Quantifying wave-induced fuel consumption and CO2 emissions: a high-fidelity free-running CFD approach

초록

Accurate estimation of ship fuel consumption and greenhouse gas (GHG) emissions is imperative for complying with increasingly stringent maritime environmental regulations. However, conventional macroscopic Life Cycle Assessments (LCA) and data-driven machine learning models frequently rely on static, calm-water assumptions, failing to explicitly capture the dynamic hydrodynamic penalties imposed by realistic sea states. The primary innovation of this research lies in establishing a direct, physics-based linkage between fully coupled 6-DoF hydrodynamic penalties and macroscopic Tank-to-Wake CO2 emissions, overcoming the limitations of conventional static models. To achieve this, this study employs high-fidelity, free-running Computational Fluid Dynamics (CFD) simulations to rigorously quantify the wave-induced added resistance and its direct impact on fuel economy. By systematically varying wave headings and heights under a constant engine revolution, the complex fluid-structure interactions were translated into explicit operational penalties. The results reveal an extreme sensitivity to frontal wave encounters; navigating in head seas reduced the advance speed by 21.8% and precipitated a 43.6% surge in fuel consumption and CO2 emissions per nautical mile compared to the calm-water baseline. Furthermore, escalating the bow wave height to 7.2 m induced an alarming 86.0% fuel penalty ratio. Extrapolating these metrics over a 1500 NM voyage demonstrated that continuous exposure to severe sea states demands hundreds of tons of additional fuel, significantly magnifying the long-term environmental footprint. Ultimately, this study proves the fundamental necessity of incorporating explicit sea environmental factors into emission calculation formulas. The quantitatively derived hydrodynamic penalties provide robust, physics-based insights expected to significantly contribute to the advancement and refinement of future predictive models for maritime decarbonization.

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