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Enhancing the handling qualities analysis by collaborative aerodynamics surrogate modelling and aero-data fusion

Abstract : 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 with minimal computing efforts.
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Submitted on : Friday, October 16, 2020 - 2:51:09 PM
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Mengmeng Zhang, Nathalie Bartoli, Aidan Jungo, Wim Lammen, Erik Baalbergen, et al.. Enhancing the handling qualities analysis by collaborative aerodynamics surrogate modelling and aero-data fusion. Progress in Aerospace Sciences, Elsevier, 2020, 119, pp.100647. ⟨10.1016/j.paerosci.2020.100647⟩. ⟨hal-02969258⟩

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