Probabilistic Algorithm for Ballistic Parachute Transition Altitude Optimization

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Probabilistic Algorithm for Ballistic Parachute Transition Altitude Optimization

Andrew Leonard

A probabilistic algorithm for the optimization of drogue-to-main parachute transition altitude is proposed for high-altitude, low-opening ballistic airdrop. In light of the significant effects of wind and model uncertainty on impact-point dispersion, the algorithm makes use of explicit nonlinear uncertainty propagation techniques to provide a probabilistically optimal transition altitude. The algorithm begins by the specification of a desired impact-point distribution, which is propagated backward in time through the nonlinear main parachute dynamics using the stochastic Liouville equation. This spatially varying probability density is uploaded to the package before release from the aircraft. During drogue descent, the package descent trajectory through these probability densities is predicted, and an optimal drogue-to-main parachute transition altitude is identified. A key aspect of the proposed algorithm is that the desired impact-point distribution is an input to the algorithm, enabling novel capabilities to shape the resulting dispersion pattern and avoid obstacles in the target area. Furthermore, the algorithm explicitly accounts for primary sources of uncertainty affecting ballistic airdrop operations. Results demonstrate that the proposed optimization algorithm has the ability to reduce dispersion, improve accuracy, and shape impact distributions around obstacles and among multiple drop zones. Presented as Paper 2015 at the AIAA Aerodynamic Decelerator Systems Technology Conference, Daytona Beach, FL, 30 March-2 April 2015