A fast GA-ANN model and application in multi-objective optimization of the sealing ring for the subsea pipeline connector with regard of the penetration load
Journal of Mechanical Science and Technology(2024)
Abstract
In this paper, a finite element method is proposed to predict the contact pressure of the metal seal in the subsea pipeline connector with regard of the penetration load on the critical sealing surface. Further, a fast GA-ANN model is proposed on the basis of GA-ANN to optimize the sealing structure of the connector, which uses only 3–6 % of the calculation time compared to the traditional GA-ANN model. The fast GA-ANN is further coupled with NSGA-II in the multiple objective optimization of the sealing structure and the results are compared with that of the response surface methodology (RSM). In terms of the number of valid candidate points and the optimal candidate point, NSGA-II coupled fast GA-ANN model performs much better than RSM. The hydrostatic pressure tests were carried out with the optimal sealing structure by the fast GA-ANN and the results meet the design requirements very well.
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Key words
Artificial neural network,Genetic algorithm,NSGA-II,RSM,Sealing performance,Structure optimization
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