Fingerprint Fingerprint is based on mining the text of the expert's scholarly documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

  • 6 Similar Profiles
sediment Earth & Environmental Sciences
resuspension Earth & Environmental Sciences
sediments Physics & Astronomy
vegetation Earth & Environmental Sciences
laboratory experiment Earth & Environmental Sciences
physical process Earth & Environmental Sciences
prediction Earth & Environmental Sciences
laboratory Earth & Environmental Sciences

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Research Output 2012 2017

  • 8 Article
  • 1 Conference contribution
  • 1 Review article

The influence of turbulent bursting on sediment resuspension under unidirectional currents

Salim, S., Pattiaratchi, C., Tinoco, R., Coco, G., Hetzel, Y., Wijeratne, S. & Jayaratne, R. Jul 19 2017 In : Earth Surface Dynamics. 5, 3, p. 399-415 17 p.

Research output: Research - peer-reviewArticle

resuspension
sediment
sediments
critical velocity
sediment transport

A laboratory study on sediment resuspension within arrays of rigid cylinders

Tinoco, R. O. & Coco, G. Jun 1 2016 In : Advances in Water Resources. 92, p. 1-9 9 p.

Research output: Research - peer-reviewArticle

resuspension
sediment
laboratory
shield
turbulence

Development of a novel, robust, sustainable and low cost self-powered water pump for use in free-flowing liquid streams

Muriel, D. F., Tinoco, R. O., Filardo, B. P. & Cowen, E. A. Jun 1 2016 In : Renewable Energy. 91, p. 466-476 11 p.

Research output: Research - peer-reviewArticle

Liquids
Water
Water supply
Industrial applications
Carbon footprint

Exploiting surface turbulence metrics and secondary flows in straight river reaches and open channels

Cowen, E. A., Schweitzer, S. A., Citerone, V. R., King, A. T., Johnson, E. D. & Tinoco, R. O. 2016 River Flow - Proceedings of the International Conference on Fluvial Hydraulics, RIVER FLOW 2016. CRC Press/Balkema, p. 7-9 3 p.

Research output: ResearchConference contribution

machine learning
vegetation
open channel flow
train
statistical analysis