Challenges in realizing 3rd generation multidisciplinary design optimization
With the increasing complexity of airplane design, multidisciplinary design optimization (MDO) plays a more critical role in this task, which requires expertise in each domain. Hence, the MDO problem is no longer a set of various models but a distribution of competence. In this work, we study the 3r...
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| Abstract | With the increasing complexity of airplane design, multidisciplinary design optimization (MDO) plays a more critical role in this task, which requires expertise in each domain. Hence, the MDO problem is no longer a set of various models but a distribution of competence. In this work, we study the 3rd Generation MDO architecture, which has been proposed as AGILE Paradigm, where different institutes or departments are involved in solving self-contained parts of the MDO system with their unique toolset. This means that the MDO framework should support multi-discipline and multi-fidelity models and a multi-code environment. It brings three main challenges: extensive MDO problem formulation, coupling design tools, and developing models. In this work, our primary focus is on the first two challenges. We formulate the framework's requirements and technical aspects that support AGILE Paradigm. Additionally, the potential benefit of using knowledge graphs in problem formulations is discussed. Our work is supported by relevant examples. Our implementations are based on open-source software KratosMultiphysics. |
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| AbstractList | With the increasing complexity of airplane design, multidisciplinary design optimization (MDO) plays a more critical role in this task, which requires expertise in each domain. Hence, the MDO problem is no longer a set of various models but a distribution of competence. In this work, we study the 3rd Generation MDO architecture, which has been proposed as AGILE Paradigm, where different institutes or departments are involved in solving self-contained parts of the MDO system with their unique toolset. This means that the MDO framework should support multi-discipline and multi-fidelity models and a multi-code environment. It brings three main challenges: extensive MDO problem formulation, coupling design tools, and developing models. In this work, our primary focus is on the first two challenges. We formulate the framework's requirements and technical aspects that support AGILE Paradigm. Additionally, the potential benefit of using knowledge graphs in problem formulations is discussed. Our work is supported by relevant examples. Our implementations are based on open-source software KratosMultiphysics. |
| Author | Baimuratov, Ildar Kreuzeberg, Lasse Attravanam, Achyuth Warnakulasuriya, Suneth Wüchner, Roland Baars, Susanna Wittenborg, Tim Antonau, Ihar |
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| Cites_doi | 10.1016/j.jfluidstructs.2022.103513 10.1016/j.artmed.2017.07.002 10.1016/j.compfluid.2012.02.004 10.1016/j.paerosci.2020.100643 10.1145/2629489 10.1007/s11831-010-9045-2 10.1007/s00158-020-02821-y 10.1007/s10957-021-01826-x 10.2514/3.10366 10.1007/s13272-018-0312-5 10.2307/2332286 10.1038/sdata.2016.18 10.2514/6.2009-3767 10.1016/j.compfluid.2016.04.003 10.1145/3340531.3412777 10.1007/s00158-013-1006-6 10.23967/coupled.2021.048 10.3390/aerospace7070087 |
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| Keywords | multidisciplinary knowledge graphs multi-fidelity Next generation MDO multi-code environment |
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