Aircraft design and trajectory optimization are typically performed sequentially, which can lead to suboptimal results. To address this, we develop a new mission analysis and trajectory optimization tool that is efficient, robust, and modular. This enables large-scale optimization in problems involving trajectory and other disciplines. The most important feature that sets this tool apart from existing mission analysis software is the use of a computational framework to provide benefits in efficiency and modularity. Through different test cases, we are able to demonstrate the efficiency and the robustness of the developed approach. The generated mission analysis results match well with data from other tools. Runge oscillations are evident in trajectory optimization cases with insufficient number of altitude design variables. Therefore, we provide a relation between the minimum number of altitude design variables needed and the range of the mission for avoiding these oscillations efficiently.
The aircraft design optimization problem is typically formulated to maximize performance at a small number of representative operating conditions. This approach makes simplifying assumptions such as ignoring the climb and descent phases, but they can be avoided by performing simultaneous designmission-allocation optimization with surrogate models for the aircraft design disciplines. As a first step towards this goal, this paper presents a method for simultaneous allocation-mission optimization. We integrate aerodynamic and propulsion surrogates, a mission analysis tool, and allocation models within a computational framework that automates solving the coupled simulation and computing derivatives using the adjoint method for gradient-based optimization. We solve the mixed-integer allocation-mission optimization problem by using the linear allocation-only optimization to generate a good starting point and applying the branch-and-bound method to find an optimum for the mixedinteger nonlinear allocation-mission problem. The results show that this approach efficiently finds good local optima with, on average, roughly 10 node evaluations in the branch-and-bound method and most of the continuous optimizations converging almost immediately. The results show that adding next-generation aircraft to a fleet yields a 200-400 % profit increase for a 3-route test problem.
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