MP
M.J. Perdeck
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Searching for two optimal trajectories
A study on different approaches to global optimization of gravity-assist trajectories that have a backup departure opportunity
In interplanetary space missions, it is convenient to have a second departure opportunity in case the first is missed. Two distinct approaches to minimizing the maximum of the two Delta-V budgets of such a trajectory pair, are developed. The first (‘a priori’) approach optimizes the variables of both trajectories at once. The second (‘a posteriori’) approach first minimizes Delta-V budgets for a range of discrete departure epochs, and then selects the pair of which the highest Delta-V is minimum. Furthermore, five different pruning and biasing methods are developed, these prove critical for computational efficiency (number of objective function evaluations). Application to three different gravity-assist (and deep space maneuver) trajectories to Saturn, reveals that the a priori approach is more computationally efficient on a trajectory with few variables (3) and that the a posteriori approach is more computationally efficient on a trajectory with many variables (22).
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In interplanetary space missions, it is convenient to have a second departure opportunity in case the first is missed. Two distinct approaches to minimizing the maximum of the two Delta-V budgets of such a trajectory pair, are developed. The first (‘a priori’) approach optimizes the variables of both trajectories at once. The second (‘a posteriori’) approach first minimizes Delta-V budgets for a range of discrete departure epochs, and then selects the pair of which the highest Delta-V is minimum. Furthermore, five different pruning and biasing methods are developed, these prove critical for computational efficiency (number of objective function evaluations). Application to three different gravity-assist (and deep space maneuver) trajectories to Saturn, reveals that the a priori approach is more computationally efficient on a trajectory with few variables (3) and that the a posteriori approach is more computationally efficient on a trajectory with many variables (22).
Bachelor thesis
(2016)
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J.M. Bakker, S. Potkamp, R.L. Bokdam, S.A. Doesburg, J.M. de Wilde, M.M. van Rijsingen, J.J.P. van den Berg, P.M. Nijman, M.J. Perdeck, J.A. Melkert