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CFD Based Multi-Disciplinary Optimization Design of High-Performance Deep Sea Seismic Vessel

[+] Author Affiliations
Mao Xiaofei, Zhang Wenxu, Qian Jiankui, Wu Minghao

Wuhan University of Technology, Wuhan, China

Paper No. OMAE2017-62657, pp. V001T01A012; 9 pages
  • ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 1: Offshore Technology
  • Trondheim, Norway, June 25–30, 2017
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-5763-2
  • Copyright © 2017 by ASME


This paper focuses on the application of a ship hull form multi-disciplinary optimization (MDO) system based on the computational fluid dynamics (CFD). Using the iSIGHT software, the MDO system integrates an automatic geometry transformation program and high-fidelity CFD solvers for different sub-disciplines. Hydrodynamics analysis subsystem includes resistance, seakeeping and stability modules. The resistance and seakeeping is analyzed by commercial potential-flow CFD codes, the stability is assessed by in-house code. The geometry variation output can be automatically used by the numerical solvers. By means of the design of experiment (DOE) technique, a neural network metamodel is trained to predict short term motion response of the derived ships efficiently.

The system has been used in a seismic vessel’s hull form optimization to minimize the resistance and maximize the long term seakeeping operability index. Meanwhile, the stability in waves is concerned as a constraint. The hybrid MIGA-NLPQL optimization algorithm is applied for a global-to-local search in resistance optimization. For the synthesis optimization, a Pareto optimal solution set has been obtained and the final solution is achieved by trade-off analysis of the solution set. The entire automatic optimization process can be used for the preliminary design of new high performance vessels.

Copyright © 2017 by ASME



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