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We have developed an artificial neural network (ANN) based combustion model and have integrated it into a fluid mechanics code (KIVA3V) to produce a new analysis tool (titled KIVA3V-ANN) that can yield accurate HCCI predictions at very low computational cost. The neural network predicts ignition delay as a function of operating parameters (temperature, pressure, equivalence ratio and residual gas fraction). KIVA3V-ANN keeps track of the time history of the ignition delay during the engine cycle to evaluate the ignition integral and predict ignition for each computational cell. After a cell ignites, chemistry becomes active, and a two-step chemical kinetic mechanism predicts composition and heat generation in the ignited cells. KIVA3V-ANN has been validated by comparison with isooctane HCCI experiments in two different engines.

For human motion studies, which are to be used for either dynamic biomechanical analyses or development of human motion simulation models, it is important to establish an empirical motion database derived from efficient measurement and well-standardized data processing methodologies. This paper describes the motion recording and data processing system developed for modeling seated reach motions at the University of Michigan's HUMOSIM Laboratory. Both electromagnetic (Flock of Birds) and optical (Qualysis) motion capture systems are being used simultaneously to record the motion data. Using both types of devices provides a robust means to record human motion, but each has different limitations and advantages. The amount of kinematic information (DOFs), external sources of noise, shadowing, off-line marker identification/tracking time, and setup cost are key differences.