The Experts below are selected from a list of 66816 Experts worldwide ranked by ideXlab platform
Ole Sigmund - One of the best experts on this subject based on the ideXlab platform.
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minimum compliance topology optimization of shell infill composites for additive manufacturing
Computer Methods in Applied Mechanics and Engineering, 2017Co-Authors: Jun Wu, A T Clausen, Ole SigmundAbstract:Abstract Additively manufactured parts are often composed of two sub-structures, a solid shell forming their exterior and a porous infill occupying the interior. To account for this feature this paper presents a novel method for generating simultaneously optimized shell and infill in the context of minimum compliance topology optimization. Our method builds upon two recently developed approaches that extend density-based topology optimization: A coating approach to obtain an optimized shell that is filled uniformly with a prescribed porous base material, and an infill approach which generates optimized, non-uniform infill within a prescribed shell. To evolve the shell and infill concurrently, our formulation assigns two sets of design variables: One set defines the base and the coating, while the other set defines the infill structures. The resulting intermediate density distributions are unified by a material Interpolation Model into a physical density field, upon which the compliance is minimized. Enhanced by an adapted robust formulation for controlling the minimum length scale of the base, our method generates optimized shell–infill composites suitable for additive manufacturing. We demonstrate the effectiveness of the proposed method on numerical examples, and analyse the influence of different design specifications.
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Material Interpolation schemes in topology optimization
Archive of Applied Mechanics, 1999Co-Authors: Martin P Bendsoe, Ole SigmundAbstract:In topology optimization of structures, materials and mechanisms, parametrization of geometry is often performed by a grey-scale density-like Interpolation function. In this paper we analyze and compare the various approaches to this concept in the light of variational bounds on effective properties of composite materials. This allows us to derive simple necessary conditions for the possible realization of grey-scale via composites, leading to a physical interpretation of all feasible designs as well as the optimal design. Thus it is shown that the so-called artificial Interpolation Model in many circumstances actually falls within the framework of microstructurally based Models. Single material and multi-material structural design in elasticity as well as in multi-physics problems is discussed.
Krister Svanberg - One of the best experts on this subject based on the ideXlab platform.
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an alternative Interpolation scheme for minimum compliance topology optimization
Structural and Multidisciplinary Optimization, 2001Co-Authors: Mathias Stolpe, Krister SvanbergAbstract:We consider the discretized zero-one continuum topology optimization problem of finding the optimal distribution of two linearly elastic materials such that compliance is minimized. The geometric complexity of the design is limited using a constraint on the perimeter of the design. A common approach to solve these problems is to relax the zero-one constraints and Model the material properties by a power law which gives noninteger solutions very little stiffness in comparison to the amount of material used. We propose a material Interpolation Model based on a certain rational function, parameterized by a positive scalar q such that the compliance is a convex function when q is zero and a concave function for a finite and a priori known value on q. This increases the probability to obtain a zero-one solution of the relaxed problem.
Peter M Atkinson - One of the best experts on this subject based on the ideXlab platform.
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sub pixel mapping of remote sensing images based on radial basis function Interpolation
Isprs Journal of Photogrammetry and Remote Sensing, 2014Co-Authors: Qunming Wang, Wenzhong Shi, Peter M AtkinsonAbstract:Abstract In this paper, a new sub-pixel mapping (SPM) method based on radial basis function (RBF) Interpolation is proposed for land cover mapping at the sub-pixel scale. The proposed method consists of sub-pixel soft class value estimation and subsequent class allocation for each sub-pixel. The sub-pixel soft class values are calculated by RBF Interpolation. Taking the coarse proportion images as input, an Interpolation Model is built for each visited coarse pixel. First, the spatial relations between any sub-pixel within a visited coarse resolution pixel and its surrounding coarse resolution pixels are quantified by the basis function. Second, the coefficients indicating the contributions from neighboring coarse pixels are calculated. Finally, the basis function values are weighted by the coefficients to predict the sub-pixel soft class values. In the class allocation process, according to the class proportions and estimated soft class values, sub-pixels are allocated one of each available class in turn. Three remote sensing images were tested and the new method was compared to bilinear-, bicubic-, sub-pixel/pixel spatial attraction Model- and Kriging-based SPM methods. Results show that the proposed RBF Interpolation-based SPM is more accurate. Hence the proposed method provides an effective new option for SPM.
Mathias Stolpe - One of the best experts on this subject based on the ideXlab platform.
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an alternative Interpolation scheme for minimum compliance topology optimization
Structural and Multidisciplinary Optimization, 2001Co-Authors: Mathias Stolpe, Krister SvanbergAbstract:We consider the discretized zero-one continuum topology optimization problem of finding the optimal distribution of two linearly elastic materials such that compliance is minimized. The geometric complexity of the design is limited using a constraint on the perimeter of the design. A common approach to solve these problems is to relax the zero-one constraints and Model the material properties by a power law which gives noninteger solutions very little stiffness in comparison to the amount of material used. We propose a material Interpolation Model based on a certain rational function, parameterized by a positive scalar q such that the compliance is a convex function when q is zero and a concave function for a finite and a priori known value on q. This increases the probability to obtain a zero-one solution of the relaxed problem.
Tongbin Chen - One of the best experts on this subject based on the ideXlab platform.
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comparison of common spatial Interpolation methods for analyzing pollutant spatial distributions at contaminated sites
Environmental Geochemistry and Health, 2019Co-Authors: Pengwei Qiao, Yanjun Cheng, Wenxia Wei, Sucai Yang, Mei Lei, Tongbin ChenAbstract:Accurate prediction of the spatial distribution of pollutants in soils based on applicable Interpolation methods is often the basis for soil remediation in contaminated sites. However, the applicable Interpolation method has not been determined for contaminated sites due to the complex spatial distribution characteristics and stronger local spatial variability of pollutants. In this research, the prediction accuracies of three Interpolation methods (including the different values of their parameters) for the spatial distribution of benzo[b]fluoranthene (BbF) in four soil layers were compared. These included inverse distance weighting (IDW), radial basis function (RBF), ordinary kriging (OK). The results indicated: (1) IDW1 is applicable for the first layer, RBF-IMQ is applicable to the second, third, and fourth layers. (2) For IDW, the prediction error is bigger with high weight where high values and low values intersect, while the prediction error is smaller where high (or low) values aggregated distribution. (3) For RBF, if the pollutant concentration trend at the predicted location is consistent with the known points in its neighborhood, the prediction accuracy is higher. (4) IDW is suitable for fitting more drastic curved surfaces, while RBF is more effective for relatively gentle curved surfaces and OK is reasonable for curved surfaces without local outliers. (5) The Interpolation uncertainty is positively associated with the contaminant concentration and local spatial variability. Therefore, we suggest the selection of the applicable Interpolation Model must be based on the principle of the Model and the spatial distribution characteristics of the pollutants.
