A hybrid trajectory optimization procedure and a mission performance estimation technique for various low-thrust (solar electric propulsion), outer-planet missions utilizing a single Venus gravity assist is presented. Determining a propellant-minimizing low-thrust spacecraft trajectory with desirable characteristics is often time consuming due to the existence of multiple locally minimizing solutions and the highly nonconvex search space. In addition, transitioning from one locally minimizing solution to another can be quite challenging. An efficient method to determine desirable trajectories is developed to address this problem. The hybrid trajectory optimization technique has two steps to identify the desired missions. The first step is to recognize common characteristics of the optimal trajectories. The revolution ratio is introduced in categorizing trajectories by their different gravity-assist timing and a phase calculation algorithm is developed to generate the potential launch and flyby dates. The second step is applying a hybrid genetic algorithm/Solar Electric Propulsion Trajectory Optimization Program (SEPTOP) method. The genetic algorithm provides sets of initial input to SEPTOP and the SEPTOP returns the convergence error to the genetic algorithm as the fitness of the set.{09}The search can be tailored to generate trajectories with a targeted characteristic such as revolution ratio.{09}By this procedure, the targeted trajectory can be determined efficiently and quickly. Additionally, analysis on SEPTOP trajectories verifies that the second-order conditions for optimality are also satisfied.; A performance (delivered mass versus time of flight) estimation technique approximates the performance of different revolution ratio trajectories from a set of optimal trajectories using a linear least square fitting method. This technique brings further understanding of the effect of different revolution ratio outer-planet missions. It also provides performance prediction capability prior to generating optimal mission profiles.
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