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Journal of Zhejiang University SCIENCE A

ISSN 1673-565X(Print), 1862-1775(Online), Monthly

Modelling and applications of dissolution of rocks in geoengineering

Abstract: The subsoil contains many evaporites such as limestone, gypsum, and salt. Such rocks are very sensitive to water. The deposit of evaporites raises questions because of their dissolution with time and the mechanical-geotechnical impact on the neighboring zone. Depending on the configuration of the site and the location of the rocks, the dissolution can lead to surface subsidence and, for instance, the formation of sinkholes and landslides. In this study, we present an approach that describes the dissolution process and its coupling with geotechnical engineering. In the first part we set the physico-mathematical framework, the hypothesis, and the limitations in which the dissolution process is stated. The physical interface between the fluid and the rock (porous) is represented by a diffuse interface of finite thickness. We briefly describe, in the framework of porous media, the steps needed to upscale the microscopic-scale (pore-scale) model to the macroscopic scale (Darcy scale). Although the constructed method has a large range of application, we will restrict it to saline and gypsum rocks. The second part is mainly devoted to the geotechnical consequences of the dissolution of gypsum material. We then analyze the effect of dissolution in the vicinity of a soil dam or slope and the partial dissolution of a gypsum pillar by a thin layer of water. These theoretical examples show the relevance and the potential of the approach in the general framework of geoengineering problems.

Key words: Dissolution; Modelling; Scaling; Evaporite; Deformation; Plasticity

Chinese Summary  <527> 在非对称大规模MIMO系统中基于集成-迁移学习的信道参数预测

何遵文1,李悦1,张焱1,张万成1,张恺恩1,郭柳1,王海明2
1北京理工大学信息与电子学院,中国北京市,100081
2东南大学毫米波国家重点实验室,中国南京市,210096
摘要:为降低第六代移动网络中的数据处理负担和硬件成本,非对称大规模多入多出(multiple-input multiple-output,MIMO)系统被提出。然而,在非对称大规模MIMO系统中,上行和下行无线信道之间的互易性是无效的。因此,需要基站和用户设备都发送导频来预测双向信道,这会消耗更多传输和计算资源。本文提出一种基于集成迁移学习的非对称大规模MIMO系统的信道参数预测方法,可以预测多个下行信道参数,包括路径损耗、多径数、时延扩展和角度扩展。选择上行信道参数和环境特征来预测下行参数。此外,提出一种基于SHAP(SHapley Additive exPlanations)值和最小描述长度标准的两步特征选择算法,以降低由弱相关或不相关特征引起的计算复杂度和对模型准确性的负面影响。引入实例迁移方法,以支持预测模型应对在新的传播条件下难以在短时间内收集足够训练数据的问题。仿真结果表明,该方法比反向传播神经网络和3GPP TR 38.901信道模型更准确。当波束宽度或通信扇区发生变化时,所提出的基于实例迁移的方法在预测下行参数方面优于没有迁移学习的方法。

关键词组:非对称大规模MIMO系统;信道模型;集成学习;实例迁移;参数预测


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DOI:

10.1631/jzus.A2200169

CLC number:

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On-line Access:

2023-01-20

Received:

2022-03-28

Revision Accepted:

2022-07-18

Crosschecked:

2023-02-01

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