Authors:Muhammad Usman; Muhammad Moazam Fraz; Sarah A. Barman Pages: 449 - 465 Abstract: Abstract The retina is a tiny layer at the posterior pole of an eye and is made up of tissues sensitive to light, these tissues generate nerve signals that pass through the optic nerve to the brain. A retinal disorder occurs when the retina malfunctions; glaucoma, diabetic retinopathy and pathologic myopia are retinal disorders and principal causes of blindness worldwide. These retinal disorders are often diagnosed and treated by an ophthalmologist. However, to accurately assess a retinal disease, ophthalmologist would need qualitative and quantitative analysis of the disease, it’s early and current statistics, but acquisition of these measurements are not possible through manual techniques, there should be automated computer aided diagnosis (CAD) systems to assist ophthalmologists. In this comprehensive review, an analysis and evaluation has been performed of different computer vision and image processing approaches applied to OCT images for automatic diagnosis of retinal disorders. We also reported disease causes, symptoms and pathologies manifestations within OCT images, which can serve as baseline knowledge for development of an automated CAD system. Hence, this disease specific review offers a good understanding to analyze visual impairments from retinal OCT images which will help researcher to design enhanced therapeutic systems for retinal disorders. PubDate: 2017-07-01 DOI: 10.1007/s11831-016-9174-3 Issue No:Vol. 24, No. 3 (2017)

Authors:L. Behera; S. Chakraverty Pages: 481 - 494 Abstract: Abstract Understanding dynamic behavior of carbon nanotubes has been of interest to researchers because of its practical applications. Recent studies show that nonlocal elasticity theory gives better results in the vibration of carbon nanotubes. The necessity of nonlocal elasticity theory, calibration of nonlocal parameter and application of nonlocal elasticity theory in various studies related to vibration of carbon nanotubes are discussed. This review emphasizes the application of nonlocal elasticity theory in the vibration of carbon nanotubes considering various types of complicating effects, nonlinearity, functionally graded material and different beam theories. PubDate: 2017-07-01 DOI: 10.1007/s11831-016-9179-y Issue No:Vol. 24, No. 3 (2017)

Authors:T. Mukhopadhyay; S. Chakraborty; S. Dey; S. Adhikari; R. Chowdhury Pages: 495 - 518 Abstract: Abstract This paper presents a critical comparative assessment of Kriging model variants for surrogate based uncertainty propagation considering stochastic natural frequencies of composite doubly curved shells. The five Kriging model variants studied here are: Ordinary Kriging, Universal Kriging based on pseudo-likelihood estimator, Blind Kriging, Co-Kriging and Universal Kriging based on marginal likelihood estimator. First three stochastic natural frequencies of the composite shell are analysed by using a finite element model that includes the effects of transverse shear deformation based on Mindlin’s theory in conjunction with a layer-wise random variable approach. The comparative assessment is carried out to address the accuracy and computational efficiency of five Kriging model variants. Comparative performance of different covariance functions is also studied. Subsequently the effect of noise in uncertainty propagation is addressed by using the Stochastic Kriging. Representative results are presented for both individual and combined stochasticity in layer-wise input parameters to address performance of various Kriging variants for low dimensional and relatively higher dimensional input parameter spaces. The error estimation and convergence studies are conducted with respect to original Monte Carlo Simulation to justify merit of the present investigation. The study reveals that Universal Kriging coupled with marginal likelihood estimate yields the most accurate results, followed by Co-Kriging and Blind Kriging. As far as computational efficiency of the Kriging models is concerned, it is observed that for high-dimensional problems, CPU time required for building the Co-Kriging model is significantly less as compared to other Kriging variants. PubDate: 2017-07-01 DOI: 10.1007/s11831-016-9178-z Issue No:Vol. 24, No. 3 (2017)

Authors:Tomasz Zawistowski; Michał Kleiber Pages: 519 - 542 Abstract: Abstract High pressure variable displacement axial piston pumps are subject to complex dynamic phenomena. Their analysis is difficult, additionally complicated by leakage of the working fluid. Analytically gap flow is calculated with the Reynolds equation which describes the pressure distribution in a thin lubricating layer. The paper presents various approaches to analyze gap flow both in traditional axial piston pump and novel type of hydraulic pump, designed at the Polish Gdansk Institute of Technology. Because of large aspect ratio between the height of the gap and the size of pump elements, the authors present the numerical simulation approach using a local model to define a lubrication gap, linked to a global model of a pump from which boundary conditions were imported. User defined functions implemented in Fluent and Excel were used to calculate the pressure and velocity fields and assess the fluid flow rate. PubDate: 2017-07-01 DOI: 10.1007/s11831-016-9180-5 Issue No:Vol. 24, No. 3 (2017)

Authors:Deepam Goyal; Vanraj; B. S. Pabla; S. S. Dhami Pages: 543 - 556 Abstract: Abstract Condition monitoring of gearboxes which is considered as a key element of rotating machines ensures to continuously reduce and eliminate cost, unscheduled downtime and unexpected breakdowns. Although, a lot of work on condition monitoring and fault diagnosis of fixed-axis gearbox has been reported in the literature, however only a few have found their way to industrial applications. The ability of condition statistical indicators is to provide accurate and precise information about the health of various components at different levels of damage. In this paper, frequently used condition indicators are addressed domain-wise and their characteristics are stated. This paper presents the review of work to provide a wide and good reference for researchers to be utilized. The structure of a fixed-axis gearbox in addition to the unique behaviors and fault characteristics of fixed-axis gearbox has been recognized and represented. By extensively reviewing and categorizing important papers and articles, this paper is able to summarize the conditional monitoring indicators on basis of adopted methodologies. Lastly, open problems are stated and further research prospects pointed out. PubDate: 2017-07-01 DOI: 10.1007/s11831-016-9176-1 Issue No:Vol. 24, No. 3 (2017)

Authors:Garrison Stevens; Sez Atamturktur Pages: 557 - 571 Abstract: Abstract Partitioned analysis involves coupling of constituent models that resolve different scales or physics by allowing them to exchange inputs and outputs in an iterative manner. Through partitioning, simulations of complex physical systems are becoming evermore present in the scientific modeling community, making the Verification and Validation (V&V) of partitioned models to quantifying the predictive capability of their simulations increasingly important. Partitioning presents unique challenges, as well as opportunities, for the V&V community. Verification gains a new level of complexity in partitioned models, as numerical errors can easily be introduced at the coupling interface where non-matching domains and models are integrated together. For validation, partitioned analysis allows the quantification of the uncertainties and errors in constituent models through comparison against separate-effect experiments conducted in independent constituent domains. Such experimental validation is important as uncertainties and errors in the predictions of constituents can be transferred across their interfaces, either compensating for each other or accumulating during iterative coupling operations. This paper reviews published literature on methods for assessing and improving the predictive capability of strongly coupled models of physical and engineering systems with an emphasis on advancements made in the last decade. PubDate: 2017-07-01 DOI: 10.1007/s11831-016-9177-0 Issue No:Vol. 24, No. 3 (2017)

Authors:Malte Krack; Loic Salles; Fabrice Thouverez Pages: 589 - 636 Abstract: Abstract The present review article addresses the vibration behavior of bladed disks encountered e.g. in aircraft engines as well as industrial gas and steam turbines. The utilization of the dissipative effects of dry friction in mechanical joints is a common means of the passive mitigation of structural vibrations caused by aeroelastic excitation mechanisms. The prediction of the vibration behavior is a scientific challenge due to (a) the strongly nonlinear contact interactions involving local sticking, sliding and liftoff, (b) the model order required to accurately describe the dynamic behavior of the assembly, and (c) the multi-disciplinary character of the problem associated with the need to account for structural mechanical as well as fluid dynamical effects. The purpose of this article is the overview and discussion the current state of the art of vibration prediction approaches. The modeling approaches in this work embrace the description of the rotating bladed disk, the contact modeling, the consideration of aeroelastic effects, appropriate model order reduction techniques and the exploitation of the rotationally periodic nature of the problem. The simulation approaches cover the direct computation of periodic, steady-state externally forced and self-excited vibrations using the high-order harmonic balance method, the formulation of the contact problem in the frequency domain, methods for the solution of the governing algebraic equations and advanced simulation approaches, including the concept of nonlinear modes. PubDate: 2017-07-01 DOI: 10.1007/s11831-016-9183-2 Issue No:Vol. 24, No. 3 (2017)

Authors:Zhaobin Wang; Huale Li; Ying Zhu; TianFang Xu Pages: 637 - 654 Abstract: Abstract Plant recognition is closely related to people’s life. The operation of the traditional plant identification method is complicated, and is unfavorable for popularization. The rapid development of computer image processing and pattern recognition technology makes it possible for computer’s automatic recognition of plant species based on image processing. There are more and more researchers drawing their attention on the computer’s automatic identification technology based on plant images in recent years. Based on this, we have carried on a wide range of research and analysis on the plant identification method based on image processing in recent years. First of all, the research significance and history of plant recognition technologies are introduced in this paper; secondly, the main technologies and steps of plant recognition are reviewed; thirdly, more than 30 leaf features (including 16 shape features, 11 texture features, four color features), and then SVM was used to evaluate these features and their fusion features, and 8 commonly used classifiers are introduced in detail. Finally, the paper is ended with a conclusion of the insufficient of plant identification technologies and a prediction of future development. PubDate: 2017-07-01 DOI: 10.1007/s11831-016-9181-4 Issue No:Vol. 24, No. 3 (2017)

Authors:Julien Berger; Nathan Mendes; Sihem Guernouti; Monika Woloszyn; Francisco Chinesta Pages: 655 - 667 Abstract: Abstract This paper presents a review of the use of model reduction techniques for building physics applications. The use of separated representations, the so called Proper Generalised Decomposition (PGD), is particularly investigated. This technique can be applied for efficient building physics modelling at different levels: the wall and multizone models, the whole-building (coupled envelope and air volumes) simulation and their practical applications. The PGD can be formulated as a space-time representation to provide fast and accurate solutions of 2- and 3-dimensional problems at the wall or the whole-building level. Furthermore, the PGD solution allows to treat efficiently parametric problems of practical building applications. PubDate: 2017-07-01 DOI: 10.1007/s11831-016-9184-1 Issue No:Vol. 24, No. 3 (2017)

Authors:Alireza Kharazian; Francisco López-Almansa Abstract: Abstract Collision between adjoining buildings with aligned slabs is relevant, since the huge impact forces significantly modify the buildings dynamic behavior. The separation required by the regulations avoids pounding; however, even in recent buildings, impact can occur due to not fulfillment of codes and seismicity underestimation. Given the importance of this issue, a significant research effort has been undertaken worldwide, and a considerable number of papers are available. The complexity of this field and this abundance of information might require a review task. This paper presents a summary of the theoretical developments, discusses the most common simulation software, provides an overview of the previous research, offers recommendations to researchers, and identifies research needs. PubDate: 2017-09-14 DOI: 10.1007/s11831-017-9242-3

Authors:Patrick Gallinari; Yvon Maday; Maxime Sangnier; Olivier Schwander; Tommaso Taddei Abstract: Abstract Reduced basis methods for the approximation to parameter-dependent partial differential equations are now well-developed and start to be used for industrial applications. The classical implementation of the reduced basis method goes through two stages: in the first one, offline and time consuming, from standard approximation methods a reduced basis is constructed; then in a second stage, online and very cheap, a small problem, of the size of the reduced basis, is solved. The offline stage is a learning one from which the online stage can proceed efficiently. In this paper we propose to exploit machine learning procedures in both offline and online stages to either tackle different classes of problems or increase the speed-up during the online stage. The method is presented through a simple flow problem—a flow past a backward step governed by the Navier Stokes equations—which shows, however, interesting features. PubDate: 2017-08-05 DOI: 10.1007/s11831-017-9238-z

Authors:Jan Neggers; Olivier Allix; François Hild; Stéphane Roux Abstract: Abstract Since the turn of the century experimental solid mechanics has undergone major changes with the generalized use of images. The number of acquired data has literally exploded and one of today’s challenges is related to the saturation of mining procedures through such big data sets. With respect to digital image/volume correlation one of tomorrow’s pathways is to better control and master this data flow with procedures that are optimized for extracting the sought information with minimum uncertainties and maximum robustness. In this paper emphasis is put on various hierarchical identification procedures. Based on such structures a posteriori model/data reductions are performed in order to ease and make the exploitation of the experimental information far more efficient. Some possibilities related to other model order reduction techniques like the proper generalized decomposition are discussed and new opportunities are sketched. PubDate: 2017-07-28 DOI: 10.1007/s11831-017-9234-3

Authors:Domenico Borzacchiello; José V. Aguado; Francisco Chinesta Abstract: Abstract We discuss the use of hierarchical collocation to approximate the numerical solution of parametric models. With respect to traditional projection-based reduced order modeling, the use of a collocation enables non-intrusive approach based on sparse adaptive sampling of the parametric space. This allows to recover the low-dimensional structure of the parametric solution subspace while also learning the functional dependency from the parameters in explicit form. A sparse low-rank approximate tensor representation of the parametric solution can be built through an incremental strategy that only needs to have access to the output of a deterministic solver. Non-intrusiveness makes this approach straightforwardly applicable to challenging problems characterized by nonlinearity or non affine weak forms. As we show in the various examples presented in the paper, the method can be interfaced with no particular effort to existing third party simulation software making the proposed approach particularly appealing and adapted to practical engineering problems of industrial interest. PubDate: 2017-07-18 DOI: 10.1007/s11831-017-9241-4

Authors:Luc Laurent; Rodolphe Le Riche; Bruno Soulier; Pierre-Alain Boucard Abstract: Abstract Metamodeling, the science of modeling functions observed at a finite number of points, benefits from all auxiliary information it can account for. Function gradients are a common auxiliary information and are useful for predicting functions with locally changing behaviors. This article is a review of the main metamodels that use function gradients in addition to function values. The goal of the article is to give the reader both an overview of the principles involved in gradient-enhanced metamodels while also providing insightful formulations. The following metamodels have gradient-enhanced versions in the literature and are reviewed here: classical, weighted and moving least squares, Shepard weighting functions, and the kernel-based methods that are radial basis functions, kriging and support vector machines. The methods are set in a common framework of linear combinations between a priori chosen functions and coefficients that depend on the observations. The characteristics common to all kernel-based approaches are underlined. A new \(\nu \) -GSVR metamodel which uses gradients is given. Numerical comparisons of the metamodels are carried out for approximating analytical test functions. The experiments are replicable, as they are performed with an opensource available toolbox. The results indicate that there is a trade-off between the better computing time of least squares methods and the larger versatility of kernel-based approaches. PubDate: 2017-07-17 DOI: 10.1007/s11831-017-9226-3

Authors:Tanmoy Chatterjee; Souvik Chakraborty; Rajib Chowdhury Abstract: Abstract Robust design optimization (RDO) has been eminent, ascertaining optimal configuration of engineering systems in presence of uncertainties. However, computational aspect of conventional RDO can often get computationally intensive as neighborhood assessments of every solution are required to compute the performance variance and ensure feasibility. Surrogate assisted optimization is one of the efficient approaches in order to mitigate this issue of computational expense. However, the performance of a surrogate model plays a key factor in determining the optima in multi-modal and highly non-linear landscapes, in presence of uncertainties. In other words, the approximation accuracy of the model is principal in yielding the actual optima and thus, avoiding any misguide to the decision maker on the basis of false or, local optimum points. Therefore, an extensive survey has been carried out by employing most of the well-known surrogate models in the framework of RDO. It is worth mentioning that the numerical study has revealed consistent performance of a model out of all the surrogates utilized. Finally, the best performing model has been utilized in solving a large-scale practical RDO problem. All the results have been compared with that of Monte Carlo simulation results. PubDate: 2017-07-13 DOI: 10.1007/s11831-017-9240-5

Authors:K. K. Thyagharajan; T. Vignesh Abstract: Abstract Multispectral remote sensing images are the primary source in the land use and land cover (LULC) monitoring. This is achieved by LULC classification and LULC change detection. The change detection in LULC includes the detection of water bodies, forest fire, forest degradation, agriculture areas monitoring, etc. Various change detection and LULC classification methods have their own advantages and disadvantages, and no single method is optimal and finds applicability for all cases. This paper summarizes and analyses the various soft computing and feature extraction techniques used for LULC classification and change detection. Based on the average error rate, performances of the different soft computing techniques are evaluated. The broad usage of multispectral remote sensing images, object-based change detection, neural networks and various levels of image fusion methods offer more potential in LULC monitoring. PubDate: 2017-07-10 DOI: 10.1007/s11831-017-9239-y

Authors:Seunghye Lee; Jingwan Ha; Mehriniso Zokhirova; Hyeonjoon Moon; Jaehong Lee Abstract: Abstract Since the first journal article on structural engineering applications of neural networks (NN) was published, there have been a large number of articles about structural analysis and design problems using machine learning techniques. However, due to a fundamental limitation of traditional methods, attempts to apply artificial NN concept to structural analysis problems have been reduced significantly over the last decade. Recent advances in deep learning techniques can provide a more suitable solution to those problems. In this study, versatile background information, such as alleviating overfitting methods with hyper-parameters, is presented. A well-known ten bar truss example is presented to show condition for neural networks, and role of hyper-parameters in the structures. PubDate: 2017-07-03 DOI: 10.1007/s11831-017-9237-0

Authors:Christoph Meier; Alexander Popp; Wolfgang A. Wall Abstract: Abstract The present work focuses on geometrically exact finite elements for highly slender beams. It aims at the proposal of novel formulations of Kirchhoff–Love type, a detailed review of existing formulations of Kirchhoff–Love and Simo–Reissner type as well as a careful evaluation and comparison of the proposed and existing formulations. Two different rotation interpolation schemes with strong or weak Kirchhoff constraint enforcement, respectively, as well as two different choices of nodal triad parametrizations in terms of rotation or tangent vectors are proposed. The combination of these schemes leads to four novel finite element variants, all of them based on a \(C^1\) -continuous Hermite interpolation of the beam centerline. Essential requirements such as representability of general 3D, large deformation, dynamic problems involving slender beams with arbitrary initial curvatures and anisotropic cross-section shapes, preservation of objectivity and path-independence, consistent convergence orders, avoidance of locking effects as well as conservation of energy and momentum by the employed spatial discretization schemes, but also a range of practically relevant secondary aspects will be investigated analytically and verified numerically for the different formulations. It will be shown that the geometrically exact Kirchhoff–Love beam elements proposed in this work are the first ones of this type that fulfill all these essential requirements. On the contrary, Simo–Reissner type formulations fulfilling these requirements can be found in the literature very well. However, it will be argued that the shear-free Kirchhoff–Love formulations can provide considerable numerical advantages such as lower spatial discretization error levels, improved performance of time integration schemes as well as linear and nonlinear solvers and smooth geometry representation as compared to shear-deformable Simo–Reissner formulations when applied to highly slender beams. Concretely, several representative numerical test cases confirm that the proposed Kirchhoff–Love formulations exhibit a lower discretization error level as well as a considerably improved nonlinear solver performance in the range of high beam slenderness ratios as compared to two representative Simo–Reissner element formulations from the literature. PubDate: 2017-07-03 DOI: 10.1007/s11831-017-9232-5