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Efficient Numerical Multilevel Methods for the Optimization of Gas Turbine Combustion Chambers

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Book cover Flow and Combustion in Advanced Gas Turbine Combustors

Part of the book series: Fluid Mechanics and Its Applications ((FMIA,volume 1581))

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Abstract

In this paper we present an approach for the optimization of turbulent flows. To accomplish such a complex task, the general strategy has to be carefully designed. On the optimization side, we incorporate multilevel optimization algorithms. With this kind of algorithms, different levels describing a problem can be efficiently used for the optimization. Typical examples are discretization levels or models of different physical fidelity. Many optimization algorithms rely on gradient information, which is generally not available for complex problems described by partial differential equations (PDEs). Nevertheless, gradient information can be obtained from computer programs by the use of Automatic Differentiation (AD) techniques. We present a discrete adjoint approach, which was applied to the flow solver FASTEST. The numerical results show the efficiency of the adjoint mode and the optimization algorithms. They include shape optimization and boundary control examples for the Navier-Stokes Equations (NSE), Large Eddy Simulation (LES) and Reynolds Averaged Navier-Stokes (RANS) Equations.

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References

  1. Alexandrov, N., Lewis, R.: First-order frameworks for managing models in engineering optimization. In: 1st International Workshop on Surrogate Modelling and Space Mapping for Engineering Optimisation, vol. 11, pp. 16–19 (2000)

    Google Scholar 

  2. Balay, S., Brown, J., Buschelman, K., Eijkhout, V., Gropp, W., Kaushik, D., Knepley, M., McInnes, L., Smith, B., Zhang, H.: Petsc users manual revision 3.2 (2011)

    Google Scholar 

  3. Borzı, A., Schulz, V.: Multigrid methods for PDE optimization. SIAM Rev. 51(2), 361–395 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  4. Briggs, W., Henson, V., McCormick, S.: A Multigrid Tutorial. Society for Industrial Mathematics, Philadelphia (2000)

    Book  MATH  Google Scholar 

  5. Chang, K.J., Haftka, R.T., Giles, G.L., Kao, P.J.: Sensitivity-based scaling for approximating structural response. J. Aircr. 30, 283–288 (1993)

    Article  Google Scholar 

  6. Chien, K.Y.: Predictions of channel and boundary-layer flows with a low-Reynolds-number turbulence model. AIAA J. 20(1), 33–38 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  7. Christianson, B.: Reverse accumulation and attractive fixed points. Optim. Methods Softw. 3(4), 311–326 (1994)

    Article  MathSciNet  Google Scholar 

  8. Conn, A., Gould, N., Toint, P.: Trust-Region Methods, vol. 1. Society for Industrial Mathematics, Philadelphia (2000)

    Book  MATH  Google Scholar 

  9. FASTEST User Manual.: Fachgebiet Numerische Berechnungsverfahren im Maschinenbau, Technische Universität Darmstadt (2005)

    Google Scholar 

  10. Gelman, E., Mandel, J.: On multilevel iterative methods for optimization problems. Math. Programming 48(1), 1–17 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  11. Germano, M.: Turbulence: the filtering approach. J. Fluid Mech. 238, 325–336 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  12. Gratton, S., Mouffe, M., Sartenaer, A., Toint, P., Tomanos, D.: Numerical experience with a recursive trust-region method for multilevel nonlinear bound-constrained optimization. Optim. Methods Softw. 25(3), 359–386 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  13. Gratton, S., Mouffe, M., Toint, P., Weber-Mendonça, M.: A recursive trust-region method for bound-constrained nonlinear optimization. IMA J. Nume. Anal. 28(4), 827–861 (2008)

    Article  MATH  Google Scholar 

  14. Gratton, S., Sartenaer, A., Toint, P.: Recursive trust-region methods for multiscale nonlinear optimization. SIAM J. Optim. 19(1), 414–444 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  15. Griesse, R., Walther, A.: Evaluating gradients in optimal control: continuous adjoints versus automatic differentiation. J. Optim. Theory Appl. 122(1), 63–86 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  16. Griewank, A.: Achieving logarithmic growth of temporal and spatial complexity in reverse automatic differentiation. Optim. Methods Softw. 1, 35–54 (1992)

    Article  Google Scholar 

  17. Griewank, A., Walther, A.: Evaluating Derivatives, 2nd edn. SIAM, Philadephia (2008)

    MATH  Google Scholar 

  18. Gu, T., Zuo, X., Liu, X., Li, P.: An improved parallel hybrid bi-conjugate gradient method suitable for distributed parallel computing. J. Comput. Appl. Math. 226(1), 55–65 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  19. Heinkenschloss, M., Vicente, L.N.: An interface between optimization and application for the numerical solution of optimal control problems. ACM Trans. Math. Softw. 25, 157–190 (1998)

    Article  MathSciNet  Google Scholar 

  20. Hinze, M., Pinnau, R., Ulbrich, M., Ulbrich, S.: Optimization with PDE Constraints, Mathematical Modelling, vol. 23. Springer, Dordrecht (2009)

    Google Scholar 

  21. Jones, W., Launder, B.: The prediction of laminarization with a two-equation model of turbulence. Int. J. Heat Mass Trans. 15, 301–314 (1972)

    Article  Google Scholar 

  22. Kretz, F.: Auslegung, konstruktion und erprobung einer druckgradienten-messstecke. Technische Universität Darmstadt, Diplomarbeit (2006)

    Google Scholar 

  23. Lewis, R., Nash, S.: A multigrid approach to the optimization of systems governed by differential equations. In: 8-th AIAA/USAF/ISSMO Symposium Multidisciplinary Analysis and Optimization (2000)

    Google Scholar 

  24. MPI: A message-passing interface standard, Version 2.2. Message Passing Interface Forum (2009)

    Google Scholar 

  25. Mouffe, M.: Multilevel optimization in infinity norm and associated stopping criteria. In: Ph.D. thesis, Institut National Polytechnique de Toulouse (2009)

    Google Scholar 

  26. Nash, S.: A multigrid approach to discretized optimization problems. Optim. Methods Softw. 14(1/2), 99–116 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  27. Nash, S., Lewis, R.: Assessing the performance of an optimization-based multilevel method. Optim. Methods Softw. 26(4–5), 693–717 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  28. Quarteroni, A., Valli, A.: Domain Decomposition Methods for Partial Differential Equations. Clarendon Press, New York (1999)

    MATH  Google Scholar 

  29. ur Rehman, M., Vuik, C., Segal, G.: Preconditioners for the steady incompressible Navier-Stokes problem. Int. J. Appl. Math. 38, 223–232 (2008)

    MathSciNet  MATH  Google Scholar 

  30. Saad, Y., Schultz, M.: Gmres: a generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM J. Sci. Stat. Comput. 7(3), 856–869 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  31. Segar, M.K.: A SLAP for the Masses, pp. 135–155. Wiley, New York (1989)

    Google Scholar 

  32. Smagorinsky, J.: General circulation experiments with the primitive equation. Mon. Weather Rev. 91(3), 99–164 (1963)

    Article  Google Scholar 

  33. Tuncer, I. (ed.): Automatic generation of discrete adjoints for unsteady optimal flow control. ECCOMAS CFD & Optimization, Paper No. 2011-043 (2011)

    Google Scholar 

  34. Ulbrich, M., Ulbrich, S.: Automatic differentiation: a structure-exploiting forward mode with almost optimal complexity for kantorovic trees. Appl. Math. Parallel Comput. 327–357 (1996)

    Google Scholar 

  35. Walther, A.: Program reversal schedules for single- and multi-processor machines. In: Ph.D. thesis, Institute of Scientific Computing, Technical University Dresden, Germany (1999)

    Google Scholar 

  36. Yakhot, V., Orszag, S.A., Thangam, S., Gatski, T.B., Speziale, C.G.: Development of turbulence models for shear flows by a double expansion technique. Phys. Fluids 4, 1510–1520 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  37. Ziems, J.C., Ulbrich, S.: Adaptive multilevel inexact SQP methods for pde-constrained optimization. SIAM J. Optim. 21(1), 1–40 (2011)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the support of the Sonderforschungbereich 568 funded by the German Research Foundation (DFG). Moreover, the first author was supported by the Graduate School Computational Engineering and the Center of Smart Interfaces at TU Darmstadt, which are both funded by the German Research Foundation (DFG).

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Ulbrich, S., Roth, R. (2013). Efficient Numerical Multilevel Methods for the Optimization of Gas Turbine Combustion Chambers. In: Janicka, J., Sadiki, A., Schäfer, M., Heeger, C. (eds) Flow and Combustion in Advanced Gas Turbine Combustors. Fluid Mechanics and Its Applications, vol 1581. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5320-4_13

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  • DOI: https://doi.org/10.1007/978-94-007-5320-4_13

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-5319-8

  • Online ISBN: 978-94-007-5320-4

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