Application of Parallel Adjoint-Based Error Estimation and Anisotropic Grid Adaptation for Three-Dimensional Aerospace Configurations

This paper demonstrates the extension of error estimation and adaptation methods to parallel computations enabling larger, more realistic aerospace applications and the quantification of discretization errors for complex 3-D solutions. Results were shown for an inviscid sonic-boom prediction about a double-cone configuration and a wing/body segmented leading edge (SLE) configuration where the output function of the adjoint was pressure integrated over a part of the cylinder in the near field. After multiple cycles of error estimation and surface/field adaptation, a significant improvement in the inviscid solution for the sonic boom signature of the double cone was observed. Although the double-cone adaptation was initiated from a very coarse mesh, the near-field pressure signature from the final adapted mesh compared very well with the wind-tunnel data which illustrates that the adjoint-based error estimation and adaptation process requires no a priori refinement of the mesh. Similarly, the near-field pressure signature for the SLE wing/body sonic boom configuration showed a significant improvement from the initial coarse mesh to the final adapted mesh in comparison with the wind tunnel results. Error estimation and field adaptation results were also presented for the viscous transonic drag prediction of the DLR-F6 wing/body configuration, and results were compared to a series of globally refined meshes. Two of these globally refined meshes were used as a starting point for the error estimation and field-adaptation process where the output function for the adjoint was the total drag. The field-adapted results showed an improvement in the prediction of the drag in comparison with the finest globally refined mesh and a reduction in the estimate of the remaining drag error. The adjoint-based adaptation parameter showed a need for increased resolution in the surface of the wing/body as well as a need for wake resolution downstream of the fuselage and wing trailing edge in order to achieve the requested drag tolerance. Although further adaptation was required to meet the requested tolerance, no further cycles were computed in order to avoid large discrepancies between the surface mesh spacing and the refined field spacing.

[1]  Steven M. Klausmeyer,et al.  Data summary from the first AIAA Computational Fluid Dynamics Drag Prediction Workshop , 2003 .

[2]  David Anthony Venditti,et al.  Grid adaptation for functional outputs of compressible flow simulations , 2000 .

[3]  Victor Lyman,et al.  Calculated and Measured Pressure Fields for an Aircraft Designed for Sonic-boom Alleviation , 2004 .

[4]  Shahyar Pirzadeh,et al.  Three-dimensional unstructured viscous grids by the advancing-layers method , 1996 .

[5]  V. Venkatakrishnan Convergence to steady state solutions of the Euler equations on unstructured grids with limiters , 1995 .

[6]  E. Nielsen,et al.  Aerodynamic design sensitivities on an unstructured mesh using the Navier-Stokes equations and a discrete adjoint formulation , 1998 .

[7]  Michael Andrew Park,et al.  Adjoint-Based, Three-Dimensional Error Prediction and Grid Adaptation , 2002 .

[8]  M. Giles,et al.  Adjoint methods for PDEs: a posteriori error analysis and postprocessing by duality , 2002, Acta Numerica.

[9]  M. Fortin,et al.  Anisotropic mesh adaptation: towards user‐independent, mesh‐independent and solver‐independent CFD. Part I: general principles , 2000 .

[10]  William Jones An Open Framework for Unstructured Grid Generation , 2002 .

[11]  Dimitri J. Mavriplis,et al.  Adaptive Meshing Techniques for Viscous Flow Calculations on Mixed Element Unstructured Meshes , 1997 .

[12]  William Jones GridEx - An Integrated Grid Generation Package for CFD , 2003 .

[13]  Timothy J. Baker,et al.  Mesh adaptation strategies for problems in fluid dynamics , 1997 .

[14]  W. K. Anderson,et al.  Grid convergence for adaptive methods , 1991 .

[15]  Edward N. Tinoco,et al.  Summary of Data from the Second AIAA CFD Drag Prediction Workshop (Invited) , 2004 .

[16]  Rolf Rannacher,et al.  Adaptive Galerkin finite element methods for partial differential equations , 2001 .

[17]  M. Giles,et al.  Algorithm Developments for Discrete Adjoint Methods , 2003 .

[18]  Scott A. Morton,et al.  Accurate Drag Prediction Using Cobalt , 2006 .

[19]  Dominique Pelletier,et al.  Adaptivity, Sensitivity, and Uncertainty: Toward Standards of Good Practice in Computational Fluid Dynamics , 2003 .

[20]  Michael A. Park,et al.  Three-Dimensional Turbulent RANS Adjoint-Based Error Correction , 2003 .

[21]  W. K. Anderson,et al.  Implicit/Multigrid Algorithms for Incompressible Turbulent Flows on Unstructured Grids , 1995 .

[22]  Philip L. Roe,et al.  ON MULTIDIMENSIONAL POSITIVELY CONSERVATIVE HIGH-RESOLUTION SCHEMES , 1998 .

[23]  W. K. Anderson,et al.  An implicit upwind algorithm for computing turbulent flows on unstructured grids , 1994 .

[24]  Dimitri J. Mavriplis,et al.  Transonic Drag Prediction on a DLR-F6 Transport Configuration Using Unstructured Grid Solvers , 2004 .

[25]  D. Venditti,et al.  Anisotropic grid adaptation for functional outputs: application to two-dimensional viscous flows , 2003 .

[26]  Michael J. Hemsch,et al.  Statistical Analysis of Computational Fluid Dynamics Solutions from the Drag Prediction Workshop , 2004 .

[27]  W. K. Anderson,et al.  Recent improvements in aerodynamic design optimization on unstructured meshes , 2001 .

[28]  Anthony T. Patera,et al.  Asymptotic a Posteriori Finite Element Bounds for the Outputs of Noncoercive Problems: the Helmholtz , 1999 .

[29]  R. J. Mack,et al.  Determination of Extrapolation Distance with Measured Pressure Signatures from Two Low-Boom Models , 2004 .

[30]  Michael B. Giles,et al.  Adjoint Recovery of Superconvergent Functionals from PDE Approximations , 2000, SIAM Rev..

[31]  O. C. Zienkiewicz,et al.  Adaptive remeshing for compressible flow computations , 1987 .

[32]  Elizabeth M. Lee-Rausch,et al.  Three-dimensional effects on multi-element high lift computations , 2002 .

[33]  Joseph H. Morrison,et al.  Statistical Analysis of CFD Solutions from 2nd Drag Prediction Workshop (Invited) , 2004 .

[34]  D. Darmofal,et al.  An implicit, exact dual adjoint solution method for turbulent flows on unstructured grids , 2004 .

[35]  Frédéric Hecht,et al.  Anisotropic unstructured mesh adaption for flow simulations , 1997 .

[36]  John C. Vassberg,et al.  OVERFLOW Drag Prediction for the DLR-F6 Transport Configuration: A DPW-II Case Study , 2004 .

[37]  Endre Süli,et al.  The Adaptive Computation of Far-Field Patterns by A Posteriori Error Estimation of Linear Functionals , 1998 .

[38]  Michael J. Aftosmis,et al.  Multilevel Error Estimation and Adaptive h-Refinement for Cartesian Meshes with Embedded Boundaries , 2002 .

[39]  Perry A. Newman,et al.  Some Advanced Concepts in Discrete Aerodynamic Sensitivity Analysis , 2001 .

[40]  J. Peraire,et al.  A posteriori finite element bounds for linear-functional outputs of elliptic partial differential equations , 1997 .

[41]  Juan J. Alonso,et al.  Numerical and Mesh Resolution Requirements for Accurate Sonic Boom Prediction of Complete Aircraf , 2004 .

[42]  Zhi Yang,et al.  Prediction of Sonic Boom Signature Using Euler-Full Potential CFD with Grid Adaptation and Shock Fitting , 2002 .

[43]  S. Pirzadeh A Solution-Adaptive Unstructured Grid Method by Grid Subdivision and Local Remeshing , 2000 .

[44]  P. Spalart A One-Equation Turbulence Model for Aerodynamic Flows , 1992 .

[45]  Michael B. Giles,et al.  Solution Adaptive Mesh Refinement Using Adjoint Error Analysis , 2001 .