基于DMD方法的旋翼流场分解与重构

Decomposition and reconstruction of rotor flow field using dynamic mode decomposition method

  • 摘要: 模态分解是快速识别流场关键特征并有效提取流场主要信息的重要技术手段。旋翼飞行器的流场具有强非定常、非线性的特点,通常比固定翼飞行器的流场更加复杂,然而,传统的模态分解−本征正交分解(proper orthogonal decomposition, POD)方法分解得到的模态中包含多种流动频率,难以准确地捕捉旋翼流场的动态特征。为了深入认识旋翼流场的流动特征和演化规律,将动力学模态分解(dynamic mode decomposition, DMD)方法引入到旋翼流场的分析中,基于国家数值风洞HeliX软件,开展了Robin机身干扰模型的旋翼流场仿真,完成了DMD方法在旋翼流场中的分解与重构。获得了旋翼流场中各阶流动模态及其频率和增长特性,分析各阶模态所包含的流场信息的同时,建立了旋翼流场的降阶模型,进一步总结了悬停状态下流场重构误差在样本内和样本外的变化情况,以及模态数量对流场重构的影响规律。结果表明,DMD方法能够有效提取旋翼流场的主要特征,且能保留流场主要信息实现重构,可为旋翼涡系演化规律与干扰机制提供方法支撑。

     

    Abstract: Modal decomposition methods are effective techniques for rapidly identifying key features and extracting essential information from flow fields. However, rotor flow fields are highly unsteady and nonlinear, often more complex than those of fixed-wing aircraft. Traditional proper orthogonal decomposition (POD) yields modes with multiple frequency components, making it challenging to accurately capture dynamic characteristics. To deeply analyze the flow features and evolution of rotor flow fields, the dynamic mode decomposition (DMD) method is introduced. Utilizing the National Numerical Wind Tunnel (NNWT) HeliX software, simulations of the Robin fuselage interference model were conducted, and DMD-based decomposition and reconstruction of the rotor flow field were performed. Key flow modes, their frequencies, and growth characteristics were obtained. A reduced-order model was established, and reconstruction errors under hover and forward flight conditions were analyzed. Results demonstrate that the DMD method effectively extracts dominant flow features and retains critical information for reconstruction, providing methodological support for studying vortex evolution and interference mechanisms.

     

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