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Experiment-Based Modeling of Turbulent Flames with Inhomogeneous Inlets

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Abstract

An experiment-based closure framework for turbulent combustion modeling is further validated using the Sydney piloted turbulent partially premixed flames with inhomogeneous inlets. The flames are characterized by the presence of mixed mode combustion. The framework’s closure is “trained” on multi-scalar measurements to construct thermo-chemical scalar statistics parameterized in terms of principal components (PCs). Three flame conditions are used for this training, while an additional flame is used for validation. The results show that the leading PCs exhibit complex features near the jet inlet where effects of partial premixing and the presence of different burning modes are strong. These features may not be captured through a strict definition for the mixture fraction or measures of reaction progress. Further downstream, the first 2 PCs tend to be reasonably correlated with parameters that are characteristic of nonpremixed flames, including the mixture fraction and the progress variable. Comparisons of the model predictions for unconditional mean and RMS for the measured quantities show a very good qualitative and quantitative agreement with experimental statistics for all 4 flames using the same closure for the PCs governing equations.

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References

  • Arndt, C. Barlow, R.S., Dally, B., Dreizler, A., Fiorina, B., Gordon, R., Hamlington, P., Hawkes, E., Ihme, M., Janicka, J., Kempf, A., Meier, W., Mueller, M., Steinberg, A., Sutton, J., Vervisch, L: Proc. TNF Workshop, July 27–28, 2018, Dublin, Ireland

  • Barlow, R.S., Frank, J.H.: Effects of turbulence on species mass fractions in methane/air jet flames. Proc. Combust. Inst. 27, 1087–1095 (1998)

    Article  Google Scholar 

  • Barlow, R.S., Meares, S., Magnotti, G., Cutcher, H., Masri, A.R.: Local extinction and near-field structure in piloted turbulent CH4/air jet flames with inhomogeneous inlets. Combust. Flame. 162, 3516–3540 (2015)

    Article  Google Scholar 

  • Barlow, R.S., Magnotti, G., Cutcher, H.C., Masri, A.R.: On defining progress variable for Raman/Rayleigh experiments in partially-premixed methane flames. Combust. Flame. 179, 117–129 (2017)

    Article  Google Scholar 

  • Bilger, R.W., Starner, S.H., Kee, R.J.: On reduced mechanisms for methane-air combustion in nonpremixed flames. Combust. Flame. 80, 135–149 (1990)

    Article  Google Scholar 

  • Blasco, J.A., Fueyo, N., Dopazo, C., Ballester, J.: Modelling the temporal evolution of a reduced combustion chemical system with an artificial neural network. Combust. Flame. 113, 38–52 (1998)

    Article  Google Scholar 

  • Blasco, J.A., Fueyo, N., Larroya, J.C., Dopazo, C., Chen, J.-Y.: A single-step time-integrator of a methane–air chemical system using artificial neural networks. Comp. Chem. Eng. 23, 1127–1133 (1999)

    Article  Google Scholar 

  • Blasco, J.A., Fueyo, N., Dopazo, C., Chen, J.-Y.: A self-organizing-map approach to chemistry representation in combustion applications. Combust. Theo. Model. 4, 61–76 (2000)

    Article  MATH  Google Scholar 

  • Bowman, A.W., Azzalini, A.: Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford (1997)

    MATH  Google Scholar 

  • Chatzopoulos, A.K., Rigopoulos, S.: A chemistry tabulation approach via rate-controlled constrained equilibrium (RCCE) and artificial neural networks (ANNs), with application to turbulent non-premixed CH4/H2/N2 flames. Proc. Combust. Inst. 34, 1465–1473 (2013)

    Article  Google Scholar 

  • Chen, J.-Y., Blasco, J.A., Fueyo, N., Dopazo, C.: An economical strategy for storage of chemical kinetics: fitting in situ adaptive tabulation with artificial neural networks. Proc. Combust. Inst. 28, 115–121 (2000)

    Article  Google Scholar 

  • Christo, F.C., Masri, A.R., Nebot, E.M., Turanyi, T.: Utilising artificial neural network and repro-modelling in turbulent combustion. Neural Netw. Proc. IEEE Int. Conf. 2, 911–916 (1995)

    Article  Google Scholar 

  • Christo, F.C., Masri, A.R., Nebot, E.M., Pope, S.B.: An integrated PDF/neural network approach for simulating turbulent reacting systems. Symp. Combust. 26(1), 43–48 (1996)

    Article  Google Scholar 

  • Christo, F.C., Masri, A.R., Nebot, E.M.: Artificial neural network implementation of chemistry with PDF simulation of H2/CO2 flames. Combust. Flame. 106, 406–427 (1996b)

    Article  Google Scholar 

  • Coussement, A., Gicquel, O., Parente, A.: Kernel density weighted principal component analysis of combustion processes. Combust. Flame. 159, 2844–2855 (2012)

    Article  Google Scholar 

  • Cutcher, H.C., Barlow, R.S., Magnotti, G., Masri, A.R.: Turbulent flames with compositionally inhomogeneous inlets: resolved measurements of scalar dissipation rates. Proc. Combust. Inst. 36, 1737–1745 (2017)

    Article  Google Scholar 

  • Echekki, T., Mirgolbabaei, H.: Principal component transport in turbulent combustion: a posteriori analysis. Combust. Flame. 162, 1919–1933 (2015)

    Article  Google Scholar 

  • Franke, L.L., Chatzopoulos, A.K., Rigopoulos, S.: Tabulation of combustion chemistry via Artificial Neural Networks (ANNs): methodology and application to LES-PDF simulation of Sydney flame L. Combust. Flame. 185, 245–260 (2017)

    Article  Google Scholar 

  • Galindo, G., Salehi, F., Cleary, M.J., Masri, A.R.: MMC-LES simulations of turbulent piloted flames with varying levels of inlet inhomogeneity. Proc. Combust. Inst. 36, 1759–1766 (2017)

    Article  Google Scholar 

  • Galindo-Lopez, S., Salehi, F., Cleary, M.J., Masri, A.R., Neuber, C., Stein, O.T., Kronenburg, A., Varna, A., Hawkes, E.R., Sundaram, B., Klimenko, A.Y., Ge, Y.: A stochastic multiple mapping conditioning computational model in OpenFoam for turbulent combustion. Comput. Fluids. 172, 410–425 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  • Gicquel, O., Darabiha, N., Thevenin, D.: Laminar premixed hydrogen/air counterflow flame simulations using flame prolongation of ILDM with differential diffusion. Proc. Combust. Inst. 28, 1901–1908 (2000)

    Article  Google Scholar 

  • Gitushi, K.M., Ranade, R., Echekki, T.: Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion, Combust. Flame 236, 111814 (2022)

    Article  Google Scholar 

  • Ihme, M., Marsden, A.L., Pitsch, H.: Generation of optimal artificial neural networks using a pattern search algorithm: application to approximation of chemical systems. Neur. Comp. 20, 573–601 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  • Ihme, M., Schmitt, C., Pitsch, H.: Optimal artificial neural networks and tabulation methods for chemistry representation in LES of a bluff-body swirl-stabilized flame. Proc. Combust. Inst. 32, 1527–1535 (2009)

    Article  Google Scholar 

  • Ji, H., Kwon, M., Kim, S., Kim, Y.: Numerical modeling for multiple combustion modes in turbulent partially premixed jet flames. J. Mech. Sci. Tech. 32, 5511–5519 (2018)

    Article  Google Scholar 

  • Kim, N., Kim, Y.: Multi-environment probability density function approach for turbulent partially-premixed methane/air flame with inhomogeneous inlets. Combust. Flame. 182, 190–205 (2017)

    Article  Google Scholar 

  • Kleinheinz, K., Kubis, T., Trisjono, P., Bode, M., Pitsch, H.: Computational study of flame characteristics of a turbulent piloted jet burner with inhomogeneous inlets. Proc. Combust. Inst. 36, 1747–1757 (2017)

    Article  Google Scholar 

  • Kohonen, T.: Self-organized formation of topologically correct featured maps. Bio. Cyber. 43, 59–69 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  • Lu, L., Jin, P., Pang, G., Zhang, Z., Karniadakis, G.E.: Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. 3, 218–229 (2021)

    Article  Google Scholar 

  • Maio, G., Cailler, M., Darabiha, N., Fiorina, B.: Capturing multi-regime combustion in turbulent flames with a virtual chemistry approach. Proc. Combust. Inst. 38, 2559–2569 (2021)

    Article  Google Scholar 

  • Meares, S., Masri, A.R.: A modified piloted burner for stabilizing turbulent flames of inhomogeneous mixtures. Combust. Flame. 161, 484–495 (2014)

    Article  Google Scholar 

  • Meares, S., Prasad, V.N., Magnotti, G., Barlow, R.S., Masri, A.R.: Stabilization of piloted turbulent flames with inhomogeneous inlets. Proc. Combust. Inst. 35, 1477–1484 (2015)

    Article  Google Scholar 

  • Mirgolbabaei, H., Echekki, T.: A novel principal component analysis-based acceleration scheme for LES–ODT: An a priori study. Combust. Flame. 161, 898–908 (2013)

    Article  Google Scholar 

  • Mirgolbabaei, H., Echekki, T.: Nonlinear reduction of combustion composition space with kernel principal component analysis. Combust. Flame. 161, 118–126 (2014)

    Article  Google Scholar 

  • Mirgolbabaei, H., Echekki, T.: The reconstruction of thermo-chemical scalars in combustion from a reduced set of their principal components. Combust. Flame. 162, 1650–1652 (2015)

    Article  Google Scholar 

  • Mirgolbabaei, H., Echekki, T., Smaoui, N.: A nonlinear principal component analysis approach for turbulent combustion composition space. Int. J. Hydrogen Energy. 39, 4622–4633 (2014)

    Article  Google Scholar 

  • Owoyele, O., Echekki, T.: Toward computationally efficient combustion DNS with complex fuels via principal component transport. Combust. Theo. Model. 21, 770–798 (2017)

    Article  MathSciNet  Google Scholar 

  • Owoyele, O., Kundu, P., Ameen, M.M., Echekki, T., Som, S.: Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames. Int. J. Eng. Res. 21, 151–168 (2019)

    Article  Google Scholar 

  • Parente, A., Sutherland, J.C., Dally, B.B., Tognotti, G., Smith, P.J.: Investigation of the MILD combustion regime via principal component analysis. Proc. Combust. Inst. 33, 3333–3341 (2011)

    Article  Google Scholar 

  • Perry, B.A., Mueller, M.E.: Effect of multiscalar subfilter PDF models in LES of turbulent flames with inhomogeneous inlets. Proc. Combust. Inst. 37, 2287–2295 (2019)

    Article  Google Scholar 

  • Perry, B.A., Mueller, M.E., Masri, A.R.: A two mixture fraction flamelet model for large eddy simulation of turbulent flames with inhomogeneous inlets. Proc. Combust. Inst. 36, 1767–1775 (2017)

    Article  Google Scholar 

  • Peters, N.: Laminar diffusion flamelet models in non-premixed turbulent combustion. Prog. Energy Combust. Sci. 10, 319–329 (1984)

    Article  Google Scholar 

  • Pope, S.B.: Computationally efficient implementation of combustion chemistry using in-situ tabulation. Combust. Theo. Model. 1, 41–63 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  • Ranade, R., Echekki, T.: A framework for data-based turbulent combustion closure: a priori validation. Combust. Flame. 206, 490–505 (2019a)

    Article  Google Scholar 

  • Ranade, R., Echekki, T.: A framework for data-based turbulent combustion closure: a posteriori validation. Combust. Flame. 210, 279–291 (2019b)

    Article  Google Scholar 

  • Ranade, R., Alqahtani, S., Farooq, A., Echekki, T.: An ANN based hybrid chemistry framework for complex fuels. Fuel 241, 625–636 (2019a)

    Article  Google Scholar 

  • Ranade, R., Alqahtani, S., Farooq, A., Echekki, T.: An extended hybrid chemistry framework for complex hydrocarbon fuels. Fuel 251, 276–284 (2019b)

    Article  Google Scholar 

  • Ranade, R., Li, G., Li, S., Echekki, T.: An efficient machine-learning approach for PDF tabulation in turbulent combustion closure. Combust. Sci. & Tech. 193, 1258–1277 (2021)

    Article  Google Scholar 

  • Sen, B.A., Menon, S.: Linear eddy mixing based tabulation and artificial neural networks for large eddy simulations of turbulent flames. Combust. Flame. 157, 62–74 (2010)

    Article  Google Scholar 

  • Sen, B.A., Hawkes, E.R., Menon, S.: Large eddy simulation of extinction and reignition with artificial neural networks based chemical kinetics. Combust. Flame. 157, 566–578 (2010)

    Article  Google Scholar 

  • Shih, T.H., Liou, W.W., Shabbir, A., Yang, Z., Zhu, J.: A new k-ε viscosity model for high Reynolds number turbulent flows. Comput. Fluids 24, 227–238 (1995)

    Article  MATH  Google Scholar 

  • Sutherland, J.C., Parente, A.: Combustion modeling using principal component analysis. Proc. Combust. Inst. 32, 1563–1570 (2009)

    Article  Google Scholar 

  • van Oijen, J.A., de Goey, L.P.H.: Modeling of premixed laminar flames using flamelet-generated manifolds. Combust. Sci. Tech. 161, 113–137 (2000)

    Article  Google Scholar 

  • Wu, H., Ihme, M.: Compliance of combustion models for turbulent reacting flow simulations. Fuel 186, 853–863 (2016)

    Article  Google Scholar 

  • Yang, B., Pope, S.B.: An investigation of the accuracy of manifold methods and splitting schemes in the computational implementation of combustion chemistry. Combust. Flame. 112, 16–32 (1998)

    Article  Google Scholar 

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Acknowledgements

Dr. Echekki’s work is supported by the National Science Foundation under Grant No. 1941430. Dr. Masri is supported by the Australian Research Council.

Funding

Dr. Echekki’s work is supported by the National Science Foundation under grant no. 1941430. Dr. Masri is supported by the Australian Research Council.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, North Carolina State University, 1840 Entrepreneur Drive, Campus Box 7910, Raleigh, NC, 27695-7910, USA

    Rishikesh Ranade & Tarek Echekki

  2. School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, Sydney, NSW, Australia

    Assaad R. Masri

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  1. Rishikesh Ranade
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  2. Tarek Echekki
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Correspondence to Tarek Echekki.

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Appendix

Appendix

In this Appendix, we include additional results of comparisons of mean and RMS profiles of species and temperature corresponding to flames FJ200-5GP-Lr75-80 and FJ200-5GP-Lr300-59. These profiles are shown in Figs.

Fig. 10
figure 10

A posteriori mean radial profile comparison of closure framework (line) and experimental data (symbol) for Sydney flame FJ200-5GP-Lr75-80

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10 and

Fig. 11
figure 11

A posteriori mean radial profile comparison of closure framework (line) and experimental data (symbol) for Sydney flame FJ200-5GP-Lr300-59

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11 for the mean profiles of temperature, major species and mixture fraction for flames FJ200-5GP-Lr75-80 and FJ200-5GP-Lr300-59, respectively. Figures 

Fig. 12
figure 12

A posteriori RMS radial profile comparison of closure framework (line) and experimental data (symbol) for Sydney flame FJ200-5GP-Lr75-80

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12 and

Fig. 13
figure 13

A posteriori RMS radial profile comparison of closure framework (line) and experimental data (symbol) for Sydney flame FJ200-5GP-Lr300-59

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13 show the corresponding RMS profiles for both flames.

We can equally contrast the prediction of the profiles of flames FJ200-5GP-Lr75-57 (shown in Fig. 6) and FJ200-5GP-Lr300-59 (shown in Fig. 11), which have comparable inlet velocities and different degrees of inlet inhomogeneities. Although it may not be easily discernable from the plots, there are some variations between the two flames’ statistics that are captured by the model. In the near field at x/d = 5 and at x/d = 12, the peak temperature, the products’ mass fractions and H2 mass fraction in FJ200-5GP-Lr75-57 is slightly higher than in FJ200-5GP-Lr300-59. The trends are reversed at x/d = 30 with greater broadening of the mean profiles.

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Ranade, R., Echekki, T. & Masri, A.R. Experiment-Based Modeling of Turbulent Flames with Inhomogeneous Inlets. Flow Turbulence Combust 108, 1043–1067 (2022). https://doi.org/10.1007/s10494-021-00304-8

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