KJ
K. Jovanov
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Multi-disciplinary design optimization (MDO) is a field that has gained traction in recent years. It can be distinguished as a methodology that promotes a simultaneous change in design features associated to multiple disciplines in order to achieve the best possible design of a coupled multi-disciplinary problem. A typical application in aircraft design is aero-structural design optimization, where the interaction of fluids and structures on load-carrying components is considered. The recent progress in this field can be largely attributed to the increasing computational resources and parallel computing in the form of High Performance Computing Clusters. Nevertheless, the computational bottleneck which persists to this day in high-fidelity aero-structural design is the gradient computation, which is a fundamental component in the design of systems with several thousand design variables and constraints.
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Multi-disciplinary design optimization (MDO) is a field that has gained traction in recent years. It can be distinguished as a methodology that promotes a simultaneous change in design features associated to multiple disciplines in order to achieve the best possible design of a coupled multi-disciplinary problem. A typical application in aircraft design is aero-structural design optimization, where the interaction of fluids and structures on load-carrying components is considered. The recent progress in this field can be largely attributed to the increasing computational resources and parallel computing in the form of High Performance Computing Clusters. Nevertheless, the computational bottleneck which persists to this day in high-fidelity aero-structural design is the gradient computation, which is a fundamental component in the design of systems with several thousand design variables and constraints.
Conference paper
(2016)
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Kristofer Jovanov, Roeland De Breuker, Mostafa Abdalla, Christophe Blondeau