quick design guide quick tips river inflow …
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River inflow characteristics, including spatial-temporal
distributions of mean velocity, Reynolds stresses, and turbulence
intensities over the energy extraction plane of a hydrokinetic
machine are needed to quantify a site’s annual energy
production, machine loads, machine performance and levelized
cost of energy.
1. Background 3. Results 3. Results (continued)
1.Classical models generally perform well in describing river inflow
characteristics.
2.Maximum longitudinal turbulence intensities near the bed are
approximately between 10 to 20% of the local mean velocity,
similar to atmospheric boundary layer flows.
3.River inflow characterization is challenging due to the high
variability of depth and flow, which causes significant variations
of inflow mean velocity and turbulence intensity over the design
life of the machine.
5. References
6. Acknowledgements
This research was funded by the U.S. Department of Energy under
Contract DE-AC05-00OR22725. The authors also thank Bob Holmes of the
United States Geological Survey for providing data he collected on the
Missouri River.
RIVER INFLOW CHARACTERISTICS FOR HYDROKINETIC ENERGY CONVERSION
1 Oak Ridge National Laboratory, Oak Ridge, TN 37831; Mechanical Engineering Department, University of Washington, Seattle, WA
V. Neary1, D. Sale2, B. Gunawan1,
1. Carling, P. A., Z. Cao, M. J. Holland, D. A. Ervine, and K. Babaeyan-
Koopaei (2002), Turbulent flow across a natural compound channel, Water
Resour. Res., 38(12), 1270.
2. Holmes, R.R. and Garcia, M.H. (2008). Flow over bedforms in a large sand-bed
river: A field investigation. Journal of Hydraulic Research, 46(3): p. 322-333.
3. McQuivey, R.S., Summary of Turbulence Data from Rivers, Conveyance Channels,
and Laboratory Flumes. USGS Report 802-B Turbulence in Water, 1973.
4. Nezu, I. and Nakagawa, H. Turbulence in Open-Channel Flows, A.A. Balkema,
Rotterdam, 1993.
5. Nikora, V.I. and Smart, G.M., (1997). Turbulence characteristics of New Zealand
gravel-bed rivers. Journal of Hydraulic Engineering. 123(9): p. 764-773.
Table 1. Bulk flow properties of reviewed open channel flow data
Figure 3.4 Effects of large depth variability on the location of the swept area (energy extraction area)
relative to the velocity and turbulence profiles.
Figure 3.3 (a) Mean longitudinal velocity profiles. (b) Longitudinal turbulence intensity profiles. The
dashed horizontal line indicates z = 0.5m. Machines will typically operate at depths greater than 0.5m off
the bed. (c) Longitudinal turbulence intensity profiles with z normalized by the flow depth .
Figure 3.2 (a) Power law velocity profiles with z normalized by D and mean velocity normalized by
maximum mean velocity. The solid black line represents the best fit of the power law with exponent 1/α
through the data, and the resulting best fit α = 5.4 (R2 = 0.999). The dotted and dashed lines represent the
power law with exponent 1/3 and 1/12, respectively; (b) Exponential decay law profiles by Nezu and
Nakagawa (1993) compared to field measurements, with z normalized by D and normal stresses normalized
by shear velocity.
Figure 3.1. Daily flow and depth time-series record on the Missouri River, Nebraska (USGS 06610000).
Blue indicates the daily values. Brown indicates the daily mean values for the (POR). The inset plots show
the flow and depth time series during field measurements by Holmes and Garcia (2009).
Figure 1. Typical distributions of velocity and turbulence and sketch of (a) surface mounted vertical axis
turbine; and (b) bottom mounted horizontal-axis turbine.
4. Conclusions
2. Objectives
Review published turbulent flow data from large rivers, a canal and laboratory flumes to:
1. Evaluate the validity of classical models that describe the depth
variation of the time-mean velocity and turbulent Reynolds stresses 2. Determine the range of velocities and longitudinal turbulence intensities
acting on EEP
a b c
7. Point of Contact
Vincent Neary, Ph.D., P.E., Sr. Research Engineer & MHK Technology Lead, [email protected]