发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用连续数据同化将低阶Koch-Kutz模型同步至高保真数据，并训练Jacobian正则化闭合项，使修正后的模型自主运行并恢复温度谱与统计特性。
AI 中文摘要
旋转爆震发动机（RDE）表现出强非线性、多尺度的波动力学，这些动力学决定了观测到的热场。高保真模拟（DNS/LES）能够解析这些结构，但计算成本过高；而低阶模型（如一维Koch-Kutz模型）能够捕捉周向波运动，却缺乏对高频内容的表达能力。我们使用连续数据同化（nudging）将Koch-Kutz求解器与处理后的高保真温度数据同步，将预测-观测不匹配作为松弛源引入守恒能量方程；在观测时间稀疏处，插值在每次源更新时提供目标值。随着nudging强度的增加，降阶模型逐渐被拉向高保真轨迹，沿该轨迹记录的强迫项提供了模型所需修正的显式、状态依赖估计。然后，我们先验地在该记录源上训练一个Jacobian正则化的闭合项。移除观测项后，修正后的模型自主推进，保持有界，并相对于基线恢复了温度谱和守恒变量的边际统计。
英文摘要
Rotating detonation engines (RDEs) exhibit strongly nonlinear, multiscale wave dynamics that set the observed thermal field. High-fidelity simulations (DNS/LES) resolve these structures but remain computationally prohibitive, while low-order models such as the one-dimensional Koch-Kutz model capture circumferential wave motion yet lack the expressivity for high-frequency content. We use continuous data assimilation (nudging) to synchronize the Koch-Kutz solver with processed high-fidelity temperature data, introducing the prediction-observation mismatch as a relaxation source in the conserved energy equation; where observations are temporally sparse, interpolation supplies a target at every source update. As the nudging strength increases, the reduced model is progressively drawn onto the high-fidelity trajectory, and the forcing recorded along it provides an explicit, state-dependent estimate of the correction the model requires. We then train a Jacobian-regularized closure a priori on this recorded source. With the observation term removed, the corrected model advances autonomously, remains bounded, and recovers the temperature spectrum and the marginal statistics of the conserved variables relative to the baseline.
Comments20 pages, 12 figures
