Optimization of Rotary Drilling Rig Mast Structure Based on Multi-Dimensional Improved Salp Swarm Algorithm
Source abstract
Abstract
The mast is a critical component of rotary drilling rigs, which has a cross-section consisting of a rectangular shape formed by two web plates and two flange plates. Structural optimization of the mast is necessary to address the issue of excessive weight. The shortcomings of the traditional structural optimization algorithms are summarized as follows: the optimized steel plate thickness is a non-integer, where rounding upwards may increase the cost to a certain extent, but it can ensure the safety of the structure; rounding downwards its load carrying capacity may not satisfy the requirements, and thus a novel Salp Swarm Algorithm is proposed to solve the optimization problem. First, this study improves the initialization and update strategy in the traditional Salp Swarm Algorithm. In order to obtain a solution for engineering, an innovative multi-dimensional running comparison is carried out. Secondly, the optimization model of rotary drilling rigs is established based on the division of the working conditions. The objective function of the optimization model is to minimize the weight of the mast while considering the constraints of strength, stiffness, stability, and welding process. Finally, the proposed optimization algorithm and the established optimization model are applied to optimize the design of the mast for a rotary drilling rig. The empirical results demonstrate that the weight of the mast has been reduced by 20%. In addition, the Improved Salp Swarm Algorithm exhibits higher solution quality, faster iteration capability, and extreme stability in optimizing welded box sections compared to the conventional algorithm. The example shows that the Improved Salp Swarm Algorithm is applicable to the optimization problem of box sections.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vibration and Dynamic Analysis · Power Systems and Technologies · Advanced Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
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