AI 中文总结
研究针对受控动力系统在未见过参数值和新条件下瞬态预测难的问题，提出物理引导光谱参数降阶建模框架，利用动态模式分解等方法，经多系统验证，能实现外推瞬态预测，扩展了参数降阶建模应用范围。
AI 中文摘要
在未见过的参数值和新的运行条件下进行高效的参数瞬态预测具有挑战性，因为重复的高保真模拟计算成本过高。现有数据驱动代理和参数降阶模型在采样范围内表现良好，但超出范围往往失去可靠性。本研究提出了一种用于受控动力系统的物理引导光谱参数降阶建模框架。利用带控制的动态模式分解从瞬态快照中识别参数依赖的降阶光谱算子，分离内在动力学和外部控制效应。经过物理引导的参数变换后，使用二次动态模式分解在参数条件间传播对齐的光谱量和降阶算子分量，以线性和径向基函数回归为基线。基线正则化和无量纲时间映射提高了鲁棒性。通过机械传动系统、氦 - 氙闭式布雷顿循环和卡门涡街进行验证。结果表明该框架将参数降阶建模从域内近似扩展到未见过条件下受控动力系统的外推瞬态预测。
英文摘要
Efficient parametric transient prediction at unseen parameter values and under new operating conditions remains challenging because repeated high-fidelity simulations are computationally prohibitive. Existing data-driven surrogates and parametric reduced-order models perform well within sampled ranges but often lose reliability beyond them. This study proposes a physics-guided spectral parametric reduced-order modeling framework for controlled dynamical systems. Parameter-dependent reduced spectral operators are identified from transient snapshots using Dynamic Mode Decomposition with control, separating intrinsic dynamics from external control effects. After physics-guided parameter transformation, aligned spectral quantities and reduced operator components are propagated across parameter conditions using Secondary Dynamic Mode Decomposition, with linear and radial basis function regressions as baselines. Baseline regularization and nondimensional time mapping improve robustness. Validation uses a mechanical transmission system, a Helium-Xenon closed Brayton cycle, and a Karman vortex street, covering linear transient, nonlinear transient, and nonlinear periodic dynamics. For the mechanical and Brayton systems, system-level multivariable responses at unseen parameter values and under new operating conditions are predicted with relative norm errors below 1%. For the vortex-street system, nondimensional time mapping preserves dominant vortex-shedding structures across Reynolds numbers. Further analyses compare the framework with an LSTM surrogate and assess extrapolation confidence using an auxiliary error-prediction model. Overall, the framework extends parametric reduced-order modeling from in-domain approximation toward extrapolative transient prediction of controlled dynamical systems under unseen conditions.
Comments29 pages, 20 figures
