Metaheuristic optimal design of adaptive composite beams

The first author also wishes to thank the support of Project IPL/2019/MOCHVar/ISEL.The integration of active materials in composite structures confers them the capability of adapting its behaviour when submitted to loads, overcoming a merely passive response. This adaptive character can be further optimized if the adequate design variable parameters are identified and selected. This work presents a study on the use of a metaheuristic bio-inspired technique to optimize (non-)skewed adaptive composite beams where material properties vary along the length. The results illustrate the performance of the analysis-optimization package implemented as well as the possible range of behaviours, beyond the optimal configurations achieved for the set of case studies.publishersversionpublishe

Ankle Foot Orthosis (AFO) stiffness design for mitigation of ankle inversion injury

Foundation for Science and Technology (FCT), through IDMEC, under LAETA [project number UID/EMS/50022/2019]. UNIDEMI [project number UID/EMS/00667/2019].Modelling and simulation of human movement has the potential to improve the design of medical devices and rehabilitation process by enabling the identification of cause-effect relationships in individuals suffering from neurological and musculoskeletal issues. The main goal of this work was to provide a simulation-based stiffness design for an Ankle Foot Orthosis (AFO) that can help to mitigate the risk of a sprain by ankle inversion during the landing in freefall which is known to occur for subtalar angles higher than 25 degrees. Computational simulations were performed using human movement models with and without a passive AFO, to access the AFO sensitivity for the translational stiffness that prevents the cuff from translating with respect to the footplate. The Design of Experiments (DoE) methodology was used to access sensitivities between the three principal directions of the AFO stiffness. Results revealed that the ankle inversion angle was less than 25 degrees when increasingly larger values of translational stiffness were used, although a nonlinear behaviour was observed between the three principal directions of the AFO stiffness, for which injury safe design configurations were obtained.authorsversionpublishe

Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$^{3}$ isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence at 7T. We then compute localized cortical thickness and volumetric measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, Jupyter notebooks, and the containerized executables are publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upennComment: Preprint submitted to NeuroImage Project website: https://pulkit-khandelwal.github.io/exvivo-brain-upen

Dynamic Response of Soft Core Sandwich Beams with Metal-Graphene Nanocomposite Skins

Sandwich structures are able to provide enhanced strength, stiffness, and lightweight characteristics, thus contributing to an improved overall structural response. To this sandwich configuration one may associate through-thickness graded core material properties and homogeneous or graded properties nanocomposite skins. These tailor-made possibilities may provide alternative design solutions to specific problem requisites. This work aims to address these possibilities, considering to this purpose a package of three beam layerwise models based on different shear deformation theories, implemented through Kriging-based finite elements. The viscoelastic behaviour of the sandwich core is modelled using the complex method and the dynamic problem is solved in the frequency domain. A set of case studies illustrates the performance of the models

Bat-inspired optimization of multilayered adaptive structures

Adaptive structures constituted by composites and smart materials is a remarkable engineering combination that join together the already known composites’ advantages and the possibility of actively control the mechanical response of a structure. These versatile structures are able to react and interact with their surrounding environments, continuously, to accomplish specific objectives. In this work, the main objective is to study, model and predict the mechanical behaviour of adaptive structures by programming the finite element method and optimization algorithms based on micro-bats’ echolocation capacity. An integrated symbolic-numerical-graphical package devoted to the analysis of plate/beam-type structures and its meta-heuristic optimization is implemented, with capabilities of simulating active multilayered structures, constituted by a variable number of different material models. Graded mixtures of piezoelectric particles and non-active materials are also modelled along the structures’ length direction. A set of illustrative case studies are performed, for different types of structures and materials and the results obtained are discussed and conclusions are drawn.info:eu-repo/semantics/acceptedVersio

Assessing the static behavior of hybrid CNT-metal-ceramic composite plates

Functionally graded materials are commonly particulate composites characterized by a varying spatial distribution of the inclusion particles. Because of this, these materials possess a great suitability potential concerning to material properties, which can be very useful to achieve specified structural behaviors. Significant features of these materials are related to their thermal barrier properties especially when ceramic materials are involved, and to the mitigation of abrupt stresses transitions, typically found in laminates. From the manufacturing point of view as well as from the computational perspective, these materials can be thought as effectively having a continuous variation of their constituent phases and consequently their properties, or by resulting from the stacking of a specified number of layers, each having constant properties. This work presents a set of parametric studies aiming to characterize the static response of hybrid functionally graded plates, concerning to their transverse displacement profile and stresses distributions. To this purpose, one considers parameters such as different ceramic materials, plates’ aspect ratio, continuous or discrete variation of phase’s mixture through thickness, the carbon nanotubes (CNT) weight fraction contents and the type of nanotubes. The results obtained are discussed and conclusions are drawn

Metaheuristic Optimization of Functionally Graded 2D and 3D Discrete Structures Using the Red Fox Algorithm

The growing applicability of functionally graded materials is justified by their ability to contribute to the development of advanced solutions characterized by the material customization, through the selection of the best parameters that will confer the best mechanical behaviour for a given structure under specific operating conditions. The present work aims to attain the optimal design solutions for a set of illustrative 2D and 3D discrete structures built from functionally graded materials using the Red Fox Optimization Algorithm, where the design variables are material parameters. From the results achieved one concludes that the optimal selection and distribution of the different materials’ mixture and the different exponents associated with the volume fraction law significantly influence the optimal responses found. To note additionally the good performance of the coupling between this optimization technique and the finite element method used for the linear static and free vibration analyses

A study on the structural behaviour of FGM plates static and free vibrations analyses

Functionally graded materials are characterised by a determined spatial composition variation of their phases’ constituents, which enable for a closer suitability of the material properties to the desired mechanical behaviour. Concerning to the engineered construction of these materials, they can be thought as being achieved by considering a continuous variation of their phases and thus of their properties, or by considering a discrete stacking of a sufficient number of layers, in order to ensure a less abrupt variation profile of their properties. Also, depending on the nature of the applications, it may be important to consider a sandwich configuration, where the three-layered constitution may correspond to a functional requisite. With the present work, these two situations will be studied, considering different methodologies based either on a meshless method or on different approaches based on the finite element method. A comparative study of the performance and adequacy of the developed models is carried out through a set of illustrative cases focused on the study of static and free vibrations behaviour of plate structures.info:eu-repo/semantics/publishedVersio

Developments on finite element methods for medical image supported diagnostics

Variational image-processing models offer high-quality processing capabilities for imaging. They have been widely developed and used in the last two decades, enriching the fields of mathematics as well as information science. Mathematically, several tools are needed: energy optimization, regularization, partial differential equations, level set functions, and numerical algorithms. For this work we consider a second-order variational model for solving medical image problems. The aim is to obtain as far as possible fine features of the initial image and identify medical pathologies. The approach consists of constructing a regularized functional and to locally analyse the obtained solution. Some parameters selection is performed at the discrete level in the framework of the finite element method. We present several numerical simulations to test the efficiency of the proposed approach.info:eu-repo/semantics/publishedVersio