Risk assessment of gas plume dispersion in bunkering of alternative fuel through validated computational fluid dynamics approach

Abstract

Abstract Maritime decarbonization is crucial in fighting climate change. Adopting low-carbon alternative fuels could help reduce greenhouse gas (GHG) emissions by 50% by 2050 compared to 2008. While liquified natural gas (LNG) has gained a strong foothold in the maritime sector over the past decades, the trend is shifting toward adopting lower and zero-carbon hydrogen and its carriers, such as methanol and ammonia, as alternative fuels. Quantifying risks from accidental leakages during storage and transfer of these alternative fuels using numerical models is required and commonly adopted in industries. The process leverages multiple models and tools for risk assessments with different accuracy levels and costs ranging from integral models to Lagrangian techniques and Eulerian approaches such as computational fluid dynamics (CFD). Increasingly, CFD approaches are employed for near-field dispersion analysis to assess the risk of accidental leakages. Despite their high computational cost, CFD models offer better accuracy than integral and Lagrangian models. In this work, we developed a CFD-based framework as a quantitative tool for risk assessment purposes. Our framework aims to (1) offer finer gas dispersion resolution with a detailed representation of surrounding structures and geometries, (2) minimize reliance on assumptions and empirical models to capture flow dynamics and gas dispersions accurately, and (3) mitigate conservative estimates and over-design of safety measures. The framework was validated using experimental data from various field tests, including a recent one involving releasing cryogenic liquids into the atmosphere. After successfully validating and comparing with experiments and other available tools, the proposed framework investigated gas plume dispersion from accidental leakages during fuel bunkering operations in ship-to-ship and shore-to-ship scenarios. The data obtained from CFD simulations was subsequently utilized to construct a surrogate model for fast prediction of gas plume behavior under varying environmental conditions. This facilitated the development of an effective emergency response plan.