Performance evaluation and comparison of commonly used optimization algorithms for natural gas liquefaction processes

Sun, Heng; Geng, Jinliang; Na, Fengyi; Rong, Guangxin; Wang, Chao

doi:10.1016/j.egyr.2022.03.164

Cited by 17 publications

(1 citation statement)

References 35 publications

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“…Thanks to the maturity of the algorithm, multiple open-source NSGA2 implementation tools are available, and researchers and engineers can easily apply the algorithm to their own problems. At the same time, some aspects of its performance, such as computational efficiency and convergence, still have certain advantages compared to other algorithms [25][26][27]. In this study, we use the NSGA2 algorithm to coordinate the calculation of the relationship between the three objective functions.…”

Section: Response Surface Methods and Nsga2 Multi-objective Optimizat...mentioning

confidence: 99%

Multi-Objective Optimization of a Multi-Cavity, Significant Wall Thickness Difference Extrusion Profile Mold Design for New Energy Vehicles

Xu,

Jiang,

et al. 2024

Materials

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With the rapid development of the new energy vehicle market, the demand for extruded profiles for battery trays, mainly characterized by significant wall thickness differences in multiple chambers, is increasing, posing new challenges to production and quality control. This study examines the multi-objective optimization problem in the design process of aluminum profile dies with multi-cavity profiles and significant wall thickness differences. Using QFORM-extrusion professional aluminum extrusion finite element analysis software and the response surface analysis method, the standard deviation of the velocity (SDV), standard deviation of the pressure (SDP), and thick wall hydrostatic pressure (TWHP) on the profile section at the die exit are optimized. By analyzing the functional relationship between the key die structure parameters (the height of the baffle plates, the length of the bearing, and the height of the false mandrel) and the optimization objective, the optimal combination scheme of die structure parameters was obtained using the NSGA2 (non-dominated sorting genetic algorithm-2) multi-objective genetic optimization algorithm. The results show that, compared with the initial design scheme, the standard deviation of profile section velocity was reduced by 5.33%, the standard deviation of pressure was reduced by 11.16%, and the thick wall hydrostatic pressure was increased by 26.47%. The die designed and manufactured using this scheme successfully completed the hot extrusion production task, and the profile quality met the predetermined requirements, thus verifying the effectiveness of this study in optimizing the design of a multi-cavity aluminum profile die with significant differences in wall thickness for complex structures.

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Section: Response Surface Methods and Nsga2 Multi-objective Optimizat...mentioning

confidence: 99%