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引用次数: 0
摘要
对于不可分离的非经典哈密顿系统,我们提出了高效的类 K 交映方法,这些方法是半显式和能量守恒的。通过引入两份相空间并构建增强哈密顿,我们可以将非经典哈密顿系统分离成两个显式可积分部分。随后,我们就可以利用拆分和组合方法构建显式 K 交映方法。为了对相空间的两个副本施加约束,我们提供了两种具有能量守恒性质的变换。这样,我们就能得到能量守恒的半显式 K-symplectic 样方法。我们提供了两种算法来实现能量守恒的半显式 K-symplectic-like 方法,并证明了它们的收敛性。两个非对称哈密顿系统的数值结果表明,我们提出的方法的能量误差长期保持在机器精度范围内,不会出现能量漂移。此外,与同阶的典型化交映方法相比,我们提出的方法具有更高的计算效率。
Semiexplicit K‐symplectic‐like methods with energy conservation for noncanonical Hamiltonian systems
For the nonseparable noncanonical Hamiltonian systems, we propose efficient K‐symplectic‐like methods which are semiexplicit and energy‐preserving. By introducing two copies of the phase space and constructing an augmented Hamiltonian, we can separate the noncanonical Hamiltonian system into two explicitly integrable parts. Subsequently, explicit K‐symplectic methods can be constructed by using the splitting and composing method. To enforce constraints on the two copies of the phase space, we provide two transformations with energy conservation property. This enables us to obtain semiexplicit K‐symplectic‐like methods that preserve energy. Two algorithms are provided to implement the semiexplicit K‐symplectic‐like methods with energy conservation and their convergence has been proved. Numerical results on two noncanonical Hamiltonian systems demonstrate that the energy errors of our proposed methods remain bounded within machine precision over long time without exhibiting energy drift. Furthermore, the proposed methods exhibit superior computational efficiency compared to the canonicalized symplectic methods of the same order.
期刊介绍:
An international journal that aims to cover research into the development and analysis of new methods for the numerical solution of partial differential equations, it is intended that it be readily readable by and directed to a broad spectrum of researchers into numerical methods for partial differential equations throughout science and engineering. The numerical methods and techniques themselves are emphasized rather than the specific applications. The Journal seeks to be interdisciplinary, while retaining the common thread of applied numerical analysis.