Research on topology optimization method of surface support structure based on leaf vein growth process

In this paper, a biomimetic topology optimization design method that simulates the growth pattern of leaf veins is proposed for the design of the support structure of ultra-light airfoil-like solar cells in the solar powered unmanned aerial vehicle. This method simulates the optimal growth process of main vein morphology through the topology change of dynamic point groups to obtain an optimized topological main support structure and then generates a Voronoi grid structure in the area surrounded by the main support structure to increase the local support for the battery. The whole process is combined with genetic algorithm to simulate the optimal distribution strategy of leaf vein growth by inputting a small number of parameters. Compared with the traditional grid support structure, the support structure obtained by simulating the leaf vein growth optimization strategy can provide more efficient support for the solar panel and avoid damage to the solar cell

A bone structure-inspired design of bionic wing rib

This paper presents a method to generative design bionic bone structures wing ribs by simulating the formation process of soap bubble boundary. In this method, the position of the central control point is optimized by the genetic algorithm to simulate the moving process of the bubble, and the control point is simplified by the K-means classification method to improve the computational efficiency. The boundary formed by the extrusion of bubbles is filled with a bone-like grid structure to realize the optimal design of a bone-like rib structure. Compared with the traditional honeycomb sandwich structure, the bone-like rib structure can distribute the stress evenly throughout the structure, thus reducing the stress concentration and improving the bearing capacity of the structure. The method proposed in this paper provides a new idea for a multilevel structure design

Effective Utilization for Data of Natural Environment Corrosion of Materials

An analysis architecture is built to make full use of natural environment corrosion of materials data. All environmental factors, such as average temperature, average relative humidity, rainfall hours, sunshine duration, chloride ion settlement, etc., in nine atmospheric test stations, including Beijing, Wuhan, Jiangjin, and Wanning, and seawater temperature, salinity, dissolved oxygen, pH values, etc. in four marine test stations, including Qingdao, Zhoushan, Xiamen, and Yulin, are modelled monthly using a statistical analysis technique. Normal distribution, logarithmic normal distribution, Weibull distribution, and uniform distribution of the samples are checked by the Shapiro-Wilk and Kolmogorov-Smirnov methods. Subsequently, the probability distribution function, the expected population value and variance, and the confidence interval are estimated. Corrosion data for twenty-two kinds of carbon steels, low alloy steels, and stainless steels, including A3, 3C, 16Mn, 10CrMoAl, 1Cr18Ni9Ti, in atmospheric and marine test stations are analyzed. The main environmental factors for atmospheric and marine corrosion of each type of steel are determined by grey relational analysis. Subsequently, a predictive model incorporating the main corrosion environmental factors, exposure time, and corrosion rate is built by the BP artificial neural network. After the evolution of corrosion pit depth is predicted by the artificial neural network, a fracture mechanics calculation is used to evaluate the residual life of a structure with corrosion pit defects