This paper presents an algorithm to estimate unknown parameters of parachute models from flight-test data. The algorithm is based on the simultaneous-perturbation-stochastic-approximation method to minimize the prediction error (difference between model output and test data). The algorithm is simple to code and requires only the model output. Analytical gradients are not necessary. The algorithm is used to estimate aerodynamic and apparent mass coefficients for an existing parachute model.

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This paper includes a detailed description of an optimized E85 concept engine developed for medium duty applications (Class 4-6 trucks) targeting ultra-low carbon emissions while maintaining power and delivering competitive cost of ownership. The engine is a light weight, downsized and boosted in-line 4 cylinder with air handling, fuel, and combustion systems designed specifically for E85 capability, producing high brake mean effective pressure (BMEP) at high thermal efficiency. It is integrated with a 12V start/stop system including a smart alternator for improved energy management. The present work demonstrates that even with the relative difference in the cost per heating value of fuel, using E85 can be upwards of 20% lower in cost while running middle to high loads. Combining high BMEP capability and a highly downsized engine displacement can ensure operation at high specific load where engine thermal efficiency is very good even in pickup-and-delivery type drive cycles. The performance characteristics of this engine were mapped using stoichiometric combustion and a three way catalyst for emissions control. The ability to perform at or close to Maximum Brake Torque (MBT) spark timing throughout the torque curve has been facilitated by an optimized combustion system design along with direct injection. The high engine thermal efficiency and knock tolerance of this combustion system eliminates the need for fuel enrichment anywhere in the engine map.

This paper presents an algorithm to estimate unknown parameters of parachute models from flight-test data. The algorithm is based on the simultaneous-perturbation-stochastic-approximation method to minimize the prediction error (difference between model output and test data). The algorithm is simple to code and requires only the model output. Analytical gradients are not necessary. The algorithm is used to estimate aerodynamic and apparent mass coefficients for an existing parachute model.