基于数据同化的气动压力稀疏重构方法

Aerodynamic pressure field reconstruction from sparse points using data assimilation method

  • 摘要: 风洞实验中获取模型高精度压力分布至关重要,但现有测量方法仍然存在一些缺陷。为获得风洞模型的全域压力分布,本文通过集合变换卡尔曼滤波(ETKF)对风洞实验的稀疏实测数据和数值计算数据进行同化,实现了基于模型物面有限测点的全空间流场高精度重构。分别使用二维翼型RAE 2822和NACA 0012进行实验验证,RAE 2822的压力稀疏重构结果比线性理论修正更加接近实测结果,此效果在激波位置体现得尤其明显,压力系数的预测误差降低了约3%,使用ETKF修正后的迎角及马赫数集合均值计算得到的机翼升力系数和力矩系数与实验值的误差均小于1%;NACA 0012实验面向风洞测量的全场感知应用,探讨了基于少量测点进行压力重构的可行性。实验结果表明:采用机翼物面6个测点重构的压力系数,相对误差可达2.42%,且同化效果与数据点位置密切相关。

     

    Abstract: In wind tunnel experiments, the high-precision pressure distribution of models is highly required, but existing measurement methods still have certain shortcomings. In order to obtain the global pressure distribution of the wind tunnel model, this paper assimilated the sparse measured data and numerical calculation data of the wind tunnel experiment by Ensemble Transform Kalman Filter (ETKF), and realized the high-precision reconstruction of the full-space flow field based on the finite measurement points of the model surface. Two-dimensional airfoil RAE 2822 and NACA 0012 were used for experimental verification. Sparse reconstruction of pressure results of RAE 2822 is more consistent with the measured results than the linear theory correction. This effect is especially evident at the shock wave position, and the prediction error of the pressure coefficient is reduced by about 3%. The lift coefficient and moment coefficient of the wing calculated by using the modified ETKF set mean of attack angle and Mach number are less than 1% error from the experimental values. Experiment of NACA 0012 is oriented to the full-field sensing application of wind tunnel experiments and explore the feasibility of pressure reconstruction based on a small number of measurement points. The experimental results show that the relative error of pressure coefficients reconstructed using six measurement points on the wing object surface can be reduced to 2.42%, and the comparison results show that the assimilation effect is highly dependent on the data point locations.

     

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