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Local-maximum-and-minimum-preserving solution remapping technique to accelerate flow convergence for discontinuous Galerkin methods in shape optimization design. (English) Zbl 1469.65151

This article discusses a remapping technique for high-order Discontinuous Galerkin methods to reduce the computational cost of the flow simulations. The proposed technique is applied to a transonic airfoil drag minimization problem and an airfoil inverse design problem where high-order DG solvers are employed for flow simulations of the intermediate shapes.

MSC:

65M60 Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs
65M12 Stability and convergence of numerical methods for initial value and initial-boundary value problems involving PDEs
49Q10 Optimization of shapes other than minimal surfaces
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References:

[1] Bhabra, M., Nadarajah, S.: Aerodynamic shape optimization for the NURBS-enhanced discontinuous Galerkin method. In: AIAA Aviation 2019 Forum (2019). doi:10.2514/6.2019-3197
[2] Blazek, J.: Computational Fluid Dynamics: Principles and Applications. Elsevier (2005). doi:10.1016/B978-0-08-044506-9.X5000-0 · Zbl 1308.76001
[3] de Boer, A.; van der Schoot, MS; Bijl, H., Mesh deformation based on radial basis function interpolation, Comput. Struct., 85, 11-14, 784-795 (2007) · doi:10.1016/j.compstruc.2007.01.013
[4] Chan, C., Bai, H., He, D.: Blade shape optimization of the Savonius wind turbine using a genetic algorithm. Appl. Energy 213, 148-157 (2018) doi:10.1016/j.apenergy.2018.01.029. http://www.sciencedirect.com/science/article/pii/S0306261918300291
[5] Chen, G., Fidkowski, K.J.: Discretization error control for constrained aerodynamic shape optimization. J. Comput. Phys. 387, 163-185 (2019) doi:10.1016/j.jcp.2019.02.038. http://www.sciencedirect.com/science/article/pii/S002199911930155X · Zbl 1452.76208
[6] Cockburn, B.; Hou, S.; Shu, CW, The Runge-Kutta local projection discontinuous Galerkin finite element method for conservation laws IV: the multidimensional case, Math. Comput., 54, 190, 545-581 (1990) · Zbl 0695.65066 · doi:10.1090/S0025-5718-1990-1010597-0
[7] Cockburn, B.; Lin, SY; Shu, CW, TVB Runge-Kutta local projection discontinuous Galerkin finite element method for conservation laws III: one-dimensional systems, J. Comput. Phys., 84, 1, 90-113 (1989) · Zbl 0677.65093 · doi:10.1016/0021-9991(89)90183-6
[8] Cockburn, B.; Shu, CW, TVB Runge-Kutta local projection discontinuous Galerkin finite element method for conservation laws II: general framework, Math. Comput., 52, 186, 411-435 (1989) · Zbl 0662.65083 · doi:10.1090/S0025-5718-1989-0983311-4
[9] Cockburn, B., Shu, C.W.: The Runge-Kutta discontinuous Galerkin method for conservation laws V: multidimensional systems. J. Comput. Phys. 141(2), 199-224 (1998) doi:10.1006/jcph.1998.5892. http://www.sciencedirect.com/science/article/pii/S0021999198958922 · Zbl 0920.65059
[10] Hartmann, R., Houston, P.: Adaptive discontinuous Galerkin finite element methods for the compressible Euler equations. J. Comput. Phys. 183(2), 508-532 (2002) doi:10.1006/jcph.2002.7206. http://www.sciencedirect.com/science/article/pii/S0021999102972062 · Zbl 1057.76033
[11] Hartmann, R.; Houston, P., Adaptive discontinuous Galerkin finite element methods for nonlinear hyperbolic conservation laws, SIAM J. Sci. Comput., 24, 3, 979-1004 (2003) · Zbl 1034.65081 · doi:10.1137/S1064827501389084
[12] Hicks, RM; Henne, PA, Wing design by numerical optimization, J. Aircr., 15, 7, 407-412 (1978) · doi:10.2514/3.58379
[13] Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975) · Zbl 0317.68006
[14] Jameson, A., Schmidt, W., Turkel, E.: Numerical solutions of the Euler equations by finite volume methods using Runge-Kutta time-stepping schemes. In: AIAA 14th Fluid and Plasma Dynamics Conference, p. 1259 (1981). doi:10.2514/6.1981-1259
[15] Kaland, L., Sonntag, M., Gauger, N.R.: Adaptive aerodynamic design optimization for Navier-Stokes using shape derivatives with discontinuous Galerkin methods. In: D. Greiner, B. Galván, J. Périaux, N. Gauger, K. Giannakoglou, G. Winter (eds.) Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, pp. 143-158. Springer International Publishing, Cham (2015). doi:10.1007/978-3-319-11541-2_9
[16] LeVeque, R.J.: Finite volume methods for hyperbolic problems. Cambridge University Press, Cambridge (2002) · Zbl 1010.65040
[17] Li, D.; Hartmann, R., Adjoint-based airfoil optimization with discretization error control, Int. J. Numeri. Methods Fluids, 77, 1, 1-17 (2015) · doi:10.1002/fld.3971
[18] Lu, J.: An a Posteriori Error Control Framework for Adaptive Precision Optimization Using Discontinuous Galerkin Finite Element Method. Ph.D. thesis, Massachusetts Institute of Technology (2005)
[19] Lyu, Z.; Kenway, GKW; Martins, JRRA, Aerodynamic shape optimization investigations of the common research model wing benchmark, AIAA J., 53, 4, 968-985 (2015) · doi:10.2514/1.J053318
[20] Naumann, D., Evans, B., Walton, S., Hassan, O.: A novel implementation of computational aerodynamic shape optimisation using Modified Cuckoo Search. Appl. Math. Modell. 40(7), 4543-4559 (2016) doi:10.1016/j.apm.2015.11.023. http://www.sciencedirect.com/science/article/pii/S0307904X15007374 · Zbl 1459.74156
[21] Persson, PO; Peraire, J., Newton-GMRES preconditioning for discontinuous Galerkin discretizations of the Navier-Stokes equations, SIAM J. Sci. Comput., 30, 6, 2709-2733 (2008) · Zbl 1362.76052 · doi:10.1137/070692108
[22] Salmoiraghi, F.; Scardigli, A.; Telib, H.; Rozza, G., Free-form deformation, mesh morphing and reduced-order methods: enablers for efficient aerodynamic shape optimisation, Int. J. Comput. Fluid Dyn., 32, 4-5, 233-247 (2018) · Zbl 07474453 · doi:10.1080/10618562.2018.1514115
[23] Slotnick, J., Khodadoust, A., Alonso, J., Darmofal, D., Gropp, W., Lurie, E., Mavriplis, D.: CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences. NASA/CR-2014-218178, NF1676L-18332 (2014)
[24] Spall, JC, An overview of the simultaneous perturbation method for efficient optimization, Johns Hopkins APL Tech. Digest, 19, 4, 482-492 (1998)
[25] Spall, J.C.: Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control. John Wiley & Sons, New Jersey (2005) · Zbl 1088.90002
[26] Toman, U.T., Hassan, A.K.S., Owis, F.M., Mohamed, A.S.: Blade shape optimization of an aircraft propeller using space mapping surrogates. Adv. Mech. Eng. 11(7) (2019). doi:10.1177/1687814019865071
[27] Wang, J., Wang, Z., Liu, T.: Solution remapping technique to accelerate flow convergence for finite volume methods applied to shape optimization design. Numeri. Math. Theory Methods Appl. 13(4), 863-880 (2020) doi:10.4208/nmtma.OA-2019-0164. http://global-sci.org/intro/article_detail/nmtma/16957.html · Zbl 1474.49091
[28] Wang, K.; Yu, S.; Wang, Z.; Feng, R.; Liu, T., Adjoint-based airfoil optimization with adaptive isogeometric discontinuous Galerkin method, Comput. Methods Appl. Mech. Eng., 344, 602-625 (2019) · Zbl 1440.74278 · doi:10.1016/j.cma.2018.10.033
[29] Wang, Z., A perspective on high-order methods in computational fluid dynamics, Sci. China Phys. Mech. Astron., 59, 1, 614701 (2016) · doi:10.1007/s11433-015-5706-3
[30] Wang, ZJ, High-order computational fluid dynamics tools for aircraft design, Philos. Trans. Roy. Soc. A Math. Phys. Eng. Sci., 372, 2022, 20130318 (2014) · doi:10.1098/rsta.2013.0318
[31] Wang, ZJ; Fidkowski, K.; Abgrall, R.; Bassi, F.; Caraeni, D.; Cary, A.; Deconinck, H.; Hartmann, R.; Hillewaert, K.; Huynh, HT; Kroll, N.; May, G.; Persson, PO; van Leer, B.; Visbal, M., High-order CFD methods: current status and perspective, Int. J. Numer. Methods Fluids, 72, 8, 811-845 (2013) · Zbl 1455.76007 · doi:10.1002/fld.3767
[32] Xing, XQ; Damodaran, M., Application of simultaneous perturbation stochastic approximation method for aerodynamic shape design optimization, AIAA J., 43, 2, 284-294 (2005) · doi:10.2514/1.9484
[33] Zahr, M.J., Persson, P.O.: High-order, time-dependent aerodynamic optimization using a discontinuous Galerkin discretization of the Navier-Stokes equations. In: 54th AIAA Aerospace Sciences Meeting (2016). doi:10.2514/6.2016-0064
[34] Zahr, M.J., Persson, P.O.: Energetically optimal flapping wing motions via adjoint-based optimization and high-order discretizations. In: H. Antil, D.P. Kouri, M.D. Lacasse, D. Ridzal (eds.) Frontiers in PDE-Constrained Optimization, pp. 259-289. Springer, New York, NY (2018). doi:10.1007/978-1-4939-8636-1_7 · Zbl 1416.49032
[35] Zhang, X., Shu, C.W.: On maximum-principle-satisfying high order schemes for scalar conservation laws. J. Comput. Phys. 229(9), 3091-3120 (2010) doi:10.1016/j.jcp.2009.12.030. http://www.sciencedirect.com/science/article/pii/S0021999109007165 · Zbl 1187.65096
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