Adatalapú aerodinamikai modellek megalkotása redukált aeroelasztikus szimulációkhoz
Data-driven aerodynamic models for reduced-order aeroelastic simulations
Keywords:
Nonlinear aeroelasticity, data-driven methods, reduced-order modeling, flutter, /, Nemlineáris aeroelasztikus modellek, adatalapú módszerek, redukált modellezés, belebegésAbstract
Numerous methods exist to calculate time-dependent aerodynamic loads of thin elastic structures subjected to airflow. Analytical, semi-empirical, reduced-order, and CFD-based models can be utilized to calculate the aerodynamic loads acting on the structure. In this work, the applicability and accuracy of a data-based identification method for calculating the aerodynamic loads in the time domain are investigated. The most significant advantage of this technique is that it can be applied to a large variety of different geometries, and it is also accurate for large deformations and angles of attack in the nonlinear aerodynamic regime.
Kivonat
Áramlásba helyezett karcsú rugalmas szerkezetek esetén számos módszer létezik az időfüggő aerodinamikai erők meghatározására. A szerkezetre ható aerodinamikai erők számítására analitikus, szemi-empirikus, CFD szimulációs és különböző redukált modelleket alkalmazhatunk. Az aerodinamikai erők számítására egy adatalapú identifikációs módszer alkalmazhatóságát és pontosságát vizsgáljuk. A módszer legnagyobb előnye, hogy sokféle geometriák esetén alkalmazható, illetve nagy deformációk és állasszögek esetén is pontosan írja le a nemlineáris aerodinamikai jelenségeket.
References
Holierhoek, J., De Vaal, J., Van Zuijlen, A., and Bijl, H., “Comparing different dynamic stall models”, Wind Energy, 2013, Vol. 16 (1), pp. 139–158.
Boutet, J., and Dimitriadis, G., “Unsteady lifting line theory using the Wagner function for the aerodynamic and aeroelastic modeling of 3d wings”, Aerospace, 2018, Vol. 5 (3), p. 92.
Wagner, H., “Über die Entstehung des dynamischen Auftriebes von Tragflügeln”, Zeitschrift Angewandte Mathematik und Mechanik 5.1 1925, pp 17-35.
Theodorsen, T., “Report No. 496, general theory of aerodynamic instability and the mechanism of flutter”, Journal of the Franklin Institute, 1935, Vol. 219 (6), p. 766–767.
Lelkes, J., and Kalmár-Nagy, T., “Analysis of a piecewise linear aeroelastic system with and without tuned vibration absorber”, Nonlinear Dynamics, 2021, Vol. 103 (4), pp. 2997– 3018.
Lendvai, B., and Lelkes, J., “Aeroelasztikus szárnymodell numerikus vizsgálata: Numerical analysis of aeroelastic wing model”, Nemzetközi Gépészeti Konferencia–OGÉT, 2021, pp. 48–51.
Tran, C., and Petot, D., “Semi-empirical model for the dynamic stall of airfoils in view of the application to the calculation of responses of a helicopter blade in forward flight”, Sixth European Rotorcraft and Powered Lift Aircraft Forum, 1980, Paper No. 48.
Leishman, J. G., and Beddoes, T., “A Semi-Empirical model for dynamic stall”, Journal of the American Helicopter society, 1989, Vol. 34 (3), pp. 3–17.
Brunton, S. L., Proctor, J. L., and Kutz, J. N., “Discovering governing equations from data by sparse identification of nonlinear dynamical systems”, Proceedings of the National Academy of Sciences, 2016, Vol. 113 (15), p. 3932–3937.
Brunton, S. L., Kutz, J. N., Manohar, K., Aravkin, A. Y., Morgansen, K., Klemisch, J., Goebel, N., Buttrick, J., Poskin, J., Blom- Schieber, A. W., and et al., “Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning”, AIAA Journal, 2021, p. 1–26.
Sun, C., Tian, T., Zhu, X., and Du, Z., “Sparse identification of nonlinear unsteady aerodynamics of the oscillating airfoil”, Proceedings of the Institution of Mechanical Engineers, 2020, Part G: Journal of Aerospace Engineering, Vol. 235 (7), p. 809–824.
Akiba, T., Sano, S., Yanase, T., Takeru Ohta, T. and Koyama, M. “Optuna: A Next-generation Hyperparameter Optimization Framework”, 2019, In KDD.
Silva, B. D., Champion, K., Quade, M., Loiseau, J.-C., Kutz, J., and Brunton, S., “PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data”, Journal of Open Source Software, 2020, Vol. 5 (49), p. 2104.
