Monte Carlo based Robust MDO applied to Aircraft Conceptual Design: a technical-financial coupling optimization strategy

摘要

Aircraft Conceptual Design is a challenging task that requires not only the understanding of many different disciplines, but also how they interact with each other, leading to many trade-off analyses. The complexity of these interactions grows fast with the number of variables, disciplines and goals of the problem. Multidisciplinary Analysis and Optimization tools can be very helpful to explore the design space, but it is up to the engineering team to define the objective function: optimal with respect to what? For commercial aircraft segment there are two well defined objectives that mostly represent what costumers of this segment desire: minimization of block fuel or minimization of direct operating costs. The objective function is not so clear for the Executive Jets segment and it is usual to apply minimization of MTOW. This paper proposes an architecture that includes Costs, Market Share and Finances disciplines in the optimization loop, treating the market specifications provided by company's intelligence as constraints and maximizes the financial return to the shareholders. This way, the conceived solution complies with all customer needs and also provides the most attractive investment to the shareholders. A comparison is made with the traditional optimization strategy and the results shows that, although both strategies are not exactly conflicting objectives, to maximize the financial return can lead to a different design with significant improvement in financial return. Another key optimization issue treated in this work is the reliability and robustness of the design. Estimation methods have inherent model uncertainties that can not be mitigated even with in-house data calibration. Uncertainty quantification and Robust Design is held in this work by the use of Monte Carlo simulations using triangular distributions and superposition of effects. Results for the Robust Optimization showed that slight changes in the design can improve robustness in the outcomes of interest. Also, the proposed methodology reduces the computational cost of the Robust Design to almost the level of a deterministic design, presenting a significant improvement to this process.