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Lattice Boltzmann study of the double-diffusive convection in porous media with Soret and Dufour effects 具有Soret和Dufour效应的多孔介质中双扩散对流的晶格玻尔兹曼研究
IF 2.5 3区 地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-09-07 DOI: 10.1007/s10596-023-10251-0
Xuguang Yang, Yuze Zhang
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引用次数: 0
Bayesian model evaluation for multiple scenarios 多场景贝叶斯模型评价
IF 2.5 3区 地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-08-29 DOI: 10.1007/s10596-023-10241-2
S. Aanonsen, K. Fossum, T. Mannseth
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引用次数: 0
A new formulation of the surface charge/surface potential relationship in electrolytes with valence less than three 三价以下电解质表面电荷/表面电势关系的新公式
IF 2.5 3区 地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-08-29 DOI: 10.1007/s10596-023-10239-w
O. Nødland, A. Hiorth
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引用次数: 0
Deep learning discovery of macroscopic governing equations for viscous gravity currents from microscopic simulation data 从微观模拟数据中深度学习发现粘性重力流的宏观控制方程
IF 2.5 3区 地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-08-28 DOI: 10.1007/s10596-023-10244-z
Junsheng Zeng, Hao Xu, Yuntian Chen, Dong-juan Zhang
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引用次数: 0
Marginalized iterative ensemble smoothers for data assimilation 用于数据同化的边缘化迭代集成平滑器
IF 2.5 3区 地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-08-26 DOI: 10.1007/s10596-023-10242-1
A. Stordal, R. Lorentzen, K. Fossum
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引用次数: 0
A reduced-order model based on C-R mixed finite element and POD technique for coupled Stokes-Darcy system with solute transport 基于C-R混合有限元和POD技术的溶质输运耦合Stokes-Darcy系统降阶模型
IF 2.5 3区 地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-08-22 DOI: 10.1007/s10596-023-10245-y
Junpeng Song, H. Rui, Zhijiang Kang
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引用次数: 0
Data-driven modelling with coarse-grid network models 粗网格网络模型的数据驱动建模
IF 2.5 3区 地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-08-04 DOI: 10.1007/s10596-023-10237-y
Knut-Andreas Lie, S. Krogstad
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引用次数: 0
Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network 基于深度学习的基于理论引导的卷积神经网络地质模型升级方法
IF 2.5 3区 地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-08-02 DOI: 10.1007/s10596-023-10233-2
Nanzhe Wang, Q. Liao, Haibin Chang, Dongxiao Zhang
{"title":"Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network","authors":"Nanzhe Wang, Q. Liao, Haibin Chang, Dongxiao Zhang","doi":"10.1007/s10596-023-10233-2","DOIUrl":"https://doi.org/10.1007/s10596-023-10233-2","url":null,"abstract":"","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45048143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A new computational model for karst conduit flow in carbonate reservoirs including dissolution-collapse breccias 含溶蚀角砾岩的碳酸盐岩储层岩溶管道流动新计算模型
IF 2.5 3区 地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-07-31 DOI: 10.1007/s10596-023-10229-y
I. Landim, M. Murad, Patricia A. Pereira, E. Abreu
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引用次数: 0
Block constrained pressure residual preconditioning for two-phase flow in porous media by mixed hybrid finite elements 基于混合有限元的多孔介质两相流块约束压力残余预处理
3区 地球科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-07-28 DOI: 10.1007/s10596-023-10238-x
Stefano Nardean, Massimiliano Ferronato, Ahmad Abushaikha
Abstract This work proposes an original preconditioner that couples the Constrained Pressure Residual (CPR) method with block preconditioning for the efficient solution of the linearized systems of equations arising from fully implicit multiphase flow models. This preconditioner, denoted as Block CPR (BCPR), is specifically designed for Lagrange multipliers-based flow models, such as those generated by Mixed Hybrid Finite Element (MHFE) approximations. An original MHFE-based formulation of the two-phase flow model is taken as a reference for the development of the BCPR preconditioner, in which the set of system unknowns comprises both element and face pressures, in addition to the cell saturations, resulting in a $$3times 3$$ 3 × 3 block-structured Jacobian matrix with a $$2times 2$$ 2 × 2 inner pressure problem. The CPR method is one of the most established techniques for reservoir simulations, but most research focused on solutions for Two-Point Flux Approximation (TPFA)-based discretizations that do not readily extend to our problem formulation. Therefore, we designed a dedicated two-stage strategy, inspired by the CPR algorithm, where a block preconditioner is used for the pressure part with the aim at exploiting the inner $$2times 2$$ 2 × 2 structure. The proposed preconditioning framework is tested by an extensive experimentation, comprising both synthetic and realistic applications in Cartesian and non-Cartesian domains.
摘要本文提出了一种新颖的预调节器,将约束压力剩余(CPR)方法与块预调节器相结合,用于求解由全隐式多相流模型引起的线性化方程组。该预调节器被称为块CPR (BCPR),是专门为基于拉格朗日乘数的流量模型而设计的,例如由混合混合有限元(MHFE)近似生成的流量模型。基于mhfe的两相流模型的原始公式被用作BCPR预调节器开发的参考,其中系统未知数集包括单元压力和面压力,以及细胞饱和度,从而产生具有$$2times 2$$ 2 × 2内压力问题的$$3times 3$$ 3 × 3块结构雅可比矩阵。CPR方法是油藏模拟中最成熟的技术之一,但大多数研究都集中在基于两点通量近似(TPFA)的离散化解决方案上,这并不容易扩展到我们的问题表述中。因此,受CPR算法的启发,我们设计了一种专用的两阶段策略,其中在压力部分使用了块预调节器,旨在利用内部$$2times 2$$ 2 × 2结构。提出的预处理框架通过广泛的实验进行了测试,包括在笛卡尔和非笛卡尔领域的综合和现实应用。
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引用次数: 0
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Computational Geosciences
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