Chinese Journal of Ship Research is supervised by China Shipbuilding Industry Corporation (CSIC) and sponsored by China Ship Development and Design Center (CSDDC). The journal is a notable academic journal in the field of naval architecture, shipbuilding and marine engineering in China. It aims to push forward theoretical innovation and advanced technologies. Its scope covers the latest technological breakthroughs, trends, concepts, and discoveries related to the marine industry, naval architects and marine engineering.
The journal is included in CSCD.
Honorary Chairmen AI Ting-yuan, SHAO Kaiwen, WANG Jianguo
Chairman WU Xiaoguang
Vice Chairmen CAI Daming, LIN Zhongqin, MIAO Yu, SHAO Xinyu, WANG Zili, XIA Guihua, XU Qing, ZHANG Qingjie
Invited members DING Rongjun(member of CAE), GONG Xianyi(member of CAE), LU Jianxun(member of CAE), MA Yuanliang(member of CAE), PAN Jingfu(member of CAE), QIU Zhiming(member of CAE), SUN Cong(member of CAE), SUN Yufa(member of CAE), TAN Jianrong(member of CAE), WANG Shunting(member of CAE), WEN Xueyou(member of CAE), YANG Desen(member of CAE), YANG Shie(member of CAE), ZHANG Jinlin(member of CAE),ZHU Yingfu(member of CAE)
[Objectives] The structural optimization of ships usually involves response analysis to high-fidelity numerical simulations, which is time-consuming and thus limits the number of simulation calls. This intrinsic property hinders the optimization process. To promote efficient design optimization, this paper explores the use of a gradient-enhanced Kriging (GEK) surrogate model to shorten the design cycle and save design costs. A reduced GEK-based infill criterion is proposed to decrease the number of simulation calls by calculating the gradients only for sample locations where improvement occurs. [Methods] A multi-start local optimization algorithm is employed to search for the local optima of the “expected improvement (EI)” function and locate candidate infill points. The associated “approximate probability of stationary point (APSP)” values are also evaluated, and infill decisions are made according to the consistency between these two quantities, thereby improving optimization efficiency. The proposed method is then applied to the structural optimization of an underwater vehicle to increase the 7th-order natural frequency under unconstrained free vibration in an underwater environment and thereby verify the validity of the method. [Results] The result shows that compared with the benchmark form, the optimized design achieves a 14.6% increase in natural frequency, which means the method is feasible. [Conclusions] The proposed GEK-based optimization method can be generalized to cases when gradients can only be evaluated by the finite difference method.
[Objectives] Designing the hardware resources of the whole warship from the top level and making comprehensive use of the information resources among different equipment is the inevitable requirement of the informatization design of warship equipment. Under this trend, an integrated information service system for warships is proposed. [Methods] The design objectives and general plan are analyzed. The information infrastructure is used for planning the overall hardware resources. The software framework is composed of a system management module and an information service module. Then, the software design scheme and implementation ideas are discussed from the aspects of the design of an integrated application platform and information application design. In the design of the integrated application platform, the integrated design ideas for B/S applications and C/S applications with Docker technology and integrated software management warehouses are analyzed respectively. In information application, the design of the functional module of the message push service is mainly discussed to achieve “information for the crewmen and pushing on demand.” [Results] According to the above design ideas, the system design is completed, and functions such as integrated management of various applications and message pushing are implemented. [Conclusions] The operation of the system meets the design objectives, improves the informatization level of the whole warship, and also helps to enhance the efficiency of the crewmen.
[Objectives] As for the control method and recovery strategy for submarines falling deep underwater, a six-degrees-of-freedom motion model was built for an X-rudder submarine. [Methods] The control rules of the X-rudder and the drainage capacity of the submarine were analyzed, and a recovery control system was designed using the multi-objective fuzzy control method. Then, recovery control in different degrees of falling deep scenarios was simulated in the case of large depth navigation, and the recovery control system was improved on the controller and control strategy levels. Finally, the recovery ability under different velocity conditions was compared. [Results] The results show that on the controller side, the intelligent fuzzy integral links were introduced, improving the recovery efficiency. On the recovery strategy side, the original control strategy was optimized with assistant pitch and acceleration, improving the recovery ability. [Conclusions] The results of this study show that the X-rudder fuzzy control system combined with acceleration and the pitch-assisted strategy has a good falling deep recovery effect.
[Objectives] In order to obtain a simplified mathematical model of ship motion for intelligent control, this paper takes a Mariner-class vessel as the research object and proposes a sensitivity analysis method combining the standard maneuverability test and proportion-integral-differential (PID) heading control test. [Methods] Compound analysis of the control index, maneuverability index, and squared loss of typical motion state variables throughout the entire process is performed to obtain a dataset containing multi-dimensional sensitivity coefficients. A machine learning-based K-means algorithm is introduced to perform cluster analysis on the dataset. The automatic sensitivity division of hydrodynamic derivatives is completed and the model is simplified. [Results] Contrastive simulation tests of heading control and track control are carried out among the simplified model, former simplified model, and complete model, and the results show that the sensitivity analysis method proposed in this paper is effective and the model proposed in this paper has higher accuracy in control prediction. [Conclusions] The method proposed in this paper has certain significance for guiding ship motion modeling for intelligent control.
[Objectives]In response to the difficulty of ensuring the fitting accuracy and optimization efficiency of surrogate models due to the high nonlinearity in conventional reliability-based optimization design of ship structures, a reliability-based optimization method based on interest subdomain dynamic surrogate models is proposed. [Methods] This method puts forward a concept of interest subdomain on the basis of the sequential optimization and reliability assessment (SORA) method, determines the range of interest subdomains, and formulates adaptive spatial reduction rules in light of information entropy function H to reduce the design space. On this basis, this paper proposes a method of adaptive spatial reduction and sequential sampling based on interest subdomains, thereby constructing dynamic Kriging surrogate models that highly fit the interest subdomains locally with as few sample points as possible. In addition, the surrogate models and the multi-island genetic algorithm (MIGA) are embedded into the SORA method to undertake reliability-based optimization. This study also proposes a probability-constraint feasibility checking method to reduce unnecessary reliability assessment processes. A mathematical example is given to verify the reliability-based optimization method. [Results] The relative error between the optimal solution and the theoretical solution is 0.066 8%, and the number of performance function calls is 40.6% less than those of the optimal method in the references, which proves the accuracy and efficiency of this method. [ Conclusions] When the proposed method is applied to the reliability-based optimization design of a cabin structure, the total cabin mass is reduced by 0.511% compared with that in the references, and 94 fewer finite element calculation calls are required, proving the efficiency and applicability of this method.
[Objectives] The cabin-skeleton coupling structure of a blended-wing-body underwater glider is optimized using a data-driven discrete optimization concept. [ Methods] First, a Kriging-assisted discrete global optimization algorithm (KDGO) is proposed for computationally expensive black-box problems. The KDGO uses a novel infill-sampling strategy to capture the discrete sample points with better performance, and introduces a multi-start method with a data mining strategy, including multi-start optimization, projection, sampling, and selection. Second, a parametric cabin-skeleton coupling structure model is established using the finite element analysis (FEA) method under lifting deformation and deep-water pressure conditions. The buoyancy-gravity ratio and strength and stability of the cabin-skeleton structure are taken as the goal and constraints, respectively. Considering the interference between shape and cabin, and the coupling relationship between cabin and skeleton, a discrete optimization mathematical model of the overall coupling structure is established. Finally, the discrete optimization algorithm and coupling structure simulation are combined to build an overall optimization framework. [Results] By using the KDGO to conduct 200 function evaluations and comparing the optimal feasible points in design of experiments (DoE) with the global optimal feasible points after optimization, it is found that the optimized buoyancy-gravity ratio of the coupling structure is increased by nearly 40%, representing satisfactory results. [Conclusion] The results of this study can provide valuable references for the cabin-skeleton coupling structure design of blended-wing-body underwater gliders.
[Objectives] Due to the functional requirements of structures, a large number of thin-walled structures with cutouts are adopted in the structural design of aviation, aerospace, shipbuilding, and other fields, leading to a significant reduction in the load-bearing capacity of such structures. Although the curvilinear stiffening method has great potential in improving the load-bearing performance of structures with cutouts, the sharp increase in design variables presents a challenge for structural optimization. The data-driven deep learning method is used to optimize the design of hierarchical stiffened panels with cutouts reinforced by curvilinear stiffeners. [Methods] For such panels, a visual representation method of structural parameters is proposed. A deep learning network model for structural response feature learning is built for data-driven structural optimization design. [Results] The results show that compared with the classical surrogate models constructed with structural numerical parameters, the proposed structural feature learning model based on image recognition improves the prediction accuracy roughly twofold. In the optimization design of structures based on the learning model, the load-bearing capacity of hierarchical orthogonally stiffened structures increases by 10.78%, and that of hierarchical curvilinearly stiffened structures increased by18.19%. [Conclusions] The results show that this deep learning-based structural optimization method is more effective for hierarchical stiffened panels with cutouts that have large and dynamically changing numbers of design variables. Compared with the traditional straight stiffener configuration, the curvilinear stiffener method is more effective in reinforcing the load-bearing capacity of structures with cutouts.
[Objectives] The optimization design of a ship strong frame structure under the requirements of the common structural rules (CSR) is a complex and time-consuming problem. Moreover, its tremendous constraints make it difficult to judge the feasibility of any design scheme. As the approach aims at global accuracy, when adopting the static surrogate-assisted evolutionary algorithm to solve this problem, the prediction of key areas will be distorted in the case of small size samples. Aiming at the above problem, a strong ship frame optimization method based on a sequential surrogate-assisted genetic algorithm is proposed. [Methods] First, the constraints of a strong frame structure based on CSR are analyzed, and all 675 constraints are reduced to 2 positive constraints according to constraint type. Then, surrogates for objective functions and constraint functions are constructed, and a genetic algorithm based on the feasibility principle is adopted to find the optimized solution. The true response of the solution is then calculated and the surrogates updated. In addition, the expected feasibility function (EFF) criterion is applied to update the constraint surrogates in order to refine the prediction accuracy at the constraint boundaries. The above procedures are iterated several times, and the optimized global feasible solution is finally obtained. [Results] The proposed method can obtain a better solution than the static surrogate-assisted algorithm with a lower computational burden, and the weight of the design area is finally reduced by 15.55%. [Conclusions] The proposed sequential surrogate-assisted algorithm is superior to the static surrogate-assisted algorithm, and possesses good application value in the optimization of ship strong frame structures under complex constraints.
