检索结果-南通市图书馆

A data-driven reduced-order model for stiff chemical kinetics using dynamics-informed training

Energy and AI 2024年第1期15卷 181-192页

作者： Vijayamanikandan Vijayarangan harshavardhana a.uranakara Shivam Barwey Riccardo Malpica Galassi Mohammad Rafi Malik Mauro Valorani Venkat Raman Hong G.Im CCRC King Abdullah University of Science and TechnologyThuwalSaudi Arabia Argonne Leadership Computing Facility Argonne National LaboratoryLemontIL 60439USA Mechanical and Aerospace Engineering Department Sapienza University of RomeVia Eudossiana18Rome 00184Italy Department of Aerospace Engineering University of MichiganAnn ArborMI 48109USA

A data-based reduced-order model(ROM)is developed to accelerate the time integration of stiff chemically reacting systems by effectively removing the stiffness arising from a wide spectrum of chemical time ***,the objective of this work is to develop a ROM that acts as a non-stiff surrogate model for the time evolution of the thermochemical state vector(temperature and species mass fractions)during an otherwise highly stiff and nonlinear ignition *** model follows an encode-forecast-decode strategy that combines a nonlinear autoencoder(AE)for dimensionality reduction(encode and decode steps)with a neural ordinary differential equation(NODE)for modeling the dynamical system in the AE-provided latent space(forecasting step).By means of detailed timescale analysis by leveraging the dynamical system Jacobians,this work shows how data-based projection operators provided by autoencoders can inherently construct the latent spaces by removing unnecessary fast timescales,even more effectively than physics-based counterparts based on an eigenvalue analysis.A key finding is that the most significant degree of stiffness reduction is achieved through an end-to-end training strategy,where both AE and neural ODE parameters are optimized simultaneously,allowing the discovered latent space to be *** addition to end-to-end training,this work highlights the vital contribution of AE nonlinearity in the stiffness reduction *** the prediction of homogeneous ignition phenomena for H2-air and C2H4-air mixtures,the proposed ROM achieves several ordersof-magnitude increase in the integration time step size when compared to(a)a baseline CVODE solver for the full-chemical system,(b)statistical technique–principal component analysis(PCA),and(c)computational singular perturbation(CSP),a vetted physics-based stiffness-reducing modeling framework.

关键词： Stiff system Chemical kinetics Reacting flows Autoencoders Neural ODE

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：