Single- and multiobjective design optimization of a fast multihull ship: numerical and experimental results

详细信息

下载全文

推荐本文 |

• 作者：Yusuke Tahara (1) tahara@nmri.go.jp
  Daniele Peri (2)
  Emilio Fortunato Campana (2)
  Frederick Stern (3)
• 关键词：Fast multihull ship &#8211 ; Simulation ; based design &#8211 ; CFD ; based single ; and multiobjective optimization &#8211 ; Derivative ; free optimization algorithm &#8211 ; Total resistance minimization &#8211 ; Seakeeping merit function minimization &#8211 ; Verif
• 刊名：Journal of Marine Science and Technology
• 出版时间：December 2011
• 出版年：2011
• 期刊代码：84_0948-4280
• 类别：en
• 卷：16
• 期：4
• 页码：412-433
• 数据来源：sp

摘要

Numerical optimization of the initial design of a fast catamaran (high-speed sealift research model B, HSSL-B) has been carried out through a simulation-based design (SBD) framework, based on an advanced free-surface unsteady Reynolds-averaged Navier–Stokes (URANS) solver and a potential flow solver, and global optimization (GO) algorithms. The potential flow computational fluid dynamics (CFD) SBD was used to guide the more expensive URANS CFD SBD. The fluid-dynamic analysis of the flow past the catamaran proved that the use of the URANS solver was fundamental in dealing with the multihull interference problem. In the case investigated, the separation distance was small and the viscous flow quite distorted by the proximity of the hulls, so that only viscous solvers could correctly capture the flow details. Sinkage and trim effects, due to the high speed range and again to the small separation distance investigated, are also relevant. The initial HSSL-B geometry and three optimization problems, including single- and multiobjective optimization problems, proposed by designers from Bath Iron Works, were successfully optimized/solved, and finally an experimental campaign was carried out to validate the optimal design. A new verification and validation methodology for assessing uncertainties and errors in simulation-based optimization was used based on the trends, i.e., the differences between the numerically predicted improvement of the objective function and the actual improvement measured in a dedicated experimental campaign, including consideration of numerical and experimental uncertainties. Finally, the success of the optimization processes was confirmed by the experimental measurements, and trends for total resistance, sinkage, and trim between the original and optimal designs were numerically and experimentally verified and validated.