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Canals located within the monsoon freezing zone are vulnerable to frost heave damage, which poses significant risks to agricultural safety. Therefore, understanding and analyzing frost heave processes in these canal systems becomes crucial to reduce potential harm. A common method for studying this phenomenon involves developing a combined water–heat interaction model, which takes into account multiple factors such as water movement through the soil, shifts in frozen ground positions, and the way frozen earth responds to stress over time. However, it is worth noting that traditional simulation approaches using finite-element techniques struggle to capture the exact interactions between soil particles and moisture-temperature dynamics, leading to less precise visual representations of these processes. To better capture the detailed behavior of soil's water and heat conditions at a microscopic level and boost the clarity of model outputs, this study proposes a combined method that employs the Python-based numerical solver library for partial differential equations, called FiPy, and tools for the discrete-element method (DEM). This method takes advantage of the discrete-element model's ability to handle discontinuous porous materials, while also utilizing FiPy's adaptable framework for solving complex mathematical equations. Testing was performed on typical scenarios using both traditional finite-element approaches and the new FiPy-DEM combination. By comparing the accuracy levels through error assessments, the findings show that the FiPy-DEM approach can reliably forecast the soil's water–heat conditions, that is to say, offering improved predictions over conventional methods when dealing with discontinuous media characteristics and localized temperature variations. This advancement could help engineers better anticipate frost-related structural risks in canal systems, ultimately contributing to safer agricultural infrastructure in vulnerable regions.
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Zhou et al. (2025) studied this question.
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