Analysis and Research of Molten Copper Sloshing in Mould of Anode Based on VOF Method

Zheng, Junjian; Li, Guoping; Teng, Xinyi; Qiu, Zefeng

doi:10.1088/1757-899x/711/1/012031

Cited by 2 publications

(1 citation statement)

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“…Furthermore, the internal sloshing forces produced by the partially filled liquid tank can damage the tank walls structure and also influence the seakeeping behaviour of the on-board ship [4]. Additionally, in the metal industries, the pouring work of the molten metal is usually conducted by the human operator which tends to cause the metal sloshing and consequently can cause the molten metal spill out from the ladle [5]. Moreover, the stability of the tank trucks during the overturning has significantly decreased due to the sloshing force produced by the movement of the liquid in the tank.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Fractional-Order PID Controller Tuning for Liquid Slosh Suppression Using Marine Predators Algorithm

Tumari¹,

Ahmad²,

Suid³

et al. 2023

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Traditional control system development for liquid slosh problems often relies on modelbased approaches, which are challenging to implement in practice due to the chaotic and complex nature of fluid motion in containers. In response, this study introduces a data-driven fractional-order PID (FOPID) controller designed using the Marine Predators Algorithm (MPA) for suppressing liquid slosh. The MPA serves as a data-driven tuning tool to optimize the FOPID controller parameters based on a fitness function comprising the total norms of tracking error, slosh angle, and control input. A motor-driven liquid container undergoing horizontal motion is employed as a mathematical model to validate the proposed data-driven control methodology. The effectiveness of the MPA-based FOPID controller tuning approach is assessed through the convergence curve of the average fitness function, statistical results, Wilcoxon's rank test, and the ability to track the cart's horizontal position while minimizing the slosh angle and control input energy. The proposed data-driven tuning tool demonstrates superior performance compared to other recent metaheuristic optimization algorithms across the majority of evaluation criteria.

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Section: Introductionmentioning

confidence: 99%