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Interpolation Techniques for Data Reconstruction at Surface in Immersed Boundary Method

机译:浸入边界法中曲面数据的插值技术

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This work presents interpolation techniques for surface data resconstruction in immersed boundary methods. Immersed boundary methods allow the use of Cartesian grids and simple curvilinear non-body conforming grids that intersect the immersed body at multiple locations, and as a consequence, the estimation of surface properties becomes a non-trivial problem. This paper explores three techniques of surface data interpolation: Inverse Distance Weight(IDW), Inverse Distance Weight by Interpolation Point method(IDW-IP) and Inverse Distance Weight based on Upwinding(IDW-Upwind). These techniques have been applied to first order as well as higher order simulations of flow past NACA16009 airfoil at a Mach number of 0.75. Both mass conservative and fully interpolation based formulations of the immersed boundary method (Ghosh, S., Choi. J.-I., and Edwards, J. R., "Numerical Simulations of Effects of Micro Vortex Generators Using Immersed- Boundary Methods," AIAA Journal, Vol. 48, No. 1, Jan 2010, pp. 92-103) have been used. C_p plots indicate that surface data has noise, and it is more for the mass conservative formulation of the immersed-boundary method. In order to reduce noise present in the reconstructed surface pressure data, smoothing techniques such as moving average method and regression method have been investigated. The results obtained (C_l and C_d) have been compared with experimental data. Good agreement in C_l values is reported while C_d values have a relatively large error.
机译:这项工作提出了在沉浸边界方法中重建表面数据的插值技术。浸入式边界方法允许使用笛卡尔网格和简单的曲线非体形网格,这些网格在多个位置与浸没体相交,因此,表面特性的估计成为一个不小的问题。本文探讨了表面数据插值的三种技术:反距离权重(IDW),插值点法反距离权重(IDW-IP)和基于上风的反距离权重(IDW-Upwind)。这些技术已应用于流经NACA16009机翼的马赫数为0.75的一阶及更高阶模拟。基于质量保守和完全插值的沉浸边界方法(Ghosh,S.,Choi.J.-I。和Edwards,JR,“使用浸没边界方法的微涡旋发生器效应的数值模拟”,AIAA杂志) (第48卷,第1期,2010年1月,第92-103页)。 C_p图表明表面数据具有噪声,对于沉浸边界方法的质量保守公式而言,噪声更大。为了减少重建的表面压力数据中存在的噪声,已经研究了诸如移动平均法和回归法之类的平滑技术。已将获得的结果(C_1和C_d)与实验数据进行了比较。报告了C_1值的良好一致性,而C_d值具有相对较大的误差。

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