13 research outputs found
Damage behavior of glass fiber reinforced plastic laminate and bovine-derived hydroxyapatite under low-velocity impact loading / Sadjad Naderi
No full textModeling and simulation of mechanical behavior of biomaterials is a very important topic. Until now, there is no single simulation model valid for the wide range of biomaterial, which includes metal alloys, composite and porous materials. Therefore the objective of the present study is to develop a numerical model to cover evaluation of mechanical properties for two types of brittle biomaterials: hydroxyapatite (HA) as a porous material for hard tissue replacement and Glass Fiber Reinforced Plastic laminates (GFRPs) as a layered composite for dental applications. The plastic behavior of HA and the effect of porosity on the elastic-plastic behavior of HA have not been well studied and fully discovered. On the other hand, biomedical and industrial applications have focused to detect the effect of low-velocity impact on mechanical response of GFRP materials under fatigue loading.
Finite element simulation models have been developed using MSC. MARC® to study on mechanical response of these brittle materials under impact and nanoindentation. The FE-inverse technique and semi-empirical method was introduced to infer the most suitable elastoplastic material model for HA. In addition, an effective approach is proposed to design irregular porous media using MATLAB code for HA with controllable pore shapes and distributions, without requiring any prior microscopic information. The impact damage process and contact stiffness for GFRP samples were investigated incrementally until when the composites were perforated. A Fatigue Damage index (FD) was introduced to capture the unique GFRP composite characteristics. Nanoindentation on HA and low-velocity impact, tensile static and fatigue tests on GFRP were performed to verify the numerical methods.
The elastic-plastic material model has shown more reasonable mechanical behavior of HA. Proposed approach for generating irregular porous structures is easier and faster
than the other existing approaches and constructed model appears more natural and realistic. The influence of low-velocity impact should be reflected in the design of GFRP structures. Even by increasing laminate thickness, the effect of impact is higher than the effect of thickness on the fatigue behavior of glass/epoxy laminates
3D meso-scale modelling of tensile and compressive fracture behaviour of steel fibre reinforced concrete
No full textThis paper presents a novel meso-scale modelling framework to investigate the fracture process in steel fibre reinforced concrete (SFRC) under uniaxial tension and compression considering its 3D mesostructural characteristics, including different types of fibres, realistic shaped aggregates, mortar, interfacial transition zone and voids. Based on a hybrid damage model consisting of cohesive element method and damage plasticity method, a cost-effective finite element approach was proposed to simulate the fracture behaviour of SFRC in terms of stress-strain response, energy dissipation and crack morphology. The results indicated that under given conditions, the straight and hooked-end fibres improved the compressive damage tolerances of concrete over 11.5% while the spiral fibres had a negligible effect of 2.6%. The tensile macro-damage level index introduced was reduced over 15% by all fibres. Compared to straight fibres, the higher anchoring capacity of spiral fibres reduced the reinforcement performance while hooked-end fibres did not exhibit a significant influence
Alternative methods to determine the elastoplastic properties of sintered hydroxyapatite from nanoindentation testing
No full textThis study introduces alternative methods to determine the elastoplastic properties of bovine-derived Hydroxyapatite (HA) porous bone graft through a set of nanoindentation tests with a Berkovich indenter. Generally, experimental data obtained from nanoindentation tests are force displacement, hardness and elastic modulus. However, to determine plastic properties such as strength coefficient and work hardening exponent of bovine HA, analytical or inverse finite element models are required. In this paper, the effect of sintering temperature on these properties of HA is studied for the range of 1000-1400 degrees C. The direct and inverse Finite Element (FE) simulation models for nanoindentation tests were written in MSC, MARC (R) software. A special algorithm for the inverse technique was developed to infer the most suitable elastoplastic material model for HA. A semi-empirical method was adapted to calculate the elastoplastic material properties of HA. The numerical results of harder hydroxyapatite showed better agreement with the experiments while the work hardening exponent, or n-value, and strength coefficient k of hard HA were found to be 0.23 and 8.05 GPa respectively. A comparison between the experimental and predicted load-displacement curves showed that the proposed inverse technique is effective in predicting the elastoplastic material properties from the nanoindentation test with error below 4 at maximum load. (C) 2014 Elsevier Ltd. All rights reserved
Low-velocity impact damage of woven fabric composites: Finite element simulation and experimental verification
No full textThis paper addresses the response of Glass Fiber Reinforced Plastic laminates (GFRPs) under low-velocity impact. Experimental tests were performed according to ASTM: D5628 for different initial impact energy levels ranging from 9.8 J to 29.4 J and specimen thicknesses of 2, 3 and 4 mm. The impact damage process and contact stiffness were studied incrementally until a perforation phase of the layered compounds occurred, in line with a force–deflection diagram and imaging of impacted laminates. The influence that impact parameters such as velocity and initial energy had on deflection and damage of the test specimens was investigated. Finite Element Simulation (FES) was done using MSC. MARC® was additionally carried out to understand the impact mechanism and correlation between these parameters and the induced damage. The simulation and experimental results reached good accord regarding maximum contact force and contact time with insignificant amount of damage
A novel framework for realistic modelling of 3D mesostructure and fracture behaviour of recycled aggregate concrete
No full textRecycled aggregate concrete (RAC) offers a sustainable alternative to natural aggregate concrete, while its meso-scale modelling is often limited by overly simplified geometries. This study presents a novel 3D computational framework using Voronoi tessellation and hierarchical clustering to realistically generate 3D mesostructure of RAC. The model equips six distinct phases including old aggregate, old mortar, new mortar, two interfacial transition zones (ITZs) and voids, and employs splining and scaling techniques to control morphology. It produces irregular recycled aggregates with realistic size distribution, volume fraction, old mortar content, and shape descriptors in terms of sphericity, roundness and convexity. Validation indicates consistence with experimental findings in terms of geometry and composition. 3D meso-scale modelling of fracture processes in RAC under uniaxial compression can accurately capture the crack initiation at old ITZs and shear-dominated failure. The proposed modelling framework offers a robust tool for understanding the mesostructure-mechanical property relationships in RAC and optimising material design of RAC
Field-scale demonstration of in situ immobilization of heavy metals by injecting iron oxide nanoparticle adsorption barriers in groundwater
No full textPublisher Copyright: © 2020 The Author(s)Remediation of heavy metal-contaminated aquifers is a challenging process because they cannot be degraded by microorganisms. Together with the usually limited effectiveness of technologies applied today for treatment of heavy metal contaminated groundwater, this creates a need for new remediation technologies. We therefore developed a new treatment, in which permeable adsorption barriers are established in situ in aquifers by the injection of colloidal iron oxides. These adsorption barriers aim at the immobilization of heavy metals in aquifers groundwater, which was assessed in a large-scale field study in a brownfield site. Colloidal iron oxide (goethite) nanoparticles were used to install an in situ adsorption barrier in a very heterogeneous, contaminated aquifer of a brownfield in Asturias, Spain. The groundwater contained high concentrations of heavy metals with up to 25 mg/L zinc, 1.3 mg/L lead, 40 mg/L copper, 0.1 mg/L nickel and other minor heavy metal pollutants below 1 mg/L. High amounts of zinc (>900 mg/kg), lead (>2000 mg/kg), nickel (>190 mg/kg) were also present in the sediment. Ca. 1500 kg of goethite nanoparticles of 461 ± 266 nm diameter were injected at low pressure (< 0.6 bar) into the aquifer through nine screened injection wells. For each injection well, a radius of influence of at least 2.5 m was achieved within 8 h, creating an in situ barrier of 22 × 3 × 9 m. Despite the extremely high heavy metal contamination and the strong heterogeneity of the aquifer, successful immobilization of contaminants was observed in the tested area. The contaminant concentrations were strongly reduced immediately after the injection and the abatement of the heavy metals continued for a total post-injection monitoring period of 189 days. The iron oxide particles were found to adsorb heavy metals even at pH-values between 4 and 6, where low adsorption would have been expected. The study demonstrated the applicability of iron oxide nanoparticles for installing adsorption barriers for containment of heavy metals in contaminated groundwater under real conditions.Peer reviewe
Thermomechanical advantages of functionally graded dental posts: A finite element analysis
No full textSleep well in Småland, whether you prefer a castle or a hut : Performing persuasion through patterns of you in tourism discourse
No full textPersuasion is especially prominent in genres with a strong aim to affect others’ behaviour. Contemporary persuasion studies often centre on domains such as advertising, politics and media, and the present study targets tourism. Previous work on persuasion in tourism discourse has focused on word choice and collocations, while this study addresses how persuasion is performed through broader rhetorical functions. The English version of the official tourism website for Sweden was compiled into a 53,296-word corpus. Word frequency data showed that you was highly frequent. All examples involving you (N=450) were analysed inductively to identify persuasive rhetorical functions. Seven functions emerged: Specifying tourist identities; Constructing helpful/expert guide; Building rapport; Anticipating reader reactions (focusing on the visitor and/or guide); and Personifying the destination; Presenting options; Imagining scenarios (focusing on the destination). The most frequent function was found to be Constructing helpful/expert guide, whose frequency contrasts sharply to Anticipating reader reactions and Personifying the destination, with the remaining functions falling in between. The frequency analysis details how the seven functions co-occur; it revealed a common pattern of overlapping functions. © 2024 The Author
A discrete element solution method embedded within a Neural Network
No full textThis paper introduces a novel methodology, the Neural Network framework for the Discrete Element Method (NN4DEM), as part of a broader initiative to harness specialised AI hardware and software environments, marking a transition from traditional computational physics programming approaches. NN4DEM enables GPU-parallelised computations by mapping particle data (coordinates and velocities) onto uniform grids as solution fields and computing contact forces by applying mathematical operations that can be found in convolutional neural networks (CNN). Essentially, this framework transforms a DEM problem into a series of layered “images” composed of pixels, using stencil operations to compute the DEM physics, which is inherently local. The method revolves around custom kernels, with operations prescribed by the laws of physics for contact detection and interaction. Therefore, unlike conventional AI methods, it eliminates the need for training data to determine network weights. NN4DEM utilises libraries such as PyTorch for relatively easier programmability and platform interoperability. This paper presents the theoretical foundations, implementation and validation of NN4DEM through hopper test benchmarks. An analysis of the results from random packing cases highlights the ability of NN4DEM to scale to 3D models with millions of particles. The paper concludes with potential research directions, including further integration with other physics-based models and applications across various multidisciplinary fields
Optimised hammer drilling bit design using artificial neural networks trained by FDEM-generated data
No full textInternational audienc
