Towards better predicting the settling velocity of film-shaped microplastics based on experiment and simulation data

The properties of microplastics determine their settling velocities and affect the fates and migration pathways of microplastics. This paper has simulated the settling velocities of film–shaped microplastics, which are present in natural aquatic environments. The numerical results provided more data...

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Veröffentlicht in:Marine pollution bulletin 2024-06, Vol.203, p.116493-116493, Article 116493
Hauptverfasser: Ji, Chaoqun, Zhang, Jinfeng, Liu, Guangwei, Zhang, Qinghe, Xing, Enbo
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Sprache:eng
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Zusammenfassung:The properties of microplastics determine their settling velocities and affect the fates and migration pathways of microplastics. This paper has simulated the settling velocities of film–shaped microplastics, which are present in natural aquatic environments. The numerical results provided more data to fit the terminal settling velocities of film–shaped microplastics. Comparison between the particle definition and the equivalent spherical diameter confirmed that the particle definition is more suitable for film–shaped microplastics. In the transitional flow regime, CD decreases linearly with Re. As Re further increases, CD gradually converges at approximately 1.20. By integrating the experimental and simulated data, a new explicit formula for predicting the settling velocity of film–shaped microplastics has been presented with the optimal shape parameter f. The presented formula achieves better performance (MAPE = 6.6 %, RMSE = 16.8 %, and R2 = 0.99) than the existing formulas for settling velocity for film–shaped microplastics, closely rivaling that of the ensemble learning algorithm. [Display omitted] •Supply universal settling velocity data for film-shaped MPs by direct numerical simulation•Settling mechanism of film-shaped MPs was investigated.•A model for predicting settling velocity of film-shaped MP incorporating optimal shape parameter was presented.•Better predictive performance than existing formulas and comparable to machine learning algorithm
ISSN:0025-326X
1879-3363
DOI:10.1016/j.marpolbul.2024.116493