发表机构
Union College（联合学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究采用集合卡尔曼反演在线校准用于二维湍流的谱涡粘性闭合模型，验证了该无导数框架构建多参数数据驱动湍流闭合模型的潜力。
AI 中文摘要
大涡模拟（LES）仅解析湍流流动中含能的大尺度结构，同时对未解析的亚格子尺度效应进行建模，因此其精度高度依赖亚格子尺度闭合模型。本研究针对受迫二维湍流，采用集合卡尔曼反演（EKI）对谱粘性闭合模型进行在线校准。该闭合模型包含一个依赖波数的粘性函数，由16维向量参数化。将一组潜在参数向量输入低分辨率LES模拟，将得到的动能谱与直接数值模拟（DNS）参考谱在LES解析的波数范围内进行比较，随后通过集合统计量迭代更新谱粘性参数以减小模型与数据的偏差。通过在谱空间中将LES与滤波后的DNS数据进行比较，利用谱失配的收敛性评估校准后闭合模型的精度。结果表明，基于EKI的在线校准作为一种无导数框架，在构建具有多个参数的数据驱动湍流闭合模型方面具有潜力。
英文摘要
Large Eddy Simulation (LES) resolves only the large, energy-containing structures of turbulent flows while modeling the effects of unresolved subgrid scales. As a result, the ac- curacy of LES strongly depends on the subgrid-scale closure. This work investigates the online calibration of a spectral viscosity closure for forced two-dimensional turbulence using ensemble Kalman Inversion (EKI). The closure consists of a wavenumber-dependent viscosity function, which is parameterized by a 16-dimensional vector. An ensemble of potential parameter vec- tors is passed through coarse resolution LES simulations, and the resulting kinetic energy spec- tra are compared with a DNS reference spectrum up to the LES-resolved wavenumber range. Then, ensemble statistics iteratively update the spectral viscosity parameters in order to reduce the model-data discrepancy. The convergence of the spectral mismatch is used to assess the accuracy of the calibrated closure by comparing LES with the filtered DNS data in spectral space. The results demonstrate the potential of EKI-based online calibration as a derivative- free framework for constructing data-driven turbulence closures with multiple parameters.
Comments12 pages, 3 figures, WCCM-ECCOMAS 2026 Conference Proceedings
