The Experts below are selected from a list of 63675 Experts worldwide ranked by ideXlab platform
Glaucio H. Paulino - One of the best experts on this subject based on the ideXlab platform.
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Virtual element method (VEM)-based topology optimization: an integrated framework
Structural and Multidisciplinary Optimization, 2019Co-Authors: Anderson Pereira, Ivan F. M. Menezes, Glaucio H. PaulinoAbstract:We present a virtual element method (VEM)-based topology optimization framework using polyhedral elements, which allows for convenient handling of non-Cartesian design domains in three dimensions. We take full advantage of the VEM properties by creating a unified approach in which the VEM is employed in both the structural and the optimization phases. In the structural problem, the VEM is adopted to solve the three-dimensional elasticity equation. Compared to the finite element method, the VEM does not require numerical integration (when linear elements are used) and is less sensitive to degenerated elements (e.g., ones with skinny faces or small edges). In the optimization problem, we introduce a Continuous Approximation of material densities using the VEM basis functions. When compared to the standard element-wise constant Approximation, the Continuous Approximation enriches the geometrical representation of structural topologies. Through two numerical examples with exact solutions, we verify the convergence and accuracy of both the VEM Approximations of the displacement and material density fields. We also present several design examples involving non-Cartesian domains, demonstrating the main features of the proposed VEM-based topology optimization framework. The source code for a MATLAB implementation of the proposed work, named PolyTop3D , is available in the (electronic) Supplementary Material accompanying this publication.
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Virtual element method (VEM)-based topology optimization: an integrated framework
Structural and Multidisciplinary Optimization, 2019Co-Authors: Heng Chi, Anderson Pereira, Ivan F. M. Menezes, Glaucio H. PaulinoAbstract:We present a virtual element method (VEM)-based topology optimization framework using polyhedral elements, which allows for easy handling of non-Cartesian design domains in three dimensions. We take full advantage of the VEM properties by creating a unified approach in which the VEM is employed in both the structural and the optimization phases of the framework. In the structural problem, the VEM is adopted to solve the three-dimensional elasticity equation. Compared to the finite element method (FEM), the VEM does not require numerical integration and is less sensitive to degenerated elements (e.g., ones with skinny faces or small edges). In the optimization problem, we introduce a Continuous Approximation of material densities using VEM basis functions. As compared to the standard element-wise constant one, the Continuous Approximation enriches geometrical representations of structural topologies. Through two numerical examples with exact solutions, we verify the convergence and accuracy of both the VEM Approximations of the displacement and material density fields. We also present several design examples involving non-Cartesian domains, demonstrating the main features of the proposed VEM-based topology optimization framework. The source code for a MATLAB implementation of the proposed work, named PolyTop3D , will be made available in the (electronic) Supplementary Material accompanying this publication.
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Large Scale Topology Optimization Using Preconditioned Krylov Subspace Recycling and Continuous Approximation of Material Distribution
AIP Conference Proceedings, 2008Co-Authors: Eric De Sturler, Shun Wang, Glaucio H. PaulinoAbstract:Large‐scale topology optimization problems demand the solution of a large number of linear systems arising in the finite element analysis. These systems can be solved efficiently by special iterative solvers. Because the linear systems in the sequence of optimization steps change slowly from one step to the next, we can significantly reduce the number of iterations and the runtime of the linear solver by recycling selected search spaces from previous linear systems, and by using preconditioning and scaling techniques. We also provide a new implementation of the 8‐node brick (B8) element for the Continuous Approximation of material distribution (CAMD) approach to improve designs of functionally graded materials. Specifically, we develop a B8/B8 implementation in which the element shape functions are used for the Approximation of both displacements and material density at nodal locations. Finally, we evaluate the effectiveness of several solver and preconditioning strategies, and we investigate large‐scale ...
Karen Smilowitz - One of the best experts on this subject based on the ideXlab platform.
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Advancements in Continuous Approximation models for logistics and transportation systems: 1996–2016
Transportation Research Part B: Methodological, 2018Co-Authors: Sina Ansari, Mehmet Başdere, Yanfeng Ouyang, Karen SmilowitzAbstract:Continuous Approximation (CA) is an efficient and parsimonious technique for modeling complex logistics problems. In this paper, we review recent studies that develop CA models for transportation, distribution and logistics problems with the aim of synthesizing recent advancements and identifying current research gaps. This survey focuses on important principles and key results from CA models. In particular, we consider how these studies fill the gaps identified by the most recent literature reviews in this field. We observe that CA models are used in a wider range of applications, especially in the areas of facility location and integrated supply chain management. Most studies use CA as an alternative and a complement to discrete solution approaches; however, CA can also be used in combination with discrete approaches. We conclude with promising areas of future work.
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advancements in Continuous Approximation models for logistics and transportation systems 1996 2016
Transportation Research Part B-methodological, 2018Co-Authors: Sina Ansari, Mehmet Başdere, Yanfeng Ouyang, Karen SmilowitzAbstract:Continuous Approximation (CA) is an efficient and parsimonious technique for modeling complex logistics problems. In this paper, we review recent studies that develop CA models for transportation, distribution and logistics problems with the aim of synthesizing recent advancements and identifying current research gaps. This survey focuses on important principles and key results from CA models. In particular, we consider how these studies fill the gaps identified by the most recent literature reviews in this field. We observe that CA models are used in a wider range of applications, especially in the areas of facility location and integrated supply chain management. Most studies use CA as an alternative and a complement to discrete solution approaches; however, CA can also be used in combination with discrete approaches. We conclude with promising areas of future work.
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Comments on: Continuous Approximation models in freight distribution management
TOP, 2017Co-Authors: Karen SmilowitzAbstract:The paper presents a review of the use of Continuous Approximation modeling for freight distribution management. The authors take a unique approach to reviewing Continuous Approximation models in the freight distribution literature. They anchor their review around an in-depth analysis of two fundamental results upon which much of the Continuous Approximation literature is built: the asymptotic Approximation formula for the optimal traveling salesman problem (TSP) tour length from Beardwood et al. (1959) and the strip strategy to build implementable TSP tours from Daganzo (1984). Whereas other Continuous Approximation survey papers focus primarily on application areas and the resulting advances in methodology [e.g., Langevin et al. (1996) for the period up to 1996 and Ansari et al. (2017) for the period from 1996 to 2016], this paper structures the review in the context of these two seminal contributions. As such, the authors create a valuable narrative of the evolution of Continuous Approximation models.
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A Continuous Approximation approach for assessment routing in disaster relief
Transportation Research Part B: Methodological, 2013Co-Authors: Michael Huang, Karen Smilowitz, Burcu BalcikAbstract:In this paper, we focus on the assessment routing problem which routes teams to different communities to assess damage and relief needs following a disaster. To address time-sensitivity, the routing problem is modeled with the objective of minimizing the sum of arrival times to beneficiaries. We propose a Continuous Approximation approach which uses aggregated instance data to develop routing policies and cost Approximations. Numerical tests are performed that demonstrate the effectiveness of the cost Approximations at predicting the true implementation costs of the policies and compare the policies against more complex solution approaches. The Continuous Approximation approach yields solutions which can be easily implemented; further, this approach reduces the need for detailed data and the computational requirements to solve the problem.
Yanfeng Ouyang - One of the best experts on this subject based on the ideXlab platform.
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Continuous Approximation for demand balancing in solving large scale one commodity pickup and delivery problems
Transportation Research Part B-methodological, 2018Co-Authors: Chao Lei, Yanfeng OuyangAbstract:Abstract The one-commodity pickup and delivery problem (1-PDP) has a wide range of applications in the real world, e.g., for repositioning bikes in large cities to guarantee the sustainable operations of bike-sharing systems. It remains a challenge, however, to solve the problem for large-scale instances. This paper proposes a hybrid modeling framework for 1-PDP, where a continuum Approximation (CA) approach is used to model internal pickup and delivery routing within each of multiple subregions, while matching of net surplus or deficit of the commodity out of these subregions is addressed in a discrete model with a reduced problem size. The interdependent local routing and system-level matching decisions are made simultaneously, and a Lagrangian relaxation based algorithm is developed to solve the hybrid model. A series of numerical experiments are conducted to show that the hybrid model is able to produce a good solution for large-scale instances in a short computation time.
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Advancements in Continuous Approximation models for logistics and transportation systems: 1996–2016
Transportation Research Part B: Methodological, 2018Co-Authors: Sina Ansari, Mehmet Başdere, Yanfeng Ouyang, Karen SmilowitzAbstract:Continuous Approximation (CA) is an efficient and parsimonious technique for modeling complex logistics problems. In this paper, we review recent studies that develop CA models for transportation, distribution and logistics problems with the aim of synthesizing recent advancements and identifying current research gaps. This survey focuses on important principles and key results from CA models. In particular, we consider how these studies fill the gaps identified by the most recent literature reviews in this field. We observe that CA models are used in a wider range of applications, especially in the areas of facility location and integrated supply chain management. Most studies use CA as an alternative and a complement to discrete solution approaches; however, CA can also be used in combination with discrete approaches. We conclude with promising areas of future work.
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advancements in Continuous Approximation models for logistics and transportation systems 1996 2016
Transportation Research Part B-methodological, 2018Co-Authors: Sina Ansari, Mehmet Başdere, Yanfeng Ouyang, Karen SmilowitzAbstract:Continuous Approximation (CA) is an efficient and parsimonious technique for modeling complex logistics problems. In this paper, we review recent studies that develop CA models for transportation, distribution and logistics problems with the aim of synthesizing recent advancements and identifying current research gaps. This survey focuses on important principles and key results from CA models. In particular, we consider how these studies fill the gaps identified by the most recent literature reviews in this field. We observe that CA models are used in a wider range of applications, especially in the areas of facility location and integrated supply chain management. Most studies use CA as an alternative and a complement to discrete solution approaches; however, CA can also be used in combination with discrete approaches. We conclude with promising areas of future work.
Anderson Pereira - One of the best experts on this subject based on the ideXlab platform.
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Virtual element method (VEM)-based topology optimization: an integrated framework
Structural and Multidisciplinary Optimization, 2019Co-Authors: Heng Chi, Anderson Pereira, Ivan F. M. Menezes, Glaucio H. PaulinoAbstract:We present a virtual element method (VEM)-based topology optimization framework using polyhedral elements, which allows for easy handling of non-Cartesian design domains in three dimensions. We take full advantage of the VEM properties by creating a unified approach in which the VEM is employed in both the structural and the optimization phases of the framework. In the structural problem, the VEM is adopted to solve the three-dimensional elasticity equation. Compared to the finite element method (FEM), the VEM does not require numerical integration and is less sensitive to degenerated elements (e.g., ones with skinny faces or small edges). In the optimization problem, we introduce a Continuous Approximation of material densities using VEM basis functions. As compared to the standard element-wise constant one, the Continuous Approximation enriches geometrical representations of structural topologies. Through two numerical examples with exact solutions, we verify the convergence and accuracy of both the VEM Approximations of the displacement and material density fields. We also present several design examples involving non-Cartesian domains, demonstrating the main features of the proposed VEM-based topology optimization framework. The source code for a MATLAB implementation of the proposed work, named PolyTop3D , will be made available in the (electronic) Supplementary Material accompanying this publication.
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Virtual element method (VEM)-based topology optimization: an integrated framework
Structural and Multidisciplinary Optimization, 2019Co-Authors: Anderson Pereira, Ivan F. M. Menezes, Glaucio H. PaulinoAbstract:We present a virtual element method (VEM)-based topology optimization framework using polyhedral elements, which allows for convenient handling of non-Cartesian design domains in three dimensions. We take full advantage of the VEM properties by creating a unified approach in which the VEM is employed in both the structural and the optimization phases. In the structural problem, the VEM is adopted to solve the three-dimensional elasticity equation. Compared to the finite element method, the VEM does not require numerical integration (when linear elements are used) and is less sensitive to degenerated elements (e.g., ones with skinny faces or small edges). In the optimization problem, we introduce a Continuous Approximation of material densities using the VEM basis functions. When compared to the standard element-wise constant Approximation, the Continuous Approximation enriches the geometrical representation of structural topologies. Through two numerical examples with exact solutions, we verify the convergence and accuracy of both the VEM Approximations of the displacement and material density fields. We also present several design examples involving non-Cartesian domains, demonstrating the main features of the proposed VEM-based topology optimization framework. The source code for a MATLAB implementation of the proposed work, named PolyTop3D , is available in the (electronic) Supplementary Material accompanying this publication.
Ivan F. M. Menezes - One of the best experts on this subject based on the ideXlab platform.
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Virtual element method (VEM)-based topology optimization: an integrated framework
Structural and Multidisciplinary Optimization, 2019Co-Authors: Heng Chi, Anderson Pereira, Ivan F. M. Menezes, Glaucio H. PaulinoAbstract:We present a virtual element method (VEM)-based topology optimization framework using polyhedral elements, which allows for easy handling of non-Cartesian design domains in three dimensions. We take full advantage of the VEM properties by creating a unified approach in which the VEM is employed in both the structural and the optimization phases of the framework. In the structural problem, the VEM is adopted to solve the three-dimensional elasticity equation. Compared to the finite element method (FEM), the VEM does not require numerical integration and is less sensitive to degenerated elements (e.g., ones with skinny faces or small edges). In the optimization problem, we introduce a Continuous Approximation of material densities using VEM basis functions. As compared to the standard element-wise constant one, the Continuous Approximation enriches geometrical representations of structural topologies. Through two numerical examples with exact solutions, we verify the convergence and accuracy of both the VEM Approximations of the displacement and material density fields. We also present several design examples involving non-Cartesian domains, demonstrating the main features of the proposed VEM-based topology optimization framework. The source code for a MATLAB implementation of the proposed work, named PolyTop3D , will be made available in the (electronic) Supplementary Material accompanying this publication.
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Virtual element method (VEM)-based topology optimization: an integrated framework
Structural and Multidisciplinary Optimization, 2019Co-Authors: Anderson Pereira, Ivan F. M. Menezes, Glaucio H. PaulinoAbstract:We present a virtual element method (VEM)-based topology optimization framework using polyhedral elements, which allows for convenient handling of non-Cartesian design domains in three dimensions. We take full advantage of the VEM properties by creating a unified approach in which the VEM is employed in both the structural and the optimization phases. In the structural problem, the VEM is adopted to solve the three-dimensional elasticity equation. Compared to the finite element method, the VEM does not require numerical integration (when linear elements are used) and is less sensitive to degenerated elements (e.g., ones with skinny faces or small edges). In the optimization problem, we introduce a Continuous Approximation of material densities using the VEM basis functions. When compared to the standard element-wise constant Approximation, the Continuous Approximation enriches the geometrical representation of structural topologies. Through two numerical examples with exact solutions, we verify the convergence and accuracy of both the VEM Approximations of the displacement and material density fields. We also present several design examples involving non-Cartesian domains, demonstrating the main features of the proposed VEM-based topology optimization framework. The source code for a MATLAB implementation of the proposed work, named PolyTop3D , is available in the (electronic) Supplementary Material accompanying this publication.