One-Dimensional Modeling of Turbulent Premixed Jet Flames - Comparison to DNS

Punati, Naveen; Wang, Haiou; Hawkes, Evatt R.; Sutherland, James C.

doi:10.1007/s10494-016-9721-x

Cited by 6 publications

(1 citation statement)

References 51 publications

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“…The shear-driven flow sustains strong turbulence-flame interactions and is characteristic of practical conditions in gas turbines. The same DNS have been previously used in studies fractal flame characteristics [41] and in modeling in studies of different TPDF mixing models [27], one-dimensional turbulence models [42], and strained flamelet models [43].…”

Section: Dns Database Descriptionmentioning

confidence: 99%

A mixing timescale model for TPDF simulations of turbulent premixed flames

Kuron

Ren

Hawkes

et al. 2017

Combustion and Flame

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Transported probability density function (TPDF) methods are an attractive modeling approach for turbulent flames as chemical reactions appear in closed form. However, molecular micro-mixing 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 turbulence-dominated 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 mechanical-to-scalar 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 time-varying 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. The new mixing timescale model is compared with the constant mechanical-to-scalar mixing timescale ratio coupled with the Euclidean Minimum Spanning Tree (EMST) mixing model, as well as a laminar flamelet closure by Pope and Anand (S.B. Pope, M.S. Anand, Proc. Combust. Inst. 20 (1984) 403-410). 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 mechanical-to-scalar mixing timescale model under-predicts the flame speed. 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.

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Section: Dns Database Descriptionmentioning

confidence: 99%

A mixing timescale model for TPDF simulations of turbulent premixed flames

Kuron

Ren

Hawkes

et al. 2017

Combustion and Flame

Self Cite

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Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches

Zdybał

Malik

Coussement

et al. 2023

Lecture Notes in Energy

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Data-driven modeling of complex dynamical systems is becoming increasingly popular across various domains of science and engineering. This is thanks to advances in numerical computing, which provides high fidelity data, and to algorithm development in data science and machine learning. Simulations of multicomponent reacting flows can particularly profit from data-based reduced-order modeling (ROM). The original system of coupled partial differential equations that describes a reacting flow is often large due to high number of chemical species involved. While the datasets from reacting flow simulation have high state-space dimensionality, they also exhibit attracting low-dimensional manifolds (LDMs). Data-driven approaches can be used to obtain and parameterize these LDMs. Evolving the reacting system using a smaller number of parameters can yield substantial model reduction and savings in computational cost. In this chapter, we review recent advances in ROM of turbulent reacting flows. We demonstrate the entire ROM workflow with a particular focus on obtaining the training datasets and data science and machine learning techniques such as dimensionality reduction and nonlinear regression. We present recent results from ROM-based simulations of experimentally measured Sandia flames D and F. We also delineate a few remaining challenges and possible future directions to address them. This chapter is accompanied by illustrative examples using the recently developed Python software, PCAfold. The software can be used to obtain, analyze and improve low-dimensional data representations. The examples provided herein can be helpful to students and researchers learning to apply dimensionality reduction, manifold approaches and nonlinear regression to their problems. The Jupyter notebook with the examples shown in this chapter can be found on GitHub at https://github.com/kamilazdybal/ROM-of-reacting-flows-Springer.

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Investigating the trend of hydrogen’s flame velocity profile in relation to pressure and temperature above and below the adiabatic point

Al-Thehabey

2021

International Journal of Hydrogen Energy

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One-Dimensional Modeling of Turbulent Premixed Jet Flames - Comparison to DNS

Cited by 6 publications

References 51 publications

A mixing timescale model for TPDF simulations of turbulent premixed flames

A mixing timescale model for TPDF simulations of turbulent premixed flames

Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches

Investigating the trend of hydrogen’s flame velocity profile in relation to pressure and temperature above and below the adiabatic point

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