Inter-comparison and Evaluation of a Single-Point, Acoustic Doppler Current Sensor. Mounted on a TABS I1 Spar Buoy. J.N. Walpert, N.L. Guinasso Jr., L.L. Lee ...
Inter-comparison and Evaluation of a Single-Point, Acoustic Doppler Current Sensor Mounted on a TABS I1 Spar Buoy J.N. Walpert, N.L. Guinasso Jr., L.L. Lee III Geochemical and Environmental Research Group, Texas A&M University 727 Graham Rd, College Station, TX 77845
F.J. Kelly Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi 6300 Ocean Dr., Corpus Christi, TX 78412
Abstract
of the buoys are within ten nautical miles of the coast, a region of very strong biofouling pressure. Fouling on the tips of the electromagnetic sensor has proven a major cause of error. Although the rubber surrounding the sensor is factoryimpregnated with tributyl tin, and GERG further protects the sensors with its own marine growth inhibiting paint, fouling is still the leading cause of deterioration in data quality as deployment duration increases. The tips of the electrodes cannot be coated with growth inhibitor. When barnacles adhere to the sensor tips, current measurements become unstable and erratic. An acoustic Doppler current sensor, which can be completely coated with a marine growth inhibiting paint, would theoretically be much less prone to errors induced by fouling. The current velocity information is contained in the phase shift of the backscattered signal, which fouling does not affect, and the anti-foulant coating does not appreciably attenuate the signal. A brief experiment was designed to test the suitability of the Aanderaa DCS 3500R sensor for use on a coastal TABS spar buoy.
The Geochemical and Environmental Research Group of Texas A&M University conducted a thirty-day field experiment to compare and evaluate a single-point, Doppler current sensor for use on the Texas Automated Buoy System (TABS) spar buoys. Two spar buoys were deployed within 200 m of each other approximately 22 nmi. southwest of Galveston, Texas, in the Gulf of Mexico. One buoy was equipped with a Marsh-McBirney model 585 electromagnetic sensor and the other with an Aanderaa model DCS3500R single-point acoustic Doppler sensor. The sensors were located 2.5 m below the surface. A 300-kHz RD Instruments Workhorse Sentinel was moored in an upwardlooking mode between the two buoys. Statistical comparison of the data from the three sensors shows that they performed well and measured vector components of similar magnitudes. Data from the Aanderaa DCS3500R tracked the bottom-mounted ADCP more closely than did the MMI585. The experiment indicates that both the W 5 8 5 and the Aanderaa DCS3500R are suitable sensors for spar buoy deployment in a near surface dynamic environment. Of the three sensors, the MMI585 was most sensitive to fouling.
I. INTRODUCTION The Geochemical and Environmental Research Group (GERG) of Texas A&M University (TAMU) has been operating and developing the TABS system since 1995 on behalf of the Texas General Land Office (TGLO). The primary mission of TABS is to provide data for oil-spill response [1,2]. The program consists of seven moored spar-type buoys equipped with Marsh-McBirney model 585 current sensors and telemetry systems to provide near real-time currents offshore Texas [3,4]. The data are collected and processed on-board the buoys and then transmitted to GERG’s office, where they are quality controlled and displayed on the GERG Webpage (www.gergtamu .edu/tdo). Biofouling is the principal factor that limits data quality and deployment duration with the MMI585. In an effort to increase the time between service cruises, GERG undertook an intercomparison test between the MM1585, an Aanderaa DCS3500R acoustic Doppler sensor and a 300-kHz RDI Workhorse ADCP. The MMI585, interfaced to Woods Hole Group SeaPac current meter electronics, is the standard sensor on TABS buoys. Most
0-7803-6551-8/00/$10.00 02000 IEEE
Fig. 1: Map showing the locations of the TABS buoys in the Gulf of Mexico and the effect on wind and currents of a cold front passing southward across the inner shelf.
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TABLE 1 Instrument Configuration
11. THEEXPERIMENT An upward-looking, RD Instruments, 300-kHz Workhorse Sentinel ADCP and two TABS I1 spar buoys were moored within 200 m of each other in 19-m water depth southwest of Galveston, Texas, at TABS site B (Fig. 2).
t9
kl
200 m
! I
19 m
Parameter 30 min
30 min
Sample Rate
current direction) of the MMI sensor. The ADCP was moorei in an upward-looking orientation, equidistant between the twc TABS buoys. Following deployment, the diver inspected th position of the ADCP to verify its location. The data from bot; TABS buoys were telemetered to GERG s office via satellite while the ADCP recorded its data internally. Beginning abou June 18, the MMI data began to diverge from the data bein: received from the DCS3500R sensor and to develop spikes ani erratic behavior characteristic of biofouling. The ADC: mooring was recovered on June 30. The data collected wer: sufficient to provide a seventeen-day inter-comparison amon: the three sensors.
111. RESULTS Fig. 2: Deployment configuration at site B
The MMI585 is a well-established current sensor that operates on Faraday’s principal of electromagnetic induction [5]. This has been the primary current sensor of the TABS buoys since 1995. The DCS3500R is an acoustic sensor that operates on the Doppler principle. The sensor utilizes four piezioceramic, orthogonal transducers that cyclically sample alternate pairs at a rate of 10 pingdsecond, at a transmit frequency of 2 MHz. This results in an effective sampling rate of 2.5 pingslsecond or 0.4seconddcycle. The sensing volume is located from 0.5 to 2 meters from the source, allowing the instrument to be placed in a protective cage without interference to the acoustic signal. The DCS3500R contains an internal Hall effect compass and two tilt sensors that are used internally to compute the vector-averaged current components. The MMI585 sensor is coupled to the Woods Hole Group’s model 2054 electronics package, which uses a digital flux-gate compass and tilt sensors to correct the data for heading, pitch, and roll. A proprietary AID converter and CPU board are used to control the sensor and convert the data to engineering units. The upward looking 300-kHzADCP was configured to sample in 2-m bins, with the first bin starting 4 m from the transducers. All the instruments were configured to sample at the same time and record at 30-minute sample intervals. The instrument configurations are shown in Table 1. The TABS I1 buoy with the DCS3500R and the bottom mounted ADCP mooring were deployed from the University of Texas’s F W Longhorn on June 1,2000. The TABS I1 buoy with the MMI585 sensor was already on location. During initial deployment, a diver from GERG’s staff inspected the MMI sensor on site to be sure it was clear of any marine fouling. The two buoys were moored 200 m apart. The DCS3500 sensor was positioned downcoast ( predominant
The incoming data were examined for completenesz duplicate records, proper time stamps and spikes. Gaps in th. data were filled with invalid 999’s for processing purposes Problems found with the incoming data were primaril duplicate records resulting from multiple ARGOS transmission and from the Aanderaa sensor locking up and transmittin: duplicate records. The Aanderaa DCS3500R sensor requirc both a supply voltage and a control voltage, and an 800-m delay between application of the two voltages. The TAB: buoys supplied only a single, switched voltage level, so circuit was constructed to split the supply and provide for th required 800-ms delay. It was found that even with the delay the sensor sometimes would not power up. This resulted in n; new data being input to the ring buffer and duplicate record being transmitted. This situation occurred approximately 26: of the time at random intervals. Out of 900 data records, 32 were considered duplicate records and discarded. The time when duplicate records were transmitted appear as flat lines i the data time series shown in Figures 3 and 4. In all, 613 vali thirty-minute records were collected from the DCS3500R, 84 records from the MMI sensor and 900 from the ADCP durin the selected common period. Eight-day time-series plots (Figures 3 and 4) of the vectr components (east and north) from all three instruments indicni relatively good agreement among the sensors. AI1 three sensol show the same characteristics and trends. The MMI sensc (dotted line) has the widest variation from the mean of the thrc sensors, overestimating in the east component at high velocities, and to a lesser degree in the north component. Or: large gap is evident in the MMI data. This occurred betwee June 6 at 12:OO and June 7 at 18:OO. The Aanderaa DCS3500 (dashed line) more closely matched the data obtained from ti ADCP (solid line), excluding periods of lockup.
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(It is proportional to the square root of the number of pings averaged). At 45 pings per ensemble the standard deviation of the ADCP was 1 c d s . The measurement error of the MMI 585 sensor on the SeaPac current meter is stated by the manufacturer to be less than 2 c d s .
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Fig. 3: An eight-day time series of both vector components for all three sensors. The dotted line represents the MMI model 585 electromagnetic sensor, the dashed line the DCS3500R sensor and the solid line represents the ADCP.
40 I
North Component
1.2 0.65
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MMI 585 DCS3500R
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Three pairs of vector series were formed, e.g., MMI-ADCP, DCS-ADCP, and MMI-DCS. The lengths of the pairs differ because of gaps, which were ignored in the statistical analyses. To choose the most linearly independent orthogonal components, principle-axis analysis was performed on each vector series (Table 3). The resulting alongshelf component is about 64 degrees. Each series was then rotated into its individual variance ellipse components (Table 3). There is very good agreement between all three instruments.
I
Current Meter ADCP Workhorse
MMI 585
Number
Angle
PtS.
("True)
847
Major Axis
MinorAxis
(Cm'S.')
(Cm'S")
64.1
22.9
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847
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613
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3500
2000
Fig. 4: An eight-day time series of both vector components for all three sensors. The dotted line represents the MMI model 585 electromagnetic sensor, the dashed line the DCS3500R sensor and the solid line represents the ADCP.
Towards the end of June, the MMI585 sensor began to fail. In-house testing following recovery of the buoy showed the cause of the failure to be the presence of a single small barnacle covering a sensor tip. The failure is evident in Fig. 4 starting around June 18. Prior to failure, the mean velocity error of two sensors in comparison to the ADCP is small (Table 2). For this experiment's setup, the accuracy of the DCS3500 was 1.3 cm/s
62.0
613
585
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62.0
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After rotation, only basic statistics and linear regression are used to compare the vector time series from the three current meters because the series are short and those from the MMI and DCS sensors have gaps. "Standard" linear least-squares fitting assumes the abscissa values are error free and well known, which is not true for this study. Here, there are uncertainties in both coordinates, which we assume to have approximately equal weights. We used the more general algorithms that treat this case [6,7,8,9]. When the errors in both coordinates have equal weights, the best-fit line is that which minimizes the sum
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of the squares of the perpendicular distances of the points from the line [7]. The linear regression results are shown in Figures 5 through 10, and summarized in Table 4. Basic statistics for the series are summarized in Table 5.
TABLE 5 Basic statisticsfor each series component and the absolute differencebetween ADCP and each of the other two current meters.
TABLE 4 Regression results showing the slopes, intercepts and correlation coefficients
I
Regression
I
Slope
Alongshelf Cross Shelf
Intercept
I
I
Corr.
I
I
I
I
DCSvs. ADCP
I
1.02 0.99
-0.56
0.47
0.988 0.848
0.98
2.23
0.983 0.823
MMI vs.
Cross Shelf
Results of principle axis analysis show the resulting alongshelf component at about 64 degrees. Rotating the series into the individual elliptical components results in Table 3. There is very good agreement between all three instruments. Regressing the components into alongshelf and cross shelf components results in Figures 5 through 10.
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Fig. 6: MMI versus ADCP resolved to alongshelf components
Fig. 5 : MMI versus the ADCP resolved to crosshelf components
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Fig. 7: Aanderaa DCS3.500 versus the ADCP resolved to crossshelf components
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Fig. 8: Aanderaa DCS3500 versus the ADCP resolved to alongshelf components
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Fig. IO: MMI sensor versus DCS3500R crosshelf
The alongshelf component of the rotated data from the DCS3500R sensor shows good correlation with the ADCP with slope characteristics of 0.99. IV. CONCLUSIONS Results from the experiment indicate that both the Aanderaa DCS3500R Doppler sensor and the Marsh-McBimey model 585 electromagnetic sensor are suitable for use on a spar buoy. The MM1585 and the DCS3500R are each highly correlated with Workhorse ADCP in the alongshelf direction (b0.978). In the cross-shelf direction the DCS3500R is better correlated with the ADCP (Rd.848) than the MMI585 (R=0.762). The susceptibility of the MMI sensor to error caused by barnacle growth on the sensor tips is a major concern. Without some other local current reference, the error is very difficult to quantify. The susceptibility of the Aanderaa sensor to the effects of marine fouling, although theOretiCdly not a factor, will have to be determined through practice. The use of the DCS3500 sensor will continue at site B in order to evaluate the impact of biofouling. V. ACNOWLEDGMENTS The authors would like to thank the Texas General Land Office for their support of the TABS program under the Interagency Cooperation Contract 00-101R. In particular we thank Dr. Robert (Buzz) Martin for his encouragement and support. The authors would also like to express their gratitude to Mr. Paul Clark, and Mr. Mike Fredericks for their help in preparing and deploying the instrumentation under difficult circumstances. Their attention to detail resulted in a successful experiment.
Fig. 9: MMI sensor versus DCS3500R alongshelf
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VI.
REFERENCES
Martin, R. A., F. J. Kelly, Linwood L. Lee 111, and Norman L. Guinasso, Jr., 1997: Texas Automated Buoy System: Real-time currents for oil spill response. Proceedings of the 1997 International Oil Spill Conference, April 7- 10, 1997, Fort Lauderdale, FL. Kelly, F.J., N.L. Guinasso, Jr., L.L. Lee 111, G.F.Chaplin, B.A. Magnell, and R.D. Martin, Jr., 1998: Texas Automated Buoy System (TABS): A public resource. Proceedings of the Oceanology Intemational98 Exhibition and Conference, 1013 March 1998, Brighton UK, Vol. 1, pp. 103-112. Magnell, B.A., F.J. Kelly, and R.A. Arthur, 1998: A new telemetering environmental buoy for offshore applications. Proceedings of the Oceanology International 98 Exhibition and Conference, 10-13 March 1998, Brighton UK, Vol. 1, pp. 179-197. Chaplin, G.F. and F.J. Kelly, 1995: Surface current measurement network using cellular telephone telemetry. Proceedings of the IEEE Fifth Working Conference on Current Measurement. Feb. 7-9, 1995, St. Petersburg, F1. Aubrey, D.G., W.D. Spencer, and J.H. Trowbridge, 1984: Dynamic Response of Electromagnetic Current Meters. Woods Hole Oceanog. Inst. Tech. Rept. WHOI-84-20. 150 PP. Reed, B.C., 1992: Linear least-squares fits with errors in both coordinates. 11: Comments on parameter variances. Am. J. Phys. 60 (1).
Reed, B.C., 1989: Linear least-squares fits with errors in both coordinates. Am. J. Phys. 57 (7). Emery, W.J. and R.E. Thomson. 1998. Data Analysis Methods in Physical Oceanography. Pergamon. 634 pp. Sprent, P., and G.A. Dolby 1980: The geometric mean functional relationship.Biometrics, 36,547-550. VII. DISCLAIMER
This paper does not necessarily reflect the views or policies of the Texas General Land Office, Texas A&M University, Texas A&M University-Corpus Christi, or the other agencies providing financial and service support. Mention of trade names or commercial products does not constitute a commercial endorsement or recommendation for use.
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