Current unmanned aerial vehicles have been designed by applying the traditional approach to aircraft conceptual design which has drawbacks in terms of the individual analysis of each discipline involved in the conception of new aircraft, the reliance on the designer’s experience and intuition, and the inability of evaluating all possible design solutions. Multidisciplinary analysis and optimization focus on solving these problems, by synthesizing all the disciplines involved and accounting for their mutual interaction. This study presents a multidisciplinary analysis and optimization method for conceptually designing electrical flying-wing micro-unmanned aerial vehicles. The conceptual design task was formulated as a non-linear mathematical programming problem. The method considers the trimming of the UAV during each mission profile phase, consisting of the climb, cruise, and descent. We used two algorithms, one for design space exploration and another for optimization. Typical examples of solving conceptual design problems were considered in the work: the modernization of an existing UAV; the effect of the change of the payload and endurance change on the takeoff weight; and the influence of different static margins on aerodynamic characteristics. The advantages of using this design method are the remotion of additional internal cycles to solve the sizing equation at each optimization step, and the possibility of not only obtaining a unique optimal solution but also a vector of optimal solutions.

The aircraft conceptual design step requires a substantial number of aerodynamic configuration evaluations. Since the wing is the main aircraft lifting element, the focus is on solving direct and reverse design problems. The former could be solved using a low-cost computational model, but the latter is unlikely, even for these models. Surrogate modeling is a technique for simplifying complex models that reduces computational time. In this work, a surrogate aerodynamic model, based on the implementation of a multilayer perceptron (MLP), is presented. The input data consist of geometrical characteristics of the wing and airfoil and flight conditions. Some of the MLP hyperparameters are defined using evolutionary algorithms, learning curves, and cross-validation methods. The MLP predicts the aerodynamic coefficients (drag, lift, and pitching moment) with high agreement with the substituted aerodynamic model. The MLP can predict the aerodynamic characteristics of compressible flow up to 0.6 M. The developed MLP has achieved up to almost 800 times faster in computing time than the model on which it was trained. The application of the developed MLP will enable the rapid study of the effects of changes in various parameters and flight conditions on flight performance, related to the design and modernization of new vehicles.