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

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 fideli...

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Published in:	Progress in aerospace sciences Vol. 119; p. 100647
Main Authors:	Zhang, Mengmeng, Bartoli, Nathalie, Jungo, Aidan, Lammen, Wim, Baalbergen, Erik, Voskuijl, Mark
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.11.2020 Elsevier
Subjects:	Aero-database AGILE Computational aerodynamics CPACS Engineering Sciences Mathematics MDO Multi-fidelity analysis Physics Sampling approaches Surrogate model repository Surrogate-modeling Computational aerodynamics CPACS Sampling approaches Surrogate model repository AGILE Surrogate-modeling MDO Multi-fidelity analysis Aero-database multi-fidelity analysis sampling approaches Surrogate Model Repository aero-database computational aerodynamics surrogate-modeling Multidisciplinary design optimization
ISSN:	0376-0421, 1873-1724
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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 5% with minimal computing efforts.
AbstractList	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. 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.
ArticleNumber	100647
Author	Jungo, Aidan Baalbergen, Erik Voskuijl, Mark Lammen, Wim Zhang, Mengmeng Bartoli, Nathalie
Author_xml	– sequence: 1 givenname: Mengmeng orcidid: 0000-0003-4991-5503 surname: Zhang fullname: Zhang, Mengmeng email: mengmeng.zhang@airinnova.se organization: Airinnova AB, Stockholm, Sweden – sequence: 2 givenname: Nathalie orcidid: 0000-0002-6451-2203 surname: Bartoli fullname: Bartoli, Nathalie email: Nathalie.Bartoli@onera.fr organization: ONERA/DTIS, Université de Toulouse, Toulouse, France – sequence: 3 givenname: Aidan orcidid: 0000-0001-7501-8385 surname: Jungo fullname: Jungo, Aidan email: aidan.jungo@cfse.ch organization: CFS Engineering, Lausanne, Switzerland – sequence: 4 givenname: Wim surname: Lammen fullname: Lammen, Wim email: wim.lammen@nlr.nl organization: NLR, Netherlands Aerospace Centre, Collaborative Engineering Systems Department, Amsterdam, the Netherlands – sequence: 5 givenname: Erik surname: Baalbergen fullname: Baalbergen, Erik email: erik.baalbergen@nlr.nl organization: NLR, Netherlands Aerospace Centre, Collaborative Engineering Systems Department, Amsterdam, the Netherlands – sequence: 6 givenname: Mark orcidid: 0000-0001-9193-1203 surname: Voskuijl fullname: Voskuijl, Mark email: M.Voskuijl@tudelft.nl organization: Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
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Keywords	Computational aerodynamics CPACS Sampling approaches Surrogate model repository AGILE Surrogate-modeling MDO Multi-fidelity analysis Aero-database multi-fidelity analysis sampling approaches Surrogate Model Repository aero-database computational aerodynamics surrogate-modeling Multidisciplinary design optimization
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Snippet	In the modern aircraft design process numerical simulation is one of the key enablers. However, computational time increases exponentially with the level of...
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Title	Enhancing the handling qualities analysis by collaborative aerodynamics surrogate modelling and aero-data fusion
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