Ship Hull Form Optimization Design for KCS Considering Uncertainty of Ship Speed
- Taiwen Chen (Shanghai Jiao Tong University) | Aiqin Miao (Shanghai Jiao Tong University) | Decheng Wan (Shanghai Jiao Tong University)
- Document ID
- International Society of Offshore and Polar Engineers
- The 28th International Ocean and Polar Engineering Conference, 10-15 June, Sapporo, Japan
- Publication Date
- Document Type
- Conference Paper
- 2018. International Society of Offshore and Polar Engineers
- Uncertainty optimization, OPTShip-SJTU, Stochastic programming, Ship hull form optimization
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At present, most of ship hull form optimization design is based on deterministic parameters. However, in the actual engineering problem, there are many unavoidable uncertain factors. For instance, the ship speed may vary around the design speed due to wind, waves, flow and other environmental or human factors while sailing. The optimal ship based on deterministic parameters may fail to reach the predetermined target while parameters change. An uncertainty optimization method based on stochastic programming is applied to reduce the resistance of KCS considering uncertainty of ship speed in this paper. The whole optimization process is based on an in-house ship hull form optimization solver, OPTShip-SJTU. Comparison and analysis of the resistance and flow field between initial ship and optimal ship validates the rationality and effectiveness of uncertainty optimization based on stochastic programming in ship hull form optimization design field.
As a core part of ship overall design, ship hull form design based on simulation-based design (SBD) technology, which is developed by combining the optimization technique and computational fluid dynamics (CFD) technique, plays a more and more important role in ship design field. There are three crucial elements for SBD optimization design process, automatic modification of ship hull geometry, high precision simulation method, and advanced optimization algorithms.
However, a large number of alternatives should be evaluated during the optimization process. Establishing the approximation model as a surrogate is an efficient way to reduce calculation burden. Despite the high efficiency, approximation models have the disadvantage at the same time: the establishment of approximation models needs amounts of precision results as input, and output results are sensitive to internal parameters, so inevitably, error of output will occur due to some uncontrollable causes, which is expressed as uncertainty. Although this error or uncertainty has a small value in most cases, large deviation of the whole system can also be generated by continuous iterative computation. Therefore, it has an important theoretical and practical significance for considering the uncertainty of approximate model.
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