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A procedure for obtaining the optimum disk serration and blade root geometry is presented. The procedure uses the -finite element method, a suitable objective function and a standard mathematical programming technique as its basis. The objective functions investigated are the mean von Mises stress concentration factor, the coefficient of efficiency, and the hoop stress and radial stress concentration factors. The mathematical programming techniques considered are the Steepest Descent Technique, the Hill Algorithm and the Box Method. Results presented in this paper include the relative cost and the degree of success achieved by the design optimization procedure.

This study investigates using driver prediction to anticipate energy usage over a 160-meter look-ahead distance for a series, plug-in, hybrid-electric vehicle to improve conventional thermostatic powertrain control. Driver prediction algorithms utilize a hidden Markov model to predict route and a regression tree to predict speed over the route. Anticipated energy consumption is calculated by integrating force vectors over the look-ahead distance using the predicted incline slope and vehicle speed. Thermostatic powertrain control is improved by supplementing energy produced by the series generator with regenerative braking during events where anticipated energy consumption is negative, typically associated with declines or decelerations.