PLIF investigation and numerical analysis of a heavy-duty gas turbine DLN combustor

Yuan Feng , Xiaodong Ren , Chunwei Gu

Propulsion and Energy ›› 2025, Vol. 1 ›› Issue (1) : 23

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Propulsion and Energy ›› 2025, Vol. 1 ›› Issue (1) :23 DOI: 10.1007/s44270-025-00031-9
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PLIF investigation and numerical analysis of a heavy-duty gas turbine DLN combustor

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Abstract

This study investigates the mixing field of a 300MW F-class heavy-duty dry low NOx (DLN) combustor using PLIF experiments and numerical simulations. Innovative optical schemes, shading designs, and tracer gas generation systems were developed to facilitate the successful execution of the experiments. Numerical simulations assessed various turbulence models by analyzing velocity distribution and mixing characteristics. The results indicate that the combustor’s headend design exhibits strong mixing capabilities, with consistent mixing field profiles observed under varying flow conditions and identical equivalence ratios. Among the turbulence models, large eddy simulation (LES) most accurately reproduced experimental results, especially in terms of velocity distribution, while Reynolds-averaged Navier–Stokes models with a default turbulent Schmidt number of 0.7 significantly underestimated the mixing rate. Additionally, reducing the turbulent Schmidt number enhanced the mixing rate, with a value of 0.2 in the Realizable k-ε model providing results closely aligned with experimental and LES findings. The experimental and numerical methodologies presented in this study provide valuable insights for future research on mixing phenomena in similar combustor designs. Future work may focus on exploring the complex flow and mixing mechanisms within the premixing tube of DLN combustors.

Keywords

Heavy-duty DLN combustor / PLIF experiments / Tracer gas generation / Turbulence model / Turbulent Schmidt number

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Yuan Feng, Xiaodong Ren, Chunwei Gu. PLIF investigation and numerical analysis of a heavy-duty gas turbine DLN combustor. Propulsion and Energy, 2025, 1(1): 23 DOI:10.1007/s44270-025-00031-9

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Funding

Tsinghua-Toyota Joint Research Fund

National Natural Science Foundation of China(52176039)

National Science and Technology Major Project(J2019-I-0009-0009)

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