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首页> 外文期刊>International Journal of Heat and Mass Transfer >Sparce Subspace Learning and Characteristic Based Split for Modelling Artificial Ground Freezing
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Sparce Subspace Learning and Characteristic Based Split for Modelling Artificial Ground Freezing

机译：稀疏的子空间学习与人造冻结模拟的特征

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Artificial Ground Freezing technique is often used to build underground structures, such as urban tunnels. modeling activities are necessary to optimize the design of the freezing process. The paper introduces a numerical solution of the phenomenon of water freezing for various soil types, using the finite element method combined with the model order reduction technique. In particular, the characteristic based split method is used as a base to build a model reduction scheme by means of Sparse Subspace Learning. The aim of this study is to develop a mathematical model able to carry out numerical results with negligible CPU time, by varying three parameters reproducing the characteristics of the soil, i.e. the porosity, the Darcy number and the Rayleigh number. The obtained results show how the soil parameters influence the freezing process and the dimension of ice wall.
机译：人造冻结技术通常用于构建地下结构，如城市隧道。 建模活动是优化冻结过程的设计。 本文介绍了各种土壤类型的水冻结现象的数值解决方案，采用有限元方法与模型顺序减少技术相结合。 特别地，基于特征的分流方法用作基础，以通过稀疏的子空间学习来构建模型减少方案。 本研究的目的是开发一种能够通过改变三个参数来进行具有可忽略的CPU时间来进行数值模型的数学模型，即再现土壤的特征，即孔隙率，达西数和瑞利数。 所得结果显示土壤参数如何影响冻结过程和冰墙的尺寸。

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