局部动能传感器用于自适应网格和算法细化
Local kinetic sensors for adaptive mesh and algorithm refinement
AI总结：
本文提出新型细化传感器，用于自适应网格和算法细化，适用于离散速度和格子玻尔兹曼方法。通过利用单粒子分布函数信息，提出多种细化标准，有效捕捉复杂流体特征。
摘要：
本文提出新型细化传感器，用于自适应网格和算法细化，适用于离散速度和格子玻尔兹曼方法。虽然基于宏观变量的细化标准可以纯局部方式实现，从而更可扩展，但利用离散速度和格子玻尔兹曼方法中单粒子分布函数的信息，可以引入新的细化传感器，允许在许多相关流体机械领域中解决特征流特征，例如在压缩、湍流和非平衡流或非理想流中出现的特征。经过详细验证后，新型细化传感器被展示用于自适应网格细化到离散速度玻尔兹曼求解器，用于压缩、粘性及非平衡流，展示出有前途的结果。所提传感器建立了一种准确、高效且可扩展的动能模拟方法，为研究复杂流体动力学问题提供了有价值的工具，并为未来更具体的流体问题扩展铺平了道路。
英文摘要：
This paper presents novel refinement sensors for the application to adaptive mesh and algorithm refinement (AMAR) with kinetic models, such as discrete velocity and lattice Boltzmann methods. While refinement criteria for AMAR based on macroscopic variables can be replicated in a purely local, and therefore more scalable, way, the main advantage that can be leveraged when working with discrete velocity and lattice Boltzmann methods is the accessibility of information from the one-particle distribution function. With this accessibility, a novel palette of refinement sensors is introduced, allowing for a set of neatly tailored refinement criteria applicable to resolve characteristic flows features in many relevant domains of fluid mechanics, for instance, those emerging in compressible, turbulent, and non-equilibrium flows or non-ideal fluids. After detailed validation, novel refinement sensors are showcased for the application of adaptive mesh refinement (AMR) to a discrete velocity Boltzmann solver for compressible, viscous, and non-equilibrium flows, demonstrating promising results. The proposed sensors establish an accurate, efficient and scalable approach to kinetic simulations with AMAR, offering a valuable tool for studying complex problems in fluid dynamics and paving the way for future extensions to more specific flow problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
