Efficient Radial Basis Function Mesh Deformation Methods for Aircraft Icing

Abstract

This paper presents an evaluation of efficient radial basis function mesh deformation for complex iced geometries. Given the high computational cost of mesh deformation, state-of-the-art radial basis function techniques are used for data reduction. The principle procedures adopted are multi-level greedy surface point selection and volume point reduction. The multi-level greedy surface point selection reduces the control point list to increase the efficiency of the interpolation operation and the volume point reduction improves the computational cost of the volume mesh update operation which is important for large data sets. The study demonstrates the capabilities of radial basis function mesh deformation in both two and three-dimensions. Furthermore, it compares localised ice deformation to more standardized test cases with global deformation. The convergence history of the multi-level greedy point selection is assessed in terms of number of control points and computational cost for all the test cases. The location of the selected control points near the ice accretion illustrates the effectiveness of the method for localised deformation. The results show that the radial basis function mesh deformation performs well for both the two and three-dimensional test cases. The reduction of the relative surface error for the three-dimensional test cases understandably requires a larger number of control points and thus results in a higher computational cost. Nevertheless, the data-reduction schemes presented in this work represent a significant improvement to standard radial basis function mesh deformation for three-dimensional aircraft icing tests with large data-sets.