Single- and multiobjective design optimization of a fast multihull ship: numerical and experimental results
Single- and multiobjective design optimization of a fast multihull ship: numerical and experimental results
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DOI：
10.1007/s00773-011-0137-y
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发表时间：
2011-08
影响因子：
2.6
通讯作者：
Y. Tahara;D. Peri;E. Campana;F. Stern
中科院分区：
文献类型：
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作者：
Y. Tahara;D. Peri;E. Campana;F. Stern
基于先进的自由面非定常雷诺平均Navier-Stokes（URANS）解算器和势流解算器以及全局优化（GO）算法，通过基于模拟的设计（SBD）框架，对快速双体船（高速海运研究模型B，HSSL-B）的初始设计进行了数值优化。势流计算流体动力学（CFD）SBD用于指导更昂贵的URANS CFD SBD。通过对双体船流场的流体动力学分析，证明了URANS解算器的使用是处理多体干扰问题的基础。在所研究的情况下，分离距离很小，粘性流由于船体的接近而相当扭曲，因此只有粘性解算器才能正确地捕捉流场细节。由于高速范围和所研究的小分离距离，下沉和配平效应也是相关的。初始HSSL-B的几何形状和三个优化问题，包括单目标和多目标优化问题，由巴斯钢铁厂的设计师提出，成功地优化/解决，最后进行了实验活动，以验证最优设计。基于趋势，使用了一种新的验证和确认方法，用于评估基于模拟的优化中的不确定性和误差，即，目标函数的数值预测改进与专用实验活动中测量的实际改进之间的差异，包括考虑数值和实验不确定性。最后，通过实验测量证实了优化过程的成功，并对原始设计和优化设计之间的总阻力、下沉和配平趋势进行了数值和实验验证。
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.