A Data-Driven Model for Ice-Breaking Resistance of Structure Based on Non-Smooth Discrete Element Method and Artificial Neural Network Method-Reference-Cited by-同舟云学术

A Data-Driven Model for Ice-Breaking Resistance of Structure Based on Non-Smooth Discrete Element Method and Artificial Neural Network Method

Published:2023-02-22 Issue:3 Volume:11 Page:469
ISSN:2077-1312
Container-title:Journal of Marine Science and Engineering
language:en
Short-container-title:JMSE

Author:

Sun Zhe¹²,Qiu Yongjin¹,Yang Biye³,Jia Zichen¹,Zhang Guiyong¹⁴⁵,Zong Zhi¹⁴⁵

Affiliation:

1. School of Naval Architecture and Ocean Engineering, Dalian University of Technology, Dalian 116024, China

2. State Key Laboratory of Deep Sea Mineral Resources Development and Utilization Technology, Changsha 410012, China

3. Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Postboks 8900, NO-7491 Trondheim, Norway

4. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian 116024, China

5. Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, Shanghai 200240, China

Abstract

In this paper, a data-driven model based on the Non-smooth Discrete Element Method (NDEM) and Artificial Neural Network Method (ANN) is proposed for the computation of the ice-breaking resistance of the structure. The idea of so-called “meta-modelling”, which means establishing an Artificial Neural Network (ANN) model based on a pre-computed ice failure database to avoid the time-consuming direct resolving of the ice fracture process, was integrated in the non-smooth discrete element method (NDEM). The developed model was validated by simulating the ice-breaking process of the cone structure, and the computational results match well with the experimental ones in the literature. After that, the effects of various parameters on the ice-breaking resistance were analyzed by the developed model. It was found that the factors that have great influence on the resistance of cone structure in level ice condition are the cone angle, navigation velocity and ice thickness.

Funder

National Natural Science Foundation of China

Open Project of State Key Laboratory of Deep Sea Mineral Resources Development and Utilization Technology

Young Scholar Supporting Project of Dalian City

Fundamental Research Fund for the Central Universities of China

Publisher

MDPI AG

Subject

Ocean Engineering,Water Science and Technology,Civil and Structural Engineering

Link

https://www.mdpi.com/2077-1312/11/3/469/pdf

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