Design, commissioning and performance of a device to vary the turbulence in a recirculating flume

Ambient turbulent flow structures are one of the key drivers that will determine the rate of wake recovery downstream of tidal turbines. For second and third generation arrays or farms such a parameter is critical for the determination of inter-device spacing and the optimisation of energy extraction per unit surface area. At present offshore flow characterisation is dominated by seabed or surface-mounted diverging-beam acoustic Doppler profilers that whilst having a good spatial capture cannot characterise turbulent flow structures to the same accuracy as single point converging laboratory-scale velocimeters. So a paradox presently exists: We can measure the (mean) flow characteristics at real tidal energy sites but lack the ability to accuracy ascertain high-frequency flow characteristic at discrete spatial locations. This is possible at laboratory-scale with convergent-beam devices but as we do not know the real site conditions replication at small-scale can only be approximated. To date there has been few laboratory studies where the ambient flow turbulence has been varied. The standard method is to generate turbulence from a static structure such as a grid. Here we have developed an articulated rig that has the ability to oscillate cylindrical members along two axes in the flow upstream of tidal turbine models. Initial results presented in this paper show the effect upon the ambient flow that the turbulence-generating rig can impose and the effects upon wake dissipation for varying levels of turbulent length and time scales. Also the formation and insistence of turbulent structures shed from the device are reported. As expected increasing ambient turbulence intensity serves to dissipate the turbine wake more rapidly and whilst we cannot directly relate these laboratory flow characteristics to full-scale tidal energy sites at present it is hoped that offshore measurement technology and that of laboratory replication can converge so that device performance prediction can be performed at smaller-scale and at a corresponding lower cost to the technology