854 research outputs found

    DarSIA: An Open-Source Python Toolbox for Two-Scale Image Processing of Dynamics in Porous Media

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    Understanding porous media flow is inherently a multi-scale challenge, where at the core lies the aggregation of pore-level processes to a continuum, or Darcy-scale, description. This challenge is directly mirrored in image processing, where pore-scale grains and interfaces may be clearly visible in the image, yet continuous Darcy-scale parameters may be what are desirable to quantify. Classical image processing is poorly adapted to this setting, as most techniques do not explicitly utilize the fact that the image contains explicit physical processes. Here, we extend classical image processing concepts to what we define as “physical images” of porous materials and processes within them. This is realized through the development of a new open-source image analysis toolbox specifically adapted to time-series of images of porous materials.publishedVersio

    Multi-scale Modelling for Materials Design in Additive Manufacturing

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    Additively manufactured (AMed) austenitic stainless steels (SSs) possess exceptional properties like high strength and toughness. However, it is unclear how they perform under long-term exposure to high-temperature conditions, such as those found in nuclear reactors. These properties arise due to complex microstructures that develop during additive manufacturing (AM), including nanoscale dislocation cellular structures, microscale sub-grains with a high density of low-angle grain boundaries (LAGBs), and high dislocation density. Although the quasi-static mechanical properties of AM austenitic SSs, such as 316L SS, have been systematically investigated, the creep behaviour of such alloys is still a new area of research, with some experimental studies conducted in recent years. Additionally, the mechanical properties of most AMed alloys are anisotropic due to texture formation, and creep behaviour can be significantly influenced by microstructural differences in various building directions. Furthermore, the presence of AM-characterised microstructure is a notable feature of AM materials, and the size and shape of the pores can greatly influence stress concentration during loading. Thus, it is critical to quantify the effects of AM-characterised microstructure on the mechanical properties of materials. As experimental methods have limitations for studying material properties, it is necessary to use computational modelling to extrapolate existing experimental data, especially for highly time-consuming experiments such as creep testing. This study aims to provide a modelling framework for characterizing the evolution of microstructure and high-temperature creep behaviour in AM and wrought austenitic SSs, considering the impact of the initial microstructure. For AM materials, there are two types of samples: horizontally-built samples (loading direction parallel to the building direction) and vertically-built samples (loading direction vertical to the building direction). The choice of AM materials with different built directions is for studying the effect of the relative loading direction to the building direction on material creep behaviour. The materials strengthening mechanisms, including lattice friction, solid solution strengthening, dislocation hardening, and precipitation hardening, are quantified in detail. In addition to data from literature and experiments used to evaluate each strengthening mechanism, the precipitation evolution during the creep process is simulated through the thermokinetics calculation using Thermo-Calc software. Differently fabricated materials are originally simulated under the visco-plasticity self-consistent (VPSC) framework, using the materials' own characteristics as input. The creep mechanical responses of AM and wrought materials are compared, and the dominant deformation mechanisms are revealed and quantitatively compared. Due to the limitations of the VPSC, only the primary stage and secondary stage of creep behaviour are captured. Based on this, the same physics-based model is employed under the crystal plasticity finite element method (CP-FEM) framework, which is full-field, and combined with the Gurson-Tvergaard-Needleman (GTN) damage model to capture the tertiary stage creep deformation. The original crystal plasticity model is highly microstructure-sensitive, and the detailed local structure can be analyzed through the finite element method. Therefore, the original electron backscatter diffraction (EBSD) information is pictured by MATLAB and used for materials input under the CP-FEM framework. In addition, DREAM3D software is used to extract microstructure information from raw EBSD data. The tertiary creep stages of horizontally-built and vertically-built AMed samples are simulated and compared, revealing that damage tends to accumulate on grain boundaries that are perpendicular to the loading direction. Additionally, the effects of AM-induced pores on creep deformation are evaluated by introducing them into the CP-FEM input. As selecting a specific region on the original EBSD data cannot summarize the overall AM materials characteristics, an artificial input is randomly generated through a Voronoi diagram by MATLAB with assigned grain orientation. The artificial input is characterized by AM-induced elongated grain structure to study the effects of high-angle grain boundaries (HAGBs) on materials creep behaviour, especially the damage evolution

    Review of Methods to Solve Desiccation Cracks in Clayey Soils

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    This paper reviews numerical methods used to simulate desiccation cracks in clayey soils. It examines five numerical approaches: Finite Element (FEM), Lattice Boltzmann (LBM), Discrete Element (DEM), Cellular Automaton (CAM), and Phase Field (PFM) Methods. The paper presents a simplified description of the methods, including their basic numerical formulations. Several factors such as the multiphase nature of soils, heterogeneity, nonlinearities, coupling, scales of analysis, and computational aspects are discussed. The review highlights the characteristics, strengths, and limitations of each method. FEM shows a good capacity to deal with the thermo-hydromechanical behavior of clays when drying that complement well with the ability of DEM to deal with particle interactions as well as LBM, PFM, and CAM to deal with complex crack patterns. The article concludes by reviewing the integration of multiple numerical methods to enhance the simulation of desiccation cracks in clayey soils and proposing what is the best option to continue improving the study of this problem

    Advances in Micro- and Nanomechanics

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    This book focuses on recent advances in both theoretical and experimental studies of material behaviour at the micro- and nano-scales. Special attention is given to experimental studies of nanofilms, nanoparticles and nanocomposites as well as tooth defects. Various experimental techniques were used. Magneto- and thermoelastic coupling were considered, as were nonlocal models of thin structures

    2022 Review of Data-Driven Plasma Science

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    Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

    Machine Learning and Its Application to Reacting Flows

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    This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

    Nonlocal Graph-PDEs and Riemannian Gradient Flows for Image Labeling

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    In this thesis, we focus on the image labeling problem which is the task of performing unique pixel-wise label decisions to simplify the image while reducing its redundant information. We build upon a recently introduced geometric approach for data labeling by assignment flows [ APSS17 ] that comprises a smooth dynamical system for data processing on weighted graphs. Hereby we pursue two lines of research that give new application and theoretically-oriented insights on the underlying segmentation task. We demonstrate using the example of Optical Coherence Tomography (OCT), which is the mostly used non-invasive acquisition method of large volumetric scans of human retinal tis- sues, how incorporation of constraints on the geometry of statistical manifold results in a novel purely data driven geometric approach for order-constrained segmentation of volumetric data in any metric space. In particular, making diagnostic analysis for human eye diseases requires decisive information in form of exact measurement of retinal layer thicknesses that has be done for each patient separately resulting in an demanding and time consuming task. To ease the clinical diagnosis we will introduce a fully automated segmentation algorithm that comes up with a high segmentation accuracy and a high level of built-in-parallelism. As opposed to many established retinal layer segmentation methods, we use only local information as input without incorporation of additional global shape priors. Instead, we achieve physiological order of reti- nal cell layers and membranes including a new formulation of ordered pair of distributions in an smoothed energy term. This systematically avoids bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To access the perfor- mance of our approach we compare two different choices of features on a data set of manually annotated 3 D OCT volumes of healthy human retina and evaluate our method against state of the art in automatic retinal layer segmentation as well as to manually annotated ground truth data using different metrics. We generalize the recent work [ SS21 ] on a variational perspective on assignment flows and introduce a novel nonlocal partial difference equation (G-PDE) for labeling metric data on graphs. The G-PDE is derived as nonlocal reparametrization of the assignment flow approach that was introduced in J. Math. Imaging & Vision 58(2), 2017. Due to this parameterization, solving the G-PDE numerically is shown to be equivalent to computing the Riemannian gradient flow with re- spect to a nonconvex potential. We devise an entropy-regularized difference-of-convex-functions (DC) decomposition of this potential and show that the basic geometric Euler scheme for inte- grating the assignment flow is equivalent to solving the G-PDE by an established DC program- ming scheme. Moreover, the viewpoint of geometric integration reveals a basic way to exploit higher-order information of the vector field that drives the assignment flow, in order to devise a novel accelerated DC programming scheme. A detailed convergence analysis of both numerical schemes is provided and illustrated by numerical experiments

    Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

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    Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results. Hence, in this paper, we survey the key studies that are published between 2014 and 2022, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites containing masks of the aforementioned tissues are thoroughly discussed, highlighting the organizers' original contributions and those of other researchers. Also, the metrics used excessively in the literature are mentioned in our review, stressing their relevance to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing, such as the scarcity of many studies on the vessels' segmentation challenge and why their absence needs to be dealt with sooner than later. 2022 The Author(s)This publication was made possible by an Award [GSRA6-2-0521-19034] from Qatar National Research Fund (a member of Qatar Foundation). The contents herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National LibraryScopu

    Concepts in low-cost and flow NMR

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