Excitability of Ocean Rogue Waves – Numerical Prediction and Early Warning by combining Wave Physics, Numerical Simulation and Data Driven Methods

海洋异常波浪的兴奋性——结合波浪物理学、数值模拟和数据驱动方法的数值预测和预警

• 批准号: 277972093
• 负责人: Professor Dr. Norbert Hoffmann
• 金额: --
• 依托单位: Institut für Strukturdynamik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/277972093?language=en
• 关键词: Excitability; Ocean; Rogue; Waves; Numerical

Achieving short-term predictions of rogue waves on the ocean is widely considered an important objective for offshore operations and shipping. At present none of the approaches and methods published or used today can be considered satisfying. Prediction times at present are on the order of a minute or less. Probably the most important reason for this is the inability to integrate the wave amplitude dependent dispersion effects properly into a computating and simulation environment that is numerically efficient enough to have real-time capability. Furthermore, also small errors in the initial conditions extracted from the data sources grow quickly during the prediction, which is unavoidable with a view to the usually chaotic or even weakly turbulent wave dynamics under consideration. The present state-of-the-art is thus far below what is needed, and it is far below what our knowledge on nonlinear wave physics suggests is possible, which ranges in the order of five to fifteen minutes, or even beyond. Two key obstacles still need to be overcome: First, from the available wave-state measurement data, initial or initial-boundary conditions have to be identified for the subsequent prediction process. At present this identification process forms the weakest part in the process chain. Second, the prediction processes themselves need to reach a substantially higher quality and numerical efficiency, if real-time capability is intended. At present three related research fields of (i) understanding the physics of rogue ocean waves, (ii) predicting the short-term occurrence of rogue ocean waves by numerical simulation, and (iii) employing data driven methods in the context of rogue ocean waves, are largely disconnected. We propose to integrate the three fields of knowledge and methods to develop novel hybrid approaches. The vision is to predict the sea state evolution and the occurrence of rogue wave events up to the limit given by the theoretical horizon of predictability of the specific chaotic sea state. This proposal aims at studying optimised combinations of all three fields mentioned to push the boundary of how one can achieve predictions closer to the theoretically possible horizon of predictability. We strive to overcome the present limitations by a combination of exploiting wave physics, advanced numerical simulation, and data driven methods. It is our aim to reach prediction times of 5 to 10 minutes. This implies a sufficient accuracy in terms of amplitude and phase error of the prediction within the desired time frame. The objective is to keep both amplitude and phase errors within an error bound of +/- 10 % within the prediction period. We expect such a method to subsequently find wide applicability in many fields, like sea-keeping, ship routing, early warning to evacuate dangerous zones on ships or platforms, construction of offshore wind plants, ocean energy devices, etc.

实现对海洋上无赖海浪的短期预测被广泛认为是近海作业和航运的一个重要目标。目前，目前出版或使用的所有途径和方法都不能被认为是令人满意的。目前的预测时间在一分钟或更短的数量级。可能最重要的原因是不能将依赖于波幅的色散效应适当地集成到计算和模拟环境中，该计算和模拟环境在数值上足够有效以具有实时能力。此外，在预测过程中，从数据源提取的初始条件中的小误差也迅速增长，这对于所考虑的通常是混沌的甚至是弱湍流的波动动力学来说是不可避免的。因此，目前的最先进水平远远低于所需的水平，也远远低于我们关于非线性波动物理的知识所表明的可能水平，即大约5到15分钟，甚至更长时间。仍然需要克服两个关键障碍：第一，从现有的波态测量数据中，必须为随后的预测过程确定初始或初始边界条件。目前，这一识别过程是过程链中最薄弱的部分。其次，如果想要实时能力，预测过程本身需要达到显著更高的质量和数值效率。目前，(I)理解无赖海浪的物理，(Ii)通过数值模拟预测无赖海浪的短期发生，以及(Iii)在无赖海浪的背景下使用数据驱动方法，这三个相关的研究领域基本上是相互脱节的。我们建议整合这三个领域的知识和方法来开发新的混合方法。其愿景是预测海态的演变和流浪事件的发生，达到特定混沌海态的理论可预测性范围所给出的极限。这项建议旨在研究上述三个领域的优化组合，以推动人们如何实现预测的边界更接近理论上可能的可预测性地平线。我们努力通过利用波动物理、先进的数值模拟和数据驱动方法的组合来克服目前的限制。我们的目标是达到5到10分钟的预测时间。这意味着在所需时间帧内的预测的幅度和相位误差方面具有足够的精度。目标是在预测期内将幅度和相位误差都保持在+/-10%的误差范围内。我们预计，这种方法随后将在许多领域找到广泛的适用性，如耐波性、船舶路线、船舶或平台上危险区域的早期疏散预警、海上风力发电站的建设、海洋能源设备等。