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Observations of velocity conditions near a hydroelectric turbine draft tube exit using ADCP measurements
Flow Measurement and Instrumentation Flow Measurement and Instrumentation 18 (2007) 148–155 www.elsevier.com/locate/flowmeasinst
Observations of velocity conditions near a hydroelectric turbine draft tube exit using ADCP measurements Christopher B. Cook ∗ , Marshall C. Richmond, John A. Serkowski Pacific Northwest National Laboratory, PO Box 999, Richland, WA, 99352, USA Received 29 September 2006; received in revised form 7 February 2007; accepted 4 April 2007
Abstract Measurement of flow characteristics near hydraulic structures is an ongoing challenge because of the need to obtain rapid measurements of time-varying velocity over a relatively large spatial domain. This paper discusses use of an acoustic Doppler current profiler (ADCP) to measure the rapidly diverging flow exiting from an operating hydroelectric turbine draft tube exit. The resolved three-dimensional velocity vectors show a highly complex and helical flow pattern developed near to and downstream of the exit. Velocity vectors were integrated across the exit and we computed an uneven percentage of flow (67%/33%) passing through the two draft tube barrels at a mid-range turbine discharge, consistent with physical model results. In addition to the three-dimensional velocity vectors, the individual one-dimensional velocities measured by each of the four ADCP beams can be separately used as calibration and validation data sets for numerical and physical models. This technique is demonstrated by comparing along-beam ADCP velocity measurements to data collected in a scaled physical model. c 2007 Elsevier Ltd. All rights reserved.
Keywords: Acoustic Doppler current profiler; Draft tube; Hydroelectric turbine; Velocity measurement; Discharge measurement
1. Introduction Kaplan-type hydroelectric turbines with adjustable runner blades are generally used in low-head plants with moderate to high discharge (Roberson et al. [1]). Swirling flow generated by the runner then enters a conical diffuser followed by an elbow-type draft tube that typically has multiple barrels. The purpose of the draft tube is to act as a diffuser by reducing flow velocity and recovering dynamic pressure back into static pressure. Swirling, turbulent flow within the draft tubes is three-dimensional (3-D) and exhibits a wide range of unsteady velocity, pressure, and wall friction variations that have been observed in laboratory experiments (Ciocan and Avellan [2], Gebart et al. [3]) and simulations using 3-D computational fluid dynamics (CFD) models (Paik et al. [4], Lai et al. [5]). The expanding cross-sectional area in the draft tube causes adverse pressure gradient conditions that can lead to repeated occurrences of flow separation phenomena, ranging ∗ Corresponding address: Pacific Northwest National Laboratory, Hydrology Group, PO Box 999, 99352 Richland, WA, USA. Tel.: +1 509 375 6878; fax: +1 509 372 6089. E-mail address: [email protected] (C.B. Cook).
c 2007 Elsevier Ltd. All rights reserved. 0955-5986/$ - see front matter doi:10.1016/j.flowmeasinst.2007.04.002
from localized flow reversals to large-scale stall-like behavior depending on the turbine operating point. These conditions result in an uneven distribution of discharge between the barrels (Mauri et al. [6]) and a decrease in turbine operating efficiency. An unsteady vortex rope can form immediately downstream of the runner, which can induce unsteady pressure pulsations, especially at partial load operating conditions (Skotak et al. [7]). Vortex breakdown (Sarpkaya [8]) can also occur in the conical diffuser section. The aggregate effect of these phenomena is to produce highly unsteady and non-uniform flow in the draft tube that then propagates into the tailrace. In addition to effects on flow conditions and turbine operating efficiency, turbulent flow within the draft tube is of concern because of its potential effect upon fish (Cada [9]). The increased turbulent shear elevates the risk of harming juvenile anadromous fish that may be passing through the turbine during their downstream migration (Neitzel et al. [10], Deng et al. [11]). Considering the importance of the draft tube to engineering design and fish passage, both physical and CFD models should be validated through comparisons to fullscale field measurements of velocity within the draft tube and immediately downstream. However, collecting prototype data
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near an operating Kaplan-type turbine requires improvements in measurement and data analysis methods. Instruments utilizing the Doppler shift are commonly used to measure velocity conditions in rivers, reservoirs, and lakes. Among this class of instruments, acoustic Doppler current profilers (ADCPs) in particular are commonly used to measure discharge (Gordon et al. [12]), as well as collect longterm observations of currents in a fixed location (Schott and Johns [13], Cook et al. [14]). ADCPs are generally selected because they can profile long distances, do not disrupt the measured flow field (i.e., are non-intrusive), and are robust even in the most unfavorable of flow conditions. ADCPs typically use three or more non-orthogonal acoustic beams to resolve the 3-D flow field (Lohrmann et al. [15], Simpson [16]). Since the beams diverge from the transducer head, beam velocity measurements are separated, often by relatively large distances. If flow heterogeneity is large between the individual beam measurements, such as what might be expected downstream of a draft tube exit, the fundamental premise used to resolve the 3-D flow field is no longer valid. However, individual beam velocity data remains accurate under most conditions as calibration and validation data sets for CFD and physical models. The use of individual acoustic beam information is not novel. Variations in individual beam velocities have been used to extract flow turbulence information, such as turbulent kinetic energy and shear production (Williams and Simpson [17], Stacey et al. [18], Stacey et al. [19]). Turbulence and higher order statistical information regarding the flow field is generally extracted using a variance technique. However, this method a priori assumes homogeneity of the flow field between each orthogonal beam pair and therefore should not be used in zones where mean flow velocity gradients are rapidly changing within the beam swath. Head configuration and the number of acoustic beams vary the turbulence quantities that can be measured even under homogeneous flow conditions (Ott et al. [20]). An alternative to using ADCPs or similar acoustic profilers is to measure draft tube velocities with point probes such as propeller meters, electromagnetic meters, or acoustic Doppler velocimeters (ADV). Of these, the ADV is attractive because it can simultaneously measure all three velocity components and turbulent fluctuations (Smith and Sale [21]). However, the measurement volume is small (approximately 6 mm in diameter and 9 mm in height) and the probe must be traversed through the flow field using a support frame that is typically deployed in an existing gate slot and moved with a crane. These considerations place limits on the area of the draft tube flow that can be easily sampled. Furthermore, the velocity magnitude within a draft tube may exceed the measurement range of the ADV and complicate analysis of the data (Smith and Sale [21]). This paper presents ADCP measurements of velocity conditions immediately downstream of an operating hydroelectric Kaplan turbine draft tube at the John Day Dam on the lower Columbia River. In this near-field zone, the flow field cannot be assumed sufficiently homogeneous to resolve 3-D velocity vectors. Techniques are discussed for developing an index to determine flow homogeneity that can be used for determining
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when 3-D velocity vectors should be resolved. In addition, a technique is presented for utilizing individual beam data (in homogeneous and heterogeneous flow field zones) to develop validation and calibration data sets for 3-D CFD and physical models. Finally, the generalization of this approach to other types of flows and conditions is discussed. 2. Powerhouse-mounted ADCP data collection The set-up involved use of a custom-made RD Instruments 600 kHz Workhorse Series ADCP with 6 deg off-centerline angle (see angle θ in Fig. 1). The four-beam ADCP transducers were spaced at 90 deg azimuth intervals from one another, often referred to as a Janus configuration The half-power beam width, defined as the angular measure from the beam centerline to an acoustic energy of −3 dB relative to the peak was 0.45 deg (Devine [22]). Likewise, the first side lobe peak was −35 dB at 3 deg while the third side lobe was −56 dB at 7 deg (Devine [22]). Each ADCP measurement consisted of four independent one-dimensional (1-D) water velocity profile measurements along the axis of each acoustic beam. The volume ensonified by each range-gated bin along the acoustic beam was relatively small. At a distance of 15 m from the ADCP head (the approximate distance from the ADCP to the bottom of the draft tube), each acoustic beam was approximately shaped like the frustum of a right circular cone with diameters (half-power beam widths) of approximately 0.35 and 0.36 m and a height (bin length) of 0.5 m. The volume integrated by each ADCP beam used to compute the alongbeam velocity at this distance was 0.049 m3 . For comparison, the volume integrated by each beam at a distance of 2 m from the ADCP head was 0.009 m3 . The ADCP was mounted above the Unit 16 Barrel A Exit so that the acoustic beams could be directed into the draft tube while the ADCP itself would be out of the direct path of the emerging jet. The tailrace zone immediately downstream of the draft tube barrel exit was known to be hydraulically complex from boat-mounted ADCP studies of the region (Mannheim and Sweeney [23], Cook et al. [24]). The ADCP was articulated using a dual-axis Remote Oceans Systems (ROS) PT-25 rotator (see Fig. 1) controlled from the surface. The ROS PT-25 is a heavy-duty rotator capable of producing 40 N m of torque on each axis. The ADCP was mounted directly to the rotators, and the center of gravity of the unit was not more than 0.5 m away from the rotator. The ADCP weight in water was approximately 4.5 kg (aluminum housing, no battery). The mounting bracket was then placed in location by hard-hat SCUBA divers and secured by rock bolts hammered into the concrete. The deployment location in context with Unit 16 is shown in Fig. 2. The ADCP was mounted on top of the 0.75 m thick Barrel A crown to minimize the dive depth for the SCUBA divers (approximately 15 m). The draft tube is approximately 11.1 m wide and 12.2 m tall at the exit. Based on physical model results (Davidson [25]), the ADCP was mounted off the centerline axis of Barrel A and above the predicted zone of largest velocity magnitude. This deployment location maximized the measured along-beam velocity components.
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Fig. 1. Illustration showing the 600 kHz ADCP attached to the dual-axis rotator and mounting bracket (left). The beam velocity schematic (right) defines the coordinate system used to resolve the four one-dimensional beam velocity components into three-dimensional Cartesian velocity vectors.
single-ping 3-D earth transformed Doppler uncertainty. For comparison, the 3-D earth transformed Doppler uncertainty for a 20 deg ADCP is 17.4 cm/s or approximately twice the alongbeam value. The 3-D earth transformed Doppler uncertainty for an ensemble of measurements can be reduced if the observed flow field is relatively invariant over the sampling period. The reduction occurs because Doppler uncertainty is uncorrelated from measurement to measurement, so averaging over time reduces the uncertainty according to the relationship σ = √σs , N where σ is the ensemble standard deviation, σs is the singleping standard deviation, and N is the number of single-ping measurements (Gordon [27]). For example, by sampling at 1.33 Hz, the 3-D ensemble Doppler uncertainty for a 6 deg ADCP measurement could be reduced from 65.5 cm/s to less than 8.5 cm/s using a 1 min averaging period. Fig. 2. Schematic showing the ADCP mounted above the Barrel A draft tube exit of Turbine Unit 16. The individual acoustic beams emanating from the ADCP were drawn (conservatively) using a beam spread of 3 deg from the centerline of each beam. Each turbine unit draft tube exit is 11.1 m wide and 12.2 m tall.
The ADCP was placed 3.0 m from the inside of the dividing wall between Units 15 and 16. Turbine units on either side of Unit 16 were not operating during the deployment. The ADCP deployment scripts were configured so that a complete velocity profile from all four beams was collected at a frequency of 1.33 Hz. Each profile consisted of a single along-beam velocity measurement, collected in mode 1, which was range gated by the ADCP into 0.5 m bins. This period of 0.75 s between profile measurements includes the time necessary for the ADCP to transmit and receive the acoustic ping (<0.1 s), process the reflected acoustic data, and to prepare to transmit the next ping. The along-beam velocity measurements are the most accurate velocity measure computed by the ADCP; however, accuracy varies according to bin size and ambiguity velocity. With a 0.5 m bin size and a programmed ambiguity velocity of 300 cm/s, the along-beam Doppler uncertainty is 9.7 cm/s (Thomas [26]). These velocity errors are approximately seven times the Doppler uncertainty of 3-D earth transformed velocities for the 6 deg ADCP, which are 65.5 cm/s. The drawback associated with use of the custommade 6 deg ADCP used in this study is the relatively large
3. One-dimensional (along-beam) velocity filters Two indices were developed to analyze the ADCP data collected at the powerhouse. The first index is a measure of how much temporal velocity variation occurred over the sampling period. The second index is a measure of the homogeneity of the flow field between the four beams. The temporal variation index was based on the mean velocity in a specific bin over the sampling period. This mean velocity was subtracted from each individual reading to generate a time series of velocity fluctuations. The square root of the timeaveraged mean of the squared velocity fluctuations was then evaluated. This index is sometimes referred to as the 1-D turbulence intensity; however, because of the relatively large bin size (0.5 m) and the relatively slow sampling rate (1.33 Hz), this phrase is not used and instead is called a root mean square (RMS) velocity fluctuation. A sample of along-beam velocity data collected at 6.1 m from the ADCP is shown in Fig. 3. Total discharge through the Unit 16 turbine was approximately 433.3 m3 /s (varied 2.8 m3 /s during the sampling period), and the sample was collected over a period of approximately 10.5 min (845 measurements). The computed RMS fluctuations were 0.49 m/s, which was typical for measurements taken within close proximity to the draft tube exit. Profiles of mean velocities and associated RMS values from all four beams for the sampling period are shown in Fig. 4.
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four values were then subtracted from each other and squared: D1 = (Wa − Wb )2
D2 = (Wa − Wc )2
D3 = (Wa − Wd )2
D4 = (Wb − Wc )2
D5 = (Wb − Wd )
D6 = (Wc − Wd ) .
2
(3)
2
Finally, the homogeneity index was computed by computing the square-root mean of the D values: r D1 + D2 + D3 + D4 + D5 + D6 Homogeneity Index = . 6 (4)
Fig. 3. Sample of 845 s of along-beam data from Beam 1 at a distance of 6.1 m from the transducer. Mean velocity for the period is −1.33 m/s (moving away from transducer head; thick line) and the RMS velocity fluctuations are ±0.49 m/s (thin black lines).
Measured RMS velocity fluctuation values generally ranged between 0.15 and 0.9 m/s between all data sets, while mean velocities reached values in excess of 3 m/s. Profiles of the four ADCP beam data can be transformed into a profile of 3-D velocity vectors if the flow field measured by each beam is uniform in the plane perpendicular to the transducers’ mutual axis. For this study, a coordinate system was developed that is oriented relative to the instrument head. The vertical (or W ) velocity direction is aligned with the centerline of the ADCP, and the U and V velocity directions are the horizontal directions. The equations to compute the 3-D instrument oriented velocity vectors are: 1 (B1 − B2) 2 sin(θ ) 1 (B4 − B3) V = 2 sin(θ ) 1 W = (B1 + B2 + B3 + B4) 4 cos(θ )
U=
(1)
where θ is the ADCP beam angle (6 deg) and B1 through B4 are the four ADCP beams (see Fig. 1). A homogeneity index was computed by first calculating various three beam velocity combinations: 1 (B1 + B2 + B3) 3 cos(θ ) 1 Wb = (B1 + B2 + B4) 3 cos(θ ) 1 Wc = (B1 + B3 + B4) 3 cos(θ ) 1 (B2 + B3 + B4). Wd = 3 cos(θ ) Wa =
(2)
To compute these values, the time-averaged along-beam values were used since the RMS velocity fluctuations already provided an index for investigating temporal variations. These
This homogeneity index was computed for all powerhouse ADCP data. It was found to be highly effective at removing erroneous values from the data set and provided a sliding index that could be adjusted depending upon need (data purity versus some meaningful, although noisy, data). A typical profile of homogeneity index values for the ADCP measurements described above is shown in Fig. 5(A), which was generated using the data set discussed in Figs. 3 and 4. The maximum homogeneity index value was 0.13 m/s and the mean value was 0.09 m/s; both of which were a relatively small percentage of the median along-beam velocity magnitude (2.8 m/s). After reviewing all data sets collected during the study, a screening homogeneity index value of 0.21 m/s was selected for this application. The set of 3-D velocity vectors with homogeneity index values less than 0.21 m/s were then translated and rotated into a real-space coordinate system using information regarding orientation of the ADCP during the measurement; interpretation of the complete data set is discussed more fully in the following sections. To illustrate this final data processing step, the data set discussed in Figs. 3 through 5(A), along with line work detailing the powerhouse structure and bathymetry, were processed and are shown in Fig. 5(B) and 5(C). For this example, the ADCP was articulated to measure water velocities in the zone downstream of the draft tube exit between Barrel A of Unit 16 and Barrel C of Unit 15. 4. Discharge measurements and jet decay with distance from the exit Velocity data collected during the two days of this deployment (February 17–18, 2005) were obtained when Unit 16 was operating at the mid-range of its discharge capacity. During data collection the discharge was held approximately constant by operators of the dam, although small variations did occur due to hydroelectric power demand requirements. Mean discharge during data collection period was 415.9 m3 /s with a range of +34.4 m3 /s (+4.0%) to −16.6 m3 /s (−8.3%). Velocity vectors within 0.7 m of the Barrel A exit were interpolated onto a uniform planar grid and are displayed graphically in Fig. 6. The flow is helical in nature and large variations in magnitude were noted in all three velocity components. The maximum U -direction velocity component was along the side of the draft tube barrel closest to Unit 15. U -direction magnitudes decreased toward the opposite side of
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Fig. 4. Profiles of mean along-beam velocities (circles) and RMS fluctuations (asterisks) for each of the four ADCP beams. Data were computed by averaging 845 s of data. The black lines across Beam 1 values at a distance of 6.1 m facilitates comparison to the time-series values shown in Fig. 3.
Fig. 5. Time-series averaged data collected for 845 s starting at 14:46:24 on February 17, 2005. (A) Profiles of water velocity magnitude and homogeneity index values. A black line at 6.1 m from the head has been placed to facilitate comparison to data shown in Figs. 3 and 4. (B) Elevation view of 3-D velocity vectors with line work illustrating the draft tube exit and approximate bathymetry. The vector at 6.1 m from the head has been called out. (C) Plan view of 3-D velocity vectors in perspective to the surrounding draft tube exits.
the draft tube, and some negative values were detected. The V and W -direction velocity patterns were equally complex across the plane, although the W -direction velocities were generally oriented upward; consistent with orientation of the draft tube. An uneven distribution of turbine discharge between the two draft tube barrels associated with the turbine unit was observed in scaled physical models of the turbine unit (Davidson [25]). During the eight-hour period when the ADCP measured velocities within 0.7 m of the Barrel A exit, the mean discharge for the Turbine Unit 16 (both barrels) was 403.6 m3 /s (+4.3/−
15.5 m3 /s). ADCP measured discharge for Barrel A, computed by integrating the U velocity components, was 269.0 m3 /s. The resulting distribution of flow between the two barrels estimated by the ADCP was therefore 67% for Barrel A and 33% for Barrel C. Discharge reported by the physical model at a turbine unit discharge of 498 m3 /s (closest reported value) was 70% for Barrel A and 30% for Barrel C (Davidson [25]). These results are nearly identical to those computed by the ADCP data set. With distance downstream from the draft tube exit, the discharge jet interacted with the bathymetry and responded to
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Fig. 7. Perspective (top) and plan (bottom) views of 3-D velocity vectors exiting the Barrel A draft tube exit. Data was collected using two ADCP sweeps, one parallel to the exit face and one perpendicular to the exit and into the tailrace. The length of each arrow is proportional to velocity magnitude. Arrows have been shaded by elevation to help illustrate the variation with depth.
Fig. 6. U, V , and W velocity components from top to bottom, respectively, viewed from the tailrace looking upstream and into Barrel A of the Unit 16 draft tube. Barrel C of Unit 16 is on the left side of each graphic. Black dots represent the location of ADCP measured velocity points (center of the four beams) used to construct the contour plots.
tailrace conditions. Velocity vectors in the upper part of the water column were oriented downstream as they left the draft tube exit and did not vary with distance downstream (Fig. 7). However, near the bottom of the draft tube, the discharge jet quickly encountered the upwardly sloping bathymetry. In response, the vectors turned toward Unit 15, which was not operating. Because this portion of the discharge jet was compressed vertically, velocity magnitudes in the lower portion of the water column decreased only gradually with distance from the draft tube exit. 5. Application of along-beam velocities to CFD and physical models Along-beam velocities measured by each ADCP beam can be used separately from the resolved 3-D velocity vectors
(or in situations when the homogeneity index is too high) as calibration and validation data sets for CFD or physical models. For the ADCP data set to be applied, the modeled values must first be projected onto the acoustic path of the ADCP beam (Fig. 8). The model-generated velocity vector V is decomposed into the corresponding X, Y , and Z scalar velocity components; V = Vx i + Vy j + Vz k. If vector n is a unit vector oriented in the direction of the ADCP beam, the dot product result of V and n produces the scalar Vbin(a) . This value can then be compared directly to the along-beam ADCP measured value. This investigation compared only one directional component of the modeled velocity vector to the ADCP along-beam measurement. An inherent limitation of along-beam data is that a specific component value could result from an infinite number of 3-D vectors. However, assuming comparisons at other locations agree, the likelihood of sampling two different flow fields decreases due to mass conservation. Confidence in model-generated results can, therefore, be increased assuming a sufficient number of points are validated. The measured velocities were compared to those collected in a physical model similar in geometry to Unit 16 at John Day Dam (Davidson [25]). Measurements in the physical model were collected using a laser Doppler velocity (LDV) meter,
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projected value is 1.3 m/s, and the measured ADCP beam value is 1.7 m/s (difference of 0.4 m/s or 23%). Slight differences in values were expected since the discharge passing through the turbine unit was different between the physical model and prototype. However, this example illustrates, that velocity trends (the relative increase and decrease in magnitude near the exit) are similar between the two data sets. 6. Conclusions
Fig. 8. Graphical representation of an along-beam velocity magnitude measured by the ADCP. The 1-D scalar velocity magnitude for bin(a) is oriented in the direction of the beam, n, and is equal to the dot product operation of the velocity vector V and n.
Fig. 9. Comparison of along-beam ADCP measurements (thick black arrows) to LDV physical model measurements (Davidson [25]). LDV measurements are shown as thin black arrows, and the components projected onto the ADCP beam are shown as thick gray arrows in the insets. The path of the ADCP beams are shown as dots, however the beam path only intersects the LDV plane in the region of the insets.
with a stated accuracy of 3.0 mm/s (prototype scale). The non-intrusive measurements were collected in two-dimensional planes parallel to the draft tube barrel centerline. Data collected in the physical model were along a vertical cross-section that passed through the centerline of Barrel C and downstream into the tailrace (Fig. 9). Turbine unit discharge (both barrels) was 498 m3 /s in the physical model. The centerline of the ADCP was not aligned with the LDV measurement plane; however, the ADCP was articulated so that individual ADCP beams intersected the plane. The LDV plane velocity measurements closest to the intersecting ADCP beams were used for this example. These points are not exactly coincident, but are separated in 3-D space by a finite distance; 0.85 m for draft tube exit comparison and 3.47 m for the tailrace comparison (Fig. 9). For the draft tube exit comparison, the magnitude of the LDV velocity vector is 0.55 m/s; however, when projected onto the ADCP beam, the component along the beam is 0.4 m/s. The corresponding ADCP beam value is 0.6 m/s, or a difference of 33%. For the tailrace comparison, the LDV vector magnitude is 1.4 m/s, the
While measurement of 3-D water velocities in the farfield tailrace zone using ADCPs has become fairly routine (Simpson [16], Gordon [28]), these techniques have limitations for collecting velocity measurements near draft tube exits of operating turbines. Our technique for collecting and processing ADCP data in the near-field zone is distinct from other acoustic collection methods such as acoustic scintillation (Lemon and Billenness [29], Lemon et al. [30]) and acoustic Doppler velocimeters (Smith and Sale [21]). For example, a profile of 3D velocity vectors can be resolved at draft tube exits, assuming the homogeneity index is suitably low. The present analysis provided a filter for determining the suitability of resolved 3-D velocity vectors using the four 1-D scalar values collected via a narrow beam spread (6 deg) ADCP. The filter was based on a homogeneity index derived using the redundant velocity-component data from a conventional four-beam Janus-configured ADCP. This index was highly effective at isolating measurements along 1-D velocity profiles unsuitable for resolving into 3-D velocity vectors. The resolved flow field showed a highly complex and helical flow pattern as the jet exited the draft tube. Data sets involving 1-D and filtered 3-D velocity vectors were used to characterize the flow field within the draft tube exit and the near-field zone immediately downstream. The 1D velocity data were compared to a limited data set collected in a physical model of the draft tube exit, and velocity trends were similar between the two. When 3-D velocity vectors were integrated across the draft tube exit, the computed flow split between the two barrels was nearly identical to physical model results (i.e., 67% for Barrel A and 33% from Barrel C). Information on near-field data allows for optimization of turbine operations to reduce flow separation and/or large asymmetries of discharge between the exit barrels. Velocity profiles collected in the near-field tailrace can help resolve issues of cross-flow and flow interaction when various combinations of turbine units are operating. Collectively, this information helps both engineers and fisheries managers improve tailrace conditions for the safe egress of migratory juvenile fish that pass hydroelectric dams via the turbine unit. Acknowledgements This work was supported by the US Army Corps of Engineers, Portland District, though contract DE-AC0576RL01830. The authors would like to thank Mr. Sean Askelson, Mr. Kyle McCune, Mr. Nathan Higa, and Dr. Laurie Ebner of the District for their assistance throughout the study.
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Geophysical Research—Oceans 1987;92(C5):5169–76. [14] Cook CB, Orlob GT, Huston DW. Simulation of wind-driven circulation in the Salton sea: Implications for indigenous ecosystems. Hydrobiologia 2002;473:59–75. [15] Lohrmann A, Hackett B, Roed LP. High resolution measurements of turbulence, velocity and stress using a pulse-to-pulse coherent sonar. Journal of Atmospheric and Oceanic Technology 1990;7:19–37. [16] Simpson MR. Discharge measurements using a broad-band acoustic Doppler current profiler. Open-File Report 01-1. US Geological Survey. 2001. 121 pp. [17] Williams E, Simpson JH. Uncertainties in estimates of Reynolds stress and TKE production rate using the ADCP variance method. Journal of Atmospheric and Oceanic Technology 2004;21(2):347–57. [18] Stacey MT, Monismith SG, Burau JR. Measurements of Reynolds stress profiles in unstratified tidal flow. Journal of Geophysical Research— Oceans 1999;104(C5):10933–49. [19] Stacey MT, Monismith SG, Burau JR. Observations of turbulence in a partially stratified estuary. Journal of Physical Oceanography 1999;29(8): 1950–70. [20] Ott MW, Dewey R, Garrett C. Reynolds stresses and secondary circulation in a stratified rotating shear flow. Journal of Physical Oceanography 2002; 32(11):3249–68. [21] Smith BT, Sale MJ. Measurement and analysis of unsteady flow in hydro intakes and draft tubes. In: Proceedings of hydrovision 2006. 2006. [22] Devine P. Workhorse ceramics specs. 2003: pp. Email communication on 26 February 2005. Teledyne-RD Instruments. [23] Mannheim C, Sweeney C. Acoustic doppler current profiler and point velocity measurement field data collection: Lower Columbia River projects. Report no. 9000-313-800. ENSR International. 2003. [24] Cook CB, Dibrani B, Serkowski JA, Richmond MC, Titzler PS, Dennis GW. Acoustic doppler current profiler measurements in the tailrace of John Day Dam. Pacific Northwest National Laboratory, PNNL-15627. 2006, 85 pp. [25] Davidson RA. Lower Granite Project draft tube study. Engineer Research and Development Center (ERDC). US Army Corps of Engineers. 2004. [26] Thomas HAS. Single ping radial standard deviations. Email communication on 28 September 2005. Teledyne-RD Instruments. 2005. [27] Gordon L. Acoustic Doppler current profiler principles of operation: A practical primer. RD Instruments. San Diego, 1996. [28] Gordon RL. Acoustic measurement of river discharge. Journal of Hydraulic Engineering 1989;115(7):925–36. [29] Lemon DD, Billenness D. Developing guidelines for using the acoustic scintillation flow meter to measure turbine discharges in short intakes. In: Proceedings of hydrovision 2002. 2002. [30] Lemon DD, Chave RA, Stone M. Field trial of an ASFM monitor at lower granite dam. In: Proceedings of hydrovision 2006. 2006.
Observations of velocity conditions near a hydroelectric turbine draft tube exit using ADCP measurements Christopher B. Cook ∗ , Marshall C. Richmond, John A. Serkowski Pacific Northwest National Laboratory, PO Box 999, Richland, WA, 99352, USA Received 29 September 2006; received in revised form 7 February 2007; accepted 4 April 2007
Abstract Measurement of flow characteristics near hydraulic structures is an ongoing challenge because of the need to obtain rapid measurements of time-varying velocity over a relatively large spatial domain. This paper discusses use of an acoustic Doppler current profiler (ADCP) to measure the rapidly diverging flow exiting from an operating hydroelectric turbine draft tube exit. The resolved three-dimensional velocity vectors show a highly complex and helical flow pattern developed near to and downstream of the exit. Velocity vectors were integrated across the exit and we computed an uneven percentage of flow (67%/33%) passing through the two draft tube barrels at a mid-range turbine discharge, consistent with physical model results. In addition to the three-dimensional velocity vectors, the individual one-dimensional velocities measured by each of the four ADCP beams can be separately used as calibration and validation data sets for numerical and physical models. This technique is demonstrated by comparing along-beam ADCP velocity measurements to data collected in a scaled physical model. c 2007 Elsevier Ltd. All rights reserved.
Keywords: Acoustic Doppler current profiler; Draft tube; Hydroelectric turbine; Velocity measurement; Discharge measurement
1. Introduction Kaplan-type hydroelectric turbines with adjustable runner blades are generally used in low-head plants with moderate to high discharge (Roberson et al. [1]). Swirling flow generated by the runner then enters a conical diffuser followed by an elbow-type draft tube that typically has multiple barrels. The purpose of the draft tube is to act as a diffuser by reducing flow velocity and recovering dynamic pressure back into static pressure. Swirling, turbulent flow within the draft tubes is three-dimensional (3-D) and exhibits a wide range of unsteady velocity, pressure, and wall friction variations that have been observed in laboratory experiments (Ciocan and Avellan [2], Gebart et al. [3]) and simulations using 3-D computational fluid dynamics (CFD) models (Paik et al. [4], Lai et al. [5]). The expanding cross-sectional area in the draft tube causes adverse pressure gradient conditions that can lead to repeated occurrences of flow separation phenomena, ranging ∗ Corresponding address: Pacific Northwest National Laboratory, Hydrology Group, PO Box 999, 99352 Richland, WA, USA. Tel.: +1 509 375 6878; fax: +1 509 372 6089. E-mail address: [email protected] (C.B. Cook).
c 2007 Elsevier Ltd. All rights reserved. 0955-5986/$ - see front matter doi:10.1016/j.flowmeasinst.2007.04.002
from localized flow reversals to large-scale stall-like behavior depending on the turbine operating point. These conditions result in an uneven distribution of discharge between the barrels (Mauri et al. [6]) and a decrease in turbine operating efficiency. An unsteady vortex rope can form immediately downstream of the runner, which can induce unsteady pressure pulsations, especially at partial load operating conditions (Skotak et al. [7]). Vortex breakdown (Sarpkaya [8]) can also occur in the conical diffuser section. The aggregate effect of these phenomena is to produce highly unsteady and non-uniform flow in the draft tube that then propagates into the tailrace. In addition to effects on flow conditions and turbine operating efficiency, turbulent flow within the draft tube is of concern because of its potential effect upon fish (Cada [9]). The increased turbulent shear elevates the risk of harming juvenile anadromous fish that may be passing through the turbine during their downstream migration (Neitzel et al. [10], Deng et al. [11]). Considering the importance of the draft tube to engineering design and fish passage, both physical and CFD models should be validated through comparisons to fullscale field measurements of velocity within the draft tube and immediately downstream. However, collecting prototype data
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near an operating Kaplan-type turbine requires improvements in measurement and data analysis methods. Instruments utilizing the Doppler shift are commonly used to measure velocity conditions in rivers, reservoirs, and lakes. Among this class of instruments, acoustic Doppler current profilers (ADCPs) in particular are commonly used to measure discharge (Gordon et al. [12]), as well as collect longterm observations of currents in a fixed location (Schott and Johns [13], Cook et al. [14]). ADCPs are generally selected because they can profile long distances, do not disrupt the measured flow field (i.e., are non-intrusive), and are robust even in the most unfavorable of flow conditions. ADCPs typically use three or more non-orthogonal acoustic beams to resolve the 3-D flow field (Lohrmann et al. [15], Simpson [16]). Since the beams diverge from the transducer head, beam velocity measurements are separated, often by relatively large distances. If flow heterogeneity is large between the individual beam measurements, such as what might be expected downstream of a draft tube exit, the fundamental premise used to resolve the 3-D flow field is no longer valid. However, individual beam velocity data remains accurate under most conditions as calibration and validation data sets for CFD and physical models. The use of individual acoustic beam information is not novel. Variations in individual beam velocities have been used to extract flow turbulence information, such as turbulent kinetic energy and shear production (Williams and Simpson [17], Stacey et al. [18], Stacey et al. [19]). Turbulence and higher order statistical information regarding the flow field is generally extracted using a variance technique. However, this method a priori assumes homogeneity of the flow field between each orthogonal beam pair and therefore should not be used in zones where mean flow velocity gradients are rapidly changing within the beam swath. Head configuration and the number of acoustic beams vary the turbulence quantities that can be measured even under homogeneous flow conditions (Ott et al. [20]). An alternative to using ADCPs or similar acoustic profilers is to measure draft tube velocities with point probes such as propeller meters, electromagnetic meters, or acoustic Doppler velocimeters (ADV). Of these, the ADV is attractive because it can simultaneously measure all three velocity components and turbulent fluctuations (Smith and Sale [21]). However, the measurement volume is small (approximately 6 mm in diameter and 9 mm in height) and the probe must be traversed through the flow field using a support frame that is typically deployed in an existing gate slot and moved with a crane. These considerations place limits on the area of the draft tube flow that can be easily sampled. Furthermore, the velocity magnitude within a draft tube may exceed the measurement range of the ADV and complicate analysis of the data (Smith and Sale [21]). This paper presents ADCP measurements of velocity conditions immediately downstream of an operating hydroelectric Kaplan turbine draft tube at the John Day Dam on the lower Columbia River. In this near-field zone, the flow field cannot be assumed sufficiently homogeneous to resolve 3-D velocity vectors. Techniques are discussed for developing an index to determine flow homogeneity that can be used for determining
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when 3-D velocity vectors should be resolved. In addition, a technique is presented for utilizing individual beam data (in homogeneous and heterogeneous flow field zones) to develop validation and calibration data sets for 3-D CFD and physical models. Finally, the generalization of this approach to other types of flows and conditions is discussed. 2. Powerhouse-mounted ADCP data collection The set-up involved use of a custom-made RD Instruments 600 kHz Workhorse Series ADCP with 6 deg off-centerline angle (see angle θ in Fig. 1). The four-beam ADCP transducers were spaced at 90 deg azimuth intervals from one another, often referred to as a Janus configuration The half-power beam width, defined as the angular measure from the beam centerline to an acoustic energy of −3 dB relative to the peak was 0.45 deg (Devine [22]). Likewise, the first side lobe peak was −35 dB at 3 deg while the third side lobe was −56 dB at 7 deg (Devine [22]). Each ADCP measurement consisted of four independent one-dimensional (1-D) water velocity profile measurements along the axis of each acoustic beam. The volume ensonified by each range-gated bin along the acoustic beam was relatively small. At a distance of 15 m from the ADCP head (the approximate distance from the ADCP to the bottom of the draft tube), each acoustic beam was approximately shaped like the frustum of a right circular cone with diameters (half-power beam widths) of approximately 0.35 and 0.36 m and a height (bin length) of 0.5 m. The volume integrated by each ADCP beam used to compute the alongbeam velocity at this distance was 0.049 m3 . For comparison, the volume integrated by each beam at a distance of 2 m from the ADCP head was 0.009 m3 . The ADCP was mounted above the Unit 16 Barrel A Exit so that the acoustic beams could be directed into the draft tube while the ADCP itself would be out of the direct path of the emerging jet. The tailrace zone immediately downstream of the draft tube barrel exit was known to be hydraulically complex from boat-mounted ADCP studies of the region (Mannheim and Sweeney [23], Cook et al. [24]). The ADCP was articulated using a dual-axis Remote Oceans Systems (ROS) PT-25 rotator (see Fig. 1) controlled from the surface. The ROS PT-25 is a heavy-duty rotator capable of producing 40 N m of torque on each axis. The ADCP was mounted directly to the rotators, and the center of gravity of the unit was not more than 0.5 m away from the rotator. The ADCP weight in water was approximately 4.5 kg (aluminum housing, no battery). The mounting bracket was then placed in location by hard-hat SCUBA divers and secured by rock bolts hammered into the concrete. The deployment location in context with Unit 16 is shown in Fig. 2. The ADCP was mounted on top of the 0.75 m thick Barrel A crown to minimize the dive depth for the SCUBA divers (approximately 15 m). The draft tube is approximately 11.1 m wide and 12.2 m tall at the exit. Based on physical model results (Davidson [25]), the ADCP was mounted off the centerline axis of Barrel A and above the predicted zone of largest velocity magnitude. This deployment location maximized the measured along-beam velocity components.
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Fig. 1. Illustration showing the 600 kHz ADCP attached to the dual-axis rotator and mounting bracket (left). The beam velocity schematic (right) defines the coordinate system used to resolve the four one-dimensional beam velocity components into three-dimensional Cartesian velocity vectors.
single-ping 3-D earth transformed Doppler uncertainty. For comparison, the 3-D earth transformed Doppler uncertainty for a 20 deg ADCP is 17.4 cm/s or approximately twice the alongbeam value. The 3-D earth transformed Doppler uncertainty for an ensemble of measurements can be reduced if the observed flow field is relatively invariant over the sampling period. The reduction occurs because Doppler uncertainty is uncorrelated from measurement to measurement, so averaging over time reduces the uncertainty according to the relationship σ = √σs , N where σ is the ensemble standard deviation, σs is the singleping standard deviation, and N is the number of single-ping measurements (Gordon [27]). For example, by sampling at 1.33 Hz, the 3-D ensemble Doppler uncertainty for a 6 deg ADCP measurement could be reduced from 65.5 cm/s to less than 8.5 cm/s using a 1 min averaging period. Fig. 2. Schematic showing the ADCP mounted above the Barrel A draft tube exit of Turbine Unit 16. The individual acoustic beams emanating from the ADCP were drawn (conservatively) using a beam spread of 3 deg from the centerline of each beam. Each turbine unit draft tube exit is 11.1 m wide and 12.2 m tall.
The ADCP was placed 3.0 m from the inside of the dividing wall between Units 15 and 16. Turbine units on either side of Unit 16 were not operating during the deployment. The ADCP deployment scripts were configured so that a complete velocity profile from all four beams was collected at a frequency of 1.33 Hz. Each profile consisted of a single along-beam velocity measurement, collected in mode 1, which was range gated by the ADCP into 0.5 m bins. This period of 0.75 s between profile measurements includes the time necessary for the ADCP to transmit and receive the acoustic ping (<0.1 s), process the reflected acoustic data, and to prepare to transmit the next ping. The along-beam velocity measurements are the most accurate velocity measure computed by the ADCP; however, accuracy varies according to bin size and ambiguity velocity. With a 0.5 m bin size and a programmed ambiguity velocity of 300 cm/s, the along-beam Doppler uncertainty is 9.7 cm/s (Thomas [26]). These velocity errors are approximately seven times the Doppler uncertainty of 3-D earth transformed velocities for the 6 deg ADCP, which are 65.5 cm/s. The drawback associated with use of the custommade 6 deg ADCP used in this study is the relatively large
3. One-dimensional (along-beam) velocity filters Two indices were developed to analyze the ADCP data collected at the powerhouse. The first index is a measure of how much temporal velocity variation occurred over the sampling period. The second index is a measure of the homogeneity of the flow field between the four beams. The temporal variation index was based on the mean velocity in a specific bin over the sampling period. This mean velocity was subtracted from each individual reading to generate a time series of velocity fluctuations. The square root of the timeaveraged mean of the squared velocity fluctuations was then evaluated. This index is sometimes referred to as the 1-D turbulence intensity; however, because of the relatively large bin size (0.5 m) and the relatively slow sampling rate (1.33 Hz), this phrase is not used and instead is called a root mean square (RMS) velocity fluctuation. A sample of along-beam velocity data collected at 6.1 m from the ADCP is shown in Fig. 3. Total discharge through the Unit 16 turbine was approximately 433.3 m3 /s (varied 2.8 m3 /s during the sampling period), and the sample was collected over a period of approximately 10.5 min (845 measurements). The computed RMS fluctuations were 0.49 m/s, which was typical for measurements taken within close proximity to the draft tube exit. Profiles of mean velocities and associated RMS values from all four beams for the sampling period are shown in Fig. 4.
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four values were then subtracted from each other and squared: D1 = (Wa − Wb )2
D2 = (Wa − Wc )2
D3 = (Wa − Wd )2
D4 = (Wb − Wc )2
D5 = (Wb − Wd )
D6 = (Wc − Wd ) .
2
(3)
2
Finally, the homogeneity index was computed by computing the square-root mean of the D values: r D1 + D2 + D3 + D4 + D5 + D6 Homogeneity Index = . 6 (4)
Fig. 3. Sample of 845 s of along-beam data from Beam 1 at a distance of 6.1 m from the transducer. Mean velocity for the period is −1.33 m/s (moving away from transducer head; thick line) and the RMS velocity fluctuations are ±0.49 m/s (thin black lines).
Measured RMS velocity fluctuation values generally ranged between 0.15 and 0.9 m/s between all data sets, while mean velocities reached values in excess of 3 m/s. Profiles of the four ADCP beam data can be transformed into a profile of 3-D velocity vectors if the flow field measured by each beam is uniform in the plane perpendicular to the transducers’ mutual axis. For this study, a coordinate system was developed that is oriented relative to the instrument head. The vertical (or W ) velocity direction is aligned with the centerline of the ADCP, and the U and V velocity directions are the horizontal directions. The equations to compute the 3-D instrument oriented velocity vectors are: 1 (B1 − B2) 2 sin(θ ) 1 (B4 − B3) V = 2 sin(θ ) 1 W = (B1 + B2 + B3 + B4) 4 cos(θ )
U=
(1)
where θ is the ADCP beam angle (6 deg) and B1 through B4 are the four ADCP beams (see Fig. 1). A homogeneity index was computed by first calculating various three beam velocity combinations: 1 (B1 + B2 + B3) 3 cos(θ ) 1 Wb = (B1 + B2 + B4) 3 cos(θ ) 1 Wc = (B1 + B3 + B4) 3 cos(θ ) 1 (B2 + B3 + B4). Wd = 3 cos(θ ) Wa =
(2)
To compute these values, the time-averaged along-beam values were used since the RMS velocity fluctuations already provided an index for investigating temporal variations. These
This homogeneity index was computed for all powerhouse ADCP data. It was found to be highly effective at removing erroneous values from the data set and provided a sliding index that could be adjusted depending upon need (data purity versus some meaningful, although noisy, data). A typical profile of homogeneity index values for the ADCP measurements described above is shown in Fig. 5(A), which was generated using the data set discussed in Figs. 3 and 4. The maximum homogeneity index value was 0.13 m/s and the mean value was 0.09 m/s; both of which were a relatively small percentage of the median along-beam velocity magnitude (2.8 m/s). After reviewing all data sets collected during the study, a screening homogeneity index value of 0.21 m/s was selected for this application. The set of 3-D velocity vectors with homogeneity index values less than 0.21 m/s were then translated and rotated into a real-space coordinate system using information regarding orientation of the ADCP during the measurement; interpretation of the complete data set is discussed more fully in the following sections. To illustrate this final data processing step, the data set discussed in Figs. 3 through 5(A), along with line work detailing the powerhouse structure and bathymetry, were processed and are shown in Fig. 5(B) and 5(C). For this example, the ADCP was articulated to measure water velocities in the zone downstream of the draft tube exit between Barrel A of Unit 16 and Barrel C of Unit 15. 4. Discharge measurements and jet decay with distance from the exit Velocity data collected during the two days of this deployment (February 17–18, 2005) were obtained when Unit 16 was operating at the mid-range of its discharge capacity. During data collection the discharge was held approximately constant by operators of the dam, although small variations did occur due to hydroelectric power demand requirements. Mean discharge during data collection period was 415.9 m3 /s with a range of +34.4 m3 /s (+4.0%) to −16.6 m3 /s (−8.3%). Velocity vectors within 0.7 m of the Barrel A exit were interpolated onto a uniform planar grid and are displayed graphically in Fig. 6. The flow is helical in nature and large variations in magnitude were noted in all three velocity components. The maximum U -direction velocity component was along the side of the draft tube barrel closest to Unit 15. U -direction magnitudes decreased toward the opposite side of
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Fig. 4. Profiles of mean along-beam velocities (circles) and RMS fluctuations (asterisks) for each of the four ADCP beams. Data were computed by averaging 845 s of data. The black lines across Beam 1 values at a distance of 6.1 m facilitates comparison to the time-series values shown in Fig. 3.
Fig. 5. Time-series averaged data collected for 845 s starting at 14:46:24 on February 17, 2005. (A) Profiles of water velocity magnitude and homogeneity index values. A black line at 6.1 m from the head has been placed to facilitate comparison to data shown in Figs. 3 and 4. (B) Elevation view of 3-D velocity vectors with line work illustrating the draft tube exit and approximate bathymetry. The vector at 6.1 m from the head has been called out. (C) Plan view of 3-D velocity vectors in perspective to the surrounding draft tube exits.
the draft tube, and some negative values were detected. The V and W -direction velocity patterns were equally complex across the plane, although the W -direction velocities were generally oriented upward; consistent with orientation of the draft tube. An uneven distribution of turbine discharge between the two draft tube barrels associated with the turbine unit was observed in scaled physical models of the turbine unit (Davidson [25]). During the eight-hour period when the ADCP measured velocities within 0.7 m of the Barrel A exit, the mean discharge for the Turbine Unit 16 (both barrels) was 403.6 m3 /s (+4.3/−
15.5 m3 /s). ADCP measured discharge for Barrel A, computed by integrating the U velocity components, was 269.0 m3 /s. The resulting distribution of flow between the two barrels estimated by the ADCP was therefore 67% for Barrel A and 33% for Barrel C. Discharge reported by the physical model at a turbine unit discharge of 498 m3 /s (closest reported value) was 70% for Barrel A and 30% for Barrel C (Davidson [25]). These results are nearly identical to those computed by the ADCP data set. With distance downstream from the draft tube exit, the discharge jet interacted with the bathymetry and responded to
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Fig. 7. Perspective (top) and plan (bottom) views of 3-D velocity vectors exiting the Barrel A draft tube exit. Data was collected using two ADCP sweeps, one parallel to the exit face and one perpendicular to the exit and into the tailrace. The length of each arrow is proportional to velocity magnitude. Arrows have been shaded by elevation to help illustrate the variation with depth.
Fig. 6. U, V , and W velocity components from top to bottom, respectively, viewed from the tailrace looking upstream and into Barrel A of the Unit 16 draft tube. Barrel C of Unit 16 is on the left side of each graphic. Black dots represent the location of ADCP measured velocity points (center of the four beams) used to construct the contour plots.
tailrace conditions. Velocity vectors in the upper part of the water column were oriented downstream as they left the draft tube exit and did not vary with distance downstream (Fig. 7). However, near the bottom of the draft tube, the discharge jet quickly encountered the upwardly sloping bathymetry. In response, the vectors turned toward Unit 15, which was not operating. Because this portion of the discharge jet was compressed vertically, velocity magnitudes in the lower portion of the water column decreased only gradually with distance from the draft tube exit. 5. Application of along-beam velocities to CFD and physical models Along-beam velocities measured by each ADCP beam can be used separately from the resolved 3-D velocity vectors
(or in situations when the homogeneity index is too high) as calibration and validation data sets for CFD or physical models. For the ADCP data set to be applied, the modeled values must first be projected onto the acoustic path of the ADCP beam (Fig. 8). The model-generated velocity vector V is decomposed into the corresponding X, Y , and Z scalar velocity components; V = Vx i + Vy j + Vz k. If vector n is a unit vector oriented in the direction of the ADCP beam, the dot product result of V and n produces the scalar Vbin(a) . This value can then be compared directly to the along-beam ADCP measured value. This investigation compared only one directional component of the modeled velocity vector to the ADCP along-beam measurement. An inherent limitation of along-beam data is that a specific component value could result from an infinite number of 3-D vectors. However, assuming comparisons at other locations agree, the likelihood of sampling two different flow fields decreases due to mass conservation. Confidence in model-generated results can, therefore, be increased assuming a sufficient number of points are validated. The measured velocities were compared to those collected in a physical model similar in geometry to Unit 16 at John Day Dam (Davidson [25]). Measurements in the physical model were collected using a laser Doppler velocity (LDV) meter,
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projected value is 1.3 m/s, and the measured ADCP beam value is 1.7 m/s (difference of 0.4 m/s or 23%). Slight differences in values were expected since the discharge passing through the turbine unit was different between the physical model and prototype. However, this example illustrates, that velocity trends (the relative increase and decrease in magnitude near the exit) are similar between the two data sets. 6. Conclusions
Fig. 8. Graphical representation of an along-beam velocity magnitude measured by the ADCP. The 1-D scalar velocity magnitude for bin(a) is oriented in the direction of the beam, n, and is equal to the dot product operation of the velocity vector V and n.
Fig. 9. Comparison of along-beam ADCP measurements (thick black arrows) to LDV physical model measurements (Davidson [25]). LDV measurements are shown as thin black arrows, and the components projected onto the ADCP beam are shown as thick gray arrows in the insets. The path of the ADCP beams are shown as dots, however the beam path only intersects the LDV plane in the region of the insets.
with a stated accuracy of 3.0 mm/s (prototype scale). The non-intrusive measurements were collected in two-dimensional planes parallel to the draft tube barrel centerline. Data collected in the physical model were along a vertical cross-section that passed through the centerline of Barrel C and downstream into the tailrace (Fig. 9). Turbine unit discharge (both barrels) was 498 m3 /s in the physical model. The centerline of the ADCP was not aligned with the LDV measurement plane; however, the ADCP was articulated so that individual ADCP beams intersected the plane. The LDV plane velocity measurements closest to the intersecting ADCP beams were used for this example. These points are not exactly coincident, but are separated in 3-D space by a finite distance; 0.85 m for draft tube exit comparison and 3.47 m for the tailrace comparison (Fig. 9). For the draft tube exit comparison, the magnitude of the LDV velocity vector is 0.55 m/s; however, when projected onto the ADCP beam, the component along the beam is 0.4 m/s. The corresponding ADCP beam value is 0.6 m/s, or a difference of 33%. For the tailrace comparison, the LDV vector magnitude is 1.4 m/s, the
While measurement of 3-D water velocities in the farfield tailrace zone using ADCPs has become fairly routine (Simpson [16], Gordon [28]), these techniques have limitations for collecting velocity measurements near draft tube exits of operating turbines. Our technique for collecting and processing ADCP data in the near-field zone is distinct from other acoustic collection methods such as acoustic scintillation (Lemon and Billenness [29], Lemon et al. [30]) and acoustic Doppler velocimeters (Smith and Sale [21]). For example, a profile of 3D velocity vectors can be resolved at draft tube exits, assuming the homogeneity index is suitably low. The present analysis provided a filter for determining the suitability of resolved 3-D velocity vectors using the four 1-D scalar values collected via a narrow beam spread (6 deg) ADCP. The filter was based on a homogeneity index derived using the redundant velocity-component data from a conventional four-beam Janus-configured ADCP. This index was highly effective at isolating measurements along 1-D velocity profiles unsuitable for resolving into 3-D velocity vectors. The resolved flow field showed a highly complex and helical flow pattern as the jet exited the draft tube. Data sets involving 1-D and filtered 3-D velocity vectors were used to characterize the flow field within the draft tube exit and the near-field zone immediately downstream. The 1D velocity data were compared to a limited data set collected in a physical model of the draft tube exit, and velocity trends were similar between the two. When 3-D velocity vectors were integrated across the draft tube exit, the computed flow split between the two barrels was nearly identical to physical model results (i.e., 67% for Barrel A and 33% from Barrel C). Information on near-field data allows for optimization of turbine operations to reduce flow separation and/or large asymmetries of discharge between the exit barrels. Velocity profiles collected in the near-field tailrace can help resolve issues of cross-flow and flow interaction when various combinations of turbine units are operating. Collectively, this information helps both engineers and fisheries managers improve tailrace conditions for the safe egress of migratory juvenile fish that pass hydroelectric dams via the turbine unit. Acknowledgements This work was supported by the US Army Corps of Engineers, Portland District, though contract DE-AC0576RL01830. The authors would like to thank Mr. Sean Askelson, Mr. Kyle McCune, Mr. Nathan Higa, and Dr. Laurie Ebner of the District for their assistance throughout the study.
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