Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

A Data-Driven Strategy Model to Incorporate Expertise Experience in Hull Form Design andOptimization
Wenyu Sun; Xiyang Liu; Anran Zheng; Jinfang Wei; Jingpu Chen
China Ship Scientific Research Center, Wuxi, China
Taihu Laboratory of Deepsea Technological Science, China

problem that accompanies the development of shipbuilding industry.
The complex geometry of hull form, coupling hydrodynamic systemand customized design requirements make it become a sophisticated job.
Traditionally, senior engineers fulfill a hull form design task mainly byinheriting the geometry from historical designs or similar ships andvalidate the performance through towing tank test. With thedevelopment of computational method and more advanced computeradded approaches, engineers could evaluate multiple hydrodynamic
performance before model test. Meanwhile, many integrated
approaches including performance evaluation, geometry modeling andoptimizer have been proposed and applied to solve specific hull form
optimization problems following the early research of Tahara(Campana et al., 2009 and Tahara et al., 2011).

ABSTRACTThe application of stochastic optimization approaches in hull form
design meets challenges due to computational resources and thecompatibility with flexible design requirements and relatively obscureknowledge of senior engineers. In this paper, we proposed a novel datadriven framework from the perspective of geometry design, physics
constrains, reasoning for next step direction and the solving of reversed
problem in hull form design. To achieve this goal, the design knowledge
is represented from three components, namely design region, direction
and optimization inference. For hull form deformation, a spatial
decomposition method is applied to resolve the geometric features ofhull form and provides a parameter-free approach to impose geometricvariation based on fuzzy experts’ knowledge. The magnitude anddirection of design thus could be represented by the eigenvalue andeigenvector of the Gaussian-Kernel based decomposition. For
performance prediction, a point-cloud based deep learning method isapplied to construct the surrogate model directly from hull form
geometry to resultant quantities. Thus, the optimization strategy modelthat supports forward performance prediction, optimization constrains
and flexible design requirements is constructed under Bayesianinference framework. At last, three typical application scenarios arepresented to demonstrate the capability of the framework. Results showthat the proposed approach could provide a uniformed framework fordesign problem with different design requirements. The priori experts
could be imposed at any time along the design procedure and theinference model could help to find the most satisfactory designs. A
complete application is also made for an 80000DWT bulk carrier andthe results validated by towing tank test indicates a reduction of 5.6%
on propeller delivery power.

In practice, hull form design and optimization is always a multi-object
task with flexible demands. With new hull forms, it is needed toevaluate the quantities of interests (QoIs) like resistance, flow field andpropulsion with CFD method or towing tank test. In design phase,
solving Reynolds Averaged Navier-Stokes (RANS) equation is themost state of art method to achieve the hydrodynamic performance of
new designs. This progress will take few hours for bare hull resistancecalculation and days for a complete simulation of self-propelled
condition even with coarse grid. One solution is to utilize potentialbased method like Neumann-Michell theory (Liu et al., 2019), Rankine
Panel method (Chen et al., 2016) et al. when wave-making resistance(Yang et al., 2015) or wave added resistance (Kuroda et al., 2010) is tobe optimized. For hull form under self-propulsion condition, somehybrid potential and RANS methods will be employed (Sun et al., 2019;
Miao and Wan, 2020). He et al. (2019b) also considers the impact ofpropeller by a simple actuator disk theory. Another way is to build
surrogate models from high fidelity data to speed up the searching ofcandidates. Kriging model (Lin et al., 2018), Response surface method(Kim et al., 2011) and polynomial chaos expansions (Wei et al., 2019)are all widely used in previous studies to find the relationship between
the design parameters and ship hydrodynamics. As the geometry is acomplex surface with 3D curvature, how to represent the hull form isanother import problem in design and optimization and attracts many
research efforts. The Free-Form Deformation method is widely used inmany studies (Zhao et al., 2017; Liu et al, 2019) by embedding the hullform into a lattice. Cheng et al (2018) use Delaunay triangulation

KEY WORDS: Hull form design, Machine learning, Design strategy,
Bayesian inference

INTRODUCTION
Hull form design and optimization is a traditional and important

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