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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, nearyvs@ornl.gov