Static And Dynamic Analysis Of Overhead Crane Bridge Structure And Multi-objective Optimization Research

At present, the domestic overhead crane structure design is mostly based on the static design method, it makes some bridge crane structures exist quality problems, poor dynamic performance and other problems. To solve these problems, this paper based on the finite element theory, and the multi-objective optimization method is introduced into the design of bridge crane, to achieve the purpose of better dynamic performance of bridge crane and lighter structural weight, under the condition of strength and stiffness constraints. The main contents of this paper are as follows:First of all, to establish the finite element model of bridge structure according to the drawings provided by enterprise, and making a static analysis based on the finite element analysis software. the static analysis results are extracted as the state variables of the following optimization design. Then making a dynamic analysis(modal analysis and harmonic response analysis) on the basis of static analysis, getting the first six order natural frequencies and the natural frequency of the biggest effect on its dynamic performance, and using the results as an optimization objective function of bridge crane.Next, the transient dynamic simulation of the optimization model is verified when the fully loaded car in the mid-span location. And the displacement response curve, the velocity response curve and the acceleration response curve are obtained. The lifting dynamic load coefficient is revised too.Finally, according to the results we have got, establishing a multi-objective optimization model of bridge crane. The objective function are lightest quality(minimum cross-sectional area) and largest third order natural frequency. With the size constraint, strength constraints, stiffness constraints, overall stability and process constraints as constraint conditions. Then using the NSGA-II algorithm to optimize the multi-objective design, and the finally Pareto solution are obtained. Selecting a group from the Pareto solution concentration as the final optimal design scheme. And the optimization design scheme is compared with the initial scheme to verify the feasibility of the optimization result.