An adaptive multiscale method for quasi-static crack growth

This paper proposes an adaptive atomistic- continuum numerical method for quasi-static crack growth. The phantom node method is used to model the crack in the continuum region and a molecular statics model is used near the crack tip. To ensure self-consistency in the bulk, a virtual atom cluster is used to model the material of the coarse scale. The coupling between the coarse scale and fine scale is realized through ghost atoms. The ghost atom positions are interpolated from the coarse scale solution and enforced as boundary conditions on the fine scale. The fine scale region is adaptively enlarged as the crack propagates and the region behind the crack tip is adaptively coarsened. An energy criterion is used to detect the crack tip location. The triangular lattice in the fine scale region corresponds to the lattice structure of the (111) plane of an FCC crystal. The Lennard-Jones potential is used to model the atom–atom interactions. The method is implemented in two dimensions. The results are compared to pure atomistic simulations; they show excellent agreement.     

A Systematic Molecular Dynamics Investigation on the Graphene Polymer Nanocomposites for Bulletproofing

In modern physics and fabrication technology, simulation of projectile and target collision is vital to improve design in some critical applications, like; bulletproofing and medical applications. Graphene, the most prominent member of two dimensional materials presents ultrahigh tensile strength and stiffness. Moreover, polydimethylsiloxane (PDMS) is one of the most important elastomeric materials with a high extensive application area, ranging from medical, fabric, and interface material. In this work we considered graphene/PDMS structures to explore the bullet resistance of resulting nanocomposites. To this aim, extensive molecular dynamic simulations were carried out to identify the penetration of bullet through the graphene and PDMS composite structures. In this paper, we simulate the impact of a diamond bullet with different velocities on the composites made of single- or bi-layer graphene placed in different positions of PDMS polymers. The underlying mechanism concerning how the PDMS improves the resistance of graphene against impact loading is discussed. We discuss that with the same content of graphene, placing the graphene in between the PDMS result in enhanced bullet resistance. This work comparatively examines the enhancement in design of polymer nanocomposites to improve their bulletproofing response and the obtained results may serve as valuable guide for future experimental and theoretical studies.     

改进实用型LED生物光源系列

通过采用平面LED点阵、半球曲面内LED点阵、半球曲面与菲涅耳透镜结合这三种光学设计,本文研制出具有不同光强和光照均匀差别的三种实用型LED生物光源,研究了每个光源中的四个工作参数对其辐射照度的影响,并使用统计分析软件SPSS拟合得出它们的辐射照度经验计算公式.     

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R² value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.     

Forecasting Damage Mechanics By Deep Learning

We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale. The predicted results by deep learning algorithms are well-agreed with experimental data.     

Free Vibration Analysis of FG-CNTRC Cylindrical Pressure Vessels Resting on Pasternak Foundation with Various Boundary Conditions

This study focuses on vibration analysis of cylindrical pressure vessels constructed by functionally graded carbon nanotube reinforced composites (FG-CNTRC). The vessel is under internal pressure and surrounded by a Pasternak foundation. This investigation was founded based on two-dimensional elastic analysis and used Hamilton’s principle to drive the governing equations. The deformations and effectivemechanical properties of the reinforced structure were elicited from the first-order shear theory (FSDT) and rule of mixture, respectively. The main goal of this study is to show the effects of various design parameters such as boundary conditions, reinforcement distribution, foundation parameters, and aspect ratio on the free vibration characteristics of the structure.     

Inverse problem of simultaneously estimating the thermal conductivity and boundary shape

An inverse analysis is used to simultaneously estimate the thermal conductivity and the boundary shape in steady-state heat conduction problems. The numerical scheme consists of a body-fitted grid generation technique to mesh the heat conducting body and solve the heat conduction equation – a novel, efficient, and easy to implement sensitivity analysis scheme to compute the sensitivity coefficients, and the conjugate gradient method as an optimization method to minimize the mismatch between the computed temperature distribution on some part of the body boundary and the measured temperatures. Using the proposed scheme, all sensitivity coefficients can be obtained in one solution of the direct heat conduction problem, irrespective of the large number of unknown parameters for the boundary shape. The obtained results reveal the accuracy, efficiency, and robustness of the proposed algorithm.     

Continuous auditing with a multi-agent system

Information technology has dramatically changed the way businesses and business information are managed. In electronic business, much of the information is in electronic format, and the resulting change in the auditing environment has forced audit professionals to audit electronic evidence. Moreover, the emergence of real time accounting reports has put increasing pressure on audit professionals to provide real-time auditing services, or continuous auditing, in which the time between the occurrence of events and the provision of an auditor's opinion is minimized to an acceptable level. This paper proposes an agent-based system for continuous auditing called the agent-based continuous audit model (ABCAM). The system can be implemented independently of the client's information system, is able to undertake automatic auditing in real time, and can easily adapt to changes in auditing requirements and information systems. Five scenarios are developed to illustrate the model.     

A computational library for multiscale modeling of material failure

We present an open-source software framework called PERMIX for multiscale modeling and simulation of fracture in solids. The framework is an object oriented open-source effort written primarily in Fortran 2003 standard with Fortran/C++ interfaces to a number of other libraries such as LAMMPS, ABAQUS, LS-DYNA and GMSH. Fracture on the continuum level is modeled by the extended finite element method (XFEM). Using several novel or state of the art methods, the piece software handles semi-concurrent multiscale methods as well as concurrent multiscale methods for fracture, coupling two continuum domains or atomistic domains to continuum domains, respectively. The efficiency of our open-source software is shown through several simulations including a 3D crack modeling in clay nanocomposites, a semi-concurrent FE-FE coupling, a 3D Arlequin multiscale example and an MD-XFEM coupling for dynamic crack propagation.