Fracture process in cortical bone: X-FEM analysis of microstructured models

Bones tissues are heterogeneous materials that consist of various microstructural features at different length scales. The fracture process in cortical bone is affected significantly by the microstructural constituents and their heterogeneous distribution. Understanding mechanics of bone fracture is necessary for reduction and prevention of risks related to bone fracture. The aim of this study is to develop a finite-element approach to evaluate the fracture process in cortical bone at micro-scale. In this study, three microstructural models with various random distributions based on statistical realizations were constructed using the global model’s framework together with a submodelling technique to investigate the effect of microstructural features on macroscopic fracture toughness and microscopic crack-propagation behaviour. Analysis of processes of crack initiation and propagation utilized the extended finite-element method using energy-based cohesive-segment scheme. The obtained results were compared with our experimental data and observations and demonstrated good agreement. Additionally, the microstructured cortical bone models adequately captured various damage and toughening mechanisms observed in experiments. The studies of crack length and fracture propagation elucidated the effect of microstructural constituents and their mechanical properties on the microscopic fracture propagation process.     

Solution approaches for a real-life resource-constrained parallel machine scheduling problem

This paper deals with a real-world scheduling problem in an injection-molding department of an electrical appliance plant. In the department, a resource-constrained parallel machine scheduling problem with machine eligibility restrictions is investigated. First, an integer-programming (IP) model with the objective of minimizing makespan is developed for the entire problem. Since this entire IP model has a huge number of variables, it cannot handle the problem efficiently. To obtain more efficient results, two solution approaches, namely IP/IP and IP/constraint programming (CP) both of which partition the entire problem into loading and scheduling sub-problems, are proposed. The loading phase, in which an IP loading model assigns the jobs to machines with the aim of minimizing maximum load on the machines and operators, is the same for both approaches. Subsequently, in the scheduling phase, the IP/IP approach uses an IP scheduling model while the IP/CP approach applies a CP scheduling model to obtain the final schedule of the jobs. Computational results show that the proposed solution methods improve makespan values for almost all test problems in comparison to the entire IP model. In particular, the IP/IP approach performs better in the test problems with greater number of operators, whereas IP/CP approach provides quick and practical results in almost all test problems and gives relatively more efficient makespan values when the resource constraints are tight (i.e., the case of smaller number of operators).     

Photo‐curable highly water‐repellent nanocomposite coatings

Modification and use of natural products have gained a lot of interest in recent years due to their environmental friendliness and their availability from different sources. In this study, (castor oil)‐based photo‐curable highly hydrophobic coatings were prepared and characterized. Castor oil was first modified with 3‐isocyanatopropyltriethoxysilane and then hydrolyzed prior to the coating preparation. The resulting precursor was mixed with norbornyl acrylate and hexanediol diacrylate, and highly roughened hydrophobic coatings were prepared with the aid of fluorinated/nonfluorinated alkoxysilane coupling agents and hydrophobic fumed nanosilica particles. The coatings were applied on borofloat glass. The addition of fluorine and nanosilica showed a significant impact on the properties of the coatings. As the fluorine and nanosilica contents were increased in the formulations, flame retardancy and the contact angle values of the coatings increased. The surface roughness of the coatings increased with the addition of hydrophobic fumed nanosilica particles. Also, the relation between the surface energy and the contact angle values of the coatings was investigated. J. VINYL ADDIT. TECHNOL., 19:31–38, 2013. © 2013 Society of Plastics Engineers     

Finite element modelling of thermally bonded bicomponent fibre nonwovens: Tensile behaviour

Nonwovens are polymer-based engineered textiles with a random microstructure and hence require a numerical model to predict their mechanical performance. This paper focuses on finite element (FE) modelling the elastic–plastic mechanical response of polymer-based core/sheath type thermally bonded bicomponent fibre nonwoven materials. The nonwoven fabric is treated as an assembly of two regions having distinct mechanical properties: fibre matrix and bond points. The fibre matrix is composed of randomly oriented core/sheath type fibres acting as load-transfer link between bond points. Random orientation of individual fibres is introduced into the model in terms of the orientation distribution function (ODF) in order to determine the material’s anisotropy. The ODF is obtained by analysing the data acquired with scanning electron microscopy (SEM) and X-ray micro computed tomography (CT). On the other hand, bond points are treated as a deformable bicomponent composite material composed of the sheath material as matrix and the core material as fibres having random orientations. An algorithm is developed to calculate the anisotropic material properties of these regions based on properties of fibres and manufacturing parameters such as the planar density, core/sheath ratio and fibre diameter. Having distinct anisotropic mechanical properties for two regions, the fabric is modelled with shell elements with thicknesses identical to those of the bond points and fibre matrix. Finally, nonwoven specimens are subjected to tensile tests along different loading directions with respect to the machine direction of the fabric. The force–displacement curves obtained in these tests are compared with the results of FE simulations.     

ZnO Nanorods as Antireflective Coatings for Industrial‐Scale Single‐Crystalline Silicon Solar Cells

In this work, both planar and textured, industrial scale (156 mm × 156 mm) single‐crystalline silicon (Si) solar cells have been fabricated using zinc oxide (ZnO) nanorods as antireflection coating (ARC). ZnO nanorods were grown in a few minutes via hydrothermal method within a commercially available microwave oven. Relative improvement in excess of 65% in the reflectivity was observed for both planar and textured Si surfaces. Through ZnO nanorods, effective lifetime (τ_eff) measurements were presented to investigate the surface passivation property of such an ARC layer. ZnO nanorods increased the τ_eff from 9 to 71 μs at a carrier injection level of 10¹⁵ cm^?3. Increased carrier lifetime revealed the passivation effect of the ZnO nanorods in addition to their ARC property. 33% and 16% enhancement in the photovoltaic conversion efficiency was obtained in planar and textured single‐crystalline solar cells, respectively. Our results reveal the potential of ZnO nanorods as ARC that can be deposited through simple solution‐based methods and the method investigated herein can be simply adapted to industrial scale fabrication.     

A fuzzy extended DELPHI method for adjustment of statistical time series prediction: An empirical study on dry bulk freight market case

This paper investigates the forecasting accuracy of fuzzy extended group decisions in the adjustment of statistical benchmark results. DELPHI is a frequently used method for implementing accurate group consensus decisions. The concept of consensus is subject to expert characteristics and it is sometimes ensured by a facilitator’s judgment. Fuzzy set theory deals with uncertain environments and has been adapted for DELPHI, called fuzzy-DELPHI (FD). The present paper extends the recent literature via an implementation of FD for the adjustment of statistical predictions. We propose a fuzzy-DELPHI adjustment process for improvement of accuracy and introduced an empirical study to illustrate its performance in the validation of adjustments of statistical forecasts in the dry bulk shipping index.     

Predicting flow conditions over stepped chutes based on ANFIS

Chute flow may be either smooth or stepped. The flow conditions in stepped chutes have been classified into nappe, transition and skimming flows. In this paper, characteristics of flow conditions are presented systematically under a wide range of critical flow depth, step height and chute slope. The Adaptive Network Based Fuzzy Inference System (ANFIS) is used to predict flow conditions in stepped chutes using critical flow depth, step height and chute slope information. The proposed model performance is determined by threefold cross validation method. The evaluated classification accuracy of ANFIS model is 99.01%. The test results showed that the proposed ANFIS model can be used successfully for complex process control in hydraulic systems.     

Area aggregation and time-scale modeling for sparse nonlinear networks

Model reduction and aggregation are of key importance for simulation and analysis of large-scale systems, such as molecular dynamics, large swarms of robotic vehicles, and animal aggregations. We study a nonlinear network which exhibits areas of internally dense and externally sparse interconnections. The densely connected nodes in these areas synchronize in the fast time-scale, and behave as aggregate nodes that dominate the slow dynamics of the network. We first derive a singular perturbation model which makes this time-scale separation explicit and, next, prove the validity of the reduced-model approximation on the infinite time interval.     

Prediction of swelling pressures of expansive soils using soft computing methods

Lateral and vertical swelling pressures associated with expansive soils cause damages on structures. These pressures must be predicted before the structures are constructed in order to prevent the damages. The magnitude of the stresses can decrease rapidly when volume changes are partly allowed. Therefore, a material, which has a high compressibility, must be placed between expansive soils and the structures in both horizontal and vertical directions in order to decrease transmitted swelling pressure on structures. There are numerous techniques recommended for estimating the swelling pressures. However, these techniques are very complex and time-consuming. In this study, a new estimation model to predict the pressures is developed using experimental data. The data were collected in the laboratory using a newly developed device and experimental setup also. In the experimental setup, a rigid steel box was designed to measure transmitted swelling pressures in lateral and vertical directions. In the estimation model, approaches of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are employed. In the first stage of the study, the lateral and vertical swelling pressures were measured with different thicknesses of expanded polystyrene geofoam placed between one of the vertical walls of the steel box and the expansive soil in the laboratory. Then, ANN and ANFIS approaches were trained using these results of the tests measured in the laboratory as input for the prediction of transmitted lateral and vertical swelling pressures. Results obtained showed that ANN-based prediction and ANFIS approaches could satisfactorily be used to estimate the transmitted lateral and vertical swelling pressures of expansive soils.