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The foremost aim of the work presented in this paper is to improve fuel economy and decrease CO2 emissions by reducing the aerodynamic drag of passenger vehicles. In vehicle development, computer aided engineering (CAE) methods have become a development driver tool rather than a design assessment tool. Exploring and developing the capabilities of current CAE tools is therefore of great importance. An efficient method for vehicle shape optimization has been developed using recent years' advancements in neural networks and evolutionary optimization. The proposed method requires the definition of design variables as the only manual work. The optimization is performed on a solver approximation instead of the real solver, which considerably reduces computation time. A database is generated from simulations of sampled configurations within the pre-defined design space. The database is used to train an artificial neural network which acts as an approximation to the simulations.

The Inflatable Curtain (IC) has shown great potential to reduce head injuries in side impacts. This study explores and presents enhanced performance in two steps of improved activation algorithms. Crash data analysis, 21 full scale crash tests and component tests in a custom built drop tower rig have been performed. The IC performance in wider crash scenarios, including side impacts outside the occupant compartment (L-type impacts), was evaluated. Both statistical crash data and in-depth studies were used. It was found in the analysis of real life crashes that moderate to fatal head injuries can occur without intrusion in the occupant compartment. In L-type side impacts, the motion of the occupant relative to the vehicle interior may cause a head impact of sufficiently high severity to cause moderate to severe head injuries. A combined analysis of real world crash data and crash test results indicates that a substantial reduction in moderate to fatal head injuries can be achieved.

A structured, semi-automatic method for reducing a high-fidelity engine model to a fast running one has been developed. The principle of this method rests on the fact that, under certain assumptions, the computationally expensive components of the simulation can be substituted with simpler ones. Thus, the computation speed increases substantially while the physical representation of the engine is retained to a large extent. The resulting model is not only suitable for fast running simulations, but also usable and updatable in later stages of the development process. The thrust of the method is that the calibration of the fast running components is achieved by use of automatically selected neural networks. Two illustrative examples demonstrate the methodology. The results show that the methodology achieves substantial increase in computation speed and satisfactory accuracy.

This article describes an expert system for objective rating of subjective characteristics like driving comfort. The system uses radial basis neural networks that can be trained on any dynamic properties, for example acceleration. Training and retraining can be done in real-time. The system includes a measure of the reliability of automatic judgement, which can be used to signal when new training may be necessary. The article shows in detail how the system has been used to automatically judge gearshift comfort for automatic transmission. Tests indicate that the system's accuracy and consistency are as good as one of Volvo's best experts.

During 1990, the Volvo Car Corporation will Introduce a new In-line six-cylinder engine featuring three litre displacement, twin overhead camshafts and 24 valves, designated the B6304F. The engine has been designed and adapted for Volvo's top-of-the-line model 960, and it has been developed to meet the market's high demands on comfort, performance, reliability, economy and environmental friendliness. The engine has been designed and manufactured with the help of advanced CAE technology. The engine structure consists of five basic aluminium parts. This construction contributes to the low engine weight of 182 kg including auxiliary units, oil and wiring. The engine's gas flow has been optimized with the help of data simulation and laser measurement technology so as to ensure efficient utilization of energy. Fuel injection and ignition timing are regulated and controlled by an advanced electronic control system, the Bosch Motronic 1.8.