This paper proposes a novel approach to build refined shell models. The focus is on the free vibrations of composite panels, and the node-dependent-kinematics is used to select shell theories node-wise. The methodology shown in this work can provide at least two sets of information. First, it optimizes the use of shell models by indicating the minimum number of refined models to use. Then, it highlights which areas of the structures are more vulnerable to non-classical effects. Moreover, by varying various problem features, e.g., boundary conditions, thickness, and stacking sequence, the influence of those parameters on the modelling strategy is evaluated. The results suggest the predominant influence of thickness and boundary conditions and the possibility to improve the quality of the solution via the proper use of the refinement strategy.
Best Spatial Distributions of Shell Kinematics Over 2D Meshes for Free Vibration Analyses / Petrolo, M.; Carrera, E.. - In: AEROTECNICA MISSILI & SPAZIO. - ISSN 2524-6968. - STAMPA. - 99:3(2020), pp. 217-232. [10.1007/s42496-020-00045-3]
Best Spatial Distributions of Shell Kinematics Over 2D Meshes for Free Vibration Analyses
M. Petrolo;E. Carrera
2020
Abstract
This paper proposes a novel approach to build refined shell models. The focus is on the free vibrations of composite panels, and the node-dependent-kinematics is used to select shell theories node-wise. The methodology shown in this work can provide at least two sets of information. First, it optimizes the use of shell models by indicating the minimum number of refined models to use. Then, it highlights which areas of the structures are more vulnerable to non-classical effects. Moreover, by varying various problem features, e.g., boundary conditions, thickness, and stacking sequence, the influence of those parameters on the modelling strategy is evaluated. The results suggest the predominant influence of thickness and boundary conditions and the possibility to improve the quality of the solution via the proper use of the refinement strategy.File | Dimensione | Formato | |
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PC_ATMS_2020_Revised.pdf
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https://hdl.handle.net/11583/2843264