge, which might signi cantly reduce the complexity of experimental designs required for process development since signi cant terms may be identi ed in silico. Quality by Design The incorporation of functional relationships between inputs, parameters, and product quality (or ef ciency), which inherently imply magnitude and directionality, enables the use of a process design space as a tool for multiobjective process optimization. Furthermore, the model-based representation of knowledge is compatible with concepts of risk management, enabling more exible operation since the risk associated with extrapolation could be predicted. Control System Development Model-based design space development offers an ideal segue between process and control development. Quite literally, a model-based design space would provide the template for development of feedforward process control models. Moreover, development of a process design space using a model-based framework would facilitate control system validation and identi cation of science-based, in-process, and release speci cations.

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Planning in Intelligent Systems: Aspects, Motivations, and Methods, Edited by Wout van Wezel, Rene Jorna, and Alexander Meystel Copyright # 2006 John Wiley & Sons, Inc.

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