Structure Shape Optimization Design Considering Geometric Nonlinear And Its Application In Engineering

Spacial frame is a shape used commonly in engineering. Its structure mechanics performance can be improved greatly and its weight can be lightened by use of shape optimization too.Many scholars have done extensive research on spacial frame optimization design. But these studies are almost based on the theory of limited flexibility in small deformation linear elasticity theory, without considering the structure of the nonlinear element. In actual structure, after bearing load, there is nonlinear distortion effect. This thesis analysis and summarizes the domestic and international researchers'work about shape optimization, and then concentrates on the research on spacial frame optimization design considering geometric nonlinear.Based on the nonlinear finite element theories, this thesis adopts Total Lagrange description to deduce geometric nonlinear tangent stiffness expressions for spacial frame,considering effects of high nonlinearity.This thesis deduces computational formulations of displacement sensitivity and stress sensitivity in structural shape optimization considering geometric nonlinear element. In the calculation of stress sensitivity, the modules required to cut the stiffness matrix of node coordinates few.In order to simplify calculation and make program conveniently, this thesis uses half- parser method.Based on displacement harmonization,we deduce a dummy stiffness element to simulate the excursion connection between spacial frame element,which simulates the actual structure more precise.Compile a program of spacial frame optimization design. And use this optimization program computate the boom structure shape of heavy bucket-wheel stacker & reclaimer; Based on shape optimization,we use second optimization technology to optimize structure section size. The optimization results are satisfied.The computation indicates that considering the geometric nonlinear element can enhance the calculation efficiency and precision of optimization design evidently in sensitivity analysis.