A mixing timescale model for TPDF simulations of turbulent premixed flames
Transported probability density function (TPDF) methods are an attractive modeling approach for turbulent flames as chemical reactions appear in closed form. However, molecular micromixing needs to be modeled and this modeling is considered a primary challenge for TPDF methods. In the present study, a new algebraic mixing rate model for TPDF simulations of turbulent premixed flames is proposed, which is a key ingredient in commonly used molecular mixing models. The new model aims to properly account for the transition in reactive scalar mixing rate behavior from the limit of turbulencedominated mixing to molecular mixing behavior in flamelets. An a priori assessment of the new model is performed using direct numerical simulation (DNS) data of a lean premixed hydrogen–air jet flame. The new model accurately captures the mixing timescale behavior in the DNS and is found to be a significant improvement over the commonly used constant mechanicaltoscalar mixing timescale ratio model. An a posteriori TPDF study is then performed using the same DNS data as a numerical test bed. The DNS provides the initial conditions and timevarying input quantities, including the mean velocity, turbulent diffusion coefficient, and modeled scalar mixing rate for the TPDF simulations, thus allowing an exclusive focus onmore »
 Authors:

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 Univ. of Connecticut, Storrs, CT (United States). Dept. of Mechanical Engineering
 Tsinghua Univ., Beijing (China). Center for Combustion Energy. School of Aerospace Engineering
 The Univ. of New South Wales, Sydney, NSW (Australia). School of Photovoltaic and Renewable Energy Engineering
 Sandia National Lab. (SNLCA), Livermore, CA (United States). Combustion Research Facility
 Publication Date:
 Report Number(s):
 SAND201612325J; SAND20177036J
Journal ID: ISSN 00102180; PII: S0010218016303741
 Grant/Contract Number:
 AC0494AL85000; SC0008622; 91441202; 51476087; DeAC0494AL85000
 Type:
 Accepted Manuscript
 Journal Name:
 Combustion and Flame
 Additional Journal Information:
 Journal Volume: 177; Journal ID: ISSN 00102180
 Publisher:
 Elsevier
 Research Org:
 Univ. of Connecticut, Storrs, CT (United States); Sandia National Lab. (SNLCA), Livermore, CA (United States); Tsinghua Univ., Beijing (China); The Univ. of New South Wales, Sydney, NSW (Australia)
 Sponsoring Org:
 USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC22); National Natural Science Foundation of China (NNSFC); Australian Research Council (Australia)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; transported probability density function; turbulent premixed flames; Euclidean minimum spanning tree; mixing timescale models; direct numerical simulation
 OSTI Identifier:
 1346569
 Alternate Identifier(s):
 OSTI ID: 1372306; OSTI ID: 1396723
Kuron, Michael, Ren, Zhuyin, Hawkes, Evatt R., Zhou, Hua, Kolla, Hemanth, Chen, Jacqueline H., and Lu, Tianfeng. A mixing timescale model for TPDF simulations of turbulent premixed flames. United States: N. p.,
Web. doi:10.1016/j.combustflame.2016.12.011.
Kuron, Michael, Ren, Zhuyin, Hawkes, Evatt R., Zhou, Hua, Kolla, Hemanth, Chen, Jacqueline H., & Lu, Tianfeng. A mixing timescale model for TPDF simulations of turbulent premixed flames. United States. doi:10.1016/j.combustflame.2016.12.011.
Kuron, Michael, Ren, Zhuyin, Hawkes, Evatt R., Zhou, Hua, Kolla, Hemanth, Chen, Jacqueline H., and Lu, Tianfeng. 2017.
"A mixing timescale model for TPDF simulations of turbulent premixed flames". United States.
doi:10.1016/j.combustflame.2016.12.011. https://www.osti.gov/servlets/purl/1346569.
@article{osti_1346569,
title = {A mixing timescale model for TPDF simulations of turbulent premixed flames},
author = {Kuron, Michael and Ren, Zhuyin and Hawkes, Evatt R. and Zhou, Hua and Kolla, Hemanth and Chen, Jacqueline H. and Lu, Tianfeng},
abstractNote = {Transported probability density function (TPDF) methods are an attractive modeling approach for turbulent flames as chemical reactions appear in closed form. However, molecular micromixing needs to be modeled and this modeling is considered a primary challenge for TPDF methods. In the present study, a new algebraic mixing rate model for TPDF simulations of turbulent premixed flames is proposed, which is a key ingredient in commonly used molecular mixing models. The new model aims to properly account for the transition in reactive scalar mixing rate behavior from the limit of turbulencedominated mixing to molecular mixing behavior in flamelets. An a priori assessment of the new model is performed using direct numerical simulation (DNS) data of a lean premixed hydrogen–air jet flame. The new model accurately captures the mixing timescale behavior in the DNS and is found to be a significant improvement over the commonly used constant mechanicaltoscalar mixing timescale ratio model. An a posteriori TPDF study is then performed using the same DNS data as a numerical test bed. The DNS provides the initial conditions and timevarying input quantities, including the mean velocity, turbulent diffusion coefficient, and modeled scalar mixing rate for the TPDF simulations, thus allowing an exclusive focus on the mixing model. Here, the new mixing timescale model is compared with the constant mechanicaltoscalar mixing timescale ratio coupled with the Euclidean Minimum Spanning Tree (EMST) mixing model, as well as a laminar flamelet closure. It is found that the laminar flamelet closure is unable to properly capture the mixing behavior in the thin reaction zones regime while the constant mechanicaltoscalar mixing timescale model underpredicts the flame speed. Furthermore, the EMST model coupled with the new mixing timescale model provides the best prediction of the flame structure and flame propagation among the models tested, as the dynamics of reactive scalar mixing across different flame regimes are appropriately accounted for.},
doi = {10.1016/j.combustflame.2016.12.011},
journal = {Combustion and Flame},
number = ,
volume = 177,
place = {United States},
year = {2017},
month = {2}
}