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

Study on numerical simulation of complex wave based on deep reinforcement learning method
Xin Chen a, b, d, Dawei You *, c, e, Enjin Zhao a, b, d, Kui Zhu fa

Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, Chinab
College of Marine Science and Technology, China University of Geosciences, Wuhan, Chinac South China Sea Information Center of State Oceanic Administration, Guangzhou, ChinadShenzhen Research Institute, China University of Geosciences, Shenzhen, ChinaeKey Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou, Chinaf
College of Electrical Engineering, Naval University of Engineering, Wuhan, China

waves: firstly, on-site observation study by manual or equipment,

ABSTRACTsecondly, simulation study in a laboratory, and thirdly, theoreticalanalysis study. Due to the complexity and variability of waves, it isdifficult to obtain data for theoretical observations, and theoreticalstudies deal with irregular waves (Zhao, 2020). In the field ofnumerical simulation, the OpenFOAM open-source software has alarge consumer base. The software has the advantage of being editablesource code and having fine control granularity. However, as far as isknown, OpenFOAM is slightly less powerful than other numericalsimulation software in the field of wave creation. However, wave
calculation is very important in computational fluid dynamics.
Therefore, a large number of scholars at home and abroad haveconducted a lot of research in the field of wave generation in the earlydays. Some research about the principle was published, but the sourcecode was not made public and was only available for internal use in thenumerical simulation software developed independently. Higuera(2012a) developed olaFlow based on ihFoam. At present, the mosteffective open-source wave generation software is the wave2foam
toolkit and the olaFlow toolkit, (Higuera, 2014) which are based on thesecondary development of OpenFOAM (Hu, 2016) In this paper, weuse the olaFlow toolkit to modify it based on the case of“wavemakerFlume” (Higuera, 2018), and the solitary waves created aremanually superimposed. Using DRL (Deep reinforcement learning)technology coupled with traditional numerical simulation models, afree wave-making model is trained. The original case can only generate
regular waves, but the observed waves are usually irregular waves tosolve practical problems. Qu (2016) studied the comparison of solitary
waves and tsunami waves, where the effects of two different
waveforms have been analyzed. It was concluded that the main
component of a tsunami waves acts at completely different spatial andtemporal scales than solitary waves. The linear overlay theory of waves
also guarantees the stability of the solver in the original case. Besides,
there are also precedents for the combination of deep reinforcement
learning and OpenFOAM dynamic grid technology (Xie, 2021). Basedon the above ideas, this paper decides to use DRL technology toNumerical simulation of waves has always been a very importantresearch topic in the field of marine engineering. However, thelimitations of wave simulation by commercial simulation software arefound to be very obvious in practical experiments in the field ofcomputational fluid dynamics. In a real marine environment, it isrelatively difficult to simulate irregular waves. Many researchers preferto write their desired irregular waves in open-source numericalsimulation software, but writing them by themselves requires complex
derivation of formulas. In addition, the poor accuracy of simulated
waves can lead to a variety of problems. In this paper, the traditional
numerical wave-making method is improved and optimized.
Combining with the current stable deep learning framework in thecomputer field, we propose a model of superposition of solitary waves
based on deep reinforcement learning to simulate complex waves in thereal world. Solitary waves are used as the basis for deformation andsuperposition to simulate various complex waveforms. A combinationof deep learning and the open-source software OpenFOAM is used totrain a model that can be used to approximate the irregular waves
needed in practice. By using deep learning to fit a function instead of acomplex computational formula and by automatically exploring thewave stacking strategy, the intelligent body learns to fit the target wave
automatically during interaction with the environment in order to obtainthe simulation of irregular waves. It provides new ideas for the future
wave simulation direction and constructs more realistic waves. In thefuture, we will investigate the direction of a further combination ofdeep learning and computational fluid dynamics.

KEY WORDS: deep reinforcement learning, OpenFOAM,
numerical simulation, solitary waves, irregular waves
INTRODUCTION
In the field of coastal engineering, there are three ways to study

2072