Mesh deformation on 3D complex configurations using multistep radial basis functions interpolation

Abstract The Radial Basis Function (RBF) method with data reduction is an effective way to perform mesh deformation. However, for large deformations on meshes of complex aerodynamic configurations, the efficiency of the RBF mesh deformation method still needs to be further improved to fulfill the demand of practical application. To achieve this goal, a multistep RBF method based on a multilevel subspace RBF algorithm is presented to further improve the efficiency of the mesh deformation method in this research. A whole deformation is divided into a series of steps, and the supporting radius is adjusted in accordance with the maximal displacement error. Furthermore, parallel computing is applied to the interpolation to enhance the efficiency. Typical deformation problems of the NASA Common Research Model (CRM) configuration, the DLR-F6 wing-body-nacelle-pylon configuration, and the DLR-F11 high-lift configuration are tested to verify the feasibility of this method. Test results show that the presented multistep RBF mesh deformation method is efficient and robust in dealing with large deformation problems over complex geometries.

[1]  Christian B. Allen,et al.  Efficient mesh motion using radial basis functions with data reduction algorithms , 2008, J. Comput. Phys..

[2]  Zhufeng Yue,et al.  Review: Layered elastic solid method for the generation of unstructured dynamic mesh , 2010 .

[3]  C. Allen,et al.  Unified fluid–structure interpolation and mesh motion using radial basis functions , 2008 .

[4]  Jun Liu,et al.  RBFs-MSA Hybrid Method for Mesh Deformation , 2012 .

[5]  David M. Schuster,et al.  Computational Aeroelasticity: Success, Progress, Challenge , 2003 .

[6]  Gang Wang,et al.  Improved Point Selection Method for Hybrid-Unstructured Mesh Deformation Using Radial Basis Functions , 2013 .

[7]  Esther Andrés-Pérez,et al.  A novel surface mesh deformation method for handling wing-fuselage intersections , 2017 .

[8]  Juanmian Lei,et al.  Radial basis function mesh deformation based on dynamic control points , 2017 .

[9]  Ning Qin,et al.  Fast dynamic grid deformation based on Delaunay graph mapping , 2006 .

[10]  Christian B. Allen,et al.  Efficient and exact mesh deformation using multiscale RBF interpolation , 2017, J. Comput. Phys..

[11]  Siva Nadarajah,et al.  Efficient Reduced-Radial Basis Function-Based Mesh Deformation Within an Adjoint-Based Aerodynamic Optimization Framework , 2016 .

[12]  Sergio Pissanetzky,et al.  Sparse Matrix Technology , 1984 .

[13]  O. Brodersen,et al.  Drag Prediction of Engine -Airframe Interference Effects Using Unstructured Navier -Stokes Calculations , 2001 .

[14]  H. Bijl,et al.  Mesh deformation based on radial basis function interpolation , 2007 .

[15]  J. Batina UNSTEADY EULER ALGORITHM WITH UNSTRUCTURED DYNAMIC MESH FOR COMPLEX – AIRCRAFT AERODYNAMIC ANALYSIS , 1991 .

[16]  Ning Qin,et al.  Delaunay graph and radial basis function for fast quality mesh deformation , 2015, J. Comput. Phys..

[17]  J. Batina Unsteady Euler airfoil solutions using unstructured dynamic meshes , 1989 .

[18]  Hong Liu,et al.  Efficient mesh motion using radial basis functions with volume grid points reduction algorithm , 2017, J. Comput. Phys..

[19]  Eric Blades,et al.  A fast mesh deformation method using explicit interpolation , 2012, J. Comput. Phys..

[20]  Z. Gao,et al.  A new grid deformation technology with high quality and robustness based on quaternion , 2014 .

[21]  Tayfan E. Tezduyar,et al.  Stabilized Finite Element Formulations for Incompressible Flow Computations , 1991 .

[22]  Christian B Allen,et al.  Parallel efficient mesh motion using radial basis functions with application to multi‐bladed rotors , 2008 .