In the modern aircraft design process numerical simulation is one of the key enablers. However, computational time increases exponentially with the level of fidelity of the simulation. In the EU Horizon2020 project AGILE different aircraft design analysis tools relative to different levels of fidelity are used. One of the challenges is to reduce the computational time - e.g. to facilitate an efficient optimization process - by processing the analysis data of various fidelity levels in a global surrogate model. This paper focuses on fusion of data sets via an automatic iterative process embedded in the collaborative multidisciplinary analysis (MDA) chains as applied in AGILE. Surrogate modeling techniques are applied, taking into account the optimal sampling and the corresponding fidelities of the samples. This paper will detail the different steps of the proposed collaborative approach. As a test case handling qualities analysis of the AGILE reference conventional aircraft is performed, by fusing the computed aerodynamic coefficients and derivatives. A full set of aerodynamic data computed either with different levels of fidelity or with only a low-fidelity tool has been derived and evaluated. The data set with multiple levels of fidelity significantly improved the accuracy of the flight performance analysis, especially for the transonic region in which the low fidelity aerodynamic method is not reliable. Moreover, the test case shows that by combining a collaborative surrogate modeling approach with fusion of the data sets, the fidelity of the analysis data can be significantly improved giving maximum relative prediction error less than 5% with minimal computing efforts.

In this paper methodological investigations regarding an innovative Multidisciplinary Design and Optimization (MDO) approach for conceptual aircraft design are presented. These research activities are part of the ongoing EU-funded research project AGILE. The next generation of aircraft MDO processes is developed in AGILE, which targets significant reductions in aircraft development cost and time to market, leading to cheaper and greener aircraft solutions. The paper introduces the AGILE project structure and recalls the achievements of the first year of activities where a reference distributed MDO system has been formulated, deployed and applied to the design and optimization of a reference conventional aircraft configuration. Then, investigations conducted in the second year are presented, all aiming at making the complex optimization workflows easier to handle, characterized by a high degree of discipline interdependencies, multi-level processes and multi-partner collaborative engineering activities. The paper focuses on an innovative approach in which knowledge-based engineering and collaborative engineering techniques are used to handle a complex aircraft design workflow. Surrogate models replacing clusters of analysis disciplines have been developed and applied to make workflow execution more efficient. The paper details the different steps of the developed approach to set up and operate this test case, involving a team of aircraft design and surrogate modelling specialists, and taking advantage of the AGILE MDO framework. To validate the approach, different executable workflows were generated automatically and used to efficiently compare different MDO formulations. The use of surrogate models for clusters of design competences have been proved to be efficient approach not only to decrease the computational time but also to benchmark different MDO formulations on a complex optimization problem.