SedimentNet — a 1D-CNN machine learning model for prediction of hydrodynamic forces in rapidly varied flows
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-08176-3.pdf
Reference47 articles.
1. Riaz MZB et al (2022) Performance evaluation of force transducer for the observation of sediment entrainment in rapidly varied flows. J Atmos Oceanic Tech 39(10):1579–1589
2. Cao D, Chiew Y-M, Yang S-Q (2016) Injection effects on sediment transport in closed-conduit flows. Acta Geophys 64(1):125–148
3. Foster, D., et al., Field evidence of pressure gradient induced incipient motion. Journal of Geophysical Research: Oceans, 2006. 111(C5).
4. Yang CT, Marsooli R, Aalami MT (2009) Evaluation of total load sediment transport formulas using ANN. Int J Sedim Res 24(3):274–286
5. Yang S-Q et al (2020) Three-dimensional velocity distribution in straight smooth channels modeled by modified log-law. J Fluids Eng. https://doi.org/10.1115/1.4044183
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