对船舶运动学建模在航海模拟器中的研究现状进行了系统梳理，重点总结了机理建模、辨识建模与数据驱动建模等主流方法的研究进展。传统方法在理论完整性和物理可解释性方面具有优势，但在高度非线性建模、实时计算效率及六自由度运动耦合处理上仍面临挑战。同时，尺度效应与实验数据获取困难制约了数值结果向实船应用的推广与可靠性。相比之下，人工智能方法能够在复杂非线性和大规模数据场景下展现更高的建模精度和适应性，但对数据依赖度高、物理可解释性不足。数字孪生、多源数据融合及机器学习等新兴技术的结合，为提升模型精度、鲁棒性和实时性提供了新思路，预计将推动船舶运动学建模与航海模拟器向智能化和数字化方向加速发展。
A systematic review of the current state of ship kinematic modeling in maritime simulators is presented, with a focus on the research progress of mainstream approaches including mechanism-based modeling, identification modeling, and data-driven modeling. Traditional methods offer advantages in theoretical completeness and physical interpretability, yet they still face challenges in highly nonlinear modeling, real-time computational efficiency, and the coupling of six degrees of freedom. Moreover, scale effects and the difficulties of acquiring experimental data restrict the reliability and applicability of numerical results to full-scale vessels. In contrast, artificial intelligence methods demonstrate higher accuracy and adaptability in handling complex nonlinear dynamics and large-scale data scenarios, but they also suffer from high data dependency and limited physical interpretability. The integration of emerging technologies such as digital twins, multi-source data fusion, and machine learning provides new avenues to enhance model precision, robustness, and real-time performance, and is expected to accelerate the advancement of ship kinematic modeling and maritime simulators toward greater intelligence and digitalization.
2026,48(7): 112-119 收稿日期：2025-7-31
DOI：10.3404/j.issn.1672-7649.2026.07.019
分类号：U666.158
基金项目：高校条件保障资助项目（xxxxx058）
作者简介：王兴众(1987-),男,博士,研究员,研究方向为船舶系统工程及模拟仿真
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