Stress integration and mesh refinement for large deformation in geomechanics

This paper first discusses alternative stress integration schemes in numerical solutions to large‐ deformation problems in hardening materials. Three common numerical methods, i.e. the total‐Lagrangian (TL), the updated‐Lagrangian (UL) and the arbitrary Lagrangian–Eulerian (ALE) methods, are discussed. The UL and the ALE methods are further complicated with three different stress integration schemes. The objectivity of these schemes is discussed. The ALE method presented in this paper is based on the operator‐split technique where the analysis is carried out in two steps; an UL step followed by an Eulerian step. This paper also introduces a new method for mesh refinement in the ALE method. Using the known displacements at domain boundaries and material interfaces as prescribed displacements, the problem is re‐analysed by assuming linear elasticity and the deformed mesh resulting from such an analysis is then used as the new mesh in the second step of the ALE method. It is shown that this repeated elastic analysis is actually more efficient than mesh generation and it can be used for general cases regardless of problem dimension and problem topology. The relative performance of the TL, UL and ALE methods is investigated through the analyses of some classic geotechnical problems. Copyright © 2005 John Wiley & Sons, Ltd.     

Finite-Element Parametric Study of the Consolidation Behavior of a Trial Embankment on Soft Clay

The paper presents a case study for numerical analysis of the consolidation behavior of an instrumented trial embankment constructed on a soft soil foundation. Details are given to the geological profile, field instrumentation, laboratory test results, and determination of soil parameters for numerical modeling. Embankment settlement is estimated based on one-dimensional consolidation analysis and nonlinear finite-element analysis following Biot’s consolidation theory. Finite-element results are calibrated against the measured field data for a period of more than 3?years. Development and dissipation of excess pore pressure, long-term settlement, and horizontal displacement are predicted and discussed in light of sensitivity of embankment performance to some critical factors through a parametric study.     

Numerical Analysis of Neutron Moisture Probe Measurements

The neutron probe has proven to be an effective means for monitoring long term in situ soil moisture variations. However, it is difficult to experimentally correlate neutron probe data (i.e., neutron counts) with accurate estimates of absolute soil moisture content, particularly for unsaturated clay soils. In this paper, a numerical model based on multigroup neutron diffusion theory is employed to predict the distribution of neutron flux in a neutron probe system. The model discretizes the neutron energy spectrum into seven intervals, with energy-dependent diffusion coefficients and parameters for each energy interval. The finite element method is employed to solve the coupled seven-group neutron diffusion equations. It is demonstrated that the numerical results compare very well with both laboratory experimental results and field measurements. The theoretical approach to neutron probe calibration described herein offers significant time and cost savings over traditional calibration methods, and potentially opens up new applications for neutron probe monitoring.     

Erratum to the ‘Load stepping schemes for critical state models’ International Journal for Numerical Methods in Engineering 2001; 50:67–93

The original article to which this Erratum refers was published in Interntational Journal for Numerical Methods in Engineering 2001; 50 :67–93.     

Application of Multiscale Learning Neural Network Based on CNN in Bearing Fault Diagnosis

With the application of intelligent manufacturing becoming more and more widely, the losses caused by mechanical faults of equipment increase. Identifying and troubleshooting faults in an early stage are important. The process of traditional data-driven fault diagnosis method includes data acquisition, fault classification, and feature extraction, in which classification accuracy is directly affected by the result of feature extraction. As a common deep learning method in image recognition, the convolutional neural network (CNN) demonstrates good performance in fault diagnosis. CNN can adaptively extract features from original signals and eliminate the effect of conventional handcrafted features. In this study, a multiscale learning neural network that contains one-dimension (1D) and two-dimension (2D) convolution channels is proposed. The network can learn the local correlation of adjacent and nonadjacent intervals in periodic signals, such as vibration data. The Paderborn data set is came into use to demonstrate the classification accuracy of the method which is brought forward, which includes three conditions of healthy, outer ring (OR) damage and inner ring (IR) damage. The classification accuracy of the method which is put forward is up to 98.58%. The same dataset was applied to test the classification accuracy of support vector machine (SVM) for comparison. And the proposed multiscale learning neural network demonstrates considerable improvements.

     

Particle finite element analysis of the granular column collapse problem

The problem of granular column collapse is investigated by means of an axisymmetric version of the particle finite element method (PFEM). The granular medium is represented by a simple rate-independent plasticity model and the frictional contact between the granular flow and its rigid basal surface is accounted for. In the version of the PFEM developed for this study, the governing equations of the boundary value problem are cast in terms of an optimization problem and solved using mathematical programming tools. The agreement between model and experiment is generally satisfactory, quantitatively as well as qualitatively. However, the friction angle of the granular material, as well as the exact interface conditions between the base and granular material, are shown to have a relatively significant influence on the results.     

Preparation and characterization of ketoprofen-loaded microspheres for embolization

To deliver drug locally and relieve the syndrome of pain after uterine artery embolization, N-[tris (hydroxymethyl) methyl] acrylamide-gelatin microspheres were prepared based on inverse suspension polymerization and then separated into a number of subgroups (150–350, 350–560, 560–710, 710–1,000, and 1,000–1,430 μm) by wet-sieving. The microspheres were dried by lyophilization or by washing with anhydrous ethanol. And ketoprofen was loaded by soaking dried blank microspheres into concentrated ketoprofen ethanol solution. The ketoprofen loading level in different subgroups of microspheres was measured and found higher when the microspheres were dried by lyophilization. Equilibrium water content and mean diameters of microspheres decreased after drug loading, especially in subgroups with larger size. The microspheres went through the catheter without any difficulty. Compression and relaxation tests were performed on microspheres before lyophilization, embosphere™, microspheres after lyophilization and ketoprofen loading microspheres. The Young’s moduli were 54.74, 64.19, 98.15, and 120.44 kPa, respectively. The release of ketoprofen from microspheres in different subgroups was studied by using the USPII method and T-cell apparatus, respectively. The results indicate that the release rate of ketoprofen depends upon the diameter of microspheres, the type of dissolution apparatus and the flow rate of media in the case that T-cell apparatus was applied. The CH50 test shows that the activation of complement by ketoprofen-loaded microspheres was lower than by blank ones.     

金融信息安全走安全管理平台之路

随着信息技术的高速发展,金融机构已经越来越依靠 IT来保证机构自身的存在和发展。一方面,金融机构的业 务系统不可避免地要和互联网相连,成为不安全的互联网 的一部分,使金融机构处于一个共享的风险环境;另一方 面,由于当前金融机构的主要业务都离不开IT平台,都要