AI 中文总结
本研究针对非互易二元流体湍流，用二维NRCHNS模型的DNS数据，分析拉格朗日示踪剂统计，首次表征其多尺度特性，并评估扩散模型生成合成轨迹模拟该统计的性能，同时指出相关应用挑战。
AI 中文摘要
生成式人工智能的最新进展在包括湍流在内的传统流体流动领域展现出巨大应用潜力。这些方法能否推广到非互易二元流体系统诱导的新型湍流研究中？为回答该问题，我们分析非互易二元流体湍流中的拉格朗日示踪剂粒子统计，该湍流近期已在非互易Cahn-Hilliard-Navier-Stokes（NRCHNS）模型中被研究。我们通过对二维（2D）NRCHNS模型进行大量伪谱直接数值模拟（DNSs）获取真实数据。研究得到粒子加速度和速度分量概率分布函数（PDFs）的诸多有趣结果：速度分量PDF呈双峰分布，与二维流体湍流的对应结果完全不同；我们将该双峰性与欧拉速度分量中的车道型结构相关联。此外，我们首次在非互易流体动力学中，通过拉格朗日速度增量、其结构函数、峭度及多尺度指数比对拉格朗日多尺度进行表征。最后，我们使用生成式扩散模型获取NRCHNS系统的合成拉格朗日轨迹，评估其模拟DNS所得拉格朗日统计的有效性，并强调生成式人工智能在非互易系统应用中的未解决挑战。
英文摘要
Recent advances in generative artificial intelligence have led to significant potential applications in conventional fluid flows, including those that are turbulent. Can these methods be carried over to studies of novel types of turbulence, such as turbulence induced by non-reciprocity in binary-fluid systems? To answer this question, we analyze the statistics of Lagrangian-tracer particles in non-reciprocal binary-fluid turbulence, which has been studied recently in the non-reciprocal Cahn-Hilliard-Navier-Stokes (NRCHNS). We obtain our ground-truth data via extensive pseudospectral direct numerical simulations (DNSs) of the two-dimensionsl (2D) NRCHNS model. Our study yields a variety of intriguing results for probability distribution functions (PDFs) for particle accelerations and velocity-component PDFs; the latter turn out to be bimodal, completely unlike their 2D-fluid-turbulence counterparts. We relate this bimodality to lane-type structures in Eulerian-velocity components. Furthermore, we characterize Lagrangian multiscaling via Lagrangian velocity increments, their structure functions and flatnesses, and multiscaling exponent ratios, for the first time in non-reciprocal hydrodynamics. Finally, we use generative diffusion models to obtain synthetic Lagrangian trajectories for the NRCHNS system, assess how effectively they can emulate the Lagrangian statistics that we obtain from our DNSs, and highlight open challenges in the application of generative artificial intelligence in non-reciprocal systems.
Comments29 pages, 16 figures
