Investigation of an Atmospheric Gas Turbine Model Combustor with Large-Eddy Simulation Using Finite-Rate Chemistry

References

• Berger, F. M., T. Hummel, M. Hertweck, J. Kaufmann, B. Schuermans, and T. Sattelmayer. 2017. High-frequency thermoacoustic modulation mechanisms in swirl-stabilized gas turbine combustors - part I: Experimental investigation of local flame response. J. Eng. Gas Turbines Power 139 (7):071501. doi:10.1115/1.4035591.
   Web of Science ®Google Scholar
• Boudier, G., N. Lamarque, G. Staffelbach, L. Y. M. Gicquela, and T. Poinsot. 2009. Thermo-acoustic stability of a helicopter gas turbine combustor using large eddy simulation. Int. J. Aeroacoustics 8 (1):69–93. doi:10.1260/147547209786234975.
   Web of Science ®Google Scholar
• Buschhagen, T., R. Gejji, J. Philo, L. Tran, J. E. P. Bilbao, and C. D. Slabaugh. 2018. Experimental investigation of self-excited combustion instabilities in a lean, premixed, gas turbine combustor at high pressure. J. Eng. Gas Turbines Power 140 (11):111503. doi:10.1115/1.4039760.
   Web of Science ®Google Scholar
• Charlette, F., C. Meneveau, and D. Veynante. 2002. A power-law flame wrinkling model for LES of premixed turbulent combustion part I: Non-dynamic formulation and initial tests. Combust. Flame 131 (1–2):159–80. doi:10.1016/S0010-2180(02)00400-5.
   Web of Science ®Google Scholar
• Colin, O., F. Ducros, D. Veynante, and T. Poinsot. 2000. A thickened flame model for large eddy simulations of turbulent premixed combustion. Phys. Fluids 12 (7):1843–63. doi:10.1063/1.870436.
   Web of Science ®Google Scholar
• Franzelli, B., E. Riber, L. Y. M. Gicquel, and T. Poinsot. 2011. Large eddy simulation of combustion instabilities in a lean partially premixed swirled flame. Combust. Flame 159 (2):621–37. doi:10.1016/j.combustflame.2011.08.004.
   Web of Science ®Google Scholar
• Goodwin, D. G., R. L. Speth, H. K. Moffat, and B. W. Weber. 2018. Cantera: An object-oriented software toolkit for chemical kinetics, thermodynamics, and transport processes. Zenodo. doi:10.5281/zenodo.1174508.
   Google Scholar
• Gruhlke, P., H. Janbazi, I. Wlokas, C. Beck, and A. M. Kempf. 2020. Investigation of a high karlovitz, high pressure premixed jet flame with heat losses by LES. Combust. Sci. Technol. 192 (11):2138–70. doi:10.1080/00102202.2020.1781101.
   Web of Science ®Google Scholar
• Gruhlke, P., H. Janbazi, P. Wollny, I. Wlokas, C. Beck, B. Janus, and A. M. Kempf. 2021. Large-eddy simulation of a lifted high-pressure jet-flame with direct chemistry. Combust. Sci. Technol. 194 (14):2978–3002. doi:10.1080/00102202.2021.1903886.
   Web of Science ®Google Scholar
• Janbazi, H., K. Roderigo, I. Wlokas, and A. M. Kempf. 2019. Development of skeletal kinetic mechanisms at various conditions for methane combustion. Paper presented at 17th International Conference on Numerical Combustion, Aachen, Germany, May 06.
   Google Scholar
• Kempf, A. M., S. Wysocki, and M. Pettit. 2012. An efficient, parallel low-storage implementation of Klein’s turbulence generator for LES and DNS. Comput. Fluids 60:58–60. doi:10.1016/j.compfluid.2012.02.027.
   Web of Science ®Google Scholar
• Klein, M., A. Sadiki, and J. Janicka. 2003. A digital filter based generation of inflow data for spatially developing direct numerical or large eddy simulations. J. Comput. Phys. 186 (2):652–65. doi:10.1016/S0021-9991(03)00090-1.
   Web of Science ®Google Scholar
• Lammel, O., M. Stöhr, P. Kutne, C. Dem, W. Meier, and M. Aigner. 2012. Experimental analysis of confined jet flames by laser measurement techniques. J. Eng. Gas Turbines Power 134 (4):041506. doi:10.1115/1.4004733.
   Web of Science ®Google Scholar
• Legier, J. P., T. Poinsot, and D. Veynante. 2000. Dynamically thickened flame LES model for premixed and non-premixed turbulent combustion. Proc. of the Summer Program. Palo Alto, Pages 157–68.
   Google Scholar
• Lu, T., and C. K. Law. 2008. A criterion based on computational singular perturbation for the identification of quasi steady state species: A reduced mechanism for methane oxidation with NO chemistry. Combust. Flame 154 (4):761–74. doi:10.1016/j.combustflame.2008.04.025.
   Web of Science ®Google Scholar
• Nanjaiah, M., A. Pilipodi-Best, M. R. Lalanne, P. Fjodorow, C. Schulz, S. Cheskis, A. M. Kempf, I. Wlokas, and I. Rahinov. 2021. Experimental and numerical investigation of iron-doped flames: FeO formation and impact on flame temperature. Proc. Combust. Inst. 38 (1):1249–57. doi:10.1016/j.proci.2020.07.006.
   Web of Science ®Google Scholar
• Nanjaiah, M., K. Roderigo, H. Janbazi, A. M. Kempf, and I. Wlokas. 2021. Compact, global skeletal reaction mechanism for the combustion of xylene/air and n-butanol/air. Paper presented at 10th European Combustion Meeting, Naples, Italy, April 15.
   Google Scholar
• Picciani, M. A., E. S. Richardson, and S. A. Navarro-Martinez. 2018. A thickened stochastic fields approach for turbulent combustion simulation. Flow Turbul. Combust. 101 (4):1119–36. doi:10.1007/s10494-018-9954-y.
   PubMed Web of Science ®Google Scholar
• Poinsot, T. J., and S. K. Lelef. 1992. Boundary conditions for direct simulations of compressible viscous flows. J. Comp. Phys 101 (1):104–29. doi:10.1016/0021-9991(92)90046-2.
   Web of Science ®Google Scholar
• Poinsot, T., and D. Veynante. 2011. Theoretical and numerical combustion. 3rd ed. Toulouse: T. Poinsot.
   Google Scholar
• Polifke, W., C. Wall, and P. Moin. 2006. Partially reflecting and non-reflecting boundary conditions for simulation of compressible viscous flow. J. Comput. Phys. 213 (1):437–49. doi:10.1016/j.jcp.2005.08.016.
   Web of Science ®Google Scholar
• Proch, F., and A. M. Kempf. 2015. Modeling heat loss effects in the large eddy simulation of a model gas turbine combustor with premixed flamelet generated manifolds. Proc. Comb. Inst. 35 (3):3337–45. doi:10.1016/j.proci.2014.07.036.
   Google Scholar
• Sharifi, V., C. Beck, B. Janus, and A. M. Kempf. 2021. Design and testing of a high frequency thermoacoustic combustion experiment. AIAA J. 59 (8):3127–43. doi:10.2514/1.J060072.
   Google Scholar
• Sharifi, V., C. Beck, and A. M. Kempf. 2018. Large-eddy simulation of acoustic flame response to high-frequency transverse excitations. AIAA J. 57 (1):327–40. doi:10.2514/1.J056818.
   Google Scholar
• Sikalo, N., O. Hasemann, C. Schulz, A. M. Kempf, and I. Wlokas. 2014. A genetic algorithm-based method for the automatic reduction of reaction mechanisms. Int. J. Chem. Kinet. 46 (1):41–59. doi:10.1002/kin.20826.
   Web of Science ®Google Scholar
• Sikalo, N., O. Hasemann, C. Schulz, A. M. Kempf, and I. Wlokas. 2015. A genetic algorithm–based method for the optimization of reduced kinetics mechanisms. Int. J. Chem. Kinet. 47 (11):695–723. doi:10.1002/kin.20942.
   Web of Science ®Google Scholar
• Smith, G. P., D. M. Golden, M. Frenklach, N. W. Moriarty, B. Eiteneer, M. Goldenberg, C. T. Bowman, R. K. Hanson, S. Song, W. C. Gardiner, et al. 2000. http://www.me.berkeley.edu/gri_mech/.
   Google Scholar
• van Leer, B. 1974. Towards the ultimate conservative difference scheme. II. Monotonicity and conservation combined in a second-order scheme. J. Comput. Phys. 14 (4):361–70. doi:10.1016/0021-9991(74)90019-9.
   Web of Science ®Google Scholar
• Wang, G., M. Boileau, and D. Veynante. 2011. Implementation of a dynamic thickened flame model for large eddy simulations of turbulent premixed combustion. Combust. Flame 158 (11):2199–213. doi:10.1016/j.combustflame.2011.04.008.
   Web of Science ®Google Scholar
• Yoshizawa, A., and K. Horiuti. 1985. A statistically-derived subgrid-scale kinetic energy model for the large-eddy simulation of turbulent flows. J. Phys. Society Japan 54 (8):2834–39. doi:10.1143/JPSJ.54.2834.
   Web of Science ®Google Scholar