Optimisation of an underwater vehicle rotary docking skirt structure

This paper presents a comprehensive procedure for the parametric design and analysis of a underwater vehicle rotary docking skirt (RDS) structure. Different types of software were integrated to improve the design efficiency, and different techniques were used to establish an approximate model of the RDS structure with high fitting accuracy. Nondominated sorting genetic algorithm II (NSGA-II) was improved by dynamic adjustment of the population size according to the number of iterations and new crossover and mutation operations (i.e. INSGA-II), which was then applied to a multi-objective optimisation model of the RDS structure. A Pareto solution set was obtained, and a sensitivity analysis was performed on the design variables and objective functions of four representative optimisation schemes. These results can be used to provide a richer selection for designers to reference when designing RDS structures.Abbreviations: RDS; Rotary docking skirt; NSGA-II; Nondominated sorting genetic algorithm II

• Record URL:
  • https://doi.org/10.1080/17445302.2023.2226517
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/17445302
• Supplemental Notes:
  • © 2023 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Liu, Feng
  • Tian, Zhen
• Publication Date: 2024-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1085-1103
• Serial:
  • Ships and Offshore Structures
  • Volume: 19
  • Issue Number: 8
  • Publisher: Taylor & Francis
  • ISSN: 1744-5302
  • Serial URL: http://www.tandfonline.com/tsos20

Subject/Index Terms

• TRT Terms: Design; Genetic algorithms; Optimization; Parametric analysis; Underwater vehicles
• Subject Areas: Data and Information Technology; Design; Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01931433
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 19 2024 4:59PM