Study of optimal periodic cruise trajectories via tradespace visualization

The determination of optimal periodic cruise trajectories which incorporate initial states as design variables are studied in this paper. A two-level optimization method which separately deals with initial states and control variables is introduced. The outer loop parameter optimization problem is handled by tradespace visualization driven by a genetic algorithm, while the inner loop optimal control problem is solved using the direct shooting method. A multi-objective (fuel rate and heat load) optimal periodic cruise problem is analyzed. The distribution of objectives and constraints in these two loops are discussed. Initial guesses of control variables of periodic cruise trajectories with different initial states are given through the corresponding maximum glide trajectories. Approximation models, built through a response surface methodology, are used to replace the inner loop optimization model to generate the Pareto frontier during the process of tradespace visualization. Thus, the designer can get the whole picture of optimal designs subject to different constraints.