The Experts below are selected from a list of 2598 Experts worldwide ranked by ideXlab platform
Brent E Stucker - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Multi-Scale Simulation Architecture for the Prediction of Performance Metrics of Parts Fabricated Using Additive Manufacturing
Metallurgical and Materials Transactions A, 2015Co-Authors: Deepankar Pal, Nachiket Patil, Chong Teng, Kai Zeng, Brent E StuckerAbstract:In this study, an overview of the computational tools developed in the area of metal-based additively manufactured (AM) to simulate the performance metrics along with their experimental validations will be presented. The performance metrics of the AM fabricated parts such as the inter- and intra-layer strengths could be characterized in terms of the melt pool dimensions, solidification times, cooling rates, granular microstructure, and phase morphologies along with defect distributions which are a function of the energy source, scan pattern(s), and the material(s). The four major areas of AM Simulation included in this study are thermo-mechanical constitutive relationships during fabrication and in-service, the use of Euler angles for gaging static and dynamic strengths, the use of algorithms involving intelligent use of matrix algebra and homogenization extracting the spatiotemporal nature of these processes, a fast GPU architecture, and specific challenges targeted toward attaining a faster than real-time Simulation efficiency and accuracy.
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A New and Efficient Multi-Scale Simulation Architecture for Prediction of Performance Metrics for Parts Fabricated Using Additive Manufacturing
TMS 2015 144th Annual Meeting & Exhibition, 2015Co-Authors: Deepankar Pal, Brent E StuckerAbstract:Performance metrics for parts made using Additive Manufacturing (AM), such as inter- and intra-layer strength, are a function of the energy source, scan pattern(s) and material(s). Similarly, residual stress, surface finish and part distortion are a function of the state change of the material(s), scan pattern(s), overall geometry and post-fabrication steps. A brief overview of newly developed computational tools for metal based AM to simulate performance metrics along with their experimental validations are discussed in this paper, including five major areas of AM Simulation. These five areas are: (1) thermo-mechanical constitutive relationships for process predictions during fabrication and part predictions in-service, (2) the use of Euler angles for gauging static and dynamic strengths, (3) the intelligent use of matrix algebra and homogenization to extract the spatiotemporal nature of AM processes, (4) a fast GPU architecture and (5) agorithms targeted towards attaining an accurate faster than real-time Simulation efficiency.
P. A. Deymier - One of the best experts on this subject based on the ideXlab platform.
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A Perspective on Multi-Scale Simulation: Toward Understanding Water-silica
Journal of Computer-Aided Materials Design, 2006Co-Authors: S. B. Trickey, Hai-ping Cheng, Keith Runge, P. A. DeymierAbstract:We discuss the conceptual and practical developments that evolved over the past seven-plus years when our multidisciplinary team took on the challenge of understanding a single complex system – water and silica – through Multi-Scale modeling and Simulation. The discussion provides the context for the ten contributions, from various groupings of the team, that make this coordinated special issue. In the evolution of our project, we have come to appreciate the need for a framework that essentially defines the intellectual basis of computational science. We have found that the usual utilitarian notion of Simulation is lacking a conceptual counterpart: by itself, it does not address the scientific challenge of analyzing complex phenomena, such as chemo-mechanical processes, across various length scales. The problem of water and silica is prototypical with regard to many complex systems of current interest. In them, the effects of chemical activity and dynamical stress are involved simultaneously and essentially. This essential dependence presents opportunities for a Multi-Scale strategy that combines quantum and classical methods of Simulation. As often is the case with “obvious” approaches, one encounters many subtle aspects. We summarize the issues we have encountered, thus laying the ground work for the detailed topical papers that follow.
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Selection of domains for coarse and fine levels of description in mixed-potential Simulations
Journal of Computer-Aided Materials Design, 2006Co-Authors: P. A. Deymier, Kidong Oh, Krishna Muralidharan, G. Frantzikonis, K. RungeAbstract:An example of a mixed-potential molecular dynamics Simulation is presented for amorphous silica. Pair potentials are used in the roles of embedding and embedded regions to allow us to study on-the-fly selection of a region for the more accurate description by embedded potentials. Brittle fracture with fast crack growth is the example that we choose to elucidate the characteristics of an amorphous system that lead to reliable prediction of the location of the initial fracture in the unbiased system. We conclude that a properly chosen wavelet analysis will permit such on-the-fly identification of those regions in a Multi-Scale Simulation whose properties need to be described at the highest available accuracy.
Gabriele Pozzetti - One of the best experts on this subject based on the ideXlab platform.
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The XDEM Multi-physics and Multi-Scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications
Particuology, 2019Co-Authors: Bernhard Peters, Maryam Baniasadi, Xavier Besseron, Alvaro Antonio Estupinan Donoso, Mohammad Mohseni, Gabriele PozzettiAbstract:Abstract The extended discrete element method (XDEM) multi-physics and Multi-Scale Simulation platform is being developed at the Institute of Computational Engineering, the University of Luxembourg. The platform is an advanced multi-physics Simulation technology that combines flexibility and versatility to establish the next generation of multi-physics and Multi-Scale Simulation tools. For this purpose, the Simulation framework relies on coupling various predictive tools based on an Eulerian and Lagrangian approach. The Eulerian approach represents the wide field of continuum models; the Lagrangian approach is perfect for characterising discrete phases. Continuum models thus include classical Simulation tools, such as computational fluid dynamics Simulation and finite element analysis, while an extended configuration of the classical discrete element method addresses the discrete (e.g., particulate) phase. Apart from predicting the trajectories of individual particles, XDEM-suite extends the application of the XDEM to estimating the thermodynamic state of each particle using advanced and optimised algorithms. The thermodynamic state may include temperature and species distributions due to chemical reaction and external heat sources. Hence, coupling these extended features with either computational fluid dynamics Simulation or finite element analysis opens a wide range of applications as diverse as pharmaceuticals, agriculture, food processing, mining, construction and agricultural machinery, metals manufacturing, energy production and systems biology.
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The XDEM Multi-physics and Multi-Scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications
arXiv: Computational Engineering Finance and Science, 2018Co-Authors: Bernhard Peters, Maryam Baniasadi, Xavier Besseron, Alvaro Antonio Estupinan Donoso, Mohammad Mohseni, Gabriele PozzettiAbstract:The XDEM multi-physics and Multi-Scale Simulation platform roots in the Ex- tended Discrete Element Method (XDEM) and is being developed at the In- stitute of Computational Engineering at the University of Luxembourg. The platform is an advanced multi- physics Simulation technology that combines flexibility and versatility to establish the next generation of multi-physics and Multi-Scale Simulation tools. For this purpose the Simulation framework relies on coupling various predictive tools based on both an Eulerian and Lagrangian approach. Eulerian approaches represent the wide field of continuum models while the Lagrange approach is perfectly suited to characterise discrete phases. Thus, continuum models include classical Simulation tools such as Computa- tional Fluid Dynamics (CFD) or Finite Element Analysis (FEA) while an ex- tended configuration of the classical Discrete Element Method (DEM) addresses the discrete e.g. particulate phase. Apart from predicting the trajectories of individual particles, XDEM extends the application to estimating the thermo- dynamic state of each particle by advanced and optimised algorithms. The thermodynamic state may include temperature and species distributions due to chemical reaction and external heat sources. Hence, coupling these extended features with either CFD or FEA opens up a wide range of applications as diverse as pharmaceutical industry e.g. drug production, agriculture food and processing industry, mining, construction and agricultural machinery, metals manufacturing, energy production and systems biology.
Maryam Baniasadi - One of the best experts on this subject based on the ideXlab platform.
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The XDEM Multi-physics and Multi-Scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications
Particuology, 2019Co-Authors: Bernhard Peters, Maryam Baniasadi, Xavier Besseron, Alvaro Antonio Estupinan Donoso, Mohammad Mohseni, Gabriele PozzettiAbstract:Abstract The extended discrete element method (XDEM) multi-physics and Multi-Scale Simulation platform is being developed at the Institute of Computational Engineering, the University of Luxembourg. The platform is an advanced multi-physics Simulation technology that combines flexibility and versatility to establish the next generation of multi-physics and Multi-Scale Simulation tools. For this purpose, the Simulation framework relies on coupling various predictive tools based on an Eulerian and Lagrangian approach. The Eulerian approach represents the wide field of continuum models; the Lagrangian approach is perfect for characterising discrete phases. Continuum models thus include classical Simulation tools, such as computational fluid dynamics Simulation and finite element analysis, while an extended configuration of the classical discrete element method addresses the discrete (e.g., particulate) phase. Apart from predicting the trajectories of individual particles, XDEM-suite extends the application of the XDEM to estimating the thermodynamic state of each particle using advanced and optimised algorithms. The thermodynamic state may include temperature and species distributions due to chemical reaction and external heat sources. Hence, coupling these extended features with either computational fluid dynamics Simulation or finite element analysis opens a wide range of applications as diverse as pharmaceuticals, agriculture, food processing, mining, construction and agricultural machinery, metals manufacturing, energy production and systems biology.
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The XDEM Multi-physics and Multi-Scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications
arXiv: Computational Engineering Finance and Science, 2018Co-Authors: Bernhard Peters, Maryam Baniasadi, Xavier Besseron, Alvaro Antonio Estupinan Donoso, Mohammad Mohseni, Gabriele PozzettiAbstract:The XDEM multi-physics and Multi-Scale Simulation platform roots in the Ex- tended Discrete Element Method (XDEM) and is being developed at the In- stitute of Computational Engineering at the University of Luxembourg. The platform is an advanced multi- physics Simulation technology that combines flexibility and versatility to establish the next generation of multi-physics and Multi-Scale Simulation tools. For this purpose the Simulation framework relies on coupling various predictive tools based on both an Eulerian and Lagrangian approach. Eulerian approaches represent the wide field of continuum models while the Lagrange approach is perfectly suited to characterise discrete phases. Thus, continuum models include classical Simulation tools such as Computa- tional Fluid Dynamics (CFD) or Finite Element Analysis (FEA) while an ex- tended configuration of the classical Discrete Element Method (DEM) addresses the discrete e.g. particulate phase. Apart from predicting the trajectories of individual particles, XDEM extends the application to estimating the thermo- dynamic state of each particle by advanced and optimised algorithms. The thermodynamic state may include temperature and species distributions due to chemical reaction and external heat sources. Hence, coupling these extended features with either CFD or FEA opens up a wide range of applications as diverse as pharmaceutical industry e.g. drug production, agriculture food and processing industry, mining, construction and agricultural machinery, metals manufacturing, energy production and systems biology.
Deepankar Pal - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Multi-Scale Simulation Architecture for the Prediction of Performance Metrics of Parts Fabricated Using Additive Manufacturing
Metallurgical and Materials Transactions A, 2015Co-Authors: Deepankar Pal, Nachiket Patil, Chong Teng, Kai Zeng, Brent E StuckerAbstract:In this study, an overview of the computational tools developed in the area of metal-based additively manufactured (AM) to simulate the performance metrics along with their experimental validations will be presented. The performance metrics of the AM fabricated parts such as the inter- and intra-layer strengths could be characterized in terms of the melt pool dimensions, solidification times, cooling rates, granular microstructure, and phase morphologies along with defect distributions which are a function of the energy source, scan pattern(s), and the material(s). The four major areas of AM Simulation included in this study are thermo-mechanical constitutive relationships during fabrication and in-service, the use of Euler angles for gaging static and dynamic strengths, the use of algorithms involving intelligent use of matrix algebra and homogenization extracting the spatiotemporal nature of these processes, a fast GPU architecture, and specific challenges targeted toward attaining a faster than real-time Simulation efficiency and accuracy.
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A New and Efficient Multi-Scale Simulation Architecture for Prediction of Performance Metrics for Parts Fabricated Using Additive Manufacturing
TMS 2015 144th Annual Meeting & Exhibition, 2015Co-Authors: Deepankar Pal, Brent E StuckerAbstract:Performance metrics for parts made using Additive Manufacturing (AM), such as inter- and intra-layer strength, are a function of the energy source, scan pattern(s) and material(s). Similarly, residual stress, surface finish and part distortion are a function of the state change of the material(s), scan pattern(s), overall geometry and post-fabrication steps. A brief overview of newly developed computational tools for metal based AM to simulate performance metrics along with their experimental validations are discussed in this paper, including five major areas of AM Simulation. These five areas are: (1) thermo-mechanical constitutive relationships for process predictions during fabrication and part predictions in-service, (2) the use of Euler angles for gauging static and dynamic strengths, (3) the intelligent use of matrix algebra and homogenization to extract the spatiotemporal nature of AM processes, (4) a fast GPU architecture and (5) agorithms targeted towards attaining an accurate faster than real-time Simulation efficiency.