Reproducing Kernel Particle Method Research Articles

An improved radial basis reproducing kernel particle method for geometrically nonlinear problem analysis of SMAs

In this paper, the radial basis function (RBF) without shaped parameter is utilized in the radial basis reproducing kernel particle method (RRKPM), and an improved radial basis reproducing kernel particle method (IRRKPM) is proposed. Compared with traditional RKPM, the IRRKPM effectively reduces the impact of different kernel functions on calculation precision, and is further employed to examine geometrically nonlinear problems associated with shape memory alloys (SMAs). The displacement boundary condition is enforced via the penalty function method, while the Galerkin integration method in its weak form, along with the total Lagrangian (TL) approach, is utilized to derive the geometrically nonlinear equations for SMAs within the IRRKPM framework. The equilibrium equations are then solved using the Newton Raphson (N-R) iterative method. The impact of the different penalty factor and the radius control parameter of influence domain on errors is analyzed, the computational precision of the IRRKPM is compared with the RRKPM, and the computational stability is evaluated. Finally, the suitability of the IRRKPM for the analysis of geometrically nonlinearity problems in SMAs are confirmed through specific numerical examples.

Research on the response of underwater explosion side structure based on RKPM method

Abstract Based on smoothed particle hydrodynamics and reproducing kernel particle method, this paper establishes a fluid-structure interaction three-dimensional calculation model for the dynamic response of complex structures to underwater explosion shockwave, verifies the effectiveness, accuracy and progressiveness of SPH-RKPM coupling calculation, and establishes a control condition with detonation distance as a variable to determine the dynamic response characteristics of ship side under shockwave loads generated by underwater explosion at different detonation distances.

An efficient numerical method for the distributed‐order time‐fractional diffusion equation with the error analysis and stability properties

In this manuscript, we study and examine the time‐fractional modified anomalous sub‐diffusion model of distributed‐order. Two numerical approaches are used to study the approximate solutions of the presented model. For the first approach, we use a second‐order difference method based on the L1 formula for the temporal variable. In this case, stability and convergence analysis for time discretization using the L1 formula is displayed. For the second approach, we display and demonstrate the numerical method for the full‐discrete based on the Galerkin weak form based on various kernels and shape functions of reproducing kernel particle method which do not have the ‐Kronecker property. Moreover, in this manuscript, in order to be able to use the necessary boundary conditions, the two straight strategies are used: one is the Lagrange multiplier method, and the other one is the penalty method. By using penalty method, we can the main boundary value model is changed to a new BVP with Robin boundary condition. At the end of the article, some numerical examples are presented and analyzed for the efficiency of the proposed numerical method. Also, the presented numerical method is compared with other numerical approaches, and the results of this comparison are reported in the form of a table.

Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method

Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method

Modeling concrete deposition via 3D printing using reproducing kernel particle method

The quality and geometry conformity of 3D concrete printing are the two major concerns facing autonomous construction. To investigate the geometry of printed concrete and optimize the printing strategy, the reproducing kernel particle method (RKPM) was developed and implemented for the first time to describe the flow of fresh concrete and simulate the process of 3D printing. The proposed novel numerical simulation method is associated with a Bingham constitutive model, which was determined by a rotational rheometer. Physical slump tests were performed at various resting times to investigate the time-dependent behavior of concrete. An experimental parametric study of the geometry of a single-layer printed concrete was also conducted at various printing speeds and nozzle heights. Multi-layer printing cases were performed to investigate the cross-sectional deformation over the printed layers. The simulated values of slump over time compared well with the experimental measurements. As such, the proposed RKPM ability to capture time-dependent concrete behavior has been validated. The simulations based on the initially verified RKPM method can yield precise geometry predictions of a single- and multi-layer printed concrete, proving a wide range of application scenarios of the novel RKPM modeling approach.

Research on Superelasticity of Shape Memory Alloys Based on Reproducing Kernel Particle Method

Shape memory alloys (SMAs), known for their unique superelastic effect, exhibit superior mechanical properties compared to traditional materials. In this study, the reproducing kernel particle method (RKPM) is applied to investigate the superelastic effect of the SMAs. A segmented linearization RKPM model is developed to establish the stress–strain relationship for the superelastic effect, and relevant formulas of superelasticity for the SMAs are derived. Finally, the applicability and accuracy of the RKPM in analyzing the superelastic effect of the SMAs are demonstrated through numerical examples.

Research on elasticity behavior of Ni-Ti shape memory alloys using the reproducing kernel particle method

The unique superelastic properties of the Ni-Ti shape memory alloys (SMAs) have been found extensive practical application in many engineering fields, but the Ni-Ti SMAs exhibits elastic behavior in small load, so the elastic behavior of the Ni-Ti SMAs is studied by using meshless method of reproducing kernel particle method (RKPM) in this paper. The displacement boundary conditions are applied by utilizing the penalty function method, and the impact of penalty factor and parameter of influence domain on errors are discussed, and computational stability is analyzed. At last, the correctness of the RKPM in studying the elastic behavior of the Ni-Ti SMAs is evidenced by using three numerical examples.

Data-driven modeling of an unsaturated bentonite buffer model test under high temperatures using an enhanced axisymmetric reproducing kernel particle method

In deep geological repositories for high level nuclear waste with close canister spacings, bentonite buffers can experience temperatures higher than 100 °C. In this range of extreme temperatures, phenomenological constitutive laws face limitations in capturing the thermo-hydraulic behavior of the bentonite, since the pre-defined functional constitutive laws often lack generality and flexibility to capture a wide range of complex coupling phenomena as well as the effects of stress state and path dependency. In this work, a deep neural network (DNN)-based soil–water retention curve (SWRC) of bentonite is introduced and integrated into a Reproducing Kernel Particle Method (RKPM) for conducting thermo-hydraulic simulations of the bentonite buffer. The DNN-SWRC model incorporates temperature as an additional input variable, allowing it to learn the relationship between suction and degree of saturation under the general non-isothermal condition, which is difficult to represent using a phenomenological SWRC. For effective modeling of the tank-scale test, new axisymmetric Reproducing Kernel basis functions enriched with singular Dirichlet enforcement representing heater placement and an effective convective heat transfer coefficient representing thin-layer composite tank construction are developed. The proposed method is demonstrated through the modeling of a tank-scale experiment involving a cylindrical layer of MX-80 bentonite exposed to central heating.

Meshfree digital image correlation using reproducing kernel particle method and its degenerate derivations

Meshfree digital image correlation (MF-DIC) is a recently-proposed advanced DIC technique that deeply integrates meshfree method with DIC. MF-DIC is of potential due to its close relationship with computational mechanics and the excellent balance between spatial resolution and measurement resolution. A new MF-DIC algorithm based on another advanced meshfree method, namely the reproducing kernel particle method (RKPM), is proposed. The RKPM-based MF-DIC is proven to be equivalent to the recently-proposed Element Free Galerkin Method (EFGM) based MF-DIC in certain conditions. This work also briefly introduces two additional MF-DIC methods based on smooth particle hydrodynamics (SPH) and the point interpolation method (PIM). In certain cases, they are the degenerative derivations of the RKPM-based MF-DIC. Benchmark tests on DIC Challenge 2.0 show that these MF-DIC methods also have a superior balance between spatial resolution and measurement resolution compared to state-of-the-art DIC algorithms.

Phase-field modeling of anisotropic crack propagation based on higher-order nonlocal operator theory

This paper presents a novel higher-order nonlocal operator theory for the phase-field modeling of brittle fracture in anisotropic materials. Incorporating higher order nonlocal operators can enhance the accuracy of the phase-field model by effectively capturing long-range interactions that hold significance in numerous materials. The reproducing kernel particle method is employed to derive a nonlocal differential operator to enhance computational stability and accuracy. Moreover, the proposed method eliminates the need for direct computation of derivatives of the modified kernel function, which avoids the calculation of moment matrix derivatives and improves computational efficiency. The phase-field modeling of polycrystalline materials, considering the anisotropic fracture resistance of each grain, is implemented using this numerical framework. The present method is able to capture different scenarios intergranular and transgranular crack propagation patterns in polycrystalline materials. The proposed method involves a detailed representation of the complex process of crack initiation and propagation in 2D and 3D models of polycrystalline materials.

The numerical analysis of viscoelastic problems in signal propagation using a novel meshless Hermit-type RRKPM

Since meshless reproducing kernel particle method (RKPM) is easily affected by different kernel functions in numerical accuracy and stability, the Hermit-type function is introduced to the construction of trial function on boundary nodes. Meanwhile, the radial basis function is applied to establish the trial function in internal nodes, then a meshless Hermit-type radial basis function RKPM (Hermit-type RRKPM) is constructed. The advantage of this proposed method is that the numerical solution is more stable and the accuracy is higher. Furthermore, the meshless Hermit-type RRKPM is used to analyze viscoelastic problems. Finally, to illustrate the accuracy and stability, we compare the results of the proposed method and RRKPM with finite element method (FEM) solutions in solving viscoelastic problems.

A smooth Crack-Band Model for anisotropic materials: Continuum theory and computations with the RKPM meshfree method

A smooth Crack Band Model (sCBM) is developed for anisotropic materials with both high and low magnitude of anisotropy. sCBM is primarily used as a regularization mechanism to enable capturing the correct size and shape of the fracture process zone (FPZ) as well as the smooth distribution of strain inside the zone. The versatility of the proposed sCBM formulation is demonstrated by its adaptability to any material law of choice. A Reproducing Kernel Particle Method (RKPM) is employed to discretize such formulation, the benefit of which is its ability to compute the Hessian of the displacement field (i.e., the sprain tensor), a key quantity in the sCBM theory, using only the nodal displacements as the problem unknowns. This makes the resulting formulation accurate and efficient, which is shown by the validations against size-effect and radial crushing experiments, illustrating the power of the proposed methodology for practical applications.

Static behavior of ribbed folded plate roof under vertical uniform load—Experimental study and meshfree method analysis

SummaryFolded plate roofs are widely used in building roofs due to their light weight and high stiffness. However, traditional folded plate roofs are often limited in application due to insufficient stiffness under the current trend of pursuing larger‐span roofs. Ribbed folded plate roofs can effectively solve this problem because ribs not only increase the stiffness of the roof and reduce stress concentration, but they are also easy to be formed into various shapes to meet different needs for buildings. Therefore, the detailed analysis was carried out on a reinforced concrete ribbed folded plate roof (RCRFR) under the vertical uniform load to investigate its static behavior. The bearing capacity, failure mode, load–displacement relationship, and strain variation were obtained through the test. Additionally, based on the finite element (FE) method, not only a comparison was conducted between RCRFR and reinforced concrete ribless folded plate roof (RCLFR), but the influence of material nonlinearity and geometric nonlinearity on the structure was also investigated. Moreover, the bending characteristics of RCRFR under design load were analyzed based on the reproducing kernel particle method. The experimental results showed that this structure had good mechanical properties within the design load. In the overload stage, the concrete on the underside of the structure was severely damaged. Furthermore, the yielding of the ribbed plate's reinforcing bars caused the increased vertical deformation difference between the ribbed plate and the top chord, and the ribbed plate and the central ridge beam. Eventually, the failure of the anchorage of the ribbed plate's reinforcing bars anchored in the central ridge beam and the top chord led to the loss of structural load‐bearing capacity. The FE analysis results demonstrated that ribs enhance the stiffness of the structure, with material nonlinearity having the primary impact and geometric nonlinearity exerting a secondary effect. The meshfree method analysis was in concordance with the experimental results as well as those of the FE analysis.

A neural network-based enrichment of reproducing kernel approximation for modeling brittle fracture

Numerical modeling of localizations is a challenging task due to the evolving rough solution in which the localization paths are not predefined. Despite decades of efforts, there is a need for innovative discretization-independent computational methods to predict the evolution of localizations. In this work, an improved version of the neural network-enhanced Reproducing Kernel Particle Method (NN-RKPM) is proposed for modeling brittle fracture. In the proposed method, a background reproducing kernel (RK) approximation defined on a coarse and uniform discretization is enriched by a neural network (NN) approximation under a Partition of Unity framework. In the NN approximation, the deep neural network automatically locates and inserts regularized discontinuities in the function space. The NN-based enrichment functions are then patched together with RK approximation functions using RK as a Partition of Unity patching function. The optimum NN parameters defining the location, orientation, and displacement distribution across location together with RK approximation coefficients are obtained via the energy-based loss function minimization. To regularize the NN-RK approximation, a constraint on the spatial gradient of the parametric coordinates is imposed in the loss function. Analysis of the convergence properties shows that the solution convergence of the proposed method is guaranteed. The NN enrichment allows the modeling of evolving cracks by a fixed coarse RK discretization without adaptive refinement for enhanced computational efficiency. The effectiveness of the proposed method is demonstrated by a series of numerical examples involving damage propagation and branching.

Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures

This work presents an approach for automating the discretization and approximation procedures in constructing digital representations of composites from micro-CT images featuring intricate microstructures. The proposed method is guided by the Support Vector Machine (SVM) classification, offering an effective approach for discretizing microstructural images. An SVM soft margin training process is introduced as a classification of heterogeneous material points, and image segmentation is accomplished by identifying support vectors through a local regularized optimization problem. In addition, an Interface-Modified Reproducing Kernel Particle Method (IM-RKPM) is proposed for appropriate approximations of weak discontinuities across material interfaces. The proposed method modifies the smooth kernel functions with a regularized Heaviside function concerning the material interfaces to alleviate Gibb's oscillations. This IM-RKPM is formulated without introducing duplicated degrees of freedom associated with the interface nodes commonly needed in the conventional treatments of weak discontinuities in the meshfree methods. Moreover, IM-RKPM can be implemented with various domain integration techniques, such as Stabilized Conforming Nodal Integration (SCNI). The extension of the proposed method to 3-dimension is straightforward, and the effectiveness of the proposed method is validated through the image-based modeling of polymer-ceramic composite microstructures.

Three dimensional meshfree analysis for time-Caputo and space-Laplacian fractional diffusion equation

Numerical exploration of spatial fractional differential equations in three dimensions is not trivial due to their complexity, especially for complicated problem domains. In this work, the time-Caputo and space-Laplacian fractional diffusion equations in three dimensions are analyzed using a meshfree technique. In the temporal dimension, the classical L1 finite difference scheme is used to approach the Caputo fractional derivative. The spatial discretization is realized by a three dimensional reproducing kernel particle method (RKPM), which can eliminate the dependence of shape functions on certain meshes. Therefore RKPM is very suitable to approximate the field variable in complicated three dimensional domains compared with other mesh-dependent methods. For the purpose of increasing the computational efficiency, the stabilized conforming nodal integration (SCNI) and lumped mass matrix techniques are adopted in the Galerkin meshfree formulation. In the proposed method, the tedious derivatives computing for meshfree shape functions, numerical integration for Galerkin weak form and time-consuming inverse calculating for the large size mass matrix are all realized by more efficient approaches comparing with the conventional Galerkin RKPM. Several numerical examples in various domains with structured and unstructured discretization are studied to demonstrate the proposed methodology, and the results show very favorable performance of the proposed method regarding the accuracy and effectiveness.

A Hybrid Reproducing Kernel Particle Method for Three-Dimensional Elasticity Problems

This study presents a fast meshless method called the hybrid reproducing kernel particle method (HRKPM) for the solution of three-dimensional (3D) elasticity problems. The equilibrium equations of 3D elasticity are divided into three groups of equations, and two equilibrium equations are contained in each group. By coupling the discrete equations for solving two arbitrary groups of equations, the complete solution of 3D elasticity can be obtained. For an arbitrary group of equations, the 3D elasticity problem is transformed into a series of associated two-dimensional (2D) ones, which is solved by the RKPM to derive the discrete formulae. The discrete equations of 2D problems are combined using the difference method in dimension splitting direction. Then, arbitrarily choosing another group of equilibrium equations, the discrete equation of another group of 2D problems can be obtained similarly. By combining the discrete equations for these two groups of 2D problems, the solution to an original 3D problem will be reached. The numerical results show that the HRKPM performs better than RKPM in solution efficiency.

A quasi-conforming embedded reproducing kernel particle method for heterogeneous materials

We present a quasi-conforming embedded reproducing kernel particle method (QCE-RKPM) for modeling heterogeneous materials that makes use of techniques not available to mesh-based methods such as the finite element method (FEM) and avoids many of the drawbacks in current embedded and immersed formulations which are based on meshed methods. The different material domains are discretized independently thus avoiding time-consuming, conformal meshing. In this approach, the superposition of foreground (inclusion) and background (matrix) domain integration smoothing cells are corrected by a quasi-conforming quadtree subdivision on the background integration smoothing cells. Due to the non-conforming nature of the background integration smoothing cells near the material interfaces, a variationally consistent (VC) correction for domain integration is introduced to restore integration constraints and thus optimal convergence rates at a minor computational cost. Additional interface integration smoothing cells with area (volume) correction, while non-conforming, can be easily introduced to further enhance the accuracy and stability of the Galerkin solution using VC integration on non-conforming cells. To properly approximate the weak discontinuity across the material interface by a penalty-free Nitsche’s method with enhanced coercivity, the interface nodes on the surface of the foreground discretization are also shared with the background discretization. As such, there are no tunable parameters, such as those involved in the penalty type method, to enforce interface compatibility in this approach. The advantage of this meshfree formulation is that it avoids many of the instabilities in mesh-based immersed and embedded methods. The effectiveness of QCE-RKPM is illustrated with several examples.

Coarse-graining algorithms for the Eulerian-Lagrangian simulation of particle-laden flows

In the present article, novel Coarse-Graining (CG) algorithms for the Eulerian-Lagrangian (EL) simulation of particle-laden flows are proposed. These include different variants of Reproducing Kernel Particle Methods (RKPM) and an extended Diffusion Two-Step Method (DTSM) for highly polydisperse flows. Owing to the dynamic nature of the kernel function in RKPMs, CG algorithms with high-order consistency properties are constructed and the extra physics of the fluid-particle interaction torque effect on the two-way coupling force distributed to the fluid is taken into consideration. To increase the robustness of RKPMs, they are hybridized with other simple CG algorithms in such a way that each model is activated in the range of its validity. The performance of the new CG algorithms is carefully assessed in comparison to other widely-used algorithms by devising four benchmarks and several grid-independence tests. Based on the analyses, the first-order RKPM demonstrates the best performance in terms of 8 attributes, including conservativity, grid-independency, smoothness, etc., among all algorithms and is recommended when the accuracy is of prime importance. However, due to its higher computational cost compared to the extended DTSM, the latter model would be an affordable alternative for large-scale discrete element method problems when the computational cost is critical.

A comparison of linear consistent correction methods for first-order SPH derivatives

A well-known issue with the widely used Smoothed Particle Hydrodynamics (SPH) method is the neighborhood deficiency. Near the surface, the SPH interpolant fails to accurately capture the underlying fields due to a lack of neighboring particles. These errors may introduce ghost forces or other visual artifacts into the simulation. In this work we investigate three different popular methods to correct the first-order spatial derivative SPH operators up to linear accuracy, namely the Kernel Gradient Correction (KGC), Moving Least Squares (MLS) and Reproducing Kernel Particle Method (RKPM). We provide a thorough, theoretical comparison in which we remark strong resemblance between the aforementioned methods. We support this by an analysis using synthetic test scenarios. Additionally, we apply the correction methods in simulations with boundary handling, viscosity, surface tension, vorticity and elastic solids to showcase the reduction or elimination of common numerical artifacts like ghost forces. Lastly, we show that incorporating the correction algorithms in a state-of-the-art SPH solver only incurs a negligible reduction in computational performance.