• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Marine Structures >Database of model-scale sloshing experiment for LNG tank and application of artificial neural network for sloshing load prediction
【24h】

Database of model-scale sloshing experiment for LNG tank and application of artificial neural network for sloshing load prediction

机译：液化天然气储罐模型尺度晃荡实验数据库及人工神经网络在晃荡负荷预测中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Seoul National University has conducted a considerable number of six degree-of-freedom irregular small-scale sloshing model tests 1/70-1/25 scales, particularly focusing on the tanks of liquefied natural gas (LNG) carriers. An experimental database has been created to provide information of sloshing load severity, which are obtained from a lot of the post-processed experimental results. In this paper, the summary of the database is described. The artificial neural network is trained based on the database to predict sloshing load severity. Various attributes that affect experimental results are considered. Management of these attributes and the machine learning architecture are illustrated. The prediction results are validated for several experiments that are not included in the training process. Further possibilities of using the database for model test planning and cargo hold design are discussed.
机译：首尔国立大学已经进行了大量的六次自由度不规则小规模晃荡模型测试，比例为1 / 70-1 / 25，特别是液化天然气（LNG）船的油箱。已经创建了一个实验数据库来提供晃荡载荷严重程度的信息，该信息是从许多后处理的实验结果中获得的。在本文中，描述了数据库的摘要。基于数据库训练人工神经网络，以预测晃荡载荷的严重性。考虑影响实验结果的各种属性。说明了这些属性的管理和机器学习架构。预测结果已针对训练过程中未包括的几个实验进行了验证。讨论了使用数据库进行模型测试计划和货舱设计的其他可能性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号