Load Identification For A Composite Laminated Plate And Shell

Smart structure is a kind of self-sensing and self-adaptive structure that can sense the change of the surrounding environment and can make an appropriate response in response to this change. Structural health monitoring based on the new type of sensors such as piezoelectric, optical fiber sensors, has drawn extensive attention from scholars at home and abroad. In this article, a further and systematic study about the load identification of piezoelectric smart structure was conducted in the engineering background of using piezoelectric smart structures for structural health monitoring in the aviation and aerospace areas.This paper used the method of combining genetic algorithms and finite element method to achieve the identification of amplitude and location of the load acted on the composite material structure, having a deep research on the chromosome encoding methods. The method that encoded the load amplitude and location with binary encoding method is simple, efficient, but there is a mapping error in the continuous function discretization. The string length depends on the required identification accuracy, and long chromosome will affect the calculation efficiency; The hybrid coding method of encoding the location of load with binary encoding method and encoding the amplitude of load with floating-point encoding method has greatly reduced the length of chromosomes, and significantly improved the computation efficiency and accuracy, but it was difficult to achieve the identification of non-grid node points; A discriminance method was proposed to identify the element where the concentrated load locates, which was judged by the geometrical relationship between the abscissa, vertical coordinates of load point generated from GA and four edges of every element. Furthermore, the load was allocated to nodes of the element by the equivalent distribution principle. Finally, the location of arbitrary concentrated load was identified by finite element analysis and the superior global search capacity of GA. Compared with the identification methods of neural network and inverse finite element analysis method , the present method has the advantage of being able to identify the concentrated load locating at the interior of a element and numerical examples show that this method has higher accuracy. In addition, the inversion equation of dynamic load history and method of location identification are derived based on the identification of static load. The simulation with a specific example shows that the identification of the history and the location of impact load are feasible.