Silicone coatings have been used in this study. The method adopted was the liquid drop analysis on the coated fabrics. The contact angle between a liquid drop and the fabric surface was measured with two liquids conti...Silicone coatings have been used in this study. The method adopted was the liquid drop analysis on the coated fabrics. The contact angle between a liquid drop and the fabric surface was measured with two liquids continuously and recorded by a computer. The surface energy was calculated by means of Owens method.Kinetic measurement was adopted. The contact angle of liquids on the fabric coated silicone decreased with time was found. A compound solution DX has been found, so that the contact angle of the liquids on the fabric washed with DX becomes constant, and the surface energy of the fabric can be reduced to below 15 mJ/m2.展开更多
Predicting the constitutive response of granular soils is a fundamental goal in geomechanics.This paper presents a machine learning(ML)framework for the prediction of the stress-strain behaviour and shearinduced conta...Predicting the constitutive response of granular soils is a fundamental goal in geomechanics.This paper presents a machine learning(ML)framework for the prediction of the stress-strain behaviour and shearinduced contact fabric evolution of an idealised granular material subject to triaxial shearing.The MLbased framework is comprised of a set of mini-triaxial tests which provide a benchmark for the setup and validation of the discrete element method(DEM)model of the granular materials,a parametric DEM simulation programme of virtual triaxial tests which provides datasets of micro-and macro-mechanical information,as well as a multi-layer perceptron(MLP)neural network which is trained and tested using the DEM-based datasets.The ML model only requires the initial void ratio of the granular sample as the input for predicting its constitutive response.The excellent agreement between the ML model prediction and experimental test and DEM simulation results indicates that the MLebased modelling approach is capable of capturing accurately the effects of initial void ratio on the constitutive behaviour of idealised granular materials,bypassing the need to incorporate the complex micromechanics underlying the macroscopic mechanical behaviour of granular materials.Lastly,a detailed comparison between the used MLP model and long short-term memory(LSTM)model was made from the perspective of technical algorithm,prediction accuracy,and computational efficiency.展开更多
文摘Silicone coatings have been used in this study. The method adopted was the liquid drop analysis on the coated fabrics. The contact angle between a liquid drop and the fabric surface was measured with two liquids continuously and recorded by a computer. The surface energy was calculated by means of Owens method.Kinetic measurement was adopted. The contact angle of liquids on the fabric coated silicone decreased with time was found. A compound solution DX has been found, so that the contact angle of the liquids on the fabric washed with DX becomes constant, and the surface energy of the fabric can be reduced to below 15 mJ/m2.
基金This study was supported by General Research Fund from the Research Grants Council of the Hong Kong SAR(Grant Nos.CityU 11201020 and 11207321)the National Natural Science Foundation of China(Grant No.51779213)as well as Contract Research Project(Ref.No.CEDD STD-30-2030-1-12R)from the Geotechnical Engineering Office.
文摘Predicting the constitutive response of granular soils is a fundamental goal in geomechanics.This paper presents a machine learning(ML)framework for the prediction of the stress-strain behaviour and shearinduced contact fabric evolution of an idealised granular material subject to triaxial shearing.The MLbased framework is comprised of a set of mini-triaxial tests which provide a benchmark for the setup and validation of the discrete element method(DEM)model of the granular materials,a parametric DEM simulation programme of virtual triaxial tests which provides datasets of micro-and macro-mechanical information,as well as a multi-layer perceptron(MLP)neural network which is trained and tested using the DEM-based datasets.The ML model only requires the initial void ratio of the granular sample as the input for predicting its constitutive response.The excellent agreement between the ML model prediction and experimental test and DEM simulation results indicates that the MLebased modelling approach is capable of capturing accurately the effects of initial void ratio on the constitutive behaviour of idealised granular materials,bypassing the need to incorporate the complex micromechanics underlying the macroscopic mechanical behaviour of granular materials.Lastly,a detailed comparison between the used MLP model and long short-term memory(LSTM)model was made from the perspective of technical algorithm,prediction accuracy,and computational efficiency.
文摘颗粒材料的宏观应力变形特征与其微观接触力、组构等紧密相关.一般而言,强接触系统属于颗粒内部体系的传力结构,其对应的组构张量是影响宏观应力性质的重要因素.细观数值方法 (如离散单元法)能够反映物理试验的基本规律,并且可以方便地提取宏微观数据来研究颗粒体系的应力变形机制.采用离散单元法(discrete element method, DEM)进行一系列等p等b应力路径下颗粒材料的真三轴试验,在此基础上研究了三维应力路径下颗粒材料的宏微观力学参数的演化过程、三维组构张量与应力张量多重联系以及强接触体系反映的宏观应力特征.研究表明:颗粒体系偏应力峰值状态和临界状态均存在与加载路径无关的宏微观特征;三维应力路径下组构张量与应力张量存在非共轴性,但其联合不变量演化过程表现出加载路径无关的特征;与弱接触系统的组构张量相比,强接触系统的组构张量更能反映宏观应力张量的特征;强弱接触体系的组构张量对颗粒体系宏观响应的贡献不同,其分界点存在一定取值范围,但采用平均接触力较为简单合理.
