Combined analytical and experimental approaches to dynamic component stress prediction

Plate;Beam;Rotorcraft;Load identification;Response identification

Chierichetti, Maria ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Plate;    Beam;    Rotorcraft;    Load identification;    Response identification;   

Others  :  https://smartech.gatech.edu/bitstream/1853/44850/1/chierichetti_maria_201208_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

In modern times, the ability to investigate the aeroelastic behavior of dynamic components on rotorcraft has become essential for the prediction of their useful fatigue life. At the same time, the aeroelastic modeling of a rotorcraft is particularly complex and costly.Inaccuracies in numerical predictions are mostly due to imprecisions in the structural modeling, to the presence of structural degradation or to the limited information on aerodynamic loads.The integration of experimental measurements on dynamic components such as rotor blades has the potential to improve fatigue estimation, augment the knowledge of the dynamic behavior and inform numerical models.The objective of this research is the development of a combined numerical and experimental approach, named Confluence Algorithm, that accurately predicts the response of dynamic components with a limited set of experimental data.The integration of experimental measurements into a numerical algorithm enables the continuous and accurate tracking of the dynamic strain and stress fields.The Confluence Algorithm systematically updates the numerical model of the external loads, and mass and stiffness distributions to improve the representation and extrapolation of the experimental data, and to extract information on the response of the system at non-measured locations.The capabilities of this algorithm are first verified in a numerical framework and with well-controlled lab experiments.Numerical results from a comprehensive UH-60A multibody model are then compared with available experimental data. These analyses demonstrate that the integration of the Confluence Algorithmimproves the accuracy of the numerical prediction of the dynamic response of systems characterized by a periodic behavior, even in presence of non-linearities. The algorithm enables the use of simplified models that are corrected through experimental data to achieve accurate tracking of the system.