• 제목/요약/키워드: radial point interpolation method

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Point interpolation method based on local residual formulation using radial basis functions

  • Liu, G.R.;Yan, L.;Wang, J.G.;Gu, Y.T.
    • Structural Engineering and Mechanics
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    • v.14 no.6
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    • pp.713-732
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    • 2002
  • A local radial point interpolation method (LRPIM) based on local residual formulation is presented and applied to solid mechanics in this paper. In LRPIM, the trial function is constructed by the radial point interpolation method (PIM) and establishes discrete equations through a local residual formulation, which can be carried out nodes by nodes. Therefore, element connectivity for trial function and background mesh for integration is not necessary. Radial PIM is used for interpolation so that singularity in polynomial PIM may be avoided. Essential boundary conditions can be imposed by a straightforward and effective manner due to its Delta properties. Moreover, the approximation quality of the radial PIM is evaluated by the surface fitting of given functions. Numerical performance for this LRPIM method is further studied through several numerical examples of solid mechanics.

A boundary radial point interpolation method (BRPIM) for 2-D structural analyses

  • Gu, Y.T.;Liu, G.R.
    • Structural Engineering and Mechanics
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    • v.15 no.5
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    • pp.535-550
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    • 2003
  • In this paper, a boundary-type meshfree method, the boundary radial point interpolation method (BRPIM), is presented for solving boundary value problems of two-dimensional solid mechanics. In the BRPIM, the boundary of a problem domain is represented by a set of properly scattered nodes. A technique is proposed to construct shape functions using radial functions as basis functions. The shape functions so formulated are proven to possess both delta function property and partitions of unity property. Boundary conditions can be easily implemented as in the conventional Boundary Element Method (BEM). The Boundary Integral Equation (BIE) for 2-D elastostatics is discretized using the radial basis point interpolation. Some important parameters on the performance of the BRPIM are investigated thoroughly. Validity and efficiency of the present BRPIM are demonstrated through a number of numerical examples.

A Stress Analysis of Structural Element Using Meshfree Method(RPIM) (무요소법(RPIM)을 이용한 구조 요소의 응력해석)

  • Han, Sang-Eul;Lee, Sang-Ju;Joo, Jung-Sik
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2007.04a
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    • pp.495-500
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    • 2007
  • A Meshfree is a method used to establish algebraic equations of system for the whole problem domain without the use of a predefined mesh for the domain discretization. A point interpolation method is based on combining radial and polynomial basis functions. Involvement of radial basis functions overcomes possible singularity. Furthermore, the interpolation function passes through all scattered points in an influence domain and thus shape functions are of delta function property. This makes the implementation of essential boundary conditions much easier than the meshfree methods based on the moving least-squares approximation. This study aims to investigate a stress analysis of structural element between a meshfree method and the finite element method. Examples on cantilever type plate and stress concentration problems show that the accuracy and convergence rate of the meshfree methods are high.

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A radial point interpolation method for 1D contaminant transport modelling through landfill liners

  • Praveen Kumar, R.;Dodagoudar, G.R.
    • Geomechanics and Engineering
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    • v.2 no.2
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    • pp.141-156
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    • 2010
  • In the framework of meshfree methods, a new methodology is developed based on radial point interpolation method (RPIM). This methodology is applied to a one-dimensional contaminant transport modelling in the saturated porous media. The one-dimensional form of advection-dispersion equation involving reactive contaminant is considered in the analysis. The Galerkin weak form of the governing equation is formulated using 1D meshfree shape functions constructed using thin plate spline radial basis functions. MATLAB code is developed to obtain the numerical solution. Numerical examples representing various phenomena, which occur during migration of contaminants, are presented to illustrate the applicability of the proposed method and the results are compared with those obtained from the analytical and finite element solutions. The proposed RPIM has generated results with no oscillations and they are insensitive to Peclet constraints. In order to test the practical applicability and performance of the RPIM, three case studies of contaminant transport through the landfill liners are presented. A good agreement is obtained between the results of the RPIM and the field investigation data.

The Stress Analysis of Structural Element Using Meshfree Method(RPIM) (무요소법(RPIM)을 이용한 구조 요소의 응력해석)

  • Han, Sang-Eul;Yang, Jae-Guen;Joo, Jung-Sik
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.20 no.3
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    • pp.311-319
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    • 2007
  • A Meshfree is a method used to establish algebraic equations of system for the whole problem domain without the use of a predefined mesh for the domain discretization. A point interpolation method is based on combining radial and polynomial basis functions. Involvement of radial basis functions overcomes possible singularity Furthermore, the interpolation function passes through all scattered points in an influence domain and thus shape functions are of delta function property. This makes the implementation of essential boundary conditions much easier than the meshfree methods based on the moving least-squares approximation. This study aims to investigate a stress analysis of structural element between a meshfree method and the finite element method. Examples on cantilever type plate, hollow cylinder and stress concentration problems show that the accuracy and convergence rate of the meshfree methods are high.

A Study on the Adaptive Refinement Method for the Stress Analysis of the Meshfree Method (적응적 세분화 방법을 이용한 무요소법의 응력 해석에 관한 연구)

  • Han, Sang-Eul;Kang, Noh-Won;Joo, Jung-Sik
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2008.04a
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    • pp.8-13
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    • 2008
  • In this study, an adaptive node generation procedure in the radial point interpolation method is proposed. Since we set the initial configuration of nodes by subdivision of background cell, abrupt changes of inter-nodal distance between higher and lower error regions are unavoidable. This unpreferable nodal spacing induces additional errors. To obtain the smoothy nodal configuration, it's regenerated by local Delaunay triangulation algorithm This technique was originally developed to generate a set of well-shaped triangles and tetrahedra. To demonstrate the performance of proposed scheme, the results of making optimal nodal configuration with adaptive refinement method are investigated for stress concentration problems.

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An adaptive meshfree RPIM with improved shape parameter to simulate the mixing of a thermoviscoplastic material

  • Zouhair Saffah;Mohammed Amdi;Abdelaziz Timesli;Badr Abou El Majd;Hassane Lahmam
    • Structural Engineering and Mechanics
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    • v.88 no.3
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    • pp.239-249
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    • 2023
  • The Radial Point Interpolation Method (RPIM) has been proposed to overcome the difficulties associated with the use of the Radial Basis Functions (RBFs). The RPIM has the following properties: Simple implementation in terms of boundary conditions as in the Finite Element Method (FEM). A less expensive CPU time compared to other collocation meshless methods such as the Moving Least Square (MLS) collocation method. In this work, we propose an adaptive high-order numerical algorithm based on RPIM to simulate the thermoviscoplastic behavior of a material mixing observed in the Friction Stir Welding (FSW) process. The proposed adaptive meshfree RPIM algorithm adapts well to the geometric and physical data by choosing a good shape parameter with a good precision. Our numerical approach combines the RPIM and the Asymptotic Numerical Method (ANM). A numerical procedure is also proposed in this work to automatically determine an improved shape parameter for the RBFs. The efficiency of the proposed algorithm is analyzed in comparison with an iterative algorithm.

The Distribution Analysis of PM10 in Seoul Using Spatial Interpolation Methods (공간보간기법에 의한 서울시 미세먼지(PM10)의 분포 분석)

  • Cho, Hong-Lae;Jeong, Jong-Chul
    • Journal of Environmental Impact Assessment
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    • v.18 no.1
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    • pp.31-39
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    • 2009
  • A lot of data which are used in environment analysis of air pollution have characteristics that are distributed continuously in space. In this point, the collected data value such as precipitation, temperature, altitude, pollution density, PM10 have spatial aspect. When geostatistical data analysis are needed, acquisition of the value in every point is the best way, however, it is impossible because of the costs and time. Therefore, it is necessary to estimate the unknown values at unsampled locations based on observations. In this study, spatial interpolation method such as local trend surface model, IDW(inverse distance weighted), RBF(radial basis function), Kriging were applied to PM10 annual average concentration of Seoul in 2005 and the accuracy was evaluated. For evaluation of interpolation accuracy, range of estimated value, RMSE, average error were analyzed with observation data. The Kriging and RBF methods had the higher accuracy than others.

Automatic Generation of the Input Data for Rapid Prototyping from Unorganized Point Cloud Data (임의의 점 군 데이터로부터 쾌속조형을 위한 입력데이터의 자동생성)

  • Yoo, Dong-Jin
    • Journal of the Korean Society for Precision Engineering
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    • v.24 no.11
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    • pp.144-153
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    • 2007
  • In order to generate the input data for rapid prototyping, a new approach which is based on the implicit surface interpolation method is presented. In the method a surface is reconstructed by creating smooth implicit surface from unorganized cloud of points through which the surface should pass. In the method an implicit surface is defined by the adaptive local shape functions including quadratic polynomial function, cubic polynomial function and RBF(Radial Basis Function). By the reconstruction of a surface, various types of error in raw STL file including degenerated triangles, undesirable holes with complex shapes and overlaps between triangles can be eliminated automatically. In order to get the slicing data for rapid prototyping an efficient intersection algorithm between implicit surface and plane is developed. For the direct usage for rapid prototyping, a robust transformation algorithm for the generation of complete STL data of solid type is also suggested.

Automatic NURBS Surface Generation from Unorganized Point Cloud Data (임의의 점 군 데이터로부터 NURBS 곡면의 자동생성)

  • Yoo, Dong-Jin
    • Journal of the Korean Society for Precision Engineering
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    • v.23 no.9 s.186
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    • pp.200-207
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    • 2006
  • In this paper a new approach which combines implicit surface scheme and NURBS surface interpolation method is proposed in order to generate a complete surface model from unorganized point cloud data. In the method a base surface was generated by creating smooth implicit surface from the input point cloud data through which the actual surface would pass. The implicit surface was defined by a combination of shape functions including quadratic polynomial function, cubic polynomial functions and radial basis function using adaptive domain decomposition method. In this paper voxel data which can be extracted easily from the base implicit surface were used in order to generate rectangular net with good quality using the normal projection and smoothing scheme. After generating the interior points and tangential vectors in each rectangular region considering the required accuracy, the NURBS surface were constructed by interpolating the rectangular array of points using boundary tangential vectors which assure C$^1$ continuity between rectangular patches. The validity and effectiveness of this new approach was demonstrated by performing numerical experiments for the various types of point cloud data.