Design Guidance on Device Layout within

Tidal arrays

31ST OCTOBER 2015

Dr Tom Blackmore and Prof AbuBakr S. Bahaj University of Southampton Energy and Climate Change Division / Sustainable Energy Research Group Faculty of Engineering and the Environment Highfield, Southampton, SO17 1BJ, United Kingdom (www.energy.soton.ac.uk) This report is produced for the Isle of Wight Council, and has been co-funded by ERDF under the

INTERREG IVB NWE programme. The report reflects the author’s views and the Programme

Authorities are not liable for any use that may be made of the information contained therein.

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EXECUTIVE SUMMARY The tidal industry is still in its infancy, a number of pre-commercial devices have been

tested by different developers, but as yet there are no arrays installed. The first array to

be installed is planned to be commissioned by 2016 and will consist of an array of 4

turbines installed in the Pentland Firth, Scotland, by MeyGen. The question then arises of

how best to position devices within an array to maximise energy extraction and minimise

cost and environmental impact. This will be critical to ensure economic success and public

acceptance.

A review of different experimental and numerical techniques used to investigate arrays is

presented along with the mechanisms through which devices interact, including wake and

blockage effects. Typical array configurations are defined; a single row or fence of

turbines, a multi-row array on a non-staggered grid, a multi-row array on a staggered

grid, and a non-uniform layout. A single row array will likely provide the greatest power

output as device interactions are reduced. However, if due to space constraints a multi-

row array is unavoidable, the spacing between devices should be as large as possible on a

staggered grid to minimise interaction effects. Further site specific optimisation of non-

uniform layouts is possible using combined hydrodynamic/optimisation models, however

these are still in their infancy and further development is required.

It is likely that the first arrays will be conservative in design to minimise interaction

effects and reduce the risk of damage to other devices. Having large spacing’s will be

beneficial from an installation and maintenance point of view, but will conflict with

reducing cabling costs which would require short cable runs. From an environmental

perspective it is likely that large spacing’s between turbines will result in a lower impact,

although that impact will be spread over a wider area. Therefore a smaller number of

higher rated turbines will likely offer a reduced impact compared to a larger number of

small turbines. The impact on recreation and navigation is likely to be device specific with

the largest impact for surface piercing or floating turbines. Bed mounted turbines with

large draft above will pose little impact. However, the array will impact on fishing

activities irrespective of device type. Minimum impact would be achieved for a small array

area to minimise reduction in fishing grounds. Alternatively, large spacing’s may make it

possible to fish in between devices, but this would likely impact on the cabling cost.

Overall, the issues surrounding array design are very site and device specific. Each site

will likely have different constraints and therefore the optimum design will be a

compromise between these constraints. Further work is required to improve the modelling

capabilities by incorporating more dynamic and site specific effects such as bathymetry,

sea bed, or wave and turbulence conditions. Further work is also required to improve the

prediction of environmental impacts further aided by site measurements from turbine or

array installations.

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TABLE OF CONTENTS Executive Summary ............................................................................................................................... 2

Table of Contents ................................................................................................................................... 3

Introduction ............................................................................................................................................ 4

State of the Industry ............................................................................................................................... 4

Array Modelling ...................................................................................................................................... 7

Experimental modelling ...................................................................................................................... 8

Experimental requirements ........................................................................................................... 10

Numerical modelling ......................................................................................................................... 10

Basic wake models ....................................................................................................................... 11

2D/quasi 3D resource-scale models .............................................................................................. 11

3D Computational Fluid Dynamics (CFD) RANS models ................................................................... 12

3D Computational Fluid Dynamics (CFD) LES models ...................................................................... 14

Summary .......................................................................................................................................... 15

Device interaction within arrays ............................................................................................................ 16

Wake effects ..................................................................................................................................... 16

Blockage effects ............................................................................................................................... 18

Device layouts in arrays ....................................................................................................................... 19

Array tuning and other optimisation ...................................................................................................... 22

Installation and maintenance ............................................................................................................ 22

Cabling ............................................................................................................................................. 22

Effects on physical processes ........................................................................................................... 23

Environmental impacts ..................................................................................................................... 24

Socio-economic impacts ................................................................................................................... 25

Conclusions and Recommendations .................................................................................................... 25

Further work ..................................................................................................................................... 27

References ........................................................................................................................................... 28

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INTRODUCTION This work addresses the current state of the industry in tidal stream energy array design

and provides some guidance and direction to device layout based on current publically

available information. This work is undertaken for the Isle of Wight Council and on behalf

of the Pro-Tide project.

The Isle of Wight Council is one of the Pro-Tide partners and are involved with the

development of the Perpetuus Tidal Energy Centre (PTEC) off the south coast of the Isle

of Wight in St Catherine’s race. PTEC is designed to be a test site for arrays of tidal

energy devices that is grid connected.

The University of Southampton’s Sustainable Energy Research Group

(www.energy.soton.ac.uk) works closely with the Isle of Wight Council across a number of

topics and has expertise in marine energy, buildings and cities. Of particular relevance is

their track record in tidal stream turbine research.

The aim of the report is to provide design guidance on the layout of devices within arrays

of multiple turbines. The report also covers an overview of the tidal energy industry and

the progression towards array deployment in the UK and elsewhere. A summary of

different methods used to investigate array layouts is presented followed by a discussion

on device interactions from wake and blockage effects. Typical array layouts are

presented and considerations are made on the suitability of these with different sites and

their specific flow characteristics. Finally, the report also covers some future work which is

identified to progress the marine energy industry.

STATE OF THE INDUSTRY There are two general methods for extracting energy from the tides; tidal range (also

known as barrage), and tidal stream. Tidal range consists of a break water to dam the

tide at high water which is then passed through a turbine driven by the head difference

between high and low water. This is similar to a conventional hydroelectric power station.

The La Rance power station is the most well-known which is rated at 240MW and built in

1966. More recently in 2011 the Sihwa Lake power plant was built in South Korea and is

rated at 254MW. The Swansea Bay tidal lagoon being developed in the Bristol Channel

would be the first tidal range power station in the UK with an installed capacity of 320MW.

However, one of the main barriers to the installation of tidal range power stations is the

large environmental impact from damming an estuary and the subsequent loss of habitat.

In contrast, tidal stream turbines, which are akin to underwater wind turbines, driven by

tidal currents, are considered to have a lower environmental impact and have received

much attention in recent years. (Bahaj & Myers, 2003; Bahaj, 2011, 2013)

The tidal stream industry is still at the pre-commercial development stage with many

devices undergoing testing at specialist test sites. The main test site used is the European

Marine Energy Centre (EMEC) which is in the Orkney Islands to the North of Scotland. It

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should be noted that only single devices have been installed and tested, with many now

accruing thousands of hours of operation, proving their design and reliability. The last

couple of years have seen significant changes within the tidal stream industry. Notably,

Alstom’s acquisition of Tidal Generation Limited (TGL) from Rolls-Royce in 2012 and

Atlantis’s acquisition of Marine Current Turbines Limited (MCT) from Siemens in 2015.

Clean Current turbines also announced its exit from the industry in March 2015. Whilst

the divestment of Rolls-Royce and Siemens from the tidal industry might seem worrying,

it is clear that the likes of Alstom (driven by a French Government support programme)

and Atlantis (with venture capital support) are making significant investments in the

development and progression towards commercialisation. Table 1 shows details of some

of the leading turbine designs and their developers.

TABLE 1 - STATE OF THE INDUSTRY, CURRENT DEVICES AND DEVELOPERS – SEE ALSO

(BAHAJ 2011; BAHAJ 2013).

Developer Device Rotor

diameter

Mounting Number

of rotors

Rated

Capacity

First

genera

tion d

evic

es

Atlantis Resources

Limited / MCT

AR-1000 18m Sea bed 1 1MW

AR-1500 18m Sea bed 1 1.5 MW

http://atlantisresourcesltd.com/turbines/ar-series/ar1000.html

(MCT) SeaGen S 20m Surface

piercing / twin rotors

2 2MW

http://www.seageneration.co.uk/

Andritz Hydro Hammerfest

HS1000 21m Sea bed 1 1MW

http://www.hammerfeststrom.com/

Alstom TGL Oceade

18 18m Sea bed 1 1.4MW

http://www.alstom.com/press-centre/2013/1/alstom-completes-the-acquisition-of-

tidal-generation-limited-tgl-from-rolls-royce-plc/

Voith Hy-Tide 13m Sea bed 1 1MW

http://www.voith.com/en/markets-industries/industries/hydro-power/ocean-

energies-539.html

6

OpenHydro, a DCNS company

Open-Centre turbine

16m Ducted / Surface piercing

1 2MW

http://www.openhydro.com/images.html

Schottel SIT

Instream turbine

3-5m Various 1 0.54-0.7MW

http://www.schottel.de/de/news-events/presseinformationen/news-

detail/?tx_ttnews%5Btt_news%5D=116

Nova Innovation Nova 100 4.5m Sea bed 1 0.1MW

http://www.novainnovation.co.uk/index.php/products/nova-m100

Second g

enera

tion d

evic

es

Nautricity Cormat 10m

2 Contra rotating rotors,

mid-depth tether

2 contra-rotating

0.5MW

http://www.nautricity.com/cormat/cormat-reliability/

Scotrenewables Tidal Power Ltd

SR2000 16m Floating, twin

rotor 2 2MW

http://www.scotrenewables.com/news/29-scotrenewables-award-contract-for-manufacture-of-250kw-

tidal-turbine-srtt-prototype

Sustainable Marie Energy

Ltd PLAT-O 7m

mid-depth buoyant twin rotor tethered

2 0.1MW

http://sustainablemarine.com/

Kepler energy Kepler 60m

length 10m

Sea bed mounted, transverse axis rotor

1 1-2MW

http://www.keplerenergy.co.uk/index.html

The next stage of development for the industry will be the installation of multiple devices

to form arrays of turbines (Bahaj & Myers, 2004; Myers & Bahaj, 2005). The first array to

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be installed is set to be the Meygen array in the Inner Sound in the Pentland Firth,

Scotland. The initial phase will deploy 4 turbines (1 Atlantis and 3 Andritz Hydro

Hammerfest) of 1.5MW capacity. The installation is due to be commissioned in March

2016. This initial deployment of 6MW is planned to be expanded to a full capacity of

398MW. The likes of the Perpetuus Tidal Energy Centre (PTEC), planned for array testing

on the south of the Isle of Wight, will likely be a key facility in progressing to the next

stage of the tidal energy industry. For the successful deployment of these arrays, careful

site selection and array design will be crucial to the economic success of the project. The

next section reviews different techniques for modelling arrays of turbines.

ARRAY MODELLING Arrays of turbines can be modelled using a number of different techniques, which may be

either experimental or numerical. This section provides an overview of the different

modelling approaches including required inputs and typical outputs from the models and

concludes with a summary of the different model types and their appropriateness for

different scenarios.

A turbine rotor extracts kinetic energy from a flowing stream of water, in this case the

flow resulting from the tides, and converts it into electrical energy. Figure 1 shows the

stream-tube around a turbine rotor. Due to the reduction in kinetic energy across the

turbine rotor, the downstream velocity is lower than the upstream velocity, and there is a

corresponding drop in pressure which causes the stream-tube to expand downstream. The

area contained within the stream-tube with a lower velocity than the surrounding flow is

known as the wake. Far downstream the wake will continue to expand until the pressures

have balanced and the velocity profile has recovered to the upstream values (albeit with a

slightly lower kinetic energy) and the wake is no longer noticeable. For the purpose of

array modelling, understanding this wake structure behind the turbines and how the

wakes of upstream turbines may interact with downstream turbines is the main goal. If

the wake structures can be accurately modelled then accurate predictions of energy

extraction and possible environmental impacts may be made. Similar wake structures

may be generated using porous disc rotor simulators for small scale laboratory

experiments, and using actuator discs in numerical models. These methods provide cost

effective methods for modelling of tidal turbine arrays and will be discussed further in this

section.

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Figure 1 - Stream-tube around a turbine rotor, porous disc rotor simulator, or actuator disc.

EXPERIMENTAL MODELLING Scaled experiments using laboratory facilities have been used to provide insights into

arrays and wakes through the generation of appropriate datasets which are also used in

the validation of numerical models. It is important to note that the scaling of these model

devices should be Froude number scaled based on the tidal site. The Froude number is

defined as the ratio of a flows inertial force to its gravitational force:

. (1)

Where Fr is the Froude number; U∞ is the flow velocity; g is the gravitational acceleration;

and h is the water depth. The Froude number is important in open channel flows to ensure

similarity in wave and surface behaviour. However, ensuring Froude number similarity will

likely mean differences in Reynolds number. The Reynolds number is the ratio of inertial

forces to viscous forces.

(2)

Where Re is the Reynolds number; U∞ is the flow velocity; L is a characteristic length (in

this case rotor diameter); and ν is the kinematic viscosity. While Reynolds number scaling

is important, once the flow becomes turbulent the flows can be considered Reynolds

number independent. Therefore, if it can be shown that the experiments are performed in

the turbulent regime, they will be Reynolds number independent and therefore any

differences in Reynolds number will not have a significant effect. This was demonstrated

to be the case for porous disc rotor simulators by Blackmore, Batten, Muller, et al.

(2014).

Stream-tube

Turbine rotor / Porous disc / Actuator disc

Velocity

Pressure

Velocity U∞

Uw

Ut

P∞

P∞

P+t

P-t

Pressure

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Scaled turbine rotors may be manufactured such as the 1/20th scale turbine with a 0.8m

diameter rotor developed by the University of Southampton (Bahaj et al., 2007; Galloway

et al., 2014), as shown in Figure 2. These turbines can provide valuable information on

the performance of a turbine and their wake, but in order to achieve suitably low blockage

ratios large experimental facilities are required such as circulating water channels or

towing tanks (Bahaj et al., 2007) and more recently the IFREMER circulating water

channel facility in France which is 4m wide and 2m deep (Germain et al., 2007). Some

work has been performed using 2 turbines and it was found that the downstream turbine’s

performance was decreased due to operating in the wake of the upstream turbine (Bahaj

& Myers, 2013; Mycek et al., 2013). However, installing arrays of multiple devices

becomes impractical as most facilities are not large enough to achieve representative

blockage ratios (the ratio of turbine area to channel cross-sectional area), and an

alternative method of array modelling is required. Stallard et al. (2013) used 1/70th scale,

0.27m diameter turbine rotors to investigate the wake interactions within a small array of

up to 10 turbines in different configurations. The results show that for lateral turbine

spacing’s of 2D or less, the turbine wakes merge within 4D downstream of the rotors. A

faster wake recovery is also observed for the central turbine wakes due to the increased

turbulence. For a lateral spacing of 3D or greater the turbine wakes remain similar to that

of an individual rotor.

Figure 2 - University of Southampton 1/20th scale, 0.8m diameter turbine (left) (Blackmore et al., 2015), porous disc rotor simulator, 0.15m diameter (right) (Blackmore, Batten, Muller, et al.,

2014).

Nevertheless turbines may be represented as porous disc rotor simulators (shown in

Figure 2 (left)) which represent the far field wake effects of a turbine (Myers & Bahaj,

2010). This is akin to the idea of an actuator disc turbine representation in numerical

models. Shah et al. (2014) used porous discs to model an array with 7 staggered rows, as

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shown in Figure 3. Flow velocities were mapped throughout the array to investigate the

effects of wake interactions within the array. It was found that after the third row,

conditions within the array start to repeat and the flow becomes dominated by device

generated wakes and therefore independent of the upstream flow conditions. However,

further work is required across a wider range of array layouts and inflow conditions in

order to draw broader conclusions from this work.

Figure 3 - 7 row staggered array using porous disc rotor simulators (Shah et al., 2014).

Daly et al. (2010) used the idea of a porous disc rotor simulator to represent an entire

array using a porous fence. In this work the effects of the wake recovery behind an array

and its proximity to the channel boundaries was investigated. It was shown that as the

array is moved closer to the channel side boundary, the lateral expansion of the wake is

increased and stronger flow acceleration between the array and channel boundary is

observed. This situation is likely to occur due to the length of cabling required to connect

the array to the grid. A shorter cable will be cheaper and therefore the array might be

more cost effective if sited closer to the shore. However, this could have a significant

effect on local scour and sediment transport due to increased flow acceleration.

EXPERIMENTAL REQUIREMENTS As discussed above, experimental models must be Froude number scaled and therefore

velocities and depths of a tidal site are required inputs. Also required are the rotor thrust

coefficients for defining the porous discs, and the turbine geometries for constructing

scaled rotors. With advances in 3D printing and CNC machining it is possible to create

scaled models of tidal sites using bathymetry data. This would then allow the performance

and optimisation of proposed arrays to be performed using these small scale experiments

with porous disc rotor simulators. Data from these experiments is also valuable for

validation of numerical models discussed in the next section.

NUMERICAL MODELLING Numerical models have the potential to provide information over a range of different

scales at relatively low cost. There are many different types of numerical models and it is

important to understand their differences and what information they can provide.

4 D

Porous disc rotor simulators

4 D

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BASIC WAKE MODELS

There are many different wake models that fall into this category. The advantage of using

a simple model to predict the wake behind a turbine is the low computational overhead.

Therefore information can be obtained quickly on the likely performance of a turbine

array. One of the most common wake models is the Ainslie wake model which is a

linearised solution of the Reynolds Averaged Navier-Stokes (RANS) equations that uses an

eddy viscosity model to describe the turbulent mixing (Ainslie, 1986) and the wake is

assumed to be axisymmetric. This eddy viscosity model forms the basis of the commercial

software, TidalFarmer developed by DNV-Garrad Hassan. Another approach was

developed by Larsen (1988) which uses Prandle’s turbulent boundary layer equation and

the assumption that for large Reynolds numbers the flow is axisymmetric. As for the

Ainslie model an approximate solution to the RANS equations is obtained. The wake

recovery and expansion are controlled by the thrust coefficient and turbulence intensity of

the flow. The Larsen wake model was used in the wake assessments performed for the

Perpetuus Tidal Energy Centre (PTEC) assessments (Royal Haskoning DHV, 2014).

Before these models can be used they must be calibrated to find coefficients used in their

solutions. This calibration is performed by comparison to experimental data. For the PTEC

wake assessments the experimental data of Stallard et al. (2013) was used for calibration

and the full details of their model described in Royal Haskoning DHV (2014).

A limitation of these models is the assumptions used in finding a solution to the RANS

equations, and as such it has been found that these models are prone to under predicting

power output in some situations (Gaumond et al., 2012). Whilst these models are cheap

and quick to run, they are too simplistic to capture non-linear effects that will become

more important as larger arrays are considered with site specific effects such as

turbulence and changes to the local bathymetry.

2D/QUASI 3D RESOURCE-SCALE MODELS These models are aimed at resolving the regional-scale hydrodynamics to capture the

effects of an array on the wider environment. At the initial stages they can be used to

define the available resource and identify possible locations for arrays (Blunden & Bahaj,

2006). These models are 2D depth averaged solutions of the RANS equations. Available

models include Mike21 and Telemac 2D. Bathymetry data is required to define the

numerical domain and tidal elevation and velocity data is required to validate the models.

Arrays of turbines are included in these models using added bed roughness to represent

the hydrodynamic forces the array would impose upon the surrounding flow field (Bahaj &

Blunden, 2008). The advantage of these models is their relatively low computational

overhead allowing large domains to be simulated and minimising possible boundary

effects (Coles et al., 2015).

By splitting the depth axis in these models into layers, 3D models may be created. These

models are 3D, but in each vertical layer the 2D depth averaged Navier-Stokes equations

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are solved and as such can be thought of as quasi 3D. Examples of these models are

Telemac 3D and Mike3 (see Figure 4). Due to the added complexity of these models it is

not currently practical to produce such large domains as for the 2D models. The 3D

models are used to create local-scale models to provide higher resolution data around the

array location. The larger scale 2D model can be used to provide boundary conditions for

the local-scale 3D model, as used in the PTEC assessment. It would be possible to define

individual turbines within an array as a momentum sink applied to a specific layer that the

turbine operates in. These models could therefore be used to investigate basic device

interactions within an array, whilst still capturing the regional-scale effects of the array on

the surrounding environment. However, it is likely that higher resolution models with

fewer simplifications will be required in order to capture more of the flow physics and

provide greater information on device interactions within arrays.

3D COMPUTATIONAL FLUID DYNAMICS (CFD) RANS MODELS

These models use numerical methods to solve the Reynolds-averaged Navier-Stokes

(RANS) equations without the simplifications introduced from depth averaging. The

models are typically steady state but require much higher resolution meshes to run, and

as a result require greater computational resource than 2D depth averaged models. As

such they are only really practical for relatively small areas. Turbines are typically

represented using actuator discs, or coupled with blade element momentum theory

(BEMT) codes which represent the turbines as a momentum sink equivalent to the energy

extracted by the turbine. These turbine models have been validated against experimental

data and shown to provide good levels of accuracy for predicting both the wake and

turbine performance. In general, the RANS + BEMT model is preferred over the actuator

disc as it shows better agreement to wake measurements, the power can be estimated for

a specified blade geometry, and it does not require empirical expressions to describe the

turbulence generated by the turbine (Batten et al., 2013). These models require the

domain boundaries or bathymetry data, velocity, and turbulence kinetic energy profiles.

Actuator disc models require only the rotor diameter, power and thrust coefficients of the

turbine. BEMT models required the turbine rotor geometry including the profile of the

hydrofoils used for the blades and the rotational speed of the turbine.

The advantage of these models is that individual turbines are represented and therefore

device interaction effects within arrays may be studied. For example, Nishino & Willden

(2012) used the CFD code, Fluent, with an actuator disc RANS model to study the effects

of blockage ratio (the ratio of turbine rotor area to channel cross-sectional area) on the

wake and performance of an infinite array of turbines. Their results show that the aspect

ratio of the channel has a significant effect on the wake expansion. This was therefore a

purely academic study to investigate the underlying flow physics rather than for a site

specific case. Nevertheless, it is well understood that in wide shallow channels the wake is

restricted in the vertical direction and therefore forced to expand laterally. In channels

with similar widths as the depth the wake remains much more uniform and axisymmetric.

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Such 3D flow effects could impact on the wider environment of a site that would not be

captured using the more basic wake models that assume an axisymmetric wake. Other

models have been used to investigate the importance of the device nacelle and support

structure (Edmunds et al., 2014). It was found that compared to the rotor wake, the

support structure had little effect and therefore need not be modelled to reduce

computational requirements. The model was also used to investigate an array situated off

a headland. It was concluded that the optimum array configuration would be site specific

due to changes in bathymetry and local flow features.

Waldman et al. (2014) compared the Mike 3 model to the CFD code Ansys CFX

TideModeller model for an array of turbines in Lashy Sound, Orkney. The Mike 3 model

had a grid resolution of around 100m, whereas the CFX model required a grid resolution

of approximately 24m which was further refined in the area of the turbines. These grids

were fitted to bathymetry data of the site. The Mike3 model also included the wider area

of the Pentland Firth & Orkney Waters and this model was used to define the boundary

conditions required for the TideModeller CFX model. The array consisted of 7 turbines but

no further details were presented due to the information being commercially sensitive.

Some results from this study are shown in Figure 4. The larger domain of the Mike 3

model can be seen and the individual turbines visible in the TideModeller CFX model.

Scales were not given due to the results being commercially sensitive, but they do provide

a qualitative representation of these two different types of model.

Figure 4 – Model output showing the array resolution for a small array using a low resolution Mike3 model with the array represented as added roughness(left) and a higher resolution RANS model where the individual turbines are represented in a TideModeller CFX model (right). Colours show changes in flow speed as a result of the array, red indicates flow acceleration and blue

indicates flow reduction. Figure has been reproduced with permission from Waldman et al. (2014).

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3D COMPUTATIONAL FLUID DYNAMICS (CFD) LES MODELS

Further increasing the resolution of these models leads to the Large Eddy Simulation

(LES) models. These are transient models that directly resolve the largest scales of

turbulence, while the smaller scales are modelled in a similar way to RANS. As a result

very high grid resolution is needed requiring high levels of computational resource. While

these models are currently impractical for large scale array modelling they do provide

information on dynamic effects such as turbulence. Turbines are typically represented as

actuator discs or actuator line models and device interaction within small arrays is

possible. These models have been used to investigate fundamental flow physics and

dynamic effects, such as turbulence, on the wake and performance of turbines and small

arrays.

Churchfield et al. (2013) used an LES model to investigate the wake propagation and

power produced by a small array in both staggered and non-staggered arrangements with

different spacing’s. Figure 5 shows an instantaneous view from their case with a

staggered array of counter-rotating turbines. The tip vortices shed from the blades can

clearly be seen as shown by the red surfaces. The results show the wakes generated

behind the first row of turbines, but also significant wake meandering behind the second

row of turbines due to the higher levels of turbulence. It is also shown how the power

produced increases with increased axial spacing, but the direction of rotor rotation was

found to have little effect. Blackmore et al. (2014) used an actuator disc LES model to

investigate the effects of turbulence on the wake of a turbine. It was shown how flows

with different turbulence characteristics had different effects on the wake recovery and

expansion. High turbulence intensity flows result in faster wake recovery, but it was also

shown how flows with larger scales of turbulence result in faster wake expansion and

even faster wake recovery than flows with smaller scales of turbulence. This is an

important consideration as the turbulence is likely to be site specific due to the local

bathymetry and sea bed surface, and therefore likely to affect device interaction within

arrays.

15

Figure 5 - Array of 4 turbines using an LES model, results taken from (Churchfield et al., 2010).

While LES models are currently impractical for large arrays due to the high computational

requirements they do provide valuable information on the fundamental flow physics

driving wake recovery, and hence device interactions within arrays. These transient

effects could not be captured with the more basic wake models and demonstrates where

these models are useful. However, it is possible to model small arrays as demonstrated by

an LES model of 7 turbines (Gebreslassie et al., 2015). As computational power continues

to advance, it may be possible to model whole sites using LES models to provide further

array optimisation for each individual site.

SUMMARY In this section we provide an overview of the different models available for modelling tidal

arrays as shown in Table 2. The table shows the different model types, what information

they can provide, and an example of available models.

TABLE 2 - SUMMARY OF DIFFERENT MODELS FOR INVESTIGATION TIDAL TURBINE

ARRAYS.

Type Examples Computational

resource Inputs

Outputs Comments

Experimental

Scaled

rotors or

porous discs

-

Turbine geometry

(scaled turbine) or

turbine thrust coefficient

for Porous disc rotor

simulators. Flow

velocities, turbulence,

blockage, bathymetry

Turbine power,

blade loads,

fatigue loads,

wake structure,

details of wake

propagation/recov

ery/interaction

Provides data on

turbine performance

and wake propagation

that can be used for

array

design/optimisation

and validation of

numerical models

Ainslie / Eddy

viscosity / TidalFarmer,

low Turbine geometry and

performance coefficients,

Turbine power,

array power,

Provides basic

estimates of array

16

Larsen WAsP, velocity profiles,

turbulence intensity,

bathymetry, blockage

wake profiles and

interactions

power production and

wake propagation.

Depth

averaged 2D /

quassi 3D

resource scale

models

Mike21 /

Mike3,

Telemac

2D/3D

medium

Turbine performance

coefficients, velocity

profiles, turbulence

intensity, bathymetry,

tidal constituents, ADCP

and tidal elevation data

for validation

Turbine array

power, array

wake profiles,

tidal elevations

Provides estimates of

the tidal resource and

predictions of possible

impacts of the array

on the wider

environment, such as

sediment transport,

tidal elevation, and

velocities

3D RANS

models

OpenFOAM,

Ansys CFX,

Fluent

medium-high

Turbine geometry and

performance coefficients,

velocity profiles,

turbulence intensity,

bathymetry

Turbine power,

array power,

wake profiles and

interactions, full

velocity field

dataset

Provides information

on the performance of

small arrays, wake

interactions, and

other steady-state

effects such as

blockage, channel

aspect ratio, and

velocity shear

LES models

OpenFOAM,

Ansys CFX,

Fluent, and

other

custom

codes

very high

Turbine geometry and

performance coefficients,

velocity profiles,

turbulence intensity,

turbulence length scale,

transient time series

data from site for energy

spectra calculation,

bathymetry

Turbine power,

array power,

wake profiles and

interactions, full

transient velocity

field dataset for

turbulence

analysis

Provides high

resolution information

on turbine

performance and

wake propagation in

dynamic flows (such

as turbulence) but

limited to single

turbines or small

arrays

DEVICE INTERACTION WITHIN ARRAYS This section considers how devices interact within an array of multiple turbines, and how

this might affect their performance. The primary cause of device interactions within arrays

is due to wake interactions. For example, if a turbine is placed downstream of another

turbine it will be operating in the wake region with lower velocities than the surrounding

flow. This will therefore result in reduced power output from the downstream device.

Another form of interaction is due to blockage. An example would be if turbines were

placed very close together off a headland, the flow may be diverted to flow around the

turbines rather than through them due to the high localised blockage. It is important to

understand these physical mechanisms of device interaction to aid the design of more

appropriate array layouts.

WAKE EFFECTS

As discussed previously, a turbine extracts kinetic energy from a flow and as a result

there is a region downstream of the turbine with reduced kinetic energy, or momentum,

see Figure 1. This region with reduced velocity is known as a wake and there are a

17

number of factors that influence its development and recovery downstream of the turbine.

Fast wake recovery is beneficial for an array as it means downstream turbines located in

the wake will be operating in velocities closer to the free-stream resulting in higher power

outputs.

The first effect to be considered is the proximity of a turbine to the sea bed. It has been

shown that as a turbine is moved closer to the sea bed the wake propagates further

downstream and the wake recovery is slowed (A. S. Bahaj et al., 2012). This is due to

boundary layer effects with lower velocities close to the sea bed and higher velocities

towards the surface. In this case, the wake is drawn down into the boundary layer with

lower velocities and mixing with the high momentum free-stream flow is reduced,

therefore the wake persists further downstream. It has also been shown how turbulence

can affect the wake recovery behind a turbine. Flows with higher turbulence intensities

result in faster wake recovery rates due to increased mixing between the high momentum

free-stream flow and the low momentum wake. For flows with larger turbulence length

scales (size of turbulent eddies) the wake expands at a faster rate with a corresponding

increase in wake recovery (Blackmore et al. 2014). Due to the turbulence characteristics

of the flow being site specific, turbulence effects should be considered in the design of

arrays (Milne et al., 2013; Thomson et al., 2012). Additionally, it has been noted that

faster wake recovery is observed behind a turbine that is located downstream of another

turbine due to the added turbulence generated by the device (A.S. Bahaj & Myers, 2013).

This effect was also noted by Churchfield et al. (2013) who also noticed the wakes of the

second row of turbines starting to meander in the higher turbulence flow. However, due to

the a reduction in kinetic energy of the wake behind the upstream turbine, the power

output of the downstream turbine could be significantly reduced, by over 50% reduction

in the worst case scenario (Churchfield et al., 2013).

Due to the expansion of the wake downstream of a turbine, for a row of laterally spaced

turbines their wakes will eventually merge, forming a single wake from the array. Stallard

et al. (2013) found that for 3 rotors spaced at 1.5D laterally, their individual wakes were

still visible at 6D downstream, but by 8D the wakes had completely merged to form a

single wake with roughly uniform velocity deficit. This merged wake then continues to

expand and recover with increasing distance downstream. The effect of wake merging

was also observed by Shah et al. (2014) who performed experiments on a staggered

array with 7 rows, as shown in Figure 3. These results showed that after the 3rd row of

turbines the conditions within the array become self-similar, suggesting that the wake

recovery is driven by the device generated turbulence only and the ambient flow

turbulence does not penetrate further than the 3rd-4th row. It was also shown that the

power produced by the turbines located downstream of the 3rd row produce approximately

half the power output of the first row. This therefore suggests the optimum array design

will likely consist of fewer rows of turbines (3 rows maximum) with more turbines

laterally. However, due to the sensitivity of the wake to these different parameters the

18

optimum array configuration will be site specific due to changes in bathymetry, sea bed

surface, depth, and local flow features (Edmunds et al., 2014).

BLOCKAGE EFFECTS

In relation to array spatial planning it may not be quite as simple as increasing the

number of turbines in the lateral direction. Doing so will change the aspect ratio of the

channel cross-section that the turbine operates in, and increases the blockage ratio. The

blockage ratio is defined as the ratio of turbine rotor area to channel cross-sectional area.

Increasing the blockage ratio restricts the bypass flow around the turbine forcing more of

the flow through the turbine. This increases the velocity at the turbine and thus increases

the power output. From a theoretical point of view, maximum power extraction from an

array occurs for a blockage ratio of 1, that is, the channel is completely occupied with

turbines! Garrett & Cummins (2007). In this situation all of the flow passes through the

turbines and maximum power generation is achieved. However, this will likely interfere

with other water users and have environmental implications. As the blockage ratio is

reduced, so does the power extraction which tends to the theoretical unblocked limit as

first derived by Albert Betz in 1919 - later translated and published in the reference (Betz,

1966). This effect was also discussed by Nishino & Willden (2012) who also showed the

effects of channel aspect ratio on the wake. For square shaped channels with an aspect

ratio of 1 the wake expands axisymmetrically. However, for a higher aspect ratio channel,

one that is shallow and wide, the wake is constrained by the bed and surface and is

therefore forced to expand laterally. Further experimental work has shown that the

channel aspect ratio can significantly affect the power output of a turbine. Maximum

power output is observed for aspect ratios of around 2, with around a 20% reduction in

power output for aspect ratios greater or less than this (Keogh et al., 2014).

A further complication may be realised by considering an array located off a headland. As

mentioned earlier, a tidal fence that occupies the whole channel provides the maximum

power extraction, but is unlikely to be a suitable arrangement. Therefore the array will

only partially block the channel. By increasing the density of turbines the local blockage

may be increased, resulting in velocity enhancements and increasing the power output.

However, if the local blockage is too high the flow may divert around the array and the

power output reduced. Similarly, if the array is located too close to the shore the flow

may be blocked and diverted around the array (Daly et al., 2010). It is therefore

important that site specific conditions are considered during the design of the array and

its spatial planning. Once this is done, further improvements in the array power output

may be achieved by optimisation of the siting of the turbines within the flow regime. For

example, Schluntz & Willden (2015) have shown how turbine performance may be

optimised for a given blockage ratio.

In Table 3 we present a summary of the different parameters and the effects they cause

to the wakes and performance of tidal turbines within arrays. It is clear that there are

19

many factors to consider in the design of a tidal array. It is likely that the first arrays will

be conservative with large spacing’s to aid deployment, ease of maintenance and

retrieval. However, as the industry grows, optimisation of these arrays will be possible

with careful consideration of site specific conditions and the spatial planning of the

turbines operating within the site. However, it is more likely that this level of optimisation

will only be possible with the use of scale experiments or LES models to capture these

complex dynamic effects.

DEVICE LAYOUTS IN ARRAYS This section presents typical designs of array layout and discusses what the optimum

design may be for maximum power output. The next section presents further optimisation

/ compromises that may be required to improve ease of installation, reduce cabling costs

etc.

Figure 6 shows an array of 9 turbines using the layouts that have typically been

considered in the literature. The most basic configuration is a single row, or fence of

turbines as shown in Figure 6(A). In order to introduce more turbines into a given area, or

increase the distance between the turbines multiple rows of turbines have been proposed.

These may be in a non-staggered arrangement where turbines are aligned both laterally

and axially as shown in Figure 6(B). However, to increase the distance between upstream

and downstream turbines, to reduce wake effects, staggered arrays have been proposed

as shown in Figure 6(C). Finally, Figure 6(D) shows a non-uniform array generated using

optimisation algorithms to place turbines based on specified optimisation criteria, typically

to maximise power output.

20

Figure 6 - Standard array configurations.

It is widely acknowledged that a non-staggered array has poor performance due to the

downstream turbines operating in the wake of upstream turbines, as discussed in the

previous section. Churchfield et al. (2013) demonstrated that by increasing the axial

spacing in a non-staggered array, the power output may be increased. However, they also

demonstrated that if the downstream turbines were positioned in a staggered

configuration the power output was further increased. It is therefore likely that even in

cases with very large axial spacing a staggered array will offer improved performance

over a non-staggered array. Draper & Nishino (2013) used a theoretical basis to examine

the effects of a staggered and non-staggered array and the impact on performance. Their

findings show that in all cases a staggered array produced a higher power output than for

a non-staggered array. They also suggested that for a staggered array, power output may

be increased by tuning of individual turbines. However, they also demonstrated that for

the same number of turbines, a single row of turbines, or fence (such as in Figure 6(A))

will have a higher power output than a staggered array of turbines with multiple rows (as

shown in Figure 6(C)). This was also demonstrated by Hunter et al. (2015) who showed

that the front row of a staggered array should be operated at a higher resistance

coefficient than the subsequent rows to achieve maximum power output. A further 10%

?

A) Single row /

fence

B) Non-staggered

array

C) Staggered array D) Non-uniform

configuration

Lateral

Axial

21

increase in power output was achieved when all turbines were placed on a single row. In

this configuration tuning of individual turbine performance showed a negligible

improvement in performance of the array. If the array must contain multiple rows the

lateral and axial spacing between turbines should be maximised to minimise wake

interaction effects, as is done with offshore wind farms (Draper & Nishino, 2013).

Figure 6(D) shows an alternative approach to array layout using an optimisation algorithm

to produce a non-uniform array where the turbine locations are altered to achieve

maximum power extraction. On such approach is demonstrated by Funke et al. (2014) in

which their method uses the adjoint approach to iteratively run a shallow water model to

predict the performance of an array with a set number of turbines. The turbine locations

are then altered and the model re-run. Over a number of iterations the turbine locations

will stabilise and tend towards the maximum power output. An example output of this

model is shown in Figure 7 for their Pentland Firth study. This method was also used to

investigate the design of large arrays and how the performance of a non-staggered array,

or single row may be further improved to achieve higher power outputs (Vennell et al.,

2015). As computational power advances it may be possible in the future to perform this

type of optimisation using an advanced LES model to capture dynamic effects within the

optimisation process.

Figure 7 - Optimised array using the adjoint approach for Stroma Island, Pentland Firth. Array of

256 turbines (left), detail of turbine locations (top right), and power output with iteration number (bottom right). Results taken from (Funke et al., 2014).

In summary, a single row of turbines (with no lateral constraints) will outperform a

staggered array of turbines, which will outperform a non-staggered array of turbines. If

lateral space is limited such that the array must contain multiple rows, the axial spacing

between rows should be maximised to minimise wake effects, and the total number of

rows minimised. Further optimisation is possible using optimisation algorithms linked to

22

hydrodynamic solvers to produce non-uniform layouts that can result in further increases

in power output, but further development of these optimisation models is required to

verify and validate them across a wider range of conditions.

ARRAY TUNING AND OTHER OPTIMISATION This section considers other factors that may influence the design of an array and where

compromises may have to be made.

INSTALLATION AND MAINTENANCE

The installation cost of a turbine will likely account for 16% of the initial capital cost

(Johnstone et al., 2013). In contrast, the maintenance cost are estimated to be around

£92/MWh, which assuming a 25 year design life could be double the capital cost and

represent an area where significant savings could be made (Johnstone et al., 2013).

These costs will largely be driven by the device type; for example, a turbine requiring a

mono-pile will incur a greater installation cost compared to a gravity base design.

However, the array layout can also impact upon this. For an array layout with tightly

spaced turbines the installation and maintenance costs will likely be increased as more

precise positioning equipment will be required, and the risk of damaging other turbines in

the locality is increased. Therefore large spacing between turbines in the array will allow

greater working room for their installation and maintenance. This also agrees well with

the optimum configuration for a multi-row array requiring large spacing’s to minimise

wake interactions.

Other factors such as the bathymetry, sea-bed, water depth, and distance from port will

also affect the array installation cost and should be factored in to any assessment. Site

specific effects such as wind, tide, and sea conditions will further impact upon the

installation cost due to possible downtime in rough weather. Models have been created to

help predict and optimise the installation of marine arrays to minimise downtime. Further

improvements can be made with new vessels designed for the marine energy industry,

with considerable cost savings compared to the existing vessels used from the oil and gas

industry (Morandeau et al., 2013). Overall, installation and maintenance costs will be

influenced by the device and site location.

CABLING

The connection of an array of turbines to the grid consists of the following elements

(EquiMar, 2009):

• Grid connected shore station. • Sub-sea transmission cable from shore to array. • Hub to connect transmission cable to smaller cables connecting strings of turbines.

23

Problems associated with cabling of a tidal array will be common with those of the off-

shore wind industry. The installation of the sub-sea transmission cable can represent a

significant cost of the overall budget, and therefore minimising its cost will improve the

economic performance (Bauer & Lysgaard, 2014). The cabling cost is likely to be most

affected by the distance to shore, bathymetry and shore profile (landing a cable on a cliff

will be more costly than a shallow gradient beach). The first cost saving would be to

locate the array close to a shore with suitable infrastructure for installation of the shore

station and grid connection. This will reduce the length of the transmission line, reducing

its cost. However, this may also impact on the array’s performance as it was previously

shown how an array close to the shore may cause a higher local blockage and cause the

flow to divert around the array.

Another option to reduce the cabling length would be to position the turbines close

together. For a single row of turbines this will likely offer improved performance too, but

for a multi row array the maximum spacing between devices is advised to minimise wake

interaction effects. Therefore this is likely to be a compromise between optimum power

output and minimum cabling cost, requiring a careful economic assessment. For a given

array layout, it is possible to use optimisation techniques to minimise the cabling cost, as

demonstrated for the offshore wind industry (Bauer & Lysgaard, 2014).

EFFECTS ON PHYSICAL PROCESSES

Due to the added turbulence generated by the turbines and their associated wake effects

it is thought that the installation of a tidal array could impact on the sediment transport,

scour, or coastal geomorphology. However, these effects are little understood due to the

lack of data from installed turbines, and due to the lack of high resolution bathymetry

data suitable for these studies. One such study by the University of Southampton focused

on an array in the Alderney race, Channel Islands. The baseline results showed that the

flow and sediment transport in the region was highly dynamic with large natural

variations. Depending on the array location some changes to these natural processes

would be possible, but if the array location is carefully chosen it is unlikely that the effects

of the array would be noticeable given the large natural variability (Haynes, 2014).

However, further work would be required before any firm conclusions can be drawn.

Robins et al. (2014) used a TELEMAC model of the Irish Sea to investigate the effects of

sedimentary processes for a proposed array of 10-50MW off Anglesey. Their results

showed that the inclusion of a tidal array did affect sediment processes slightly, but the

effects were localised. They concluded that due to the large natural variability in sediment

transport the change due to the tidal array was small. However, these effects are likely to

be site specific due to the local bathymetry, sea bed, tide, and wave conditions.

Further investigation on sediment transport has been undertaken in the Pentland Firth

using a Mike3 model (Fairley et al., 2015). In this study the cumulative effects of 4

proposed tidal arrays were considered. It was found that the tidal arrays caused a change

24

in bed height of less than 0.2m, this is in comparison to natural variations in bed height of

up to 5m! The effects of the tidal arrays were therefore considered insignificant. Further

modelling work was undertaken for the case of a large array of 4800 turbines in an array

of approximately 60x90 km. This work found that large marine arrays will cause a

reduction in suspended sediment in the array (van der Molen et al., 2014). However, this

was performed for such a large array that the effects on small arrays cannot be inferred.

It seems likely that, if carefully sited, tidal arrays will have low impact on physical

processes due to the large natural variability at these highly energetic tidal sites.

However, further work is required to provide a more definitive answer and it is likely that

site specific investigations will be required.

ENVIRONMENTAL IMPACTS

Optimising an array layout to minimise environmental impact has not received significant

attention. Due to there being no turbine arrays installed there is a lack of data to provide

information on how these optimisations could be performed. However, the EBAO project

has sought to answer some of these questions through modelling approaches (Smith,

2015). It was found that arrays of turbines have the potential to impact upon the

movement of marine mammals and that the impact of multiple devices is not additive.

The impact area is wider than the array location due to a masking effect caused by the

turbine noise. However, it is not possible to say what level of adaption the marine

mammals will have to an array development so the impacts are still largely unknown and

further work is required. Another risk would be due to entanglement in mooring ropes of

floating devices. This risk has been assessed and found that taught mooring ropes posed

the least threat, although it was found that the overall risk was low irrespective of the

mooring type (Harnois et al., 2015). Therefore the device mounting type is not likely to

cause any additional concerns.

Further modelling from a large array of 4800 turbines showed an increase in food levels

and reduction in wave height within the array (van der Molen et al., 2014). This would

provide an attraction for marine animals, but how this balances with the increase in noise

is unknown. Another benefit would be from fishing exclusion zones located around the

turbines or arrays. Combined with the increase in food this could prove to be beneficial for

marine wildlife.

This would also prove attractive for diving sea birds, although there is uncertainty on the

overlap of tidal sites and diving sea birds (Waggitt et al., 2014). It has been found that

the survey methods used (both shore and boat) may skew the number of observations

closer to the shore, due to it being easier to spot birds against the backdrop of a cliff. An

alternative method may be the use of radar to measure the flux of birds at a given site,

as used to measure bird collision risks in wind farms (Fijn et al., 2015). However, this

method would not distinguish between diving birds at risk from tidal devices and

migrating birds. The FLOWBEC project has been investigating the challenge of monitoring

25

the local environment around marine energy site using underwater sonar and surface

radar (Williamson et al., 2015). The developed system is capable of capturing high

resolution data on the behaviour of marine mammals, fish, and birds at highly energetic

tidal sites. The underwater sonar is capable of capturing diving birds, schools of fish, and

marine mammals. This data will be invaluable in making realistic predictions of the likely

risks that marine energy installations will pose to the local wildlife. This work is still

ongoing, but demonstrates the potential of this approach for site specific environmental

data capture.

SOCIO-ECONOMIC IMPACTS

The installation of an array will likely have an impact on recreation, navigation, and

fishing activities. The level of impact will likely be dependent on the device type. Surface

piercing turbines will cause a much greater impact on navigation and recreation due to

the danger present to ships and pleasure craft. If turbines are bottom mounted with a

large draft above them, there will likely be minimal impact on navigation or pleasure craft.

However, fishing will be impacted irrespective of device type and the level of impact

driven by the location and size of the array. If the array can be sited away from known

fishing spots this will reduce the impact. Further, if the array can be kept within a small

area, the impact on available fishing grounds will be reduced. If the array is large, it may

be possible to ensure the turbine spacing is sufficient that fishing can still take place

between the devices, although this may increase the cabling cost.

CONCLUSIONS AND RECOMMENDATIONS This report considers different factors that influence turbine interactions within arrays of

multiple turbines and other factors to be considered in array design. These factors include

wake interactions, blockage effects, installation & maintenance, cabling, physical

processes, environmental impact, and socio-economic impacts. Different modelling

techniques are also presented for modelling these arrays.

Table 3 summarises the effects of different parameters on the wake and performance of

tidal turbines within arrays. It is clear from the table that there are many factors to

consider in the design of a tidal array.

From a hydrodynamic point of view, a single row array will likely provide the greatest

power output as device interactions are minimised. However, in many situations this may

not be possible due to constraints on the array size and bathymetry. For multi-row-arrays

the turbines should be on a staggered grid and the distance between turbines maximised

to reduce device interactions. Non-uniform array layouts generated from turbine siting

optimisation models using site specific information have the potential to generate

increased power output. However, this high level of site specific optimisation will only

become possible with scale experiments or LES models to capture these complex dynamic

effects. At present these models are not currently feasible for large scale array

optimisation and further model development is required.

26

TABLE 3 - SUMMARY OF PARAMETERS THAT CAUSE EFFECTS ON THE WAKE AND TURBINE

PERFORMANCE WITHIN ARRAYS.

Parameter Effect Control

parameter Reference

Sea bed

proximity

Slower wake recovery with increased proximity of the

turbine to the sea bed due to reduced mixing between

the high momentum free-stream flow and the low

momentum wake caused by the sea bed boundary of

and the lower velocities from the boundary layer profile.

Height of

rotor above

sea bed

(A. S. Bahaj

et al., 2012)

Ambient

turbulence

Faster wake recovery with higher turbulence intensity

due to higher mixing between the high momentum

bypass flow and the low momentum wake. Faster wake

expansion and recovery with larger scales of turbulence

due to higher mixing rates.

Site specific

(Blackmore,

Batten, &

Bahaj, 2014)

Turbulence

within array

Beyond 3-4 rows of turbines flow conditions are self-

similar and repeating due to flow conditions being driven

by device generated turbulence. Power output of

turbines after 3rd row may be half that of the first row.

Number of

rows and

stream-wise

spacing

(Shah et al.,

2014)

Wake

merging

Wakes merge downstream to form a single wake behind

the array where individual turbine effects are no longer

visible.

Lateral

spacing

(Stallard et

al., 2013)

Channel

blockage

Increasing the blockage ratio of the array increases the

power output. Maximum power output will be achieved if

the channel is completely blocked and all the flow

passes through the turbines.

Array size

(Garrett &

Cummins,

2007; Nishino

& Willden,

2012)

Local, turbine

blockage

Increasing the local blockage of a turbine within an

array will increase power output. However, if the turbine

density, or blockage, within the array is too high the

flow may be diverted and forced to flow around the

array reducing power output. Similarly, if the array is

located too close to shore the flow may be restricted

and be forced around the array.

Turbine

diameter,

lateral

spacing, &

proximity of

array to

shore

(Daly et al.,

2010; Nishino

& Willden,

2012)

Aspect ratio

(ratio of

lateral

spacing to

channel

depth)

For an aspect ratio of 1 (a square channel) the wake

behind a turbine expands axisymmetrically. For an

aspect ratio greater than 1 the wake is constrained and

greater lateral expansion of the wake is observed,

possibly increasing wake merging. For an aspect ratio of

2, a peak in turbine power output is observed which is

around 20-30% greater than for all other aspect ratios.

Turbine

diameter,

lateral

spacing,

channel

depth

(Keogh et al.,

2014; Nishino

& Willden,

2012)

27

However, it is likely that the first arrays will be conservative in design to reduce the risk

of turbine interaction effects and reduce the risk of damage to other devices. Having

larger spacing between turbines will aid the installation & maintenance from a reduction in

risk to damage of neighbouring devices. From a cabling perspective larger spacing’s would

be detrimental as the cost of cabling would be increased so cable lengths should be

minimised. Environmental impacts are largely unknown as there are currently no arrays

installed and therefore there are no field measurements to identify the environmental

impact. It is however likely that large spacing’s between turbines will result in a lower

impact, although that impact will be spread over a wider area. Therefore a smaller

number of higher rated turbines will likely offer a reduced impact compared to a larger

number of small turbines. Further work is required, including the monitoring of proposed

array installations to allow array optimisation for reduced environmental impact.

Finally, the impact of an array on recreation and navigation are likely to be device

specific. Surface piercing and floating devices will pose the greatest impact to navigation;

while seabed mounted turbines with large draft above will pose little impact. However,

fishing will likely be impacted irrespective of device type. To minimise this impact the

array area should be reduced to lessen the reduction in fishing grounds. Alternatively it

may be possible to fish between devices if the spacing between turbines is sufficiently

large, but this would then impact upon cabling costs.

It is clear that the subject of tidal turbine array design is a complex problem with many

variables to consider. The optimum solution will be a compromise between these different

factors, which will be site and device specific.

FURTHER WORK While the design of array layouts is well understood for simplified cases, there is a lack of

data and understanding on the performance of arrays in real tidal sites. Further work is

required to improve current models to include more dynamic effects to produce more

realistic estimates of array performance and device interactions. While these advanced

models are currently possible, they are not feasible due to the vast computational

resources require, even for simplified cases. Advances in computational resources and

more efficient computer models will help make these models a reality for developers. This

will in turn lead to a better understanding of what the environmental impacts might be.

The effects of arrays on the environment and physical processes are also little understood

so further work is required to reduce this uncertainty. However, the biggest need for

future work is real data from array installations. Demonstration arrays are required so

real data can be captured and used to feed back into the models to improve their ability

to accurately predict array impacts for other sites.

28

ACKNOWLEDGEMENT This report is produced for the Isle of Wight Council, and has been co-funded by ERDF

under the INTERREG IVB NWE programme. The report reflects the author’s views and the

Programme Authorities are not liable for any use that may be made of the information

contained therein.

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