...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Mathematical Modelling >Machine learning closures for model order reduction of thermal fluids
【24h】

Machine learning closures for model order reduction of thermal fluids

机译：机器学习闭包，用于减少热流体的模型阶数

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

We put forth a data-driven closure modeling approach for stabilizing projection based reduced order models for the Bousinessq equations. The effect of discarded modes is taken into account using a machine learning architecture consisting of a single hidden layer feed-forward artificial neural network to achieve robust stabilization with respect to parameter changes. For training our network architecture, we implement an extreme learning machine strategy to utilize fast learning speeds and excellent generalized predictive capabilities for underlying statistical trends. The architecture is then deployed to recover reduced order model dynamics of flow phenomena which are not used in our training data set. A two-dimensional differentially heated cavity flow is used to demonstrate the advantage of the proposed framework considering a large set of modeling parameters. It is observed that the proposed closure strategy performs remarkably well in stabilizing the temporal mode evolution and represents a promising direction for closure development of predictive reduced order models for thermal fluids. (C) 2018 Elsevier Inc. All rights reserved.
机译：我们提出了一种数据驱动的闭合建模方法，用于稳定基于投影的Bousinessq方程的降阶模型。使用由单个隐藏层前馈人工神经网络组成的机器学习体系结构考虑了丢弃模式的影响，以实现针对参数更改的鲁棒稳定。为了训练我们的网络体系结构，我们实施了一种极限学习机策略，以利用快速的学习速度和出色的广义预测能力来应对潜在的统计趋势。然后部署该体系结构，以恢复流动现象的降阶模型动力学，这在我们的训练数据集中没有使用。考虑到大量的建模参数，使用二维差分加热腔流来演示所提出框架的优势。可以看出，所提出的封闭策略在稳定时间模式演化方面表现出色，并且为热流体的预测降阶模型的封闭发展提供了有希望的方向。 （C）2018 Elsevier Inc.保留所有权利。

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