Author
Listed:
- Pedro Sanchez-Cachinero
(Department of Mechanics, Campus de Rabanales, University of Cordoba, 14014 Cordoba, Spain)
- Rafael Luque-Mohedano
(Department of Mechanics, Campus de Rabanales, University of Cordoba, 14014 Cordoba, Spain)
- Rafael R. Sola-Guirado
(Department of Mechanics, Campus de Rabanales, University of Cordoba, 14014 Cordoba, Spain)
Abstract
The development of trunk shaker machines over the years has been based on test-error methods in field. Mathematical or computational models have been studied with great simplifications. This paper presents a method for modelling the dynamic behaviour of a trunk shaker with a test bench. Two mass configurations were used on the test bench as well as two different vibration frequencies on the trunk shaker. Acceleration values were recorded at different points of the system. The binomial shaker-post was computationally modelled, and its dynamic response was analysed based on a modal and transient study with a series of proposed simplifications. The results of the simulations were compared with experimentally recorded acceleration values. In both cases, a linear response to mass and frequency variation was observed in the acceleration that the shaker performed. There was a high correlation in the effective accelerations (error < 4%) between experimental and computational studies measured in the trunk shaker. However, there were higher errors when the post was used in the test in the post structure points. The greatest uncertainty in the model may lie in the assumption of contact between the attachment pad and the post, but if this is not carried out, it makes convergence in the computational calculations very difficult. The method has proved its worth in determining the dynamic behaviour of these machines.
Suggested Citation
Pedro Sanchez-Cachinero & Rafael Luque-Mohedano & Rafael R. Sola-Guirado, 2022.
"Computational Model for the Dynamic Characterisation of a Trunk Shaker,"
Agriculture, MDPI, vol. 12(12), pages 1-15, December.
Handle:
RePEc:gam:jagris:v:12:y:2022:i:12:p:2158-:d:1003943
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