检索结果-南通市图书馆

A Deep Learning Modeling Framework to Capture Mixing Patterns in reactive-transport Systems

Communications in Computational Physics 2022年第1期31卷 188-223页

作者： N.V.Jagtap M.K.Mudunuru K.B.Nakshatrala Department of Mechanical Engineering University of HoustonTexas 77204USA Atmospheric Sciences&Global Change Division Pacific Northwest National LaboratoryRichlandWashington 99352USA Department of Civil&Environmental Engineering University of HoustonHoustonTexas 77204USA

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous andanisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large andfor long-term temporal predictions. To address this knowledge gap, we will present inthis paper a deep learning (DL) modeling framework applied to predict the progress ofchemical mixing under fast bimolecular reactions. This framework uses convolutionalneural networks (CNN) for capturing spatial patterns and long short-term memory(LSTM) networks for forecasting temporal variations in mixing. By careful design ofthe framework—placement of non-negative constraint on the weights of the CNN andthe selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast,accurate, and requires minimal data for training. The time needed to obtain a forecastusing the model is a fraction (≈ O(10−6)) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinitynorm) for capturing local-scale mixing features such as interfacial mixing, only 24%to 32% of the sequence data for model training is required. To achieve the same levelof accuracy for capturing global-scale mixing features, the sequence data required formodel training is 64% to 70% of the total spatial-temporal data. Hence, the proposedapproach—a fast and accurate way to forecast long-time spatial-temporal mixin

关键词： Deep learning reactive-transport non-negative solutions spatial-temporal forecasting pattern recognition convolutional neural networks(CNN) long short-term memory(LSTM) networks

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：