  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.
