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A Ply-By-Ply Discretized 2D FEA Approach with the Integrated XFEM-CE Strategy for Predicting Multiple Failures in Laminated Composite Structures
Get PDFDelamination and matrix cracking are two common failure mechanisms in composite structures, and are usually coupled with each other, leading to multiple failures pattern. This paper proposed a fast damage prediction methodology for composite laminated structures based on the ply-by-ply 2D (two dimensional) FE model of composite laminates in the transverse plane. The layer-wise 2D FE model was firstly used in conjunction with the integrated XFEM/CE strategy, which simulated the interface delamination with cohesive elements and the intra-ply matrix crack with XFEM (extended finite element method). To realize ply-by-ply 2D FE (finite element) modeling of composite laminates, two 2D material models were developed based on the plane stress assumption and plane strain assumption, respectively. A general crack propagation scheme was developed in the framework of the integrated XFEM-CE method. Adopting the 2D material model based on the plane strain assumption, a ply-by-ply discretized 2D FEA procedure was conducted for an out-of-plane composite Pi joint under the static tensile load. The predicted load-displacement response and damage evolution process showed good agreement with the experimental results, which verified the proposed approach
Computational Modeling of Dual-Phase Ceramics with Finsler-Geometric Phase Field Mechanics
Get PDFA theory invoking concepts from differential geometry of generalized Finsler space in conjunction with diffuse interface modeling is described and implemented in finite element (FE) simulations of dual-phase polycrystalline ceramic microstructures. Order parameters accounting for fracture and other structural transformations, notably partial dislocation slip, twinning, or phase changes, are dimensionless entries of an internal state vector of generalized pseudo-Finsler space. Ceramics investigated in computations are a boron carbide-titanium diboride (B4C-TiB2) composite and a diamond-silicon carbide (C-SiC) composite. Deformation mechanisms-in addition to elasticity and cleavage fracture in grains of any phase-include restricted dislocation glide (TiB2 phase), deformation twinning (B4C and ฮฒ-SiC phases), and stress-induced amorphization (B4C phase). The metric tensor of generalized Finsler space is scaled conformally according to dilatation induced by cavitation or other fracture modes and densification induced by phase changes. Simulations of pure shear consider various morphologies and lattice orientations. Effects of microstructure on overall strength of each composite are reported. In B4C-TiB2, minor improvements in shear strength and ductility are observed with an increase in the second phase from 10 to 18% by volume, suggesting that residual stresses or larger-scale crack inhibition may be responsible for toughness gains reported experimentally. In diamond-SiC, a composite consisting of diamond crystals encapsulated in a nano-crystalline SiC matrix shows improved strength and ductility relative to a two-phase composite with isolated bulk SiC grains
Modeling and Simulation of Dynamic Unloading of Prestressed Rockmass
Get PDFDuring the excavation of deep rock, a sudden change in boundary conditions will cause the in-situ stress on the excavation surface to release instantaneously. This disturbance propagates in the form of an unloading stress wave, which will enlarge the damage field of surrounding rock. In this paper, the dynamic unloading problem of the in-situ stress in deep rock excavation is studied using theoretical, numerical, and experimental methods. First, the dynamic unloading process of rock is analyzed through adopting the wave equation, and the equivalent viscous damping coefficient of the material is taken into consideration. Calculations show that there is significant tensile strain in the rock bar when the strain rate is above 10-1 s-1. With an increase in the length or damping coefficient, the wave state will change from an underdamped to an overdamped state. Second, implicit and explicit solvers of the finite element method are employed to simulate rock unloading processes, which can be used to verify the theoretical results from one-dimensional to three-dimensional stress states. Finally, the dynamic unloading experiment of a one-dimensional bar is used to further verify the validity and accuracy of the theoretical analysis
Numerical Modeling Strategy for the Simulation of Nonlinear Response of Slender Reinforced Concrete Structural Walls
Get PDFA three-dimensional nonlinear modeling strategy for simulating the seismic response of slender reinforced concrete structural walls with different cross-sectional shapes is presented in this paper. A combination of nonlinear multi-layer shell elements and displacement-based beam-column elements are used to model the unconfined and confined parts of the walls, respectively. A uniaxial material model for reinforcing steel bars that includes buckling and low-cyclic fatigue effects is used to model the longitudinal steel bars within the structural walls. The material model parameters related to the buckling length are defined based on an analytical expression for reinforcing steel bars embedded in reinforced concrete elements, which are developed based on beam-on-springs model, and validated with experimental tests of boundary elements of structural walls available in the literature. Six experimental case studies of reinforced concrete walls with rectangular-shape, T-shape, and U-shape cross-section are used to validate the structural wall numerical modeling strategy
A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network
Get PDFRecently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we propose a hybrid approach to detect anomalies associated with electricity theft in the AMI system, based on a combination of two robust machine learning algorithms; K-means and Deep Neural Network (DNN). K-means unsupervised machine learning algorithm is used to identify groups of customers with similar electricity consumption patterns to understand different types of normal behavior. DNN algorithm is used to build an accurate anomaly detection model capable of detecting changes or anomalies in usage behavior and deciding whether the customer has a normal or malicious consumption behavior. The proposed model is constructed and evaluated based on a real dataset from the Irish Smart Energy Trials. The results show a high performance of the proposed model compared to the models mentioned in the literature
A Cross-Tenant RBAC Model for Collaborative Cloud Services
Get PDFTenants in the cloud computing environment share various services, including storage, network, computing, and applications. For better use of services in the cloud computing environment, tenants collaborate in tasks, resulting in challenges to the traditional access control. This study proposes a cross-tenant role-based access control (CT-RBAC) model for collaborative cloud services. This model covers the CT-RBAC0, CT-RBAC1, CT-RBAC2, and CT-RBAC3 models. The model not only extends the RBAC model in the multi-tenant cloud computing mode but also includes four types of authorization modes among tenants. Consequently, the role inheritance constraint is increased, and fine-grained authorization access among trusted tenants is realized
Fuzzy Search for Multiple Chinese Keywords in Cloud Environment
Get PDFWith the continuous development of cloud computing and big data technology, the use of cloud storage is more and more extensive, and a large amount of data is outsourced for public cloud servers, and the security problems that follow are gradually emerging. It can not only protect the data privacy of users, but also realize efficient retrieval and use of data, which is an urgent problem for cloud storage. Based on the existing fuzzy search and encrypted data fuzzy search schemes, this paper uses the characteristics of fuzzy sounds and polysemy that are unique to Chinese, and realizes the synonym construction of keywords through Chinese Pinyin and Chinese-English translation, and establishes the fuzzy word and synonym set of keywords. This paper proposes a Chinese multi-keyword fuzzy search scheme in a cloud environment, which realizes the fuzzy search of multiple Chinese keywords and protects the private key by using a pseudo-random function. Finally, the safety analysis and system experiments verify that the scheme has high security, good practicability, and high search success rate
Binaural Sound Source Localization Based on Convolutional Neural Network
Get PDFBinaural sound source localization (BSSL) in low signal-to-noise ratio (SNR) and high reverberation environment is still a challenging task. In this paper, a novel BSSL algorithm is proposed by introducing convolutional neural network (CNN). The proposed algorithm first extracts the spatial feature of each sub-band from binaural sound signal, and then combines the features of all sub-bands within one frame to assemble a two-dimensional feature matrix as a grey image. To fully exploit the advantage of the CNN in extracting high-level features from the grey image, the spatial feature matrix of each frame is used as input to train the CNN model. The CNN is then used to predict azimuth of sound source. The experiments show that the proposed algorithm significantly improves the localization performance of BSSL in various acoustic environments, especially to deal with low SNR and high reverberation conditions
Model Studies of Fluid-Structure Interaction Problems
No full textIn this work, we employ fluid-structure interaction (FSI) systems with immersed flexible structures with or without free surfaces to explore both Singular Value Decomposition (SVD)-based model reduction methods and mode superposition methods. For acoustoelastic FSI systems, we adopt a three-field mixed finite element formulation with displacement, pressure, and vorticity moment unknowns to effectively enforce the irrotationality constraint. We also propose in this paper a new Inf-Sup test based on the lowest non-zero singular value of the coupling matrix for the selection of reliable sets of finite element discretizations for displacement and pressure as well as vorticity moment. Our numerical examples demonstrate that mixed finite element formulations can be effectively used to predict resonance frequencies of fully coupled FSI systems within different ranges of respective physical motions, namely, acoustic, structural, and slosh motions, without the contamination of spurious (non-physical) modes with nonzero frequencies. Our numerical results also confirm that SVD-based model reduction methods can be effectively used to reconstruct from a few snapshots of transient solutions the dominant principal components with moderate level of signal to noise ratio, which may eventually open doors for simulation of long-term behaviors of both linear and nonlinear FSI systems
Turning Industrial Waste into a Valuable Bioproduct: Starch from Mango Kernel Derivative to Oil Industry Mango Starch Derivative in Oil Industry
No full textAfter industrial mango processing, tons of residues such as peels and kernels are discarded as waste. Nevertheless, almost 60% of the mango kernel is due to starch on a dry weight basis. Herein, starch from mango (Manguifera Indica L.) kernel was applied to obtain a starch fatty ester with vinyl laurate, in DMSO, under basic catalysis. FTIR, 1H and 13C NMR confirmed that a starch ester with a degree of modification of 2.6 was produced. TGA showed that the modified starch has higher thermal stability than its precursors and higher than a vinyl laurate/starch physical blend. SEM data showed that granular shape and smooth surface on mango starch changed after chemical modification to a continuous and shapeless morphology. This industrial reject derivative behaved as an efficient alternative environmentally friendly fluid loss controller in oil drilling fluids, even in conditions of high temperatures and high pressures (HTHP) drilling