This paper presents a finite element (FE) model developed using commercial FE software COMSOL to simulate the multiphysical process of pieozoelectric vibration energy harvesting (PVEH), involving the dynamic mechanical and electrical behaviours of piezoelectric macro fibre composite (MFC) on carbon fibre composite structures. The integration of MFC enables energy harvesting, sensing and actuation capabilities, with applications found in aerospace, automotive and renewable energy. There is an existing gap in the literature on modelling the dynamic response of PVEH in relation to real-world vibration data. Most simulations were either semi-analytical MATLAB models that are geometry unspecific, or basic FE simulations limited to sinusoidal analysis. However, the use of representative environment vibration data is crucial to predict practical behaviour for industrial development. Piezoelectric device physics involving solid mechanics and electrostatics were combined with electrical circuit defined in this FE model. The structure was dynamically excited by interpolated vibration data files, while orthotropic material properties for MFC and carbon fibre composite were individually defined for accuracy. The simulation results were validated by experiments with <10% deviation, providing confidence for the proposed multiphysical FE model to design and optimise PVEH smart composite structures.
In this paper, we apply the Cross-Entropy optimization (CEO) to the problem of selecting k sensors from a set of m sensors for the purpose of minimizing the error in parameter estimation. The computational complexity of finding an optimal subset through exhaustive search can grow exponentially with the numbers (m and k) of sensors. The CEO is a generalized Monte Carlo technique to solve combinatorial optimization problems. The CEO method updates its parameters from the superior samples at the previous iterations. The performance of proposed CEO-based sensor selection algorithm is better than existing sensor selection algorithm, and its effectiveness is verified through simulation results.
For the inverse finite element method (iFEM), an inappropriate scheme of strain senor distribution would cause severe degradation of the deformation reconstruction accuracy. The robustness of the strain–displacement transfer relationship and the accuracy of reconstruction displacement are the two key factors of reconstruction accuracy. Previous research studies have been focused on single-objective optimization for the robustness of the strain–displacement transfer relationship. However, researchers found that it was difficult to reach a mutual balance between robustness and accuracy using single-objective optimization. In order to solve this problem, a bi-objective optimal model for the scheme of sensor distribution was proposed for this paper, where multi-objective particle swarm optimization (MOPSO) was employed to optimize the robustness and the accuracy. Initially, a hollow circular beam subjected to various loads was used as a case to perform the static analysis. Next, the optimization model was established and two different schemes of strain sensor were obtained correspondingly. Finally, the proposed schemes were successfully implemented in both the simulation calculation and the experiment test. It was found that the results from the proposed optimization model in this paper proved to be a promising tool for the selection of the scheme of strain sensor distribution.
This paper presents a new gear dynamic model for flexibly supported gear sets aiming to improve the accuracy of gear fault diagnostic methods. In the model, the operating gear centre distance, which can affect the gear design parameters, like the gear mesh stiffness, has been selected as the iteration criteria because it will significantly deviate from its nominal value for a flexible supported gearset when it is operating.The FEA method was developed for calculation of the gear mesh stiffnesses with varying gear centre distance, which can then be incorporated by iteration into the gear dynamic model. The dynamic simulation results from previous models that neglect the operating gear centre distance change and those from the new model that incorporate the operating gear centre distance change were obtained by numerical integration of the differential equations of motion using the Newmark method. Some common diagnostic tools were utilized to investigate the difference and comparison of the fault diagnostic results between the two models. The results of this paper indicate that the major difference between the two diagnostic results for the cracked tooth exists in the extended duration of the crack event and in changes to the phase modulation of the coherent time synchronous averaged signal even though other notable differences from other diagnostic results can also be observed.
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