Nondestructive identification of multiple flaws using XFEM and a topologically adapting artificial bee colony algorithm

We present a novel algorithm based on the extended finite element method (XFEM) and an enhanced artificial bee colony (EABC) algorithm to detect and quantify multiple flaws in structures. The concept is based on recent work that have shown the excellent synergy between XFEM, used to model the forward problem, and a genetic‐type algorithm to solve an inverse identification problem and converge to the ‘best’ flaw parameters. In this paper, an adaptive algorithm that can detect multiple flaws without any knowledge on the number of flaws beforehand is proposed. The algorithm is based on the introduction of topological variables into the search space, used to adaptively activate/deactivate flaws during run time until convergence is reached. The identification is based on a limited number of strain sensors assumed to be attached to the structure surface boundaries. Each flaw is approximated by a circular void with the following three variables: center coordinates (x_c, y_c) and radius (r_c), within the XFEM framework. In addition, the proposed EABC scheme is improved by a guided‐to‐best solution updating strategy and a local search (LS) operator of the Nelder–Mead simplex type that show fast convergence and superior global/LS abilities compared with the standard ABC or classic genetic algorithms. Several numerical examples, with increasing level of difficulty, are studied in order to evaluate the proposed algorithm. In particular, we consider identification of multiple flaws with unknown a priori information on the number of flaws (which makes the inverse problem harder), the proximity of flaws, flaws having irregular shapes (similar to artificial noise), and the effect of structured/unstructured meshes. The results show that the proposed XFEM–EABC algorithm is able to converge on all test problems and accurately identify flaws. Hence, this methodology is found to be robust and efficient for nondestructive detection and quantification of multiple flaws in structures. Copyright © 2013 John Wiley & Sons, Ltd.     

A model updating strategy of non‐linear vibrating structures

The objective of this paper is to present a model updating strategy of non‐linear vibrating structures. Because modal analysis is no longer helpful in non‐linear structural dynamics, a special attention is devoted to the features extracted from the proper orthogonal decomposition and one of its non‐linear generalizations based on auto‐associative neural networks. The efficiency of the proposed procedure is illustrated using simulated data from a three‐dimensional portal frame. Copyright © 2004 John Wiley & Sons, Ltd.     

Incremental elastoplastic analysis for active macro‐zones

In this paper a strategy to perform incremental elastoplastic analysis using the symmetric Galerkin boundary element method for multidomain type problems is shown. The discretization of the body is performed through substructures, distinguishing the bem‐elements characterizing the so‐called active macro‐zones, where the plastic consistency condition may be violated, and the macro‐elements having elastic behaviour only. Incremental analysis uses the well‐known concept of self‐equilibrium stress field here shown in a discrete form through the introduction of the influence matrix (self‐stress matrix). The nonlinear analysis does not use updating of the elastic response inside each plastic loop, but at the end of the load increment only. This is possible by using the self‐stress matrix, both, in the predictor phase, for computing the stress caused by the stored plastic strains, and, in the corrector phase, for solving a nonlinear global system, which provides the elastoplastic solution of the active macro‐zones. The use of active macro‐zones gives rise to a nonlocal and path‐independent approach, which is characterized by a notable reduction of the number of plastic iterations. The proposed strategy shows several computational advantages as shown by the results of some numerical tests, reported at the end of this paper. These tests were performed using the Karnak.sGbem code, in which the present procedure was introduced as an additional module.Copyright © 2012 John Wiley & Sons, Ltd.     

Adaptive sparse grid based HOPGD: Toward a nonintrusive strategy for constructing space‐time welding computational vademecum

Simulation‐based engineering usually needs the construction of computational vademecum to take into account the multiparametric aspect. One example concerns the optimization and inverse identification problems encountered in welding processes. This paper presents a nonintrusive a posteriori strategy for constructing quasi‐optimal space‐time computational vademecum using the higher‐order proper generalized decomposition method. Contrary to conventional tensor decomposition methods, based on full grids (eg, parallel factor analysis/higher‐order singular value decomposition), the proposed method is adapted to sparse grids, which allows an efficient adaptive sampling in the multidimensional parameter space. In addition, a residual‐based accelerator is proposed to accelerate the higher‐order proper generalized decomposition procedure for the optimal aspect of computational vademecum. Based on a simplified welding model, different examples of computational vademecum of dimension up to 6, taking into account both geometry and material parameters, are presented. These vademecums lead to real‐time parametric solutions and can serve as handbook for engineers to deal with optimization, identification, or other problems related to repetitive task.     

Identifying multiple configurations of complex molecules on metal surfaces

E xperimental identification of molecular configurations in diffusion processes of large complex molecules has been a demanding topic in the field of molecular construction at solid surfaces. Such identification is needed in order to control the self‐assembly process and the properties and configurations of the resulting structures. This paper provides an overview of state‐of‐the‐art techniques for identification of molecular configurations in motion. First, a brief introduction to the conventional tools is presented, for example, low‐energy electron diffraction and IR/Raman spectroscopy. Second, currently used techniques, scanning probe microscopy, and its application in molecular configuration identification are reviewed. In the last part, a methodology combining time‐resolved tunneling spectroscopy and density functional theory calculation is reviewed in detail; this strategy has been successfully applied to two typical molecular systems, (t‐Bu)₄‐ZnPc and FePc (where Pc is phthalocyanine), with molecular rotation and laterial diffusion on the Au(111) surface.     

The effect of continuously updating control chart limits on control chart performance

An open topic within statistical process monitoring is the effect on control chart properties of updating the control chart limits during the monitoring period. The challenge is to use the correct data for updating the control limits as in‐control data could be incorrectly classified as out of control and therefore not used for re‐estimating the parameters, and out‐of‐control data could be classified as in control and therefore used for re‐estimating. In the present article, we study the effect of updating the Shewhart, cumulative sum, and exponentially weighted moving average control chart limits. We simulate different scenarios: the monitoring data could be in or out of control, and the practitioner may or may not be able to find out whether the process is indeed out of control when the control chart gives a signal. The results reveal that the variation in the performance of the conditional control charts decreases significantly as a result of updating the control chart limits when the updating data are in control and also when the updating data are out of control and the practitioner is able to classify correctly data samples that produce a signal. However, when a practitioner is not able to classify a signal correctly, the advisability of updating depends on the type of control chart and the level of data contamination.     

时滞数据动态过程传感器故障诊断的改进方法

提出利用不断更新的时滞数据矩阵建立变化的动态主元模型对某些动态系统内的传感器故障进行检测，利用变量贡献图的样本平均对故障进行识别的方法．该方法克服了传统静态主元分析方法不适于分析具有动态特性的系统传感器故障的缺陷，而且改善了常用的基于时滞数据建立的动态主元分析法对于分析变量间关系不确定的动态系统存在的局限性．最后给出了3种方法的实验比照结果，验证了方法的有效性，     

Subsystem Global Modal Parameterization for efficient simulation of flexible multibody systems

This paper presents a new model order reduction strategy for flexible multibody simulation, namely the Subsystem Global Modal Parameterization. The proposed method is based on a system‐level reduction technique, named Global Modal Parameterization, but offers significant improvements for systems with many independent DOFs. The approach splits up the motion of a mechanism or part of a mechanism into a relative motion, in which the members move relatively with respect to each other, and a global motion of the system, in which the relative position of the members does not change. The relative motion is described by a local Global Modal Parameterization model expressed in a mechanism‐attached frame, and the global motion is described by the motion of the mechanism‐attached frame. In order to improve simulation efficiency, mass invariants are used, which are also introduced in this paper. Two numerical examples are presented, which show the good accuracy and the major simulation efficiency improvements this new approach offers. Copyright © 2011 John Wiley & Sons, Ltd.     

On the Pareto optimum sensitivity analysis in multicriteria optimization

To analyse the trade‐off relations among the set of criteria in multicriteria optimization, Pareto optimum sensitivity analysis is systematically studied in this paper. Original contributions cover two parts: theoretical demonstrations are firstly made to validate the gradient projection method in Pareto optimum sensitivity analysis. It is shown that the projected gradient direction evaluated at a given Pareto optimum in the design variable space rigorously corresponds to the tangent direction of the Pareto curve/surface at that point in the objective space. This statement holds even for the change of the set of active constraints in the perturbed problem. Secondly, a new active constraint updating strategy is proposed, which permits the identification of the active constraint set change, to determine the influence of this change upon the differentiability of the Pareto curve and finally to compute directional derivatives in non‐differentiable cases. This work will highlight some basic issues in multicriteria optimization. Some numerical problems are solved to illustrate these novelties. Copyright © 2003 John Wiley & Sons, Ltd.     

Boosting the Loading of Metal Single Atoms via a Bioconcentration Strategy

Increasing the mass loading of transition metal single atoms coordinated with nitrogen in carbon‐based materials (M‐N‐C) is still challenging. Herein, inspired by the bioconcentration effect in the living body, a biochemistry strategy for the synthesis of Fe‐N‐C single atoms is demonstrated. Through introducing ferrous glycinate into the growth of fungus, the Fe atoms are bioconcentrated in hyphae. The highly dispersed Fe‐N‐C single atoms in hyphae‐derived carbon fibers (labeled as Fe‐N‐C SA/HCF) are prepared by the pyrolysis of Fe‐riched hyphae. In the bioconcentration process, the uptake of Fe ions by hyphae promotes the secretion of glutathione and ferritin, which provides additional coordination sites for Fe ions. Accordingly, the mass content of Fe in bioconcentrated Fe‐N‐C SA/HCF reaches 4.8%, which is 5.3 times larger than that of the sample prepared by the conventional pyrolysis process. The present bioconcentration strategy is further extended to the preparation of Co, Ni, and Mn single atoms. Owing to the high content of Fe‐N‐C single atoms, Fe‐N‐C SA/HCF shows the onset potential (E_onset) of 0.931 V versus reversible hydrogen electrode (RHE) and half‐wave potential (E_1/2) of 0.802 V versus RHE in oxygen reduction reaction measurements, which is comparable to the commercial Pt/C catalysts.     

An Oxygen‐Vacancy‐Rich Semiconductor‐Supported Bifunctional Catalyst for Efficient and Stable Zinc–Air Batteries

The highly oxidative operating conditions of rechargeable zinc–air batteries causes significant carbon‐support corrosion of bifunctional oxygen electrocatalysts. Here, a new strategy for the catalyst support design focusing on oxygen vacancy (OV)‐rich, low‐bandgap semiconductor is proposed. The OVs promote the electrical conductivity of the oxide support, and at the same time offer a strong metal–support interaction (SMSI), which enables the catalysts to have small metal size, high catalytic activity, and high stability. The strategy is demonstrated by successfully synthesizing ultrafine Co‐metal‐decorated 3D ordered macroporous titanium oxynitride (3DOM‐Co@TiO_xN_y). The 3DOM‐Co@TiO_xN_y catalyst exhibits comparable activities for oxygen reduction and evolution reactions, but much higher cycling stability than noble metals in alkaline conditions. The zinc–air battery using this catalyst delivers an excellent stability with less than 1% energy efficiency loss over 900 charge–discharge cycles at 20 mA cm^?2. The high stability is attributed to the strong SMSI between Co and 3DOM‐TiO_xN_y which is verified by density functional theory calculations. This work sheds light on using OV‐rich semiconductors as a promising support to design efficient and durable nonprecious electrocatalysts.     

Comparison of the identification performance of conventional FEM updating and integrated DIC

Full‐field identification methods are increasingly used to adequately identify constitutive parameters to describe the mechanical behavior of materials. This paper investigates the more recently introduced one‐step method of integrated digital image correlation (IDIC) with respect to the most commonly used two‐step method of finite element model updating (FEMU), which uses a subset‐based DIC algorithm. To make the comparison as objective as possible, both methods are implemented in the most equivalent manner and use the same FE model. Various virtual test cases are studied to assess the performance of both methods when subjected to different error sources: (1) systematic errors, (2) poor initial guesses for the constitutive parameters, (3) image noise, (4) constitutive model errors, and (5) experimental errors. Results show that, despite the mathematical similarity of both methods, IDIC produces less erroneous and more reliable results than FEMU, particularly for more challenging test cases exhibiting small displacements, complex kinematics, misalignment of the specimen, and image noise. Copyright © 2015 John Wiley & Sons, Ltd.     

Hierarchical model reduction at multiple scales

A hierarchical model reduction approach aimed at reducing computational complexity of non‐linear homogenization at multiple scales is developed. The method consists of the following salient features: (1) formulation of non‐linear unit cell problems at multiple scales in terms of eigendeformation modes that a priori satisfy equilibrium equations at multiple scales and thus eliminating the need for costly solution of discretized non‐linear equilibrium, (2) the ability to control the discretization of the eigendeformation modes at multiple scales to maintain desired accuracy, and (3) hierarchical solution strategy that requires sequential solution of single‐scale problems. A two‐scale formulation is verified against an one‐dimensional model problem for which an analytical solution can be obtained and a three‐scale formulation is validated against tube crash experiments. Copyright © 2009 John Wiley & Sons, Ltd.     

Single Fe Atom on Hierarchically Porous S,N‐Codoped Nanocarbon Derived from Porphyra Enable Boosted Oxygen Catalysis for Rechargeable Zn‐Air Batteries

Iron–nitrogen–carbon materials (Fe–N–C) are known for their excellent oxygen reduction reaction (ORR) performance. Unfortunately, they generally show a laggard oxygen evolution reaction (OER) activity, which results in a lethargic charging performance in rechargeable Zn–air batteries. Here porous S‐doped Fe–N–C nanosheets are innovatively synthesized utilizing a scalable FeCl₃‐encapsulated‐porphyra precursor pyrolysis strategy. The obtained electrocatalyst exhibits ultrahigh ORR activity (E_1/2 = 0.84 V vs reversible hydrogen electrode) and impressive OER performance (E_{j = 10} = 1.64 V). The potential gap (ΔE = E_{j = 10} ? E_1/2) is 0.80 V, outperforming that of most highly active bifunctional electrocatalysts reported to date. Furthermore, the key role of S involved in the atomically dispersed Fe–Nx species on the enhanced ORR and OER activities is expounded for the first time by ultrasound‐assisted extraction of the exclusive S source (taurine) from porphyra. Moreover, the assembled rechargeable Zn–air battery comprising this bifunctional electrocatalyst exhibits higher power density (225.1 mW cm^?2) and lower charging–discharging overpotential (1.00 V, 100 mA cm^?2 compared to Pt/C + RuO₂ catalyst). The design strategy can expand the utilization of earth‐abundant biomaterial‐derived catalysts, and the mechanism investigations of S doping on the structure–activity relationship can inspire the progress of other functional electrocatalysts.     

Solvent‐Induced Self‐Assembly Strategy to Synthesize Well‐Defined Hierarchically Porous Polymers

Porous polymers with well‐orchestrated nanomorphologies are useful in many fields, but high surface area, hierarchical structure, and ordered pores are difficult to be satisfied in one polymer simultaneously. Herein, a solvent‐induced self‐assembly strategy to synthesize hierarchical porous polymers with tunable morphology, mesoporous structure, and microporous pore wall is reported. The poly(ethylene oxide)‐b‐polystyrene (PEO‐b‐PS) diblock copolymer micelles are cross‐linked via Friedel–Crafts reaction, which is a new way to anchor micelles into porous polymers with well‐defined structure. Varying the polarity of the solvent has a dramatic effect upon the oleophobic/oleophylic interaction, and the self‐assembly structure of PEO‐b‐PS can be tailored from aggregated nanoparticles to hollow spheres even mesoporous bulk. A morphological phase diagram is accomplished to systematically evaluate the influence of the composition of PEO‐b‐PS and the mixed solvent component on the pore structure and morphology of products. The hypercrosslinked hollow polymer spheres provide a confined microenvironment for the in situ reduction of K₂PdCl₄ to ultrasmall Pd nanoparticles, which exhibit excellent catalytic performance in solvent‐free catalytic oxidation of hydrocarbons and alcohols.     

A multilevel projection‐based model order reduction framework for nonlinear dynamic multiscale problems in structural and solid mechanics

A reduction/hyper reduction framework is presented for dramatically accelerating the solution of nonlinear dynamic multiscale problems in structural and solid mechanics. At each scale, the dimensionality of the governing equations is reduced using the method of snapshots for proper orthogonal decomposition, and computational efficiency is achieved for the evaluation of the nonlinear reduced‐order terms using a carefully designed configuration of the energy conserving sampling and weighting method. Periodic boundary conditions at the microscales are treated as linear multipoint constraints and reduced via projection onto the span of a basis formed from the singular value decomposition of Lagrange multiplier snapshots. Most importantly, information is efficiently transmitted between the scales without incurring high‐dimensional operations. In this proposed proper orthogonal decomposition–energy conserving sampling and weighting nonlinear model reduction framework, training is performed in two steps. First, a microscale hyper reduced‐order model is constructed in situ, or using a mesh coarsening strategy, in order to achieve significant speedups even in non‐parametric settings. Next, a classical offline–online training approach is performed to build a parametric hyper reduced‐order macroscale model, which completes the construction of a fully hyper reduced‐order parametric multiscale model capable of fast and accurate multiscale simulations. A notable feature of this computational framework is the minimization, at the macroscale level, of the cost of the offline training using the in situ or coarsely trained hyper reduced‐order microscale model to accelerate snapshot acquisition. The effectiveness of the proposed hyper reduction framework at accelerating the solution of nonlinear dynamic multiscale problems is demonstrated for two problems in structural and solid mechanics. Speedup factors as high as five orders of magnitude are shown to be achievable. Copyright © 2017 John Wiley & Sons, Ltd.     

Local proper generalized decomposition

One of the main difficulties that a reduced‐order method could face is the poor separability of the solution. This problem is common to both a posteriori model order reduction (proper orthogonal decomposition, reduced basis) and a priori [proper generalized decomposition (PGD)] model order reduction. Early approaches to solve it include the construction of local reduced‐order models in the framework of POD. We present here an extension of local models in a PGD—and thus, a priori—context. Three different strategies are introduced to estimate the size of the different patches or regions in the solution manifold where PGD is applied. As will be noticed, no gluing or special technique is needed to deal with the resulting set of local reduced‐order models, in contrast to most proper orthogonal decomposition local approximations. The resulting method can be seen as a sort of a priori manifold learning or nonlinear dimensionality reduction technique. Examples are shown that demonstrate pros and cons of each strategy for different problems.     

Three‐dimensional finite element implementation of the nonuniform transformation field analysis

In this paper aspects of the nonuniform transformation field analysis (NTFA) introduced by Michel and Suquet (Int. J. Solids Struct. 2003; 40 :6937–6955) are investigated for materials with three‐dimensional microtopology. A novel implementation of the NTFA into the finite element method (FEM) is described in detail, whereas the NTFA was originally used in combination with the fast Fourier transformation (FFT). In particular, the discrete equivalents of the averaging operators required for the preprocessing steps and an algorithm for the implicit time integration and linearization of the constitutive equations of the homogenized material are provided. To the authors knowledge this is the first implementation of the method for three‐dimensional problems. Further, an alternative mode identification strategy is proposed with the aim of small computational cost in combination with good efficiency. The new identification strategy is applied to three‐dimensional metal matrix composites in order to investigate its effective non‐linear behaviour. The homogenized material model is implemented into ABAQUS/STANDARD. Numerical examples at integration point level and in terms of structural problems highlight the efficiency of the method for three‐dimensional problems. Copyright © 2010 John Wiley & Sons, Ltd.     

Updating robust reliability using structural test data

The concept of robust reliability is defined to take into account uncertainties from structural modeling in addition to the uncertain excitation that a structure will experience during its lifetime. A Bayesian probabilistic methodology for system identification is integrated with probabilistic structural analysis tools for the purpose of updating the assessment of the robust reliability based on dynamic test data. Methods for updating the structural reliability for both identifiable and unidentifiable models are presented. Application of the methodology to a simple beam model of a single-span bridge with soil-structure interaction at the abutments, including a case with a tuned-mass damper attached to the deck, shows that the robust reliabilities computed before and after updating with “measured” dynamic data can differ significantly.     

Extension of the Optimised Virtual Fields Method to Estimate Viscoelastic Material Parameters from 3D Dynamic Displacement Fields

In vivo measurement of the mechanical properties of soft tissues is essential to provide necessary data in biomechanics and medicine (early cancer diagnosis, study of traumatic brain injuries, etc.). Imaging techniques such as magnetic resonance elastography can provide 3D displacement maps in the bulk and in vivo, from which, using inverse methods, it is then possible to identify some mechanical parameters of the tissues (stiffness, damping, etc.). The main difficulties in these inverse identification procedures consist in dealing with the pressure waves contained in the data and with the experimental noise perturbing the spatial derivatives required during the processing. The optimised virtual fields method (OVFM) ( 26 ), designed to be robust to noise, presents natural and rigorous solution to deal with these problems. The OVFM has been adapted to identify material parameter maps from magnetic resonance elastography data consisting of 3D displacement fields in harmonically loaded soft materials. In this work, the method has been developed to identify elastic and viscoelastic models. The OVFM sensitivity to spatial resolution and to noise has been studied by analysing 3D analytically simulated displacement data. This study evaluates and describes the OVFM identification performances: Different biases on the identified parameters are induced by the spatial resolution and experimental noise. The well‐known identification problems in the case of quasi‐incompressible materials also find a natural solution in the OVFM. Moreover, an a posteriori criterion to estimate the local identification quality is proposed. The identification results obtained on actual experiments are briefly presented.