DOI: 10.5194/hess-24-5173-2020
论文题名: Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow
作者: Pizarro A. ; Dal Sasso S.F. ; Perks M.T. ; Manfreda S.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期: 11 起始页码: 5173
结束页码: 5185
语种: 英语
Scopus关键词: Flow measurement
; Reduction
; Rivers
; Spatial distribution
; Turbulent flow
; Velocimeters
; Velocity measurement
; Water management
; Distribution index
; Emerging technologies
; Image velocimetry
; Monitoring capabilities
; Particle image velocimetries
; Particle tracking velocimetry
; Spatial clustering
; Video image sequences
; Tracers
; identification method
; spatial distribution
; subsurface flow
; tracer
英文摘要: River monitoring is of particular interest as a society that faces increasingly complex water management issues. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities but have also generated new challenges for the harmonised use of devices and algorithms. In this context, optical-sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and their spatial distribution. Therefore, a principal research goal is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of tracer clustering, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used imagevelocimetry algorithms were adopted: (i) particle-tracking velocimetry (PTV) and (ii) particle image velocimetry (PIV). A descriptor of the seeding characteristics (based on seeding density and tracer clustering) was introduced based on a newly developed metric called the Seeding Distribution Index (SDI). This index can be approximated and used in practice as SDI D=v0.1/(ρ/ρcv1) where v, ρ, and ρcv1 are the spatial-clustering level, the seeding density, and the reference seeding density at v= 1, respectively. A reduction in image-velocimetry errors was systematically observed for lower values of the SDI; therefore, the optimal frame window (i.e. a subset of the video image sequence) was defined as the one that minimises the SDI. In addition to numerical analyses, a field case study on the Basento river (located in southern Italy) was considered as a proof of concept of the proposed framework. Field results corroborated numerical findings, and error reductions of about 15.9% and 16.1% were calculated - using PTV and PIV, respectively - by employing the optimal frame window. © Author(s) 2020. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162560
Appears in Collections: 气候变化与战略
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作者单位: Pizarro, A., Department of European and Mediterranean Cultures, University of Basilicata, Matera, 75100, Italy; Dal Sasso, S.F., Department of European and Mediterranean Cultures, University of Basilicata, Matera, 75100, Italy; Perks, M.T., School of Geography, Politics and Sociology, Newcastle University, Newcastle-upon-Tyne, NE1g 7RU, United Kingdom; Manfreda, S., Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Naples, 80125, Italy
Recommended Citation:
Pizarro A.,Dal Sasso S.F.,Perks M.T.,et al. Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow[J]. Hydrology and Earth System Sciences,2020-01-01,24(11)