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

Prediction Method for Flow over Submarine Based on Multi-scale Deep Neural Network
Xing Hea,b Qiaogao Huanga,b* Chengcheng Qiua,b Jingyi Bai a,ba School of Marine Science and Technology, Northwestern Polytechnical University
b Key Laboratory for Unmanned Underwater Vehicle, Northwestern Polytechnical University
Xi’an, People’s Republic of China

ABSTRACTcylinder and the velocity field around (Jin et. al., 2018), It proves thatthe data-driven model can successfully learn the relationship between
wake field and cylinder surface pressure. Meanwhile, CNN model wasused to extract features from 2-D of temporal flame, trained LSTM tosimulate the time series features of these features and then predicted thetemporal flame state. It is proved that the CNN-LSTM hybrid neuralnetwork model can predict the changing law of flame state (Huang et al.,2019). Then, scholars were not satisfied that a single neural network onlycapture limited features, Hybrid model and multi-scale model wereproposed.
Multi-scale model was that the original data are divided into smoothand pulsating parts through different sampling frequencies. Then the
low-fidelity large-scale network and high-fidelity small-scale network
are trained to realize the response and capture of different physicalprocesses (Yue et al.,2021). The deep neural network has also
extensively studied the prediction of different flow scenarios. Used thesimulation data of three groups of cylinders with different geometricprofiles to train the CNN model, and successfully predicted the fluidloads of the other ten groups of cylinders with different geometricprofiles, and for the first time realized the accurate prediction ofaerodynamic loads by CNN through flow field variables in the wake areaof two-dimensional cylinders (Miya et al., 2008). CNN model also beenused to carry out reverse predictive design of airfoil (Vinoth and Shu etal., 2019). The article established a proxy model based on residual U-Net
and convolutional LRT recursive network and applied it to predict
dynamic underwater flows in geological models (Tang et al., 2020). The
applied scene in this paper is submarine flow field.

In order to accurately predict the dramatic changes of pressurearound submarine fore region, a method of prediction on continuous timeseries variables based a multi-scale network is proposed. The multi-scale
network is constituted by long short-term memory network (LSTM). The
pressure around submarine fore region under different time period are
obtained through numerical simulation to establish the datasets as
training samples and testing samples. Firstly, the dataset is decomposed
into high frequency and low frequency by lowpass filter to train deepneural networks with two different scales respectively. Finally, A largescale and a small-scale network are trained separately to achieve theresponse and capture of different process, the trained networks were thentested by predicting the flow fields in future time steps. This work has
analyzed the influence of multi-scale neural network on the prediction
accuracy. Meanwhile we verified the reliability of the network intransition zone. The results show that predicted flow fields using themulti-scale deep neural network are in good agreement with those
calculated directly a computational fluid dynamic solver.

KEY WORDS:
Multi-scale deep network; Deep learning; Submarine; Flow field
prediction; Long short-term memory

INTRODUCTION
It is a great challenge to quickly and accurately predict flow field
because of the high and nonlinear complexity of unsteady flow field
Benefiting from the vigorous development of computing power, artificial
intelligence has developed rapidly and has been widely used in many
fields and achieved fruitful results. More and more scholars began toexplore the fusion of fluid mechanics and artificial intelligence
technology, applying to traditional fields in order to get better results.
Some articles have applied deep learning method to predict flow
field. There are many deep networks being used. Generative Adversarial
Networks (GAN) has been used to predict the unsteady laminar flow
vortex shedding on the cylinder (Lee et al., 2017), the results show thatthe prediction and CFD calculation of flow field on the qualitative andquantitative are in good agreement. Convolutional Neural Network(CNN) has been used to learn the relationship between the pressure on aTHE PROBLEM AND DATASETSThis article is to predict the pressure quantities at subsequent
moments from the prior moments. We used the values from the first to60st moments to predict the 61st The law of data prediction is shown as
Fig1. The dataset is composed of CFD values of SUBOFF submarine
model. The dynamic process of unsteady flow field is calculated by highprecision numerical simulation method, and the flow field information ateach moment is recorded.

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