EG
E. Gillebaart
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1
Including a gust analysis in an optimization framework is computationally expensive as the critical load cases are not known a priori and hence a large number of points within the flight envelope have to be analyzed. Model order reduction techniques can provide significant improvement in computational efficiency of an aeroelastic analysis. In this paper, after thorough analysis of 4 commonly used model order reduction methods, balanced proper orthogonal decomposition is selected to reduce the aerodynamic system which is based on potential flow theory. The reduced aerodynamic system is coupled to a structural solver to obtain a reduced-order aeroelastic model. It is demonstrated that the dominant modes of the aerodynamic model can be assumed to be constant for varying equivalent airspeed and Mach number, enabling the use of a single reduced model for the entire flight envelope. A dynamic aeroelastic optimization method is then formulated using the reduced-order aeroelastic model. Results show that both dynamic and static loads play a role in optimization of the wing structure. Furthermore, the worst case gust loads change during the optimization process and it is important to identify the critical loads at every iteration in the optimization.
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Including a gust analysis in an optimization framework is computationally expensive as the critical load cases are not known a priori and hence a large number of points within the flight envelope have to be analyzed. Model order reduction techniques can provide significant improvement in computational efficiency of an aeroelastic analysis. In this paper, after thorough analysis of 4 commonly used model order reduction methods, balanced proper orthogonal decomposition is selected to reduce the aerodynamic system which is based on potential flow theory. The reduced aerodynamic system is coupled to a structural solver to obtain a reduced-order aeroelastic model. It is demonstrated that the dominant modes of the aerodynamic model can be assumed to be constant for varying equivalent airspeed and Mach number, enabling the use of a single reduced model for the entire flight envelope. A dynamic aeroelastic optimization method is then formulated using the reduced-order aeroelastic model. Results show that both dynamic and static loads play a role in optimization of the wing structure. Furthermore, the worst case gust loads change during the optimization process and it is important to identify the critical loads at every iteration in the optimization.
In conventional aeroelastic analysis and optimisation methods for wing design, different geometries are used for the different steps in the process. Generally, a parametrised model is used to describe the shape of the geometry for the optimisation process. Subsequently, this model is converted into a structural and aerodynamic model for analysis purposes. These steps increase the computational effort and introduce geometrical errors. In this work, a geometrically consistent static aeroelastic analysis framework is presented. By using isogeometric analysis, the exact geometry is used in both the structural and aerodynamic models, preventing any additional computational effort for meshing and geometry retrieval steps and avoiding the introduction of geometrical errors due to the discretisation of the geometry. The separate components of the framework are described and verified, and the complete framework is demonstrated through the analysis of the realistic wing model.
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In conventional aeroelastic analysis and optimisation methods for wing design, different geometries are used for the different steps in the process. Generally, a parametrised model is used to describe the shape of the geometry for the optimisation process. Subsequently, this model is converted into a structural and aerodynamic model for analysis purposes. These steps increase the computational effort and introduce geometrical errors. In this work, a geometrically consistent static aeroelastic analysis framework is presented. By using isogeometric analysis, the exact geometry is used in both the structural and aerodynamic models, preventing any additional computational effort for meshing and geometry retrieval steps and avoiding the introduction of geometrical errors due to the discretisation of the geometry. The separate components of the framework are described and verified, and the complete framework is demonstrated through the analysis of the realistic wing model.
Low-fidelity isogeometric aeroelastic analysis has not received much attention since the introduction of the isogeometric analysis (IGA) concept, while the combination of IGA and the boundary element method in the form of the potential flow theory shows great potential. This paper presents a two-dimensional low-fidelity aeroelastic analysis and optimization framework consisting of a closely coupled isogeometric potential flow model and isogeometric curved Timoshenko beam model combined with a boundary layer model. Application of the framework to the optimization of the landing performance for an active morphing airfoil demonstrates the potential of the isogeometric aeroelastic framework.
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Low-fidelity isogeometric aeroelastic analysis has not received much attention since the introduction of the isogeometric analysis (IGA) concept, while the combination of IGA and the boundary element method in the form of the potential flow theory shows great potential. This paper presents a two-dimensional low-fidelity aeroelastic analysis and optimization framework consisting of a closely coupled isogeometric potential flow model and isogeometric curved Timoshenko beam model combined with a boundary layer model. Application of the framework to the optimization of the landing performance for an active morphing airfoil demonstrates the potential of the isogeometric aeroelastic framework.