Key Takeaways & Executive Findings
- •• • Model validation: CFD predictions of particle concentration distribution deviated by only 6.4% from experiments, and mainstream solid-phase velocity errors averaged 3.3%, confirming high fidelity for engineering design. • • Concentration effect: Ice slurry at 60% initial concentration achieved an effective shear stress ratio of 77.39%, enabling thorough cleaning of both upper and lower pipe walls; at 20% concentration, no effective shear occurred at the pipe bottom, with an overall ratio of 42.18%. • • Velocity dominance: Flow velocity is the most influential parameter; increasing from 0.2 to 1.0 m/s raised the effective shear stress ratio from negligible to 83.16%, with cumulative shear stress increasing and becoming more uniform. • • Optimal parameters: The combination of 50% initial concentration, 0.5 mm particle diameter, and 1.0 m/s flow velocity yielded an average cumulative shear stress of 11.59 Pa·s and an effective cumulative shear stress of 9.89 Pa·s, representing the best cleaning performance.
Abstract
Ice slurry pigging is an emerging technology for cleaning water supply pipelines, yet quantitative understanding of its cleaning mechanisms and optimal operating conditions remains limited. This study developed a computational fluid dynamics (CFD) model integrating the kinetic theory of granular flows (KTGF), the Euler-Euler method, and the shear stress transport (SST) model to simulate ice slurry flow and wall shear stress distribution. The model was validated against experimental data, showing a 6.4% error in particle concentration distribution, a 3.3% average error in solid-phase velocity in the mainstream region, and a pressure drop error within 20%. A total of 125 simulations were performed under varying initial concentrations (20%–60%), particle diameters (0.3–1.0 mm), and flow velocities (0.2–1.0 m/s). Results indicate that higher initial concentrations (60%) achieve effective cleaning of both upper and lower pipe walls, with an effective shear stress ratio of 77.39%. Larger particles exhibit pronounced upward movement, increasing non-uniformity in solid distribution. Flow velocity is the dominant factor affecting wall shear stress; at 1.0 m/s, the effective shear stress ratio reaches 83.16%. The optimal parameters for cumulative shear stress were identified as 50% initial concentration, 0.5 mm particle diameter, and 1.0 m/s flow velocity, yielding an average cumulative shear stress of 11.59 Pa·s. For effective cumulative shear stress, the same parameters produced 9.89 Pa·s, while the highest effective shear stress ratio (88.50%) was achieved with 0.3 mm particles at 1.0 m/s and 50% concentration. This research provides theoretical guidance for ice slurry pigging operations in water supply pipelines.
1. Introduction
Long-term operation of water supply pipelines leads to the formation of pipe scales composed of biofilms, sediments, and metal oxides. These scales increase frictional resistance, elevate energy consumption, and pose risks of secondary drinking water contamination. Traditional cleaning methods often involve chemical agents or mechanical pigging, which can be costly, environmentally intrusive, or insufficient for complex geometries. Ice slurry pigging, utilizing a two-phase fluid of ice particles (≤1 mm) in a carrier liquid, offers a promising alternative due to its non-polluting nature, low energy demand, and economic viability. However, the non-Newtonian behavior of high-concentration ice slurry complicates flow prediction, and real-time monitoring during in-situ cleaning is impractical, necessitating robust numerical models.
This study addresses the bottleneck of quantitative design by developing a CFD model that couples the kinetic theory of granular flows with the Euler-Euler approach and the SST turbulence model. The model accurately reproduces experimental flow characteristics, enabling systematic exploration of 125 parameter combinations. By introducing cumulative shear stress as a novel evaluation metric, the study identifies optimal initial concentration, particle size, and flow velocity for maximal cleaning efficiency. These findings provide actionable insights for engineers seeking to optimize ice slurry pigging protocols, reducing trial-and-error in field applications.
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LI Ziyi, TAO Hui, ZHU Qixuan, SHEN Zhouwei, TANG Yangyang, LIN Tao (2026). CFD-Based Investigation of Ice Slurry Pigging and Optimization of Cleaning Parameters. Journal of Environmental Engineering Technology. https://doi.org/10.13205/j.hjgc.202604014
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Frequently Asked Questions
How does the model account for ice melting during the pigging process, and what is the potential error in predicting cleaning performance under real-world conditions?
The model does not explicitly simulate ice melting; it assumes a constant particle size distribution. The authors acknowledge that in practice, ice melting can occur, leading to a wider particle size range. However, the model's validation against experimental data (within 6.4% for concentration and 3.3% for velocity) suggests that the error remains acceptable for engineering purposes, though field conditions may introduce additional deviations.
What is the physical explanation for the non-monotonic effect of particle diameter on cumulative shear stress, and how should engineers select particle size?
The study found no clear trend in cumulative shear stress with particle diameter, but larger particles caused greater non-uniformity in distribution, leading to higher shear on the upper pipe wall. For uniform cleaning, smaller particles (e.g., 0.3 mm) are preferable, as they yield a higher effective shear stress ratio (88.50%). However, the optimal cumulative shear stress was achieved with 0.5 mm particles, suggesting a trade-off between magnitude and uniformity.
How does flow velocity influence the cleaning mechanism, and what is the minimum velocity required for effective cleaning?
Flow velocity is the most critical factor. At 0.2 m/s, wall shear stress is insufficient for effective cleaning. At 1.0 m/s, the effective shear stress ratio reaches 83.16%, and the distribution becomes more uniform due to turbulent mixing. Therefore, a velocity of at least 1.0 m/s is recommended for practical operations.
What are the limitations of the CFD model in scaling up to real pipeline networks, and how can the results be extrapolated?
The model was validated for a single pipe geometry; scaling to complex networks with bends and junctions may require additional validation. The 125 simulations cover a range of parameters, but extrapolation beyond these ranges (e.g., higher concentrations or velocities) should be done cautiously. The authors suggest that the model provides a reliable basis for initial design, but pilot testing is recommended for critical applications.
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