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Articles

Conditional space evaluation of progress variable definitions for Cambridge/Sandia swirl flames

ORCID Icon, ORCID Icon, & ORCID Icon
Pages 736-767 | Received 11 Jul 2022, Accepted 09 Apr 2023, Published online: 12 May 2023

References

  • J. van Oijen, A. Donini, R. Bastiaans, J. ten Thije Boonkkamp, and L. de Goey, State-of-the-art in premixed combustion modeling using flamelet generated manifolds, Prog. Energy. Combust. Sci. 57 (2016), pp. 30–74.
  • A. Klimenko and R. Bilger, Conditional moment closure for turbulent combustion, Prog. Energy. Combust. Sci. 25 (1999), pp. 595–687.
  • D. Veynante and L. Vervisch, Turbulent combustion modeling, Prog. Energy. Combust. Sci. 28 (2002), pp. 193–266.
  • T. Poinsot and D. Veynante, Theoretical and numerical combustion, 2nd ed., RT Edwards, Philadelphia PA, 2005.
  • D. Dovizio, J.W. Labahn, and C.B. Devaud, Doubly conditional source-term estimation (DCSE) applied to a series of lifted turbulent jet flames in cold air, Combust. Flame.162 (2015), pp. 1976–1986.
  • P.D. Nguyen, L. Vervisch, V. Subramanian, and P. Domingo, Multidimensional flamelet-generated manifolds for partially premixed combustion, Combust. Flame. 157 (2010), pp. 43–61.
  • A.H. Mahdipour and M.M. Salehi, Localized conditional source-term estimation model for turbulent combustion, Combustion Flame. 235 (2021), p. 111715.
  • M. Ihme and Y.C. See, LES flamelet modeling of a three-stream MILD combustor: analysis of flame sensitivity to scalar inflow conditions, Proc. Combustion Inst. 33 (2011), pp. 1309–1317.
  • M. Ihme, J. Zhang, G. He, and B. Dally, Large-Eddy simulation of a Jet-in-Hot-Coflow burner operating in the Oxygen-Diluted combustion regime, Flow, Turbulence Combustion 89 (2012), pp. 449–464.
  • J. Labahn, D. Dovizio, and C. Devaud, Numerical simulation of the Delft-Jet-in-Hot-Coflow (DJHC) flame using conditional source-term estimation, Proc. Combustion Inst. 35 (2015), pp. 3547–3555.
  • J. Labahn and C. Devaud, Large Eddy simulations (LES) including conditional source-term estimation (CSE) applied to two Delft-Jet-in-Hot-Coflow (DJHC) flames, Combust. Flame.164 (2016), pp. 68–84.
  • R.W. Bilger and S.H. Starner, On reduced mechanisms for methane-air combustion in nonpremixed flames, Combust. Flame. 80 (1990), pp. 135–149.
  • X. Fang, R. Ismail, M.H. Davy, and J. Camm, Numerical Studies of Combustion Recession on ECN Diesel Spray A, Internal Combustion Engine Division Fall Technical Conference Vol. 2: Emissions Control Systems; Instrumentation, Controls, and Hybrids; Numerical Simulation; Engine Design and Mechanical Development, Proceedings of the ASME 2018 Internal Combustion Engine Division Fall Technical Conference, San Diego, USA, Vol. 11, 2018, p. v002T06A011.
  • X. Fang, R. Ismail, and M. Davy, A study on kinetic mechanisms of diesel fuel surrogate n-dodecane for the simulation of combustion recession, in WCX SAE World Congress Experience, SAE International, Detroit, 2019.
  • Z. Sun, S. Gierth, M. Pollack, C. Hasse, and A. Scholtissek, Ignition under strained conditions: unsteady flamelet progress variable modeling for diesel engine conditions in the transient counterflow configuration, Combust. Flame. 240 (2022), p. 111841.
  • M. Ihme, L. Shunn, and J. Zhang, Regularization of reaction progress variable for application to flamelet-based combustion models, J. Comput. Phys. 231 (2012), pp. 7715–7721.
  • Y.S. Niu, L. Vervisch, and P.D. Tao, An optimization-based approach to detailed chemistry tabulation: automated progress variable definition, Combust. Flame. 160 (2013), pp. 776–785.
  • A. Vasavan, P. de Goey, and J. van Oijen, A novel method to automate FGM progress variable with application to igniting combustion systems, Combustion Theory Modell. 24 (2020), pp. 221–244.
  • F. Chitgarha, F. Ommi, and M. Farshchi, Assessment of optimal reaction progress variable characteristics for partially premixed flames, Combustion Theory Modell. 26 (2022), pp. 797–830.
  • H. Gupta, O.J. Teerling, and J.A. van Oijen, Effect of progress variable definition on the mass burning rate of premixed laminar flames predicted by the flamelet generated manifold method, Combustion Theory Modell. 25 (2021), pp. 631–645.
  • A. Lipatnikov and V. Sabelnikov, Evaluation of mean species mass fractions in premixed turbulent flames: a DNS study, Proc. Combustion Inst. 38 (2021), pp. 6413–6420.
  • A. Lipatnikov, V. Sabelnikov, F. Hernández-Pérez, W. Song, and H.G. Im, A priori DNS study of applicability of flamelet concept to predicting mean concentrations of species in turbulent premixed flames at various Karlovitz numbers, Combust. Flame. 222 (2020), pp. 370–382.
  • A. Giusti and E. Mastorakos, Detailed chemistry LES/CMC simulation of a swirling ethanol spray flame approaching blow-off, Proc. Combustion Inst. 36 (2017), pp. 2625–2632.
  • A. Varna, A. Wehrfritz, E.R. Hawkes, M.J. Cleary, T. Lucchini, G. D'Errico, S. Kook, and Q.N. Chan, Application of a multiple mapping conditioning mixing model to ECN spray A, Proc. Combustion Inst.37 (2019), pp. 3263–3270.
  • W.K. Bushe and H. Steiner, Conditional moment closure for large eddy simulation of nonpremixed turbulent reacting flows, Phys. Fluids 11 (1999), pp. 1896–1906.
  • J.W. Labahn, I. Stanković, C.B. Devaud, and B. Merci, Comparative study between conditional moment closure (CMC) and conditional source-term estimation (CSE) applied to piloted jet flames, Combust. Flame. 181 (2017), pp. 172–187.
  • Z. Huang, M.J. Cleary, Z. Ren, and H. Zhang, Large Eddy simulation of a supersonic lifted hydrogen flame with sparse-Lagrangian multiple mapping conditioning approach, Combustion Flame. 238 (2021), p. 111756.
  • X. Fang, R. Ismail, K. Bushe, and M. Davy, Simulation of ECN diesel spray A using conditional source-term estimation, Combustion Theory Modell. 24 (2020), pp. 725–760.
  • X. Fang, R. Ismail, N. Sekularac, and M. Davy, On the Prediction of Spray A End of Injection Phenomenon Using Conditional Source-Term Estimation, WCX SAE World Congress Experience, SAE International, Detroit, 2020.
  • X. Fang, N. Sekularac, and M.H. Davy, Parametric Studies of a Novel Combustion Modelling Approach for Low Temperature Diesel Spray Simulation, Proceedings of the ASME 2020 Internal Combustion Engine Division Fall Technical Conference, 2020, p. v001T06A005.
  • A. Hussien and C. Devaud, Simulations of partially premixed turbulent ethanol spray flames using doubly conditional source term estimation (DCSE), Combustion Flame. 239 (2021), p. 111651.
  • D. Dovizio and C. Devaud, Doubly conditional source-term estimation (DCSE) for the modelling of turbulent stratified V-shaped flame, Combust. Flame. 172 (2016), pp. 79–93.
  • C. Devaud, W.K. Bushe, and J. Bellan, The modeling of the turbulent reaction rate under high-pressure conditions: A priori evaluation of the conditional source-term estimation concept, Combust. Flame. 207 (2019), pp. 205–221.
  • W. Bushe, Spatial gradients of conditional averages in turbulent flames, Combust. Flame. 192 (2018), pp. 314–339.
  • A. Mousemi and W. Kendal Bushe, The joint probability density function of mixture fraction, reaction progress variable, and total enthalpy in a stratified, swirl-stabilized turbulent flame, Phys. Fluids 33 (2021), p. 035106.
  • W.K. Bushe, C. Devaud, and J. Bellan, A priori evaluation of the double-conditioned conditional source-term estimation model for high-pressure heptane turbulent combustion using DNS data obtained with one-step chemistry, Combust. Flame. 217 (2020), pp. 131–151.
  • J.C. Sutherland and A. Parente, Combustion modeling using principal component analysis, Proc. Combustion Inst. 32 (2009), pp. 1563–1570.
  • M.S. Sweeney, S. Hochgreb, M.J. Dunn, and R.S. Barlow, The structure of turbulent stratified and premixed methane/air flames I: non-swirling flows, Combust. Flame. 159 (2012), pp. 2896–2911.
  • M.S. Sweeney, S. Hochgreb, M.J. Dunn, and R.S. Barlow, The structure of turbulent stratified and premixed methane/air flames II: swirling flows, Combust. Flame. 159 (2012), pp. 2912–2929.
  • A. Mousemi, W.K. Bushe, and S. Hochgreb, Evaluation of manifold representations of chemistry in stratified, swirl-stabilized flames, Combust. Flame. 229 (2021), p. 111418.
  • M. Wang, J. Huang, and W. Bushe, Simulation of a turbulent non-premixed flame using conditional source-term estimation with trajectory generated low-dimensional manifold, Proc. Combustion Inst.31 (2007), pp. 1701–1709.
  • O. Owoyele, P. Kundu, M.M. Ameen, T. Echekki, and S. Som, Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames, Int. J. Engine Res. 21 (2020), pp. 151–168.
  • M. Ihme, W.T. Chung, and A.A. Mishra, Combustion machine learning: principles, progress and prospects, Prog. Energy. Combust. Sci. 91 (2022), p. 101010.
  • H. Mirgolbabaei and T. Echekki, Nonlinear reduction of combustion composition space with kernel principal component analysis, Combust. Flame. 161 (2014), pp. 118–126.
  • M.R. Malik, P. Obando Vega, A. Coussement, and A. Parente, Combustion modeling using principal component analysis: a posteriori validation on sandia flames D, E and F, Proc. Combustion Inst. 38 (2021), pp. 2635–2643.
  • A. Parente, J. Sutherland, L. Tognotti, and P. Smith, Identification of low-dimensional manifolds in turbulent flames, Proc. Combustion Inst. 32 (2009), pp. 1579–1586.
  • J. Shlens, A tutorial on principal component analysis, 2014.
  • B.J. Isaac, A. Coussement, O. Gicquel, P.J. Smith, and A. Parente, Reduced-order PCA models for chemical reacting flows, Combust. Flame. 161 (2014), pp. 2785–2800.
  • A. Biglari and J.C. Sutherland, An a-posteriori evaluation of principal component analysis-based models for turbulent combustion simulations, Combust. Flame. 162 (2015), pp. 4025–4035.
  • B.J. Isaac, J.N. Thornock, J. Sutherland, P.J. Smith, and A. Parente, Advanced regression methods for combustion modelling using principal components, Combust. Flame. 162 (2015), pp. 2592–2601.
  • M.R. Malik, B.J. Isaac, A. Coussement, P.J. Smith, and A. Parente, Principal component analysis coupled with nonlinear regression for chemistry reduction, Combust. Flame.187 (2018), pp. 30–41.
  • A. Parente and J.C. Sutherland, Principal component analysis of turbulent combustion data: data pre-processing and manifold sensitivity, Combust. Flame. 160 (2013), pp. 340–350.
  • R. Ranade and T. Echekki, A framework for data-based turbulent combustion closure: A priori validation, Combust. Flame. 206 (2019), pp. 490–505.
  • H. Turkeri, X. Zhao, and M. Muradoglu, Large eddy simulation/probability density function modeling of turbulent swirling stratified flame series, Phys. Fluids 33 (2021), p. 025117.
  • A.N. Lipatnikov, T. Nilsson, R. Yu, X.S. Bai, and V.A. Sabelnikov, Assessment of a flamelet approach to evaluating mean species mass fractions in moderately and highly turbulent premixed flames, Phys. Fluids 33 (2021), p. 045121.