Genetic Algorithm Optimization of Rainfall Impact Force Piezoelectric Sensing Device, Analytical and Finite Element Investigation

Abstract

In this paper, rainfall droplet impact force is transformed into a measurable voltage signal output via the piezoelectric material direct effect utilized for sensing purposes. The motivating sensor is utilized to measure the peak impact forces of rainfall droplets for further analysis and processing. Constructing a sense for the impact force of rainfall droplets has great implications in many real-life applications that can provide vital information regarding the amplifications of the impact force of rainfall on soil erosion, and the impact on small creatures and plants, etc. The rainfall droplet is set to collide on a very thin aluminum plate with negligible mass that can be presented geometrically as an extended segment of the proposed sensing device. The proposed sensing device is composed of a bimorph simply supported composite-piezoelectric beam that buckles due to the effect of the rain droplets’ vertical impact force. The proposed device is designed for optimal performance in terms of the amount of voltage that can be measured. This is accomplished by having the first critical buckling load of the device as less than the impact force of the rainfall droplet. Accordingly, the well-known genetic algorithm (GA) automated optimization technique is utilized in this paper to enhance the measured voltage signal. A proof mass is added to the middle of the beam to amplify the magnitude of the measured voltage signal. The voltage signal is intended to be transferred to the PC via a data acquisition system. The rainfall droplets’ peak impact forces are obtained analytically due to the nonlinear behavior of the beam using the Euler–Bernoulli thin beams assumptions. The FE model using COMSOL 6.0 Multiphysics commercial software is used to verify the analytical results.