Authors:Giulia Scalet; Ferdinando Auricchio Pages: 545 - 589 Abstract: The need of accurately reproducing the behaviour of elastoplastic materials in computational environments for the solution of engineering problems motivates the development of efficient and robust numerical schemes. These engineering problems often involve complex designs and/or conditions and are further complicated by the necessity of employing highly nonlinear and nonsmooth elastoplastic constitutive equations and constraints to describe material behaviour. Therefore, the numerical solution of such problems is not trivial and requires careful analyses to guarantee algorithm robustness, accuracy, and convergence in a reasonable amount of time. The aim of the present paper is to provide the reader with both an analysis and discussion, helpful in choosing the suitable numerical scheme when considering the implementation of a plasticity model. After a brief overview of the fundamental concepts for classical plasticity theory, we revise the state-of-the-art of computational methods by discussing conventional and less-conventional algorithms, formulated in a unified setting to allow for a comparison. Several approaches are implemented and discussed in representative numerical simulations. PubDate: 2018-07-01 DOI: 10.1007/s11831-016-9208-x Issue No:Vol. 25, No. 3 (2018)

Authors:Georgios G. Vogiatzis; Doros N. Theodorou Pages: 591 - 645 Abstract: Following the substantial progress in molecular simulations of polymer-matrix nanocomposites, now is the time to reconsider this topic from a critical point of view. A comprehensive survey is reported herein providing an overview of classical molecular simulations, reviewing their major achievements in modeling polymer matrix nanocomposites, and identifying several open challenges. Molecular simulations at multiple length and time scales, working hand-in-hand with sensitive experiments, have enhanced our understanding of how nanofillers alter the structure, dynamics, thermodynamics, rheology and mechanical properties of the surrounding polymer matrices. PubDate: 2018-07-01 DOI: 10.1007/s11831-016-9207-y Issue No:Vol. 25, No. 3 (2018)

Authors:Ursula Rasthofer; Volker Gravemeier Pages: 647 - 690 Abstract: The variational multiscale method is reviewed as a framework for developing computational methods for large-eddy simulation of turbulent flow. In contrast to other articles reviewing this topic, which focused on large-eddy simulation of turbulent incompressible flow, this study covers further aspects of numerically simulating turbulent flow as well as applications beyond incompressible single-phase flow. The various concepts for subgrid-scale modeling within the variational multiscale method for large-eddy simulation proposed by researchers in this field to date are illustrated. These conceptions comprise (i) implicit large-eddy simulation, represented by residual-based and stabilized methods, (ii) functional subgrid-scale modeling via small-scale subgrid-viscosity models and (iii) structural subgrid-scale modeling via the introduction of multifractal subgrid scales. An overview on exemplary numerical test cases to which the reviewed methods have been applied in the past years is provided, including explicit computational results obtained from turbulent channel flow. Wall-layer modeling, passive and active scalar transport as well as developments for large-eddy simulation of turbulent two-phase flow and combustion are discussed to complete this exposition. PubDate: 2018-07-01 DOI: 10.1007/s11831-017-9209-4 Issue No:Vol. 25, No. 3 (2018)

Authors:Luis Ramírez; Xesús Nogueira; Pablo Ouro; Fermín Navarrina; Sofiane Khelladi; Ignasi Colominas Pages: 691 - 706 Abstract: In this work a higher-order accurate finite volume method for the resolution of the Euler/Navier–Stokes equations using Chimera grid techniques is presented. The formulation is based on the use of Moving Least Squares approximations in order to obtain higher-order accurate reconstruction and connectivity between the overlapped grids. The accuracy and performance of the proposed methodology is demonstrated by solving different benchmark problems. PubDate: 2018-07-01 DOI: 10.1007/s11831-017-9213-8 Issue No:Vol. 25, No. 3 (2018)

Authors:Nitin Shrivastava; Zubair Mohd. Khan Pages: 707 - 726 Abstract: It is well known that fossil fuels are depleting day by day, and with the increase in the number of vehicles the pollution has reached at an alarming stage. The need of the hour is to find an alternate fuel as well as to demote the exhaust emission and enhance the performance parameters of the internal combustion (I.C.) engine. Researches on I.C. engines are being conducted in order to come to a feasible solution. Since performing experiments on an I.C. engine is both time consuming and costly therefore many soft computing techniques are being adopted in this field. The term soft computing refers to find the solution of an inexact problem. Different soft computing techniques being used in this field are Artificial Neural Network, Fuzzy Based Approach, Adaptive Neuro Fuzzy Inference System, Gene Expression Programming, Genetic Algorithm and Particle Swarm Optimization. The motive of this work is to review the researches being carried out in the field of I.C. engine on different types of engines with various alternative fuels using these soft computing techniques. PubDate: 2018-07-01 DOI: 10.1007/s11831-017-9212-9 Issue No:Vol. 25, No. 3 (2018)

Authors:Biswarup Bhattacharyya Pages: 727 - 751 Abstract: Surrogate models are widely used to predict response function of any system and in quantifying uncertainty associated with the response function. It is required to have response quantities at some preselected sample points to construct a surrogate model which can be processed in two way. Either the surrogate model is constructed using one shot experimental design techniques, or, the sample points can be generated in a sequential manner so that optimum sample points for a specific problem can be obtained. This paper addresses a comprehensive comparisons between these two types of sampling techniques for the construction of more accurate surrogate models. Two most popular one shot sampling strategies: Latin hypercube sampling and Sobol sequence, and four different type sequential experimental designs (SED) namely, Monte Carlo intersite projected (MCIP), Monte Carlo intersite projected threshold (MCIPT), optimizer projected (OP) and LOLA-Voronoi (LV) method are taken for the present study. Two most widely used surrogate models, namely polynomial chaos expansion and Kriging are used to check the applicability of the experimental design techniques. Three different types of numerical problems are solved using the two above-mentioned surrogate models considering all the experimental design techniques independently. Further, all the results are compared with the standard Monte Carlo simulation (MCS). Overall study depicts that SED performs well in predicting the response functions more accurately with an acceptable number of sample points even for high-dimensional problems which maintains the balance between accuracy and efficiency. More specifically, MCIPT and LV method based Kriging outperforms other combinations. PubDate: 2018-07-01 DOI: 10.1007/s11831-017-9211-x Issue No:Vol. 25, No. 3 (2018)

Authors:Mijo Nikolić; Emir Karavelić; Adnan Ibrahimbegovic; Predrag Miščević Pages: 753 - 784 Abstract: This paper presents the lattice element models, as a class of discrete models, in which the structural solid is represented as an assembly of one-dimensional elements. This idea allows one to provide robust models for propagation of discontinuities, multiple cracks interaction or cracks coalescence. Many procedures for computation of lattice element parameters for representing linear elastic continuum have been developed, with the most often used ones discussed herein. Special attention is dedicated to presenting the ability of this kind of models to consider material disorder, heterogeneities and multi-phase materials, which makes lattice models attractive for meso- or micro-scale simulations of failure phenomena in quasi-brittle materials, such as concrete or rocks. Common difficulties encountered in material failure and a way of dealing with them in the lattice models framework are explained in detail. Namely, the size of the localized fracture process zone around the propagating crack plays a key role in failure mechanism, which is observed in various models of linear elastic fracture mechanics, multi-scale theories, homogenization techniques, finite element models, molecular dynamics. An efficient way of dealing with this kind of phenomena is by introducing the embedded strong discontinuity into lattice elements, resulting with mesh-independent computations of failure response. Moreover, mechanical lattice can be coupled with mass transfer problems, such as moisture, heat or chloride ions transfer which affect the material durability. Any close interaction with a fluid can lead to additional time dependent degradation. For illustration, the lattice approach to porous media coupling is given here as well. Thus, the lattice element models can serve for efficient simulations of material failure mechanisms, even when considering multi-physics coupling. The main peculiarities of such an approach have been presented and discussed in this work. PubDate: 2018-07-01 DOI: 10.1007/s11831-017-9210-y Issue No:Vol. 25, No. 3 (2018)

Authors:Pablo Moreno-García; José V. Araújo dos Santos; Hernani Lopes Pages: 785 - 815 Abstract: The first goal of this work is to present a literature review regarding the use of several sets of admissible functions in the Ritz method. The papers reviewed deal mainly with the analysis of buckling and free vibration of isotropic and anisotropic beams and plates. Theoretically, in order to obtain a correct solution, the set of admissible functions must not violate the essential or geometric boundary conditions and should also be linearly independent and complete. However, in practice, some of the sets of functions proposed in the literature present a bad numerical behavior, namely in terms of convergence, computational time and stability. Thus, a second goal of the present work is to compare the performance of several sets of functions in terms of these three features. To achieve this objective, the free vibration analysis of a fully clamped rectangular plate is carried out using six different sets of functions, along with the study of the convergence of natural frequencies and mode shapes, the computational time and the numerical stability. PubDate: 2018-07-01 DOI: 10.1007/s11831-017-9214-7 Issue No:Vol. 25, No. 3 (2018)

Authors:Emmanuel Tromme; Alexander Held; Pierre Duysinx; Olivier Brüls Pages: 817 - 844 Abstract: This paper reviews the state-of-the-art methods to perform structural optimization of flexible mechanisms. These methods are based on a system-based approach, i.e. the formulation of the design problem incorporates the time response of the mechanism that is obtained from a dynamic simulation of the flexible multibody system. The system-based approach aims at considering as precisely as possible the effects of nonlinear dynamic loading under various operating conditions. Also, the optimization process enhances most existing studies which are limited to (quasi-) static or frequency domain loading conditions. This paper briefly introduces flexible multibody system dynamics and structural optimization techniques. Afterwards, the two main methods, named the weakly and the fully coupled methods, that couple both disciplines are presented in details and the influence of the multibody system formalism is analyzed. The advantages and drawbacks of both methods are discussed and future possible research areas are mentioned. PubDate: 2018-07-01 DOI: 10.1007/s11831-017-9215-6 Issue No:Vol. 25, No. 3 (2018)

Authors:Fermin Otero; Sergio Oller; Xavier Martinez Pages: 479 - 505 Abstract: The continuous increase of computational capacity has encouraged the extensive use of multiscale techniques to simulate the material behaviour on several fields of knowledge. In solid mechanics, the multiscale approaches which consider the macro-scale deformation gradient to obtain the homogenized material behaviour from the micro-scale are called first-order computational homogenization. Following this idea, the second-order FE2 methods incorporate high-order gradients to improve the simulation accuracy. However, to capture the full advantages of these high-order framework the classical boundary value problem (BVP) at the macro-scale must be upgraded to high-order level, which complicates their numerical solution. With the purpose of obtaining the best of both methods i.e. first-order and second-order, in this work an enhanced-first-order computational homogenization is presented. The proposed approach preserves a classical BVP at the macro-scale level but taking into account the high-order gradient of the macro-scale in the micro-scale solution. The developed numerical examples show how the proposed method obtains the expected stress distribution at the micro-scale for states of structural bending loads. Nevertheless, the macro-scale results achieved are the same than the ones obtained with a first-order framework because both approaches share the same macro-scale BVP. PubDate: 2018-04-01 DOI: 10.1007/s11831-016-9205-0 Issue No:Vol. 25, No. 2 (2018)

Authors:Joshua Holgate; Alex Skillen; Timothy Craft; Alistair Revell Abstract: When scale-resolving simulation approaches are employed for the simulation of turbulent flow, computational cost can often be prohibitive. This is particularly true for internal wall-bounded flows, including flows of industrial relevances which may involve both high Reynolds number and geometrical complexity. Modelling the turbulence induced stresses (at all scales) has proven to lack requisite accuracy in many situations. In this work we review a promising family of approaches which aim to find a compromise between cost and accuracy; hybrid RANS–LES methods. We place particular emphasis on the emergence of embedded large eddy simulation. These approaches are summarised and key features relevant to internal flows are highlighted. A thorough review of the application of these methods to internal flows is given, where hybrid approaches have been shown to offer significant benefits to industrial CFD (relative to an empirical broadband modelling of turbulence). This paper concludes by providing a cost-analysis and a discussion about the emerging novel use-modalities for hybrid RANS–LES methods in industrial CFD, such as automated embedded simulation and multi-dimensional coupling. PubDate: 2018-05-18 DOI: 10.1007/s11831-018-9272-5

Authors:Tsenguun Ganbat; Heap-Yih Chong; Pin-Chao Liao; Cen-Ying Lee Abstract: Building information modeling (BIM) is gaining prominence in international construction projects (ICPs). While BIM is able to improve project performance and address certain ICP risks, it can also increase the number of risks to ICPs. This paper aims to provide a comprehensive review for addressing risks in BIM-enabled ICPs. A cross-systematic review approach was adopted to consider limited sources of references when reviewing risks in BIM-enabled ICPs as a single subject. Hence, six steps of reviews were formulated in connection with ICP risks, BIM-related risks, risk analysis and management techniques, and BIM uses. The findings of the cross-systematic review indicate (a) current and potential BIM uses in dealing with ICP risks, and (b) current and potential risk analyses and management techniques in addressing BIM-related risks. The review provides practical references for industry players and valuable insights into the future development of new BIM uses and risk management in BIM-enabled ICPs. PubDate: 2018-05-17 DOI: 10.1007/s11831-018-9265-4

Authors:Koichi Hashiguchi Abstract: Hyperelastic-based plastic constitutive equation based on the multiplicative decomposition of the deformation gradient tensor is reviewed comprehensively and its exact formulation is given for the description of the finite deformation and rotation in this article. Further, it is extended to describe the general loading behavior including the monotonic, the cyclic and the non-proportional loading behaviors by incorporating the rigorous plastic flow rules and the subloading surface model. In addition, it is extended also to the rate-dependency based on the overstress model, and the exact hyperelastic-based plastic constitutive equation of friction is formulated by incorporating the subloading-friction model. They are the exact constitutive equations describing the monotonic and the cyclic loading behavior up to the finite deformation/rotation and the friction behavior under the finite sliding/rotation with the rate-dependency, which have remained to be unsolved for a long time, although they have been required in the history of elastoplasticity theory. PubDate: 2018-05-17 DOI: 10.1007/s11831-018-9256-5

Authors:Swati Nigam; Rajiv Singh; A. K. Misra Abstract: Computer vision techniques capable of detecting human behavior are gaining interest. Several researchers have provided their review on behavior detection, however most of the reviews are focused on activity recognition only, and reviews on gesture and facial expression recognition are very few. Therefore, all of them lack to cover complete human behavior analysis. In this study, we provide a comprehensive review of human behavior detection approaches. The framework of this review is based on activity, gesture and facial expression recognition since these are the most important cues for behavior detection. These three areas are further classified in existing computational approaches. One can easily recognize from this review that hidden Markov model is widely exploited for activity recognition while motion history image is still a developing area. Haar-like features can be a valid alternative for gesture recognition. For facial expression recognition, local binary patterns feature is a very popular choice. We have reviewed behavior detection techniques, mostly developed after year 2009. The explicit advantages of this review are: (1) it provides a deep analysis of computational approaches for activity, gesture and facial expression recognition. (2) It includes both types of techniques that include single human as well as multiple human activities. (3) It considers techniques developed in the last decade only pertaining to information about the most recent techniques. (4) It provides a brief description of popular datasets used for activity, gesture and facial expression recognition. (5) It discusses open issues to provide an insight for future also. PubDate: 2018-05-17 DOI: 10.1007/s11831-018-9270-7

Authors:Guilherme Ferreira Gomes; Yohan Ali Diaz Mendez; Patrícia da Silva Lopes Alexandrino; Sebastiao Simões da Cunha; Antonio Carlos Ancelotti Abstract: The Structural Health Monitoring (SHM) technique is today the principle approach to manage the discovery and recognizable proof of damage in the most various designing areas. The need to monitor structural behavior is increasing every day but due to the development of new materials and increasingly complex structures. This leads to the development of increasingly robust and sensitive SHM methodologies and techniques. Damage Identification by means of intelligent signal processing and optimization algorithms based in vibration metrics are particularly emphasized in this paper. The methods discussed here are mainly elaborated by the evaluation of vibrational and modal data due to the great potential (and relatively easy to apply) of application. This article discusses the use of optimization algorithms and Artificial Neural Networks (ANN) for structural monitoring in the form of a brief review. This paper can be seen as a starting point of developing SHM systems and data analysis. The content of this paper aims to help engineers and researchers find a better alternative to their specific structural monitoring problems. PubDate: 2018-05-12 DOI: 10.1007/s11831-018-9273-4

Authors:Arnab Banerjee; Raj Das; Emilio P. Calius Abstract: Wave propagation through a structured medium has attracted the attention of researchers for centuries due to its relevance to problems in condensed matter physics, chemistry, optics, phononics, composite, acoustics and mechanics. Wave containing certain band of frequencies can either propagate, known as transmission, or attenuated, known as attenuation band. This band structure for a continuum and its equivalent lumped spring mass model are not identical, although continuum medium is often modelled as a chain of discrete periodic structures because the continuous and discrete is depends on the scale. These band characteristics are dependent on the properties of the units, thus the effects of different parameters, such as damping, stiffness and mass ratios, nonlinearity, on the bandwidth are compared with each other in this review. To cloak, modulate, guide, filter out or attenuate unwanted frequencies from the propagating waves, metamaterials are widely investigated as a special form of the periodic structures from the past 2 decades. The main aim of this review is to compare the bandwidth for one-dimensional periodic structures. Waves through two and three-dimensional periodic medium are not considered in the review because the key band characteristics of periodic system can be perceived in one dimensional. The methods for computing the wave transmission are evaluated in the non-dimensional domain and the band characteristics of different one-dimensional periodic structures are critically assessed in this review. This review will help to the future researchers to choose a proper periodic medium for getting a specific band phenomenon. PubDate: 2018-05-07 DOI: 10.1007/s11831-018-9268-1

Authors:K. K. Thyagharajan; I. Kiruba Raji Abstract: Plants are fundamentally important to life. Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. Because of the increasing demand for experts and calls for biodiversity, there is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on. We review several image processing methods in the feature extraction of leaves, given that feature extraction is a crucial technique in computer vision. As computers cannot comprehend images, they are required to be converted into features by individually analyzing image shapes, colors, textures and moments. Images that look the same may deviate in terms of geometric and photometric variations. In our study, we also discuss certain machine learning classifiers for an analysis of different species of leaves. PubDate: 2018-05-03 DOI: 10.1007/s11831-018-9266-3

Authors:S. K. Jeswal; S. Chakraverty Abstract: Quantum neural network is a useful tool which has seen more development over the years mainly after twentieth century. Like artificial neural network (ANN), a novel, useful and applicable concept has been proposed recently which is known as quantum neural network (QNN). QNN has been developed combining the basics of ANN with quantum computation paradigm which is superior than the traditional ANN. QNN is being used in computer games, function approximation, handling big data etc. Algorithms of QNN are also used in modelling social networks, associative memory devices, and automated control systems etc. Different models of QNN has been proposed by different researchers throughout the world but systematic study of these models have not been done till date. Moreover, application of QNN may also be seen in some of the related research papers. As such, this paper includes different models which have been developed and further the implement of the same in various applications. In order to understand the powerfulness of QNN, few results and reasons are incorporated to show that these new models are more useful and efficient than traditional ANN. PubDate: 2018-05-03 DOI: 10.1007/s11831-018-9269-0

Authors:D. Vinodha; E. A. Mary Anita Abstract: Wireless sensor networks (WSN) are made up of energy constraint tiny sensing devices which are distributed geographically to monitor inhabited remote areas by collecting the physical phenomenon like temperature, pressure etc. They play a vital role in military surveillance, environment monitoring etc. Unstructured topology in WSN results in large amount of redundant data being transmitted over the resource constraint devices which leads to energy starvation problem. Since the nodes are prone to tamper, thanks to their environment, ensuring the privacy of sensitive data being aggregated and transmitted is important. Hence data aggregation schemes which minimize the data redundancy with the guarantee of security become the attraction of research. Many secured aggregation schemes have been proposed by researchers. In this survey the various existing solutions are surveyed and an attempt is made to classify them based on the node topology and mechanisms employed for assuring privacy. PubDate: 2018-05-03 DOI: 10.1007/s11831-018-9267-2