Applications of Second Log-Wake Law for Turbulent Velocity Distributions in Laboratory Flumes and Natural Rivers

2021 ◽  
Vol 147 (9) ◽  
pp. 06021010
Author(s):  
Narendra Patel ◽  
Joshan Shahi ◽  
Junke Guo
Water Policy ◽  
2018 ◽  
Vol 20 (5) ◽  
pp. 933-952 ◽  
Author(s):  
Sameer H. Shah ◽  
Lucy Rodina

Abstract The protection of natural rivers and watersheds face important concerns related to environmental (in)justice and (in)equity. Using the Queensland Wild Rivers Act as a case study, we advocate that ethical water governance attends to multiple and diverse values, specifically in ways that: (i) locate them within stakeholders' claims of inequality that emerge from a given or practiced water ethic; and (ii) historicize and understand them as resonating or reflecting natural resource management frameworks that have led to structural injustices. This approach, combined with adaptive co-governance, can contribute to more inclusive water ethics and even support reflexive spaces where radical change in social-ecological resource governance can be imagined.


2002 ◽  
Vol 4 (1) ◽  
pp. 39-51
Author(s):  
Helen Kettle ◽  
Keith Beven ◽  
Barry Hankin

A method has been developed to estimate turbulent dispersion based on fuzzy rules that use local transverse velocity shears to predict turbulent velocity fluctuations. Turbulence measurements of flow around a rectangular dead zone in an open channel laboratory flume were conducted using an acoustic Doppler velocimeter (ADV) probe. The mean velocity and turbulence characteristics in and around the shear zone were analysed for different flows and geometries. Relationships between the mean transverse velocity shear and the turbulent velocity fluctuations are encapsulated in a simple set of fuzzy rules. The rules are included in a steady-state hybrid finite-volume advection–diffusion scheme to simulate the mixing of hot water in an open-channel dead zone. The fuzzy rules produce a fuzzy number for the magnitude of the average velocity fluctuation at each cell boundary. These are then combined within the finite-volume model using the single-value simulation method to give a fuzzy number for the temperature in each cell. The results are compared with laboratory flume data and a computational fluid dynamics (CFD) simulation from PHOENICS. The fuzzy model compares favourably with the experiment data and offers an alternative to traditional CFD models.


2014 ◽  
pp. 2173-2180 ◽  
Author(s):  
Z Vecsernyés ◽  
M Destrieux ◽  
N Andreini ◽  
J Boillat

Sign in / Sign up

Export Citation Format

Share Document