This paper presents an adaptive hyperviscosity stabilisation procedure for the Radial Basis Function-generated Finite Difference (RBF-FD) method, aimed at solving linear and non-linear advection-dominated transport equations on domains without a boundary. The approach employs a PDE-independent algorithm that adaptively determines the hyperviscosity constant based on the spectral radius of the RBF-FD evolution matrix. The proposed procedure supports general node layouts and is not tailored for specific equations, avoiding the limitations of empirical tuning and von Neumann-based estimates. To reduce computational cost, it is shown that lower monomial augmentation in the approximation of the hyperviscosity operator can still ensure consistent stabilisation, enabling the use of smaller stencils and improving overall efficiency. A hybrid strategy employing different spline orders for the advection and hyperviscosity operators is also implemented to enhance stability. The method is evaluated on pure linear advection and non-linear Burgers' equation, demonstrating stable performance with limited numerical dissipation. The two main contributions are: (1) a general hyperviscosity RBF-FD solution procedure demonstrated on both linear and non-linear advection-dominated problems, and (2) an in-depth analysis of the behaviour of hyperviscosity within the RBF-FD framework, addressing the interplay between key free parameters and their influence on numerical results.
翻译:本文提出了一种用于径向基函数生成有限差分(RBF-FD)方法的自适应超黏性稳定化流程,旨在求解无边界区域上的线性和非线性对流主导输运方程。该方法采用一种与偏微分方程无关的算法,基于RBF-FD演化矩阵的谱半径自适应地确定超黏性常数。所提出的流程支持通用的节点布局,并非针对特定方程定制,从而避免了经验性调参和基于冯·诺依曼分析的估计方法的局限性。为降低计算成本,研究表明在超黏性算子近似中使用较低阶单项式增广仍能确保一致的稳定化效果,这使得可以采用更小的模板并提高整体效率。此外,还实施了一种针对对流算子和超黏性算子采用不同样条阶数的混合策略以增强稳定性。该方法在纯线性对流和非线性Burgers方程上进行了评估,结果表明其在有限数值耗散下具有稳定的性能。两个主要贡献是:(1)在线性和非线性对流主导问题上均得到验证的通用超黏性RBF-FD求解流程;(2)对RBF-FD框架中超黏性行为的深入分析,探讨了关键自由参数之间的相互作用及其对数值结果的影响。