Prediction of Hydrodynamic Forces and Moments on Submarines Using Neural Networks

机译：基于神经网络的潜艇流体动力和力矩预测

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This paper describes a parametric identification tool for predicting the hydrodynamic forces acting on a submarine model using its motion history. The tool uses a neural network to identify the hydrodynamic forces and moments; the network was trained with data obtained from multi-degree-of-freedom captive maneuvering tests. The characteristics of the trained network are demonstrated through reconstruction of the force and moment time histories. This technique has the potential to reduce experimental time and cost by enabling a full hydrodynamic model of the vehicle to be obtained from a relatively limited number of test maneuvers.

机译：本文介绍了一种参数识别工具，可使用其运动历史来预测作用于海底模型的流体动力。该工具使用神经网络来识别流体动力和力矩。该网络使用从多自由度强制性机动测试中获得的数据进行了训练。通过重建力和力矩时间历史，可以证明训练网络的特征。通过允许从相对有限的测试操作中获得车辆的完整流体力学模型，该技术具有减少实验时间和成本的潜力。

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来源
《International Conference on Offshore Mechanics and Arctic Engineering, Jun 23-28, 2002, Oslo, Norway》|2002年|p.843-854|共12页
会议地点 Oslo(NO);Oslo(NO)
作者
Ibrahim Mohamed; Mahmoud Haddara; Christopher D. Williams; Michael Mackay;
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作者单位

Faculty of Engineering and Applied Science Memorial University of Newfoundland;

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原文格式 PDF
正文语种 eng
中图分类海洋学;
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Prediction of Hydrodynamic Forces and Moments on Submarines Using Neural Networks

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