Development of a Novel Correlation for Laminar Burning Velocity in Iso-Octane and Hydrogen Blends

Global warming has intensified environmental challenges such as more intense heat waves due to the accumulation of greenhouse gases, primarily carbon dioxide (CO2), which is heavily produced in power generation and transportation sectors, traps heat and raises the Earth's temperature. Significant measures must be taken to reduce its production and impact on our environment. Hydrogen (H2) enrichment is a promising technology that enables higher thermal efficiencies and lower exhaust emissions. However, various parameters need to be optimized for internal combustion engines (ICE), which increases experimental and computational costs. The main goal of this work is to offer a reliable correlation that can be used as an input parameter for turbulent combustion models to enhance predictions and lower the cost of running simulations. Thus, the laminar burning velocity (LBV) of binary fuel mixtures is investigated numerically over a wide range of initial conditions (300-600 K and 1-11 atm) and mixture compositions. This facilitates a better understanding of combustion characteristics and establishes a reliable basis for developing an LBV correlation for C8H18/H2/air mixtures. It was found that as the H2 ratio (D) increases, the temperature exponent trend shifts toward a richer mixture, enabling higher values for lean mixtures and lower values for rich mixtures. This phenomenon is attributed to H2's unique properties, which shift the point of maximum combustion intensity to richer mixtures. An opposite trend is observed and discussed for the pressure exponent of binary fuel mixtures. Given the nonlinear nature of binary fuel blends, a novel correlation is proposed that enables the direct estimation of LBV without requiring knowledge of the values of neat fuels. This new correlation provides accurate estimations across a wide range of initial conditions and mixture compositions, as confirmed by comparing its results with three mixing rules for binary mixtures and data points from the literature.