<div style="text-align:justify;"> In order to speed up the global optimization-based mesh smoothing, an enhanced steepest descent method is presented in the paper. Numerical experiment results show tha...<div style="text-align:justify;"> In order to speed up the global optimization-based mesh smoothing, an enhanced steepest descent method is presented in the paper. Numerical experiment results show that the method performs better than the steepest descent method in the global smoothing. We also presented a physically-based interpretation to explain why the method works better than the steepest descent method. </div>展开更多
Mesh smoothing is an essential technique for the improvement of mesh quality in finite element analysis,due to the fact that mesh quality has a large impact on the convergence of the computational scheme and the accur...Mesh smoothing is an essential technique for the improvement of mesh quality in finite element analysis,due to the fact that mesh quality has a large impact on the convergence of the computational scheme and the accuracy of the numerical results.A novel mesh smoothing method based on regular-position-guided operations is presented in this paper.The method introduced here contains two main stages:The first stage computes the regular position of each vertex based on the shape of the element and conducts regular-position-oriented-based element transformations independently;the second stage determines the finial position of each vertex according to its surrounding elements with an assembly strategy.This method is not limited to planar triangular mesh,but applicable to surface polygonal mesh.Numerical experiments on various mesh models demonstrate the effectiveness and potential of this method.展开更多
This paper proposes a vertex-estimation-based, feature-preserving smoothingtechnique for meshes. A robust mesh smoothing operator called mean value coordinates flow isintroduced to modify mean curvature flow and make ...This paper proposes a vertex-estimation-based, feature-preserving smoothingtechnique for meshes. A robust mesh smoothing operator called mean value coordinates flow isintroduced to modify mean curvature flow and make it more stable. Also the paper proposes athree-pass vertex estimation based on bilateral filtering of local neighbors which is transferredfrom image processing settings and a Quasi-Laplacian operation, derived from the standard Laplacianoperator, is performed to increase the smoothness order of the mesh rapidly whilst denoising meshesefficiently, preventing volume shrinkage as well as preserving sharp features of the mesh. Comparedwith previous algorithms, the result shows it is simple, efficient and robust.展开更多
In computational fluid dynamics(CFD),mesh-smoothing methods are widely used to refine the mesh quality for achieving high-precision numerical simulations.Specifically,optimization-based smoothing is used for high-qual...In computational fluid dynamics(CFD),mesh-smoothing methods are widely used to refine the mesh quality for achieving high-precision numerical simulations.Specifically,optimization-based smoothing is used for high-quality mesh smoothing,but it incurs significant computational overhead.Pioneer works have improved its smoothing efficiency by adopting supervised learning to learn smoothing methods from high-quality meshes.However,they pose difficulties in smoothing the mesh nodes with varying degrees and require data augmentation to address the node input sequence problem.Additionally,the required labeled high-quality meshes further limit the applicability of the proposed method.In this paper,we present graph-based smoothing mesh net(GMSNet),a lightweight neural network model for intelligent mesh smoothing.GMSNet adopts graph neural networks(GNNs)to extract features of the node’s neighbors and outputs the optimal node position.During smoothing,we also introduce a fault-tolerance mechanism to prevent GMSNet from generating negative volume elements.With a lightweight model,GMSNet can effectively smooth mesh nodes with varying degrees and remain unaffected by the order of input data.A novel loss function,MetricLoss,is developed to eliminate the need for high-quality meshes,which provides stable and rapid convergence during training.We compare GMSNet with commonly used mesh-smoothing methods on two-dimensional(2D)triangle meshes.Experimental results show that GMSNet achieves outstanding mesh-smoothing performances with 5%of the model parameters compared to the previous model,but offers a speedup of 13.56 times over the optimization-based smoothing.展开更多
文摘<div style="text-align:justify;"> In order to speed up the global optimization-based mesh smoothing, an enhanced steepest descent method is presented in the paper. Numerical experiment results show that the method performs better than the steepest descent method in the global smoothing. We also presented a physically-based interpretation to explain why the method works better than the steepest descent method. </div>
文摘Mesh smoothing is an essential technique for the improvement of mesh quality in finite element analysis,due to the fact that mesh quality has a large impact on the convergence of the computational scheme and the accuracy of the numerical results.A novel mesh smoothing method based on regular-position-guided operations is presented in this paper.The method introduced here contains two main stages:The first stage computes the regular position of each vertex based on the shape of the element and conducts regular-position-oriented-based element transformations independently;the second stage determines the finial position of each vertex according to its surrounding elements with an assembly strategy.This method is not limited to planar triangular mesh,but applicable to surface polygonal mesh.Numerical experiments on various mesh models demonstrate the effectiveness and potential of this method.
文摘This paper proposes a vertex-estimation-based, feature-preserving smoothingtechnique for meshes. A robust mesh smoothing operator called mean value coordinates flow isintroduced to modify mean curvature flow and make it more stable. Also the paper proposes athree-pass vertex estimation based on bilateral filtering of local neighbors which is transferredfrom image processing settings and a Quasi-Laplacian operation, derived from the standard Laplacianoperator, is performed to increase the smoothness order of the mesh rapidly whilst denoising meshesefficiently, preventing volume shrinkage as well as preserving sharp features of the mesh. Comparedwith previous algorithms, the result shows it is simple, efficient and robust.
基金supported by the National Key Research and Development Program of China(No.2021YFB0300101)the Youth Foundation of National University of Defense Technology,China(No.ZK2023-11)the National Natural Science Foundation of China(No.12102467)。
文摘In computational fluid dynamics(CFD),mesh-smoothing methods are widely used to refine the mesh quality for achieving high-precision numerical simulations.Specifically,optimization-based smoothing is used for high-quality mesh smoothing,but it incurs significant computational overhead.Pioneer works have improved its smoothing efficiency by adopting supervised learning to learn smoothing methods from high-quality meshes.However,they pose difficulties in smoothing the mesh nodes with varying degrees and require data augmentation to address the node input sequence problem.Additionally,the required labeled high-quality meshes further limit the applicability of the proposed method.In this paper,we present graph-based smoothing mesh net(GMSNet),a lightweight neural network model for intelligent mesh smoothing.GMSNet adopts graph neural networks(GNNs)to extract features of the node’s neighbors and outputs the optimal node position.During smoothing,we also introduce a fault-tolerance mechanism to prevent GMSNet from generating negative volume elements.With a lightweight model,GMSNet can effectively smooth mesh nodes with varying degrees and remain unaffected by the order of input data.A novel loss function,MetricLoss,is developed to eliminate the need for high-quality meshes,which provides stable and rapid convergence during training.We compare GMSNet with commonly used mesh-smoothing methods on two-dimensional(2D)triangle meshes.Experimental results show that GMSNet achieves outstanding mesh-smoothing performances with 5%of the model parameters compared to the previous model,but offers a speedup of 13.56 times over the optimization-based smoothing.
