Optimisation of transmission towers under multiple load cases and constraint conditions with the KSM-GA method

Advances in Mechanical Engineering

卷:15

Research Article

Yinqi Li¹  Songfeng Liang¹  Peng Li¹  Yuanzhi Xu² 

[1] Automotive and Transportation Engineering, Shenzhen Polytechnic College, Shenzhen City, China

[2] Kunming Yunnei Power Company Limited, Kunming City, China

关键词: Transmission tower;    structural optimisation;    KSM-GA;    multiple load cases;    secondary development of ABAQUS;   

DOI  :  10.1177/16878132231183764

 received in 2022-10-22, accepted in 2023-06-06,  发布年份 2023

来源: Sage Journals

【 摘 要 】

Transmission towers operate in complex engineering environments, such as gravity, strong winds, ice and snow, wire breaking and unbalanced loads. Owing to complicated structural parameters, multiple load cases and multiple constraint conditions, the optimal design plan of the structure is difficult to acquire. Popular intelligent algorithms (Genetic Algorithm, GA; Particle Swarm Optimisation, PSO; and others) need to spend time in structural mechanical computation and search processes. To solve this problem, the commercial FE software ABAQUS was used to build the full parametric analytical and computational sub-procedures (general static, linear buckling and cost computation) for the transmission tower under multiple load cases and constraint conditions. Then, the main algorithm procedure, KSM-GA, was developed based on the GA optimiser and Kriging Surrogate Model (KSM). The KSM-GA could import the design variables (such as cross-section properties and structural dimensions) of the transmission tower into the FE computational sub-procedures and read the results (including stresses, displacements, buckling load and weight). The results show that the KSM-GA can reduce the search time more than 30% compared with the GA, PSO and BO-GP( Bayesian Optimisation with Gaussian Process) while the training precision of the KSM is above 99% accuracy of the FE results.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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