Search

Aims High systolic blood pressure (HSBP), a significant public health challenge, has not been systematically studied in the elderly population in the context of global aging. Understanding the temporal trends of the disease burden associated with HSBP in the elderly population is essential to control and mitigate the harm caused by HSBP. Methods and results We used the estimated data derived from the Global Burden of Disease Study to analyse the disease burden of HSBP among the elderly population by region, sex, and temporal changes from 1990 to 2019. We found that the number of deaths due to HSBP increased to 7.86 (95% UI: 6.89–8.82) million, with an increase of 54.1%, and the number of disability-adjusted life years (DALYs) increased to 146 (95% UI: 130–162) million, with an increase of 52.4%. Conversely, the death and DALY rates of HSBP decreased by −27.0 and −27.8%, respectively. At the national and regional levels, Australasia and other high socio-demographic index regions have made significant improvements in the burden of HSBP, while it remains high in other regions of the world. Additionally, the burden of HSBP in older men is greater than that in older women. Conclusion Our findings indicate that the current prevention and control of HSBP in older adults is poor, with the total burden increasing significantly. There is an urgent need to implement feasible measures to resist HSBP and lessen the disparity of the global HSBP burden for older adults.

The extensive use of tetracycline hydrochloride (TCH) poses a threat to human health and the aquatic environment. Here, magnetic p-n Bi2WO6/CuFe2O4 catalyst was fabricated to efficiently remove TCH. The obtained Bi2WO6/CuFe2O4 exhibited 92.1% TCH degradation efficiency and 50.7% and 35.1% mineralization performance for TCH and raw secondary effluent from a wastewater treatment plant in a photo-Fenton-like system, respectively. The remarkable performance was attributed to the fact that photogenerated electrons accelerated the Fe(III)/Fe(II) and Cu(II)/Cu(I) conversion for the Fenton-like reaction between Fe(II)/Cu(I) and H2O2, thereby generating abundant •OH for pollutant oxidation. Various environmental factors including H2O2 concentration, initial pH, catalyst dosage, TCH concentration and inorganic ions were explored. The reactive oxidation species (ROS) quenching results and electron spin resonance (ESR) spectra confirmed that •O2− and •OH were responsible for the dark and photo-Fenton-like systems, respectively. The degradation mechanisms and pathways of TCH were proposed, and the toxicity of products was evaluated. This work contributes a highly efficient and environmentally friendly catalyst and provides a clear mechanistic explanation for the removal of antibiotic pollutants in environmental remediation.

Market homogenization leads to fierce competition in the tea market. In recent years, with the rise of “green health” and the deep-rooted concept of “innovation”. Tea packaging design innovation has become a hot spot for research in the tea industry. The continuous upgrading of tea market demand has shown vigorous vitality and energy. The study aims to examine the research on the influence of Chinese tea packaging design on product purchasing decisions and analyze its importance and influencing factors. Further, strengthen the sustainable development of tea packaging design in Yanhu District, Shanxi Province, China. Theoretical analysis was conducted of tea cognitive attitudes, tea culture, tea packaging design, and purchase decision behavior. The final online questionnaire was distributed to 321 respondents in the Yanhu District. Empirical research results indicate that all three dimensions of tea packaging design factors positively influence, but the degree of influence varies. Tea products can be enhanced through packaging design to influence purchasing decisions. Ultimately, it can explore the packaging effect of tea products and add value to the connotation of tea products and meet the multi-level demand of consumers for tea products.

Rapid generation of high-valent cobalt-oxo species (Co(IV)═O) for the removal of organic contaminants has been challenging because of the low conversion efficiency of Co(III)/Co(II) and the high activation energy barrier of the Co(II)-oxidant complex. Herein, we introduced nitrogen (N) vacancies into graphite carbon nitride imbedded with cobalt carbonate (CCH/CN-V

The hierarchical deep-learning neural network (HiDeNN) is systematically developed through the construction of structured deep neural networks (DNNs) in a hierarchical manner, and a special case of HiDeNN for representing Finite Element Method (or HiDeNN-FEM in short) is established. In HiDeNN-FEM, weights and biases are functions of the nodal positions, hence the training process in HiDeNN-FEM includes the optimization of the nodal coordinates. This is the spirit of r-adaptivity, and it increases both the local and global accuracy of the interpolants. By fixing the number of hidden layers and increasing the number of neurons by training the DNNs, rh-adaptivity can be achieved, which leads to further improvement of the accuracy for the solutions. The generalization of rational functions is achieved by the development of three fundamental building blocks of constructing deep hierarchical neural networks. The three building blocks are linear functions, multiplication, and inversion. With these building blocks, the class of deep learning interpolation functions are demonstrated for interpolation theories such as Lagrange polynomials, NURBS, isogeometric, reproducing kernel particle method, and others. In HiDeNN-FEM, enrichment functions through the multiplication of neurons is equivalent to the enrichment in standard finite element methods, that is, generalized, extended, and partition of unity finite element methods. Numerical examples performed by HiDeNN-FEM exhibit reduced approximation error compared with the standard FEM. Finally, an outlook for the generalized HiDeNN to high-order continuity for multiple dimensions and topology optimizations are illustrated through the hierarchy of the proposed DNNs.

Carbon fiber reinforced plastics (CFRPs) have attracted growing attention from transportation industry because of their superior performance over metal counterparts. The thermoforming process has been invented for mass-production of CFRP parts. This thermoforming process involves multiple steps and numerous parameters, and one of the most important process parameters is the initial blank geometry. To optimize blank geometry in thermoforming and minimize trimming work for the final part, an innovative approach integrating automatic mesh adjustment and FEA modeling method for preforming is developed as foundation of design and reported in this paper. Preliminary trial-and-error optimization algorithm demonstrates that this newly-developed approach can accurately design blank geometry under various typical process conditions.

The performance of the Photo-Fenton process is still limited by the low conversion rate of Fe3+/Fe2+ under near neutral conditions. We report a α-Fe2O3/BiOI catalyst that enhanced the efficiency of Fe2+ generation. The structure, morphology, chemical composition and chemical states of the catalyst were characterized. The α-Fe2O3/BiOI catalyst exhibited remarkable degradation performance for methyl orange (MO), phenol and tetracycline hydrochloride (TCH). The degradation efficiency of α-Fe2O3/BiOI with an optimum α-Fe2O3 loading was 3 and 10 times higher than those of pristine BiOI and α-Fe2O3, respectively. The excellent degradation performance arose from the synergistic effect of the efficient separation and transfer of photogenerated charge at the α-Fe2O3/BiOI solid-solid interface and the optimized Fenton reaction at the solid-liquid interface. The effects of operating parameters including the H2O2 concentration, solution pH, catalyst concentration and MO concentration on the degradation efficiency were investigated. Electron spin resonance results and reactive oxidation species scavenger experiments indicated that superoxide radicals could be transferred into hydroxide radicals via the activation of H2O2. A possible degradation pathway of TCH is proposed. This strategy provides a new perspective for improving the cyclic ability of Fe3+/Fe2+ over heterogeneous catalysts.

Mechanical science and engineering can use machine learning. However, data sets have remained relatively scarce; fortunately, known governing equations can supplement these data. This paper summarizes and generalizes three reduced order methods: self-consistent clustering analysis, virtual clustering analysis, and FEM-clustering analysis. These approaches have two-stage structures: unsupervised learning facilitates model complexity reduction and mechanistic equations provide predictions. These predictions define databases appropriate for training neural networks. The feed forward neural network solves forward problems, e.g., replacing constitutive laws or homogenization routines. The convolutional neural network solves inverse problems or is a classifier, e.g., extracting boundary conditions or determining if damage occurs. We will explain how these networks are applied, then provide a practical exercise: topology optimization of a structure (a) with non-linear elastic material behavior and (b) under a microstructural damage constraint. This results in microstructure-sensitive designs with computational effort only slightly more than for a conventional linear elastic analysis.

In order to represent the functionally graded properties of interphase, a multi-coated micromechanical model is developed. Based on elliptic shell integration of Green’s function, the strain disturbance in each phase is obtained. According to computational investigation of this model, the outer layer of the interphase does not bring in strain disturbance within the inner ones. To this end, a sequential computational homogenization method is proposed. The inhomogeneities are added sequentially from outside to inside. The temporary effective modulus on each stage is obtained by the Self Consistency Scheme. Then the effective modulus of the overall composites are fitted with a Mori–Tanaka estimation for practical applications. The effectiveness of present method is verified by the results of “2 + 1” and “3 + 1” models in prior researches and finite element simulations. Finally, the influence of thickness and stiffness of interphase on the composites’ effective modulus are investigated.

A multiscale elasto-plastic damage model is developed to predict the nonlinear behavior of three-dimensional (3D) braided composites. In this model, the sequential multiscale method is applied to transfer the effective properties from microscale to mesoscale, and from mesoscale to macroscale. The constituents at the microscale consist of fiber, matrix and interface which are consistent with the mesoscale ones. The fiber is considered to be elastic and brittle, and the elastic damage model is applied to degrade the stiffness. For the epoxy matrix, a coupled elasto-plastic damage model is proposed to integrate the effects of plasticity and damage, and furthermore the paraboloidal yield criterion is adopted to characterize the different types of mechanical behavior in tension and compression. The bilinear constitutive relation based on the cohesive element is used to investigate the properties of interface. A user-defined material subroutine (UMAT) in the nonlinear finite element analysis software ABAQUS is written to implement the proposed model and determine the response for 3D braided composites under quasi-static tension. The numerical simulations are compared with the corresponding experiments and the results show that they agree well with each other.

Thermal residual stress is identified as one of the major reasons of stress concentration in material’s microstructures which initiates failure in the microstructure. Considering the details of microstructural features (inclusions shape, size, and distri-bution) can provide better understanding of the thermal residual stress developed in the materials due to the temperature change. In this paper, we have extended the self-consistent clustering analysis (SCA) method for efficient and accurate modeling of thermal residual stress for thermoelastic heterogeneous materials. The governing equations of the thermoelasticity has been implemented through a eigenstrain problem and solved using a Fast Fourier Transform (FFT) based solution scheme and later extended for SCA formulation. The thermoelastic formulation of SCA method has been verified for a single inclusion (homogeneous and inhomoge-neous) problem with the analytical solution of Eshelby and the FFT-based solution for multiple clusters. An example problem with multiple inhomogeneous inclusions has been solved for different temperatures and the residual stress developed has been analyzed. Results show that the thermoelastic formulation of SCA can have order of hundred times speed up compare to the traditional FFT-based solution scheme. The proposed methodology can be implemented in thermomechanical problems and provide efficient multiscale capabilities for prediction of thermal residual stress.

The extensive use of tetracycline hydrochloride (TCH) poses a threat to human health and the aquatic environment. Here, magnetic p-n Bi

Multimodal medical images are useful for observing tissue structure clearly in clinical practice. To integrate multimodal information, multimodal registration is significant. The entropy-based registration applies a structure descriptor set to replace the original multimodal image and compute similarity to express the correlation of images. The accuracy and converging rate of the registration depend on this set. We propose a new method, logarithmic fuzzy entropy function, to compute the descriptor set. It is obvious that the proposed method can increase the upper bound value from log(r) to log(r) + ∆(r) so that a more representative structural descriptor set is formed. The experiment results show that our method has faster converging rate and wider quantified range in multimodal medical images registration.

The multi-model image registration is widely used in medical clinical diagnosis. However, the feature descriptor pairs are hard to detect between different modalities, which is a common dilemma faced in multi-modal image registration. In this paper, we detect feature descriptor pairs in the structure representation by convolutional neural network (CNN). On the one hand, structure representation can transform different modalities into a third-type modality, and it shows the potential information which do not appear at multi-modal medical image commonly. On the other hand, the matched feature points pairs which are computed by CNN can move back from structure representation to original image. Finally, the effective feature points are matched by 2NN algorithm. Extensive experiments on group-wise registration prove that this algorithm overcome the dilemma in extracting feature descriptor pairs among the multi-modal medical images.

Computational analyses of emerging materials such as fibrous composites rely on multiscale simulations. To account for the inherent uncertainty in these materials, such simulations must be integrated with statistical uncertainty quantification (UQ) and propagation (UP) methods. However, limited advancement has been made in this regard due to the significant computational costs and complexities in modeling spatially correlated structural variations coupled at different scales. In this work, a nonintrusive approach is proposed for multiscale UQ and UP to address these limitations. We introduce the top-down sampling method that allows to model nonstationary and continuous spatial variations of uncertainty sources by creating nested random fields (RFs) where the hyperparameters of an ensemble of RFs is characterized by yet another RF. We employ multiresponse Gaussian RFs in top-down sampling and leverage statistical techniques to address the considerable computational costs of multiscale simulations. We apply our approach to quantify the uncertainty in a cured woven prepreg due to spatial variations of fiber volume fraction and misalignment, fiber and matrix modulus, and yarns' architecture (i.e., angle, height, and spacing). Our results indicate that, even in linear analysis, the effect of uncertainty sources on material's response could be significant.

The multiresolution continuum theory (MCT) is implemented in FEA with a bespoke user defined element and materials. A simple dog-bone model is used to validate the code and study the effect of microscale parameters. The ability of the method to simulate the propagation of a shear band in simple shear without mesh dependence is shown. The length scale parameter is demonstrated to influence shear band width. Finally, we present a simulation of serrated chip formation in metal cutting, a case where accurate prediction of shear band formation is critical. The advantages of MCT over conventional methods are discussed. This work helps elucidate the role of the length scale and microscale parameters in MCT, and is a demonstration of a practical engineering application of the method: the simulation of high speed cutting.

Woven fiber composites have been increasingly employed as light-weight materials in aerospace, construction, and transportation industries due to their superior properties. These materials possess a hierarchical structure that necessitates the use of multiscale simulations in their modeling. To account for the inherent uncertainty in materials, such simulations must be integrated with statistical uncertainty quantification (UQ) and propagation (UP) methods. However, limited advancement has been made in this regard due to the significant computational costs and complexities in modeling spatially correlated structural variations coupled at different scales. In this work, a non-intrusive approach is proposed for multiscale UQ and UP to address these limitations. We introduce the top-down sampling method that allows to model non-stationary and continuous (but not differentiable) spatial variations of uncertainty sources by creating nested random fields (RFs) where the hyperparameters of an ensemble of RFs is characterized by yet another RF. We employ multi-response Gaussian RFs in top-down sampling and leverage statistical techniques (such as metamodeling and dimensionality reduction) to address the considerable computational costs of multiscale simulations. We apply our approach to quantify the uncertainty in a cured woven composite due to spatial variations of yarn angle, fiber volume fraction, and fiber misalignment angle. Our results indicate that, even in linear analysis, the effect of uncertainty sources on the material’s response could be significant.

Cutting a unidirectional carbon fiber-reinforced polymer (UD CFRP) structure is the basic unit for CFRP machining, which is a complex thermal-mechanically coupled process. To reveal the deformation mechanism and predict cutting force in UD CFRP micro cutting, a micro-scale fracture model for UD CFRP cutting with thermal-mechanical coupling is demonstrated in this paper, which captures the failure modes for fibers, matrix and the interface based on a micro-level RVE using a relatively simple damage-based fracture method. The thermal-mechanical coupling model at the micro scale is developed on the basis of the plastic energy dissipation and frictional heating during cutting. Failure models for the fiber, matrix and interface region are applied depending on the material properties of each of these three phases. Numerical simulations based on the above model with different fiber orientations were performed to predict the deformation and forces of different components in UD CFRP. Cutting experiments with the same fiber orientations as considered in the simulations were carried out to validate the force and deformation results. The predicted force and deformation patterns match well with evidence from our experiments. In general, the cutting force is larger than the thrust force regardless of fiber orientation. The cutting force reaches a maximum as the fiber orientation approaches 90°, but thrust forces do not vary substantially across cases. When the fiber orientation is acute, the deformation of fibers is much smaller than when the cutting angle is obtuse. Surface roughness follows the same trend with cutting angle as fiber deformation.

Compared with traditional phenomenological models, concurrent multiscale simulation is a powerful tool to capture the mechanical behavior of composites from different scales simultaneously. However, it is still a challenge to conduct a concurrent multiscale simulation for composites due to the huge computational costs. The aim of this paper is to solve the challenge by introducing an effective reduced order model (ROM), called the data-driven self-consistent clustering analysis (SCA). The SCA method solves the RVE problem into two stages. In the offline stage, the high-fidelity RVE of composites is compressed into a cluster-based RVE and the interaction tensor between two clusters is calculated. In the online stage, a cluster-based discrete incremental Lippmann-Schwinger equation is solved to get the local strain and stress responses. Based on SCA, a concurrent multiscale framework SCA2 from microscale to mesoscale is proposed to capture the non-linear behavior of 3D braided composites. Firstly, the SCA-based results for yarn RVE and braided RVE are compared with the finite element (FE) results to verify the accuracy of the algorithm. Then, the SCA2 framework is applied to simulate the uniaxial tension and compression of 3D braided composites, which is also validated with the experiments and shows great accuracy and high efficiency. The proposed SCA2 framework will be extended into a three-scale concurrent simulation for the macroscopic structure analysis.

In this work, oxygen defect-rich pencil-like ZnO nanorods were prepared by a simple hydrothermal method, and Ag and CDots co-catalysts were further introduced for modification. The morphology, structure, UV–vis diffuse reflectance, photoluminescence (PL) and electrochemical properties of the as-prepared photocatalysts were studied by a series of analytical techniques and measurements. The results indicated that pencil-like morphology and the existence of oxygen defects increased the carrier concentration and promoted charge carrier separation of ZnO. The introduction of Ag and CDots co-catalysts further improved the electron-hole pairs separation and visible light utilization of photocatalysts. Gaussian three-peak fitting of the PL spectra proved that the introduction of co-catalysts also affected the relative content of various oxygen defects in the catalyst. As a typical pollutant, tetracycline hydrochloride (TCH) was used to detect the photocatalytic performance of as-prepared catalysts. CDots/Ag/ZnO possessed the strongest TCH removal and mineralization performance, and the possible degradation mechanism and pathways of TCH were proposed. Furthermore, the consequence of the biotoxicity assessment showed that the ternary photocatalyst had good biocompatibility and low biotoxicity.

The performance of the Photo-Fenton process is still limited by the low conversion rate of Fe

The advent of advanced processing and manufacturing techniques has led to new material classes with complex microstructures across scales from nanometers to meters. In this paper, a data-driven computational framework for the analysis of these complex material systems is presented. A mechanistic concurrent multiscale method called Self-consistent Clustering Analysis (SCA) is developed for general inelastic heterogeneous material systems. The efficiency of SCA is achieved via data compression algorithms which group local microstructures into clusters during the training stage, thereby reducing required computational expense. Its accuracy is guaranteed by introducing a self-consistent method for solving the Lippmann–Schwinger integral equation in the prediction stage. The proposed framework is illustrated for a composite cutting process where fracture can be analyzed simultaneously at the microstructure and part scales.

A phytochemical investigation of the roots of Aspilia pluriseta led to the isolation of ent-kaurane-type diterpenoids and additional phytochemicals (1⁻23). The structures of the isolated compounds were elucidated based on Nuclear Magnetic Resonance (NMR) spectroscopic and mass spectrometric analyses. The absolute configurations of seven of the ent-kaurane-type diterpenoids (3⁻6, 6b, 7 and 8) were determined by single crystal X-ray diffraction studies. Eleven of the compounds were also isolated from the roots and the aerial parts of Aspilia mossambicensis. The literature NMR assignments for compounds 1 and 5 were revised. In a cytotoxicity assay, 12α-methoxy-ent-kaur-9(11),16-dien-19-oic acid (1) (IC50 = 27.3 ± 1.9 µM) and 9β-hydroxy-15α-angeloyloxy-ent-kaur-16-en-19-oic acid (3) (IC50 = 24.7 ± 2.8 µM) were the most cytotoxic against the hepatocellular carcinoma (Hep-G2) cell line, while 15α-angeloyloxy-16β,17-epoxy-ent-kauran-19-oic acid (5) (IC50 = 30.7 ± 1.7 µM) was the most cytotoxic against adenocarcinomic human alveolar basal epithelial (A549) cells.

We present a data-driven nonlinear uncertainty quantification and propagation framework to study the microstructure-induced stochastic performance of unidirectional (UD) carbon fiber reinforced polymer (CFRP) composites. The proposed approach integrates (1) microscopic image characterization, (2) stochastic microstructure reconstruction, and (3) efficient multiscale finite element simulations enabled by self-consistent clustering (SCA) analysis. To model the complex microstructural variability, the proposed UQ methods take the non-Gaussian uncertainty sources into account through a distribution-free sampling approach leveraging nonparametric and asymptotic statistical tools. A hierarchical conditional sampling strategy enables the simultaneous sampling of multiple sources of uncertainties. Our approach provides insights into the impact of microstructural variabilities, which are shown to have an increasing impact on the nonlinear responses of UD CFRP parts under progressive compression loading and ultimately on the failure rate over time. We discover that before CFRP parts start to fail, a characteristic time period emerges with distinctive uncertainty distributions specific to the microstructure variability and the probability of failure. Identifying the failure time period is crucial to the reliability prediction, which is an essential component of CFRP design.

In this work, a unified AI-framework named Hierarchical Deep Learning Neural Network (HiDeNN) is proposed to solve challenging computational science and engineering problems with little or no available physics as well as with extreme computational demand. The detailed construction and mathematical elements of HiDeNN are introduced and discussed to show the flexibility of the framework for diverse problems from disparate fields. Three example problems are solved to demonstrate the accuracy, efficiency, and versatility of the framework. The first example is designed to show that HiDeNN is capable of achieving better accuracy than conventional finite element method by learning the optimal nodal positions and capturing the stress concentration with a coarse mesh. The second example applies HiDeNN for multiscale analysis with sub-neural networks at each material point of macroscale. The final example demonstrates how HiDeNN can discover governing dimensionless parameters from experimental data so that a reduced set of input can be used to increase the learning efficiency. We further present a discussion and demonstration of the solution for advanced engineering problems that require state-of-the-art AI approaches and how a general and flexible system, such as HiDeNN-AI framework, can be applied to solve these problems.

A data-driven self-consistent clustering analysis (SCA) method is applied to investigate the progressive damage behavior of 3D braided composites. The SCA-based method is split into the offline stage and the online stage. In the offline stage, the high fidelity RVE is compressed into a reduced RVE composed of several clusters. In the online stage, the mechanical responses are calculated by solving the discretized Lippmann-Schwinger integral equation. To validate the accuracy of proposed model, the SCA-based simulation is compared with the corresponding experiments and finite element analysis (FEA). The results show that the SCA method can accurately capture the stress and damage distribution, and the predictive stiffness and strength agree well with experimental data. More importantly, with the same constitutive laws and geometric model, SCA only takes a few hundred seconds, which is 1771 times faster than FEA. Because of the high efficiency, the SCA has the potential to be applied in concurrent multiscale analysis for braided composites.

A hierarchical multiscale model is established to reveal the failure mechanism of three-dimensional (3D) braided composites at high temperature. Firstly, the tensile and bending tests of the composites were performed at three different temperatures, and the properties of microscale constituents, i.e., carbon fiber, epoxy resin and interface, were also calibrated by experiments at different temperatures. Then, the elastic-plastic damage constitutive laws were proposed to characterize the mechanical behavior of microscale and mesoscale components. These constitutive models were implemented by a user-defined subroutine UMAT in ABAQUS. Finally, based on the homogenization procedure and the multiscale analysis method, the effects of temperature on microscale, mesoscale and macroscale properties of 3D braided composites were analyzed sequentially. The results showed that the temperature has the significant effects on the performances of 3D braided composites. With the increase of temperature, the properties of 3D braided composites decreased, and the failure modes changed from fiber breakage to matrix plastic deformation. Besides, the macroscopic simulation of strain fields agreed well with the DIC measurements and the temperature-dependent failure modes agreed well with SEM observation. It is expected that the established multiscale framework can predict high-temperature behavior of 3D braided composites and reveal the different failure mechanisms at different temperatures.

Multiscale simulation of woven composites structure remains a challenge due to extremely expensive computational cost for solving the nonlinear woven Representative Volume Element (RVE). Recently, an effective and efficient Reduced Order modeling method, namely Self-consistent Clustering Analysis (SCA), is proposed to solve the RVE problem. In this work, the curse of computational cost in woven RVE problem is countered using the SCA, which maintains a considerable accuracy compared with the standard Finite Element Method (FEM). The Hill anisotropic yield surface is predicted efficiently using the woven SCA, which can accelerate the microstructure optimization and design of woven composites. Moreover, a two-scale FEM × SCA modeling framework is proposed for woven composites structure. Based on this framework, the complex behavior of the composite structures in macroscale can be predicted using microscale properties. Additionally, macroscale and mesoscale physical fields are captured simultaneously, which are hard, if not impossible, to observe using experimental methods. This will expedite the deformation mechanism investigation of composites. A numerical study is carried out for T-shaped hooking structures under cycle loading to illustrate these advantages.

This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.

In this paper, a computational procedure combining experimental data and interphase inverse modeling is presented to predict filled rubber compound properties. The Fast Fourier Transformation (FFT) based numerical homogenization scheme is applied on the high quality filled rubber 3D Transmission Electron Microscope (TEM) image to compute its complex shear moduli. The 3D TEM filled rubber image is then compressed into a material microstructure database using a novel Reduced Order Modeling (ROM) technique, namely Self-consistent Clustering Analysis (a two-stage offline database creation from training and learning, followed by data compression via unsupervised learning, and online prediction approach), for improved efficiency and accuracy. An inverse modeling approach is formulated for quantitatively computing interphase complex shear moduli in order to understand the interphase behaviors. The two-stage SCA and the inverse modeling approach formulate a three-step prediction scheme for studying filled rubber, whose loss tangent curve can be computed in agreement with test data.

It is difficult to predict the fatigue life of notched specimen due to the complexity of notch effect. Based on the continuum damage mechanics theory of Lemaitre, a modified damage law for fatigue life prediction of notched specimen combined with the fuzzy neural network method is proposed to obtain a more reliable life. According to the fatigue experimental data, the material parameters in the damage evolution equation are demarcated. Then the fatigue life prediction of notched specimen is conducted. At last, after the stage pf experiment data collection and NN training , the established NN model will be able to reduce the relative error from 20.85% to about %7.12 in the verification test. Although the life prediction after being modified may not be accurate in every local region, the combination between the theoretical method and the fuzzy neural network method will obtain a more reliable material life in the whole. DOI: http://dx.doi.org/10.5755/j01.mech.23.6.14862

In most current works related to the structure analysis of cured woven composites, the influence of yarn angle variation on the local material properties is neglected. In this paper, its influence was studied, and an integrated preforming-performance simulation model, accounting for the influence of yarn angle variation, was proposed. The multi-scale modeling approach was adopted to predict the material properties of cured woven composites with different yarn angles. The results were compared with experiments to validate its effectiveness. In the integrated preforming-performance simulation model, non-orthogonal constitutive law was used in the preforming simulation to compute yarn orientations and yarn angle distribution. To demonstrate the capabilities of the integrated simulation model, simulations were conducted and compared with experiments. The results show that the proposed simulation model has more accurate prediction than the simulation model without considering the influence of yarn angle, and the impact of yarn angle shouldn’t be neglected.

In this paper, a new method based on the Continuum Damage Mechanics (CDM) and the Genetic Algorithm and Radial Basis Function neural network method (GARBF) is propo- sed to predict fatigue life of LY12CZ notched plate. Firstly, the multiaxial fatigue damage evolution equation is derived, and the fatigue life of the notched specimen is predicted based on the CDM method. Secondly, the RBF method is introduced to modify the relative devia- tion between the theoretical result and actual life. According to the drawbacks of the RBF method, the GA is adopted to optimize network parameters to effectively improve the model quality and reduce the training error. Then, the verification test indicates that the combined method of CDM and GARBF is able to reduce the average relative error of the results of fatigue life prediction to about 7%, which shows that the new method to predict the fatigue life is more reliable. At last, compared with the predicted results of the traditional Back Propagation (BP) neural network, the GARBF model proposed in this paper has a better optimization effect and the result is more stable. This research provides a feasible way to predict the fatigue lives of the notched plate based on the CDM and GARBF method.

This is the Part II of a two-part series numerical study which investigates the improvement of surface integrity of AZ31B-O magnesium (Mg) alloy by cryogenic cutting. In Part I, material constitutive behavior and grain refinement mechanism of AZ31B-O Mg alloy under cryogenic cutting conditions were modeled based on both slip and twinning mechanisms. In this study, the material model is implemented in the two-pass cryogenic cutting finite element simulations using a commercial machining simulation software package of AdvantEdge 6.4. The microstructural evolution by nanocrystalline grain refinement and other improvement of the surface integrity of AZ31B-O Mg alloy after cryogenic cutting are simulated. With quantitative assessments, simulation results are further discussed in grain refinement, microhardness change, residual stress, and slip/twinning mechanism in the machined surface of Mg alloy by cryogenic cutting. The results show that the surface integrity of Mg components can be significantly improved by using cryogenic cooling and a larger tool edge radius.Copyright © 2015 by ASME