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Fusing wind-tunnel measurements and CFD data using constrained gappy proper orthogonal decomposition

机译：使用有限的间隙正交正交分解将风洞测量和CFD数据融合

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In this article gappy proper orthogonal decomposition (POD) is used to fuse wind-tunnel measurements and computational fluid dynamics (CFD) data to provide a consistent and more comprehensive output of greater utility. The technique is used to fuse a very limited and 'gappy' set of wind-tunnel surface pressure measurements with computational surface pressure and friction data. By integrating the resulting fused surface data over a complete aerodynamic surface, the corresponding aerodynamic coefficients are obtained. A comparison between these integrated aerodynamic coefficients and the wind-tunnel balance measurements show a better agreement than that obtained between CFD and the wind-tunnel experiment. In addition, the original gappy POD algorithm is modified and extended to provide an accurate and consistent match between the integrated coefficients evaluated from the fused data set and the wind-tunnel balance measurements, thus producing a set of data distributed over the entire aerodynamic surface that conforms to experiment. This is achieved by considering the wind-tunnel balance measurements as equality constraints in the gappy POD least-squares problem formulation. The technique is demonstrated for steady flow around an RAE 2822 airfoil and an industrial wing-body aircraft configuration. Also, an important feature of the methodology that is essential for its use in an industrial environment. namely regularization, is discussed. (C) 2019 Elsevier Masson SAS. All rights reserved.
机译：在这篇文章中，使用适当的正交分解（POD）融合风洞测量和计算流体力学（CFD）数据，以提供一致且更全面的输出，从而具有更大的实用性。该技术用于将非常有限的“橡皮布”风洞表面压力测量结果与计算的表面压力和摩擦数据融合在一起。通过将所得的融合表面数据整合到完整的空气动力学表面上，可以获得相应的空气动力学系数。这些综合的空气动力学系数与风洞平衡测量之间的比较显示出比在CFD和风洞实验之间获得的一致性更好的一致性。此外，对原始的不完整的POD算法进行了修改和扩展，以在从融合数据集评估的积分系数与风洞平衡测量结果之间提供准确一致的匹配，从而生成分布在整个空气动力学表面上的一组数据，从而符合实验。这是通过将风洞平衡测量值视为有问题的POD最小二乘问题公式中的相等约束来实现的。演示了该技术在RAE 2822机翼周围的稳定流动和工业机翼飞机的配置。同样，该方法的重要特征对于在工业环境中使用至关重要。讨论正则化。 （C）2019 Elsevier Masson SAS。版权所有。

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