High-energy orbit attainment of a nonlinear beam generator by adjusting the buckling level

2020 ◽  
Vol 312 ◽  
pp. 112164
Author(s):  
Yao Huang ◽  
Weiqun Liu ◽  
Yanping Yuan ◽  
Zutao Zhang
2014 ◽  
Vol 14 (08) ◽  
pp. 1440023 ◽  
Author(s):  
Dongxu Su ◽  
Kimihiko Nakano ◽  
Rencheng Zheng ◽  
Matthew P. Cartmell

The recent potential benefit of nonlinearity has been applying in order to improve the effectiveness of energy harvesting devices. For instance, at relatively high excitation levels, both low and high-energy responses can coexist for the same parameter combinations in a hardening type Duffing oscillator, and this provides a wider bandwidth and a higher energy harvesting effectiveness under periodic excitations. However, frequency or amplitude sweeps of the excitation must be used in order to reach a desirable high-energy orbit, and this gives a limitation on practical implementation. This paper presents a stiffness tunable nonlinear vibrational energy harvester which contains a moving magnetic end mass attached to a cantilever beam, whose nonlinearity emerges from the interaction forces with two neighboring permanent magnets facing with opposing poles. The motivating hypothesis has been that the jump from the low-energy orbit to the high-energy orbit can be triggered by tuning the stiffness of the system without changing the frequency or the amplitude of the excitation. Theoretical investigations show a methodology for tuning stiffness, and experimental tests have validated that the proposed method can be used to trigger a jump to the desirable state, and hereby this can broaden the bandwidth of the energy harvester.


2018 ◽  
Vol 112 (14) ◽  
pp. 143901 ◽  
Author(s):  
Yunshun Zhang ◽  
Rencheng Zheng ◽  
Kimihiko Nakano ◽  
Matthew P. Cartmell

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 53
Author(s):  
Massimo Giovannozzi ◽  
Ewen Maclean ◽  
Carlo Emilio Montanari ◽  
Gianluca Valentino ◽  
Frederik F. Van der Veken

A Machine Learning approach to scientific problems has been in use in Science and Engineering for decades. High-energy physics provided a natural domain of application of Machine Learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, Machine Learning has been applied to Accelerator Physics only recently, with several laboratories worldwide deploying intense efforts in this domain. At CERN, Machine Learning techniques have been applied to beam dynamics studies related to the Large Hadron Collider and its luminosity upgrade, in domains including beam measurements and machine performance optimization. In this paper, the recent applications of Machine Learning to the analyses of numerical simulations of nonlinear beam dynamics are presented and discussed in detail. The key concept of dynamic aperture provides a number of topics that have been selected to probe Machine Learning. Indeed, the research presented here aims to devise efficient algorithms to identify outliers and to improve the quality of the fitted models expressing the time evolution of the dynamic aperture.


Author(s):  
Dongxu Su ◽  
Rencheng Zheng ◽  
Kimihiko Nakano ◽  
Matthew P Cartmell

The non-linearity of a hardening-type oscillator provides a wider bandwidth and a higher energy harvesting capability under harmonic excitations. Also, both low- and high-energy responses can coexist for the same parameter combinations at relatively high excitation levels. However, if the oscillator’s response happens to coincide with the low-energy orbit then the improved performance achieved by the non-linear oscillator over that of its linear counterpart, could be impaired. This is therefore the main motivation for stabilisation of the high-energy orbit. In the present work, a schematic harvester design is considered consisting of a mass supported by two linear springs connected in series, each with a parallel damper, and a third-order non-linear spring. The equivalent linear stiffness and damping coefficients of the oscillator are derived through variation of the damper element. From this adjustment the variation of the equivalent stiffness generates a corresponding shift in the frequency–amplitude response curve, and this triggers a jump from the low-energy orbit to stabilise the high-energy orbit. This approach has been seen to require little additional energy supply for the adjustment and stabilisation, compared with that needed for direct stiffness tuning by mechanical means. Overall energy saving is of particular importance for energy harvesting applications. Subsequent results from simulation and experimentation confirm that the proposed method can be used to trigger a jump to the desirable state, thereby introducing a beneficial addition to the performance of the non-linear hardening-type energy harvester that improves overall efficiency and broadens the bandwidth.


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