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<p>Research on converting biomass into heat or power has recently plummeted owing to the importance of renewable energy sources in today’s world. Grate-firing biomass combustion furnace technology has attracted much attention due to its capability of accommodating a wide range of biomass fuels. While this technology has been well established, there is still room for improvement, which can be achieved through optimization studies using numerical simulations. Their success, however, depends on their ability to accurately reproduce the flow field inside these biomass combustion systems. Turbulent flow field inside a grate-firing biomass furnace has traditionally been modelled using RANS-based turbulence models. In this study, a sequential RANS/LES modelling framework is proposed as a practical alternative to the widely adopted RANS-based RNG <i>k</i>−ε model, and is assessed as a computationally feasible coarse scale-resolving approach under practical grid limitations rather than as a fully resolved LES. The results showed improved predictions of temperature profiles, species concentrations, and key combustion parameters, including Damköhler number, scalar dissipation rate, and turbulent flame speed. The improved characterization of the turbulent flow field including residence time within the furnace resulted in enhanced prediction of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(N{O_x}\)</EquationSource> </InlineEquation> emission and related intermediate species.</p>

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A Sequential RANS/LES Turbulence Modelling Framework for the Gas-Phase Combustion of Grate-Firing Biomass Furnaces

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • A H M Nazmush Sakib,
  • Madjid Birouk

摘要

Research on converting biomass into heat or power has recently plummeted owing to the importance of renewable energy sources in today’s world. Grate-firing biomass combustion furnace technology has attracted much attention due to its capability of accommodating a wide range of biomass fuels. While this technology has been well established, there is still room for improvement, which can be achieved through optimization studies using numerical simulations. Their success, however, depends on their ability to accurately reproduce the flow field inside these biomass combustion systems. Turbulent flow field inside a grate-firing biomass furnace has traditionally been modelled using RANS-based turbulence models. In this study, a sequential RANS/LES modelling framework is proposed as a practical alternative to the widely adopted RANS-based RNG k−ε model, and is assessed as a computationally feasible coarse scale-resolving approach under practical grid limitations rather than as a fully resolved LES. The results showed improved predictions of temperature profiles, species concentrations, and key combustion parameters, including Damköhler number, scalar dissipation rate, and turbulent flame speed. The improved characterization of the turbulent flow field including residence time within the furnace resulted in enhanced prediction of \(N{O_x}\) emission and related intermediate species.