Abstract
Numerical modeling of radiative transfer in nongray reacting media is a challenging problem in computational science and engineering. The choice of radiation models is important for accurate and efficient high-fidelity combustion simulations. Different applications usually involve different degrees of complexity, so there is yet no consensus in the community. In this paper, the performance of different radiative transfer equation (RTE) solvers and spectral models for a turbulent piloted methane/air jet flame are studied. The flame is scaled from the Sandia Flame D with a Reynolds number of 22,400. Three classes of RTE solvers, namely the discrete ordinates method, spherical harmonics method, and Monte Carlo method, are examined. The spectral models include the Planck-mean model, the full-spectrum k-distribution (FSK) method, and the line-by-line (LBL) calculation. The performances of different radiation models in terms of accuracy and computational cost are benchmarked. The results have shown that both RTE solvers and spectral models are critical in the prediction of radiative heat source terms for this jet flame. The trade-offs between the accuracy, the computational cost, and the implementation difficulty are discussed in detail. The results can be used as a reference for radiation model selection in combustor simulations.
Original language | English |
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Title of host publication | Proceedings of the ASME 2021 Heat Transfer Summer Conference, HT 2021 |
Publisher | American Society of Mechanical Engineers (ASME) |
ISBN (Electronic) | 9780791884874 |
DOIs | |
State | Published - 2021 |
Event | ASME 2021 Heat Transfer Summer Conference, HT 2021 - Virtual, Online Duration: Jun 16 2021 → Jun 18 2021 |
Publication series
Name | Proceedings of the ASME 2021 Heat Transfer Summer Conference, HT 2021 |
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Conference
Conference | ASME 2021 Heat Transfer Summer Conference, HT 2021 |
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City | Virtual, Online |
Period | 06/16/21 → 06/18/21 |
Funding
This research was supported by National Science Foundation and the Department of Energy through Grant No. NSF1258635 (WG, MFM, SR), and by the National Science Foundation under Grant No. 1756005 (CD, SR). WG and RS acknowledge the support from the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.