深水时域格林函数的微分方程求解与高效数值计算

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本文由朱仁传、申亮和毛国平三位作者共同完成,发表于上海交通大学船舶海洋与建筑工程学院,主要探讨了深水时域格林函数的常微分方程推导及其数值计算方法。在船舶动力学分析中,有效评估时域格林函数及其梯度是至关重要的,因为这直接影响到对船舶流体动力学问题的时间域分析准确性。作者们基于贝塞尔函数,成功地导出了一个描述时域格林函数及其梯度的常微分方程(ODE)。 他们的工作旨在提供一种新的数值计算策略,通过求解这个ODE来提高格林函数的计算效率。该方法的亮点在于它不仅能提升时域格林函数的计算精度,而且还能显著降低计算所需的时间。这种方法对于解决复杂的流体动力学问题具有实际意义,因为它能够在保证结果精确性的同时,优化计算流程。 文中提到,随着船舶动力学研究的深入,越来越多的趋势倾向于在时间域内研究和分析问题,因此,对于高效的时域格林函数求解技术的需求愈发迫切。作者们的研究为这一领域提供了宝贵的贡献,通过对比传统的数值计算方法,他们证明了新提出的算法在深度水环境下的优越性。 关键词包括:时域格林函数、级数与渐近表达式、常微分方程(ODE)。整个研究的介绍部分强调了当前船舶动力学研究的发展趋势,以及其在时间和空间上进行分析的重要性。这篇首发论文为深水环境中船舶动力学问题的时间域分析提供了一个创新且实用的计算工具,有望在未来的研究中推动相关领域的发展。

Please revise the paper:Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.

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