Publication: A low-complexity global optimization algorithm for temperature and pollution control in flames with complex chemistry
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Publication Date
2006
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Taylor & Francis
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Controlling flame shapes and emissions is a major objective for all combustion engineers. Considering the complexity of reacting flows, new optimization methods are required: this paper explores the application of control theory for partial differential equations to combustion. Both flame temperature and pollutant levels are optimized in a laminar Bunsen burner computed with complex chemistry using a recursive semi-deterministic global optimization algorithm. In order to keep computational time low, the optimization procedure is coupled with mesh adaptation and incomplete gradient techniques.
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[1] Williams, F.A. Combustion Theory. 1985. Addison-Welsey
[2] Pitsch, H. Improved Pollutant Predictions in Large-Eddy Simulation of Turbulent Non-Premixed Combustion by Considering Scalar Dissipation Rate Fluctuations. Proc. Comb. Inst., 29, 1971:1978. 2002
[3] Warnatz, J. Concentration, Pressure and Temperature dependence of the flame velocity in Hydrogen-Oxygen-Nitrogen Mixtures. Combustion Science and Technologie. 26, 203:213. 1981
[4] Pitsch, H. & Barths, H. & Peters, N. Three-imensional Modeling of NOx and Soot Formation in DI-Diesel Engines Using Detailed Chemistry Based on the Interactive Flamelet Approach. SAE 962057. 1996
[5] Peters, N. & Donnerhack, S. Structure and similarity of nitric oxide production in turbulent diffusion flames. 18th International Symposium on Combustion. The Combustion Institute, 33:42. 1981
[6] Lions, J.L. Oeuvres choisies de Jacques-Louis Lions. Vol. II : Contrôle - Homogénéisation. 2003. EDP Science.
[7] Burman, E. & Ern, A. & V. Giovangigli Bunsen flame simulation by finite elements on adaptively refined, unstructured triangulations, Combustion Theory Modelling 8 (1), 65:84. 2004
[8] Mohammadi, B. & Pironneau, O. Applied shape optimization for fluids, 2001. Oxford Univ. Press.
[9] Frey, P.J. & George, P.L.Mesh generation: application to finite elements. 2001. Paris: Hermès Sci. Publications.
[10] Frey, P.J. Yams: A fully automatic adaptive isotropic surface remeshing procedure. INRIA RT-0252. 2001
[11] Debiane, L. Application de l’adaptation de maillages en mecanique des fluides, combustion et traitement de l’image. 2004. Ph.D. University of Montpellier.
[12] Goldberg, D. Genetic algorithms in search, optimization and machine learning. 1989. Addison Wesley.
[13] Ivorra, B. & Mohammadi, B. & Santiago, J.G.& Hertzog, D.E. Semi-deterministic and genetic algorithms for global optimization of microfluidic protein folding devices. International Journal of Numerical Methods in Engineering. To be published. 2005
[14] Ivorra, B. & Mohammadi, B. & Redont, P.& Dumas, L. Semideterministic vs genetic algorithms for global optimization of multichannel Optical Filters. International Journal of Computational Science and Engineering. To be published. 2006
[15] Attouch, H. & Cominetti, R. A dynamical approach to convex minimization coupling approximation with the steepest descent method,J. Differential Equations, 128 (2), 519-540. 1996
[16] Mohammadi, B. & Saiac, J. H. Pratique de la simulation numérique, 2002. Dunod, Paris.
[17] Vanderplaats, G.N. Numerical optimization techniques for engineering design. 1990. Mc Graw-Hill.
[18] Giovangigli, V. Multicomponent flow modeling. 1999. Birkhauser
[19] Hecht, F. Mohammadi, B. Mesh Adaptation by Metric Control for Multi-scale Phenomena and Turbulence, AIAA 97-0859. 1997.