We present a structure-preserving discretization of the hybrid magnetohydrodynamics (MHD)-driftkinetic system for simulations of low-frequency wave-particle interactions. The model equations are derived from a variational principle, assuring energetically consistent couplings between MHD fluids and driftkinetic particles. The spatial discretization is based on a finite-element-exterior-calculus (FEEC) framework for the MHD and a particle-in-cell (PIC) method for the driftkinetic. A key feature of the scheme is the inclusion of the non-quadratic particle magnetic moment energy term in the Hamiltonian, which is introduced by the guiding-center approximation. The resulting discrete Hamiltonian structure naturally organizes the dynamics into skew-symmetric subsystems, enabling balanced energy exchange. To handle the non-quadratic energy term, we develop energy-preserving time integrators based on discrete gradient methods. The algorithm is implemented in the open-source Python package \texttt{STRUPHY}. Numerical experiments confirm the energy-conserving property of the scheme and demonstrate the capability to simulate energetic particles (EP) induced excitation of toroidal Alfv\'en eigenmodes (TAE) without artificial dissipation or mode filtering. This capability highlights the potential of structure-preserving schemes for high-fidelity simulations of hybrid systems.

翻译：本文提出了一种结构保持的混合磁流体力学(MHD)-漂移动理学离散化方法，用于低频波粒相互作用的数值模拟。该模型方程从变分原理导出，确保了磁流体与漂移动理学粒子之间能量一致的耦合关系。空间离散化基于有限元外微积分(FEEC)框架处理MHD部分，并采用粒子网格(PIC)方法处理漂移动理学部分。该方案的关键特征是在哈密顿量中包含了由导向中心近似引入的非二次粒子磁矩能量项。由此得到的离散哈密顿结构自然地将动力学组织成斜对称子系统，实现了平衡的能量交换。为处理非二次能量项，我们基于离散梯度方法开发了能量保持型时间积分器。该算法已在开源Python软件包\texttt{STRUPHY}中实现。数值实验验证了该方案的能量守恒特性，并展示了在不引入人工耗散或模式滤波的情况下模拟高能粒子(EP)激发环形阿尔芬本征模(TAE)的能力。这一能力凸显了结构保持方案在混合系统高保真模拟中的潜力。