Emerging Trends in Combustion Modeling: Numerical and Experimental Insights
Keywords:
Combustion Modeling, Industrial Furnaces, Reynolds-Averaged Navier-Stokes (RANS), Laser-Induced Fluorescence (LIF)Abstract
Combustion modeling has evolved significantly over the years, integrating both numerical and experimental approaches to enhance the understanding of complex combustion phenomena. Advances in Computational Fluid Dynamics (CFD), machine learning integration, and high-fidelity turbulence-chemistry interactions have led to improved predictive capabilities, enabling researchers and engineers to optimize combustion processes in various applications, including internal combustion engines, gas turbines, and industrial furnaces.
This review discusses the latest developments in combustion modeling, focusing on turbulence modeling techniques, such as Reynolds-Averaged Navier-Stokes (RANS), Large Eddy Simulation (LES), and Direct Numerical Simulation (DNS), which are essential for accurately capturing turbulent flow and chemical reactions. Additionally, reduced-order chemical kinetics approaches, including skeletal mechanism reduction, tabulated chemistry, and artificial intelligence (AI)-assisted modeling, are explored to improve computational efficiency while maintaining accuracy.
On the experimental front, advanced diagnostic techniques such as laser-induced fluorescence (LIF), particle image velocimetry (PIV), chemiluminescence imaging, and X-ray synchrotron methods have provided crucial insights into combustion dynamics, validating numerical models and refining predictive accuracy. The integration of high-speed imaging and non-intrusive laser diagnostics has significantly improved our ability to capture real-time flame behavior, pollutant formation, and fuel-air mixing characteristics.
Despite these advancements, significant challenges remain in achieving accurate multi-physics coupling, where the interactions between fluid dynamics, heat transfer, and chemical reactions must be precisely modeled. Additionally, the high computational cost of high-fidelity simulations continues to be a limiting factor, necessitating the development of hybrid CFD-AI models and next-generation computing techniques.
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