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An ice- air-water-NAPL multiphase model for simulating NAPL migration in subsurface system under freeze-thaw
Xiaoqin Fu1, Amalia Kokkinaki2, Xiaoqing Shi1
1School of Earth Sciences and Engineering, Nanjing University, Key Laboratory of Earth Fissures Geological Disaster, Ministry of Natural Resources, Nanjing 210023, China.
Abstract:
Non-aqueous phase liquid (NAPL) leakage poses serious threats to human health and the environment. Understanding NAPL migration and distribution in subsurface systems is crucial for developing effective remediation strategies. Multiphase flow modeling is an important tool to quantitatively describe the NAPL migration process in the subsurface. However, most multiphase flow models are built for temperatures typical of warmer climates and above freezing conditions, only considering two phases (water-NAPL) or three phases (air-water-NAPL). To date, few studies simulate NAPL migration in a four-phase system (ice-air-water-NAPL), which would be more appropriate for cold regions. In this study, we developed a coupled non-isothermal multiphase transport model to quantitatively describe NAPL migration in a four-phase (ice, gas, water, NAPL) system. The ice phase was added in the continuity equations and the constitutive relationship between unfrozen water content and temperature was applied to solve the energy and flow equations. The developed mathematical model was evaluated using a two-dimensional experiment under freeze-thaw cycles (FTCs) with an R2 = 0.8803 between the simulated and observed NAPL saturation. Next, we evaluated the effect of freezing-induced changes in pressure and density between LNAPL and DNAPL on NAPL distribution under freeze-thaw condition. Simulation results show that ignoring the impact of ice formation and thawing during freeze-thaw cycles for LNAPL and DNAPL transport simulations can result in up to a 48% and 13% difference in model predictions of local NAPL saturations respectively, affecting model predictions of overall NAPL spatial distributions and potentially predicted remediation effectiveness.
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