As tides pass through narrow channels and around headlands, high-velocity flows are produced. Depending on site-specific characteristics, the turbulence of these flows varies in time and space. When these turbulent flows encounter a tidal stream turbine (TST), the severity of turbulence affects the turbine energy extraction capability by altering blade performance. This results in reduced rotor thrust and torque [1
], which causes a detriment in turbine performance of over 10%. Prolonged exposure to high turbulent flows increases fatigue load cycles and blade bending [2
], thereby reducing the expected lifespan of a turbine. Mitigation techniques can be applied to keep design tolerances high to account for turbulence related stresses, but this also increases manufacturing costs. The quantification of turbulence remains an important factor in the optimization of energy extraction and turbine durability and thus successful tidal stream developments.
The measurement of turbulence in a fluid, at scales relevant to rotating hydrofoils, by its nature requires high-resolution measurements (>1 Hz). Originally, sensors were developed to determine atmospheric turbulence; these were designed to be unobtrusive, where the measurements do not cause disruption to the flow of the fluid. To achieve this, the principle of Doppler shift was applied. This transmits high-frequency bursts (pings) from three or more transceivers. As the sound is scattered off by suspended moving particles in the fluid, a frequency shift is observed. This shift in frequency uses the Doppler effect formula, where the particle velocity, and therefore fluid velocity, can be calculated. The combination of multiple beams on different axes allows for the velocity calculation in the x
vectors and therefore speed and direction. This was originally done for converging beam sensors [3
]. This technology was later applied to the ocean environment [4
], where an adapted method for calculating seabed friction velocity was presented for low flow speeds in the presence of waves. Acoustic Doppler Velocimeters (ADVs) offer high-resolution sample rates in the order of 10 Hz assuming a small volume flow homogeneity (5–50 mm) [5
]. However, due to the focusing of the beams, a small area can only be observed, and this limits these sensors to small profiles or single point measurements. When the beam angle configuration changes from convergent to divergent, the beams cover a much larger area. If a similar homogenous flow approximation is applied, but now over an area of 1–50 m, then the component flow directions can be calculated for a depth profile. The application of this method has been used for many years in divergent beam ADCPs. Originally operating via “Narrow-Band” acoustic pulses, instantaneous velocity measurements experienced high noise levels, limiting these datasets to time averaged results not suitable for turbulence assessment. The development of “Broadband” acoustic processing has allowed more accurate instantaneous velocity measurements for diverging beam sensors [6
]. This continues to allow velocity measurements to be made throughout the water column, but with much higher sample rates, without the need for temporal averaging. For some sensors, this means sample rates as high as 10 Hz or above can be used while maintaining low data noise levels.
The number of transceivers on a sensor has increased over the years. While it is possible to get velocity measurements from a single beam, the velocity component is only in the beam direction, providing heavy application restrictions. Three transducers were common for a number of years and this allowed the calculation of directional velocity components. When combined with a pressure sensor, surface waves could also be measured. This used the pressure, u
velocity (PUV) method to measure the surface elevation using the pressure sensor and the wave directional orbital velocity (u
) to establish wave direction [8
]. The introduction of four beam sensors with opposing angled beams on two perpendicularly aligned axis increased the accuracy of flow measurements. This allowed two measurements to be used to calculate the velocity in each horizontal vector. More recently, five beam sensors have become the new standard. These use the same beam orientation as the four beam sensors; where the introduction of a fifth vertical beam allows a direct measurement of the vertical velocity and surface elevation. While the implementation of a fifth beam and acoustic surface tracking isn’t new, it is now more commonplace and integrated as default in a wide range of standard ADCPs.
The calculation of turbulence parameters from beam velocities of a 4-beam ADCP in relatively slow velocities (<1 ms−1
) was demonstrated in [8
]. This identified the effects of Doppler noise and its implications on turbulence parameters. The Doppler noise is created as a byproduct of the velocity calculation, where a phase difference for the Doppler shift occurs for multiple returns within a spatially determined cell [9
]. More recent studies quantifying turbulence in tidal channels compared the velocities between a bed-mounted ADCP and an ADV [10
]. These studies presented a standardised metric, adopted from the wind energy industry, to quantify tidal turbulence; this is known as the turbulence intensity. The turbulence intensity can be calculated using the velocity fluctuation minus the Doppler noise. If the noise is not accounted for, the instantaneous measurements from ADCPs can overestimate the value of the standard deviation calculated from velocity. This produces higher turbulence values for the same corresponding location. The noise can be described as white noise, where it is distributed evenly across all frequencies. The effects of this noise can be mitigated by averaging; this can either be done in time, where instantaneous velocity fluctuations are averaged over a number of minutes, or in space, where velocity fluctuations can be averaged across the vertical profile. This, however, is not a viable solution when considering turbulence measurements. The quantification of the Doppler noise was improved in [12
]; this applied a polynomial least square regression method to extend the inertial range to the high-frequency end of the velocity spectrum, lowering the noise floor. Additional methods for correcting instrument noise were presented in [13
]. These use either a Noise Auto-Correlation (NAC) or Proper Orthogonal Decomposition (POD) approach to determine and remove noise. In the case of the NAC method, the noise level is calculated based on the velocity spectrum, restricting the noise reduction to the frequency domain. However, the POD method is capable of reducing noise over the spectrum and within the time domain, providing a more flexible output. In addition, these methods may be more suitable for reducing noise in the presence of waves.
The number of published studies quantifying turbulence levels using in-situ field measurements at tidal energy test sites has increased in recent years [14
]. Similar studies have been conducted in rivers [21
], however, due to differences on the onset flow conditions, topography and sedimentation, the results of these studies is of limited relevance. Both the tidal and river studies use divergent beam ADCPs to record the velocity fluctuations. Alternatively, single-beam Doppler profilers, horizontally mounted and aligned with the in-stream velocity direction, have been used for turbulence assessment [18
]. This configuration provides streamwise velocity fluctuations with no assumption of flow homogeneity, resulting in a more accurate measurement of ambient flow speeds at mid-water depth and turbine hub height. However, additional single beam profilers are required (as installed in [18
]) to provide flow information in the transverse and vertical directions. The previous studies provide a basic resource assessment, where flow velocities and directions are first quantified in time and often depth. Then a range of turbulence parameters are presented including the standard deviation of the velocity fluctuations, turbulence intensity, Reynolds stresses, Turbulence Kinetic Energy and velocity spectra. Previous studies also provide benchmarks to other locations that allow the Fall of Warness to be contextualized in terms of other potential tidal test sites.
In this study, data are presented from a sensor network at the southern end of the Fall of Warness, with the locations specified in Figure 1
. The diverging acoustic Doppler profilers (D-ADP) were deployed simultaneously to measure wave and currents over a 4 day period. The general resource and turbulence characteristics are calculated and compared for each sensor with depth and time. Specific emphasis is placed on the peak velocities of the flood and ebb tides and the average conditions, where turbulence interactions are likely to be higher. The turbulence intensity, turbulence spectrum, Reynolds stresses and integral time and length scales are presented. A direct comparison between the homogenous approximated length scale and the raw beam length scales are presented for two sensors. The data analysis indicates that along with spatial and temporal variations, sensor make and model specific features also cause variation in observed turbulence parameters. These differences are highlighted and discussed throughout this paper. The presentation of turbulence intensity and length scale provide important boundary conditions required for the simulation of tidal turbines [25
]. This is crucial for the long term performance analysis of a single or array of devices.
This study has highlighted the difference between the two co-located SE Signature 500 and Sentinel V50 sensors. These sensors were deployed at less than 1 m distance from each other within the same sensor frame. It was expected that the flow speeds and turbulence parameters for these sensors would record almost identical results. However, this was not the case. While exact sensor setup and sampling regime would provide a more robust comparison, the setups used were within reasonable tolerances conducive to turbulence measurements. These results should yield largely similar outcomes, where only the very high frequency turbulence components would differ. The sensor manufacturers, Nortek and Teledyne, were contacted in regards to data quality and measurement validation. This concluded that both SE located datasets were confirmed as credible data sources, with no significant defects or errors. Both hardware and firmware of both ADCPs has also been confirmed as healthy. Further comparison of the co-located sensor’s raw data, showed the presence of the variations in flow profile features, as shown in Figure 4
, in the streamwise orientated beam data. This suggests these differences in measurements are a result of the sensors. This measurement campaign does not provide an explanation of the cause of this variation, but brings them to the reader’s attention. Further explanation and validation of these measurement variations, e.g., by an attempt to repeat the deployments, is limited due to the nature and cost of field measurements. Additional experimental work would benefit from collecting turbulence measurements in a laboratory flow tank, where more variables can be controlled.
The analysis of the results show the RDI Sentinel sensor having a much smoother velocity profile across the water column, whereas the Signature 500 experiences a much higher velocity variation. The data used in this study purposely extracted the sensor information in its rawest beam velocities format. This seems to be the case for the Signature 500 sensor, whereas the smooth profile of the RDI Sentinel 600 suggests that some form of internal data filtering has occurred. However, this was dismissed when contact was made with the manufacturer. When the average flow conditions are plotted, this initially assumed data cleaning shows very little effect. However, during the turbulence analysis, this apparent smoothing of the velocity variation causes significant effects to the turbulence parameters. A more direct comparison should be carried out, where the sensors sample rate and bin sizes remain constant and the sampling regime remains as similar as possible, with both sensor head aligned in the same orientation, so a direct beam-by-beam comparison is possible. However, the sampling regime will have to remain staggered to avoid sensor crosstalk. Based on the results presented and discussed in the above sections, it is recommended that a standard procedure is applied to the very basic data processing for the output of raw data for all sensor manufactures. This would require a classification of “raw data” that only allows very basic quality controls parameters to be applied. This would allow a reliable use of combined sensor types for turbulence quantification.
The results from the spatially distributed sensors all show turbulence levels that are consistent with previous literature with similar flow speeds for tidal energy test sites [11
], where turbulence intensities are of the order of 0.1 for hub height water depths. This is with exception to the SP1 sensor, where a much more turbulent flow is shown due to wake effects from the southern point of Eday. This sensor experiences a very disrupted tidal flow pattern, where flow speeds during the ebb and later part of the flood tide are significantly reduced. This shows the spatial variability of the flow field, where large difference in flow and turbulence characteristics occur over a short distance. Based on these measurements it seems likely that a tidal device could operate without detriment as a result of turbulence, at the SE and SP3 locations. The SP3 sensor experiences significant turbulence and flow disruptions making it a poor location for turbine siting.
Further work should include longer term continuous turbulence measurements, where full spring, neap cycles are measured. Additional considerations may wish to address the impact of seasonality on turbulence characteristics, where winter storms bring large waves to exposed tidal sites, causing alteration to the turbulence depth profile.
This study investigates the spatial and temporal differences in turbulence in the southern entry to the Fall of Warness tidal energy test site in Orkney. Four high-resolution bottom-mounted diverging beam ADCPs were deployed to simultaneously collect flow characteristics for several nearby locations. The measurements were made on a waxing lunar phase, with tides midway between neaps and springs. The flow characteristics show an asymmetric velocity distribution, where maximum flood velocities of 2 ms−1
at 150° and ebb velocities of 1.2 ms−1
at 315° are present within the channel. The SP1 sensor, close to the easterly landmass in Figure 1
, experiences flow interference midway through the flood tide that persists into the ebb tide, this provides much lower velocities with a wider directional variation.
The comparison of data from two sensors deployed in the multi-carrier frame at the same location provides different flow results and therefore different turbulence parameters. This provides uncertainty in the accuracy of turbulence measurements using ADCPs. Further comparisons between sensors should be tested in a more controlled environment, e.g., laboratory condition to identify the origins of these variations.
Several turbulence metrics are presented, where the variation in the velocity fluctuations are assessed in depth and time. The turbulence intensities show higher values towards the seabed, relative to the local velocities. The SP1 sensor shows large levels of turbulence near the surface for the flood tide and mid-water column for the ebb tide. This is supported by the presentation of the streamwise (uw) and transverse (vw) Reynold stresses. These stresses indicate larger vertical movement of turbulence in the transverse direction as opposed to the streamwise component; which generally increases towards the seabed. The time and length integral scales show longer and larger turbulent structures during the flood tides. These larger structures are located towards the seabed for the SP1 sensor, whereas for the SP3 sensor these structures are shown nearer the surface. The streamwise and beam length scales were compared at a likely depth of a turbine hub height. This showed that while the streamwise homogenous approximation should prevent the measurement of small (<10 m) length scales, the direct comparison of the individual beam data shows a similar agreement in the results. This shows similar magnitudes length scales across the flood and ebb tide, with some discrepancies occurring, where individual beam data provide larger values and higher frequency of fluctuations. The variation in turbulence between the deployment locations indicates that turbulence values can vary highly over relatively small distances. In order to quantify turbulence for a tidal energy test site for multiple numbers of devices, the deployment of several spatially distributed ADCPs must be required. Caution should be exercised when applying these turbulence conditions to other tidal energy test sites.
Further work focusses on extended deployment durations in obtaining high-resolution datasets. These extended measurement periods will provide more data to allow a more robust quantification of the turbulence parameters for an individual site. The presence of surface gravity waves will also be investigated as well as the application of the fifth vertical beam.