An optimal tuning strategy for tidal turbines.

Tuning wind and tidal turbines is critical to maximizing their power output. Adopting a wind turbine tuning strategy of maximizing the output at any given time is shown to be an extremely poor strategy for large arrays of tidal turbines in channels. This 'impatient-tuning strategy' results in far lower power output, much higher structural loads and greater environmental impacts due to flow reduction than an existing 'patient-tuning strategy' which maximizes the power output averaged over the tidal cycle. This paper presents a 'smart patient tuning strategy', which can increase array output by up to 35% over the existing strategy. This smart strategy forgoes some power generation early in the half tidal cycle in order to allow stronger flows to develop later in the cycle. It extracts enough power from these stronger flows to produce more power from the cycle as a whole than the existing strategy. Surprisingly, the smart strategy can often extract more power without increasing maximum structural loads on the turbines, while also maintaining stronger flows along the channel. This paper also shows that, counterintuitively, for some tuning strategies imposing a cap on turbine power output to limit loads can increase a turbine's average power output.

Energy storage inherent in large tidal turbine farms.

While wind farms have no inherent storage to supply power in calm conditions, this paper demonstrates that large tidal turbine farms in channels have short-term energy storage. This storage lies in the inertia of the oscillating flow and can be used to exceed the previously published upper limit for power production by currents in a tidal channel, while simultaneously maintaining stronger currents. Inertial storage exploits the ability of large farms to manipulate the phase of the oscillating currents by varying the farm's drag coefficient. This work shows that by optimizing how a large farm's drag coefficient varies during the tidal cycle it is possible to have some flexibility about when power is produced. This flexibility can be used in many ways, e.g. producing more power, or to better meet short predictable peaks in demand. This flexibility also allows trading total power production off against meeting peak demand, or mitigating the flow speed reduction owing to power extraction. The effectiveness of inertial storage is governed by the frictional time scale relative to either the duration of a half tidal cycle or the duration of a peak in power demand, thus has greater benefits in larger channels.

Underwater behavior of sperm whales off Kaikoura, New Zealand, as revealed by a three-dimensional hydrophone array.

Observations are presented of the vocal behavior and three dimensional (3D) underwater movements of sperm whales measured with a passive acoustic array off the coast of Kaikoura, New Zealand. Visual observations and vocal behaviors of whales were used to divide dive tracks into different phases, and depths and movements of whales are reported for each of these phases. Diving depths and movement information from 75 3D tracks of whales in Kaikoura are compared to one and two dimensional tracks of whales studied in other oceans. While diving, whales in Kaikoura had a mean swimming speed of 1.57 m/s, and, on average, dived to a depth of 427 m (SD = 117 m), spending most of their time at depths between 300 and 600 m. Creak vocalizations, assumed to be the prey capture phase of echolocation, occurred throughout the water column from sea surface to sea floor, but most occurred at depths of 400-550 m. Three dimensional measurement of tracking revealed several different "foraging" strategies, including active chasing of prey, lining up slow-moving or unsuspecting prey, and foraging on demersal or benthic prey. These movements provide the first 3D descriptions underwater behavior of whales at Kaikoura.

The effect of unsteady flow due to acceleration on hydrodynamic forces acting on the hand in swimming.

This study describes the effect of hand acceleration on hydrodynamic forces acting on the human hand in angular and general motions with variable hand accelerations. Even if accelerations of a swimmer's hand are believed to have an important role in generating hydrodynamic forces on the hand, the effect of accelerations in angular and general motions on hydrodynamic forces on the swimmers hand has not been previously quantified. Understanding how hand acceleration influences force generation can provide useful information to enhance swimming performance. A hand-forearm model attached to a tri-axial load cell was constructed to measure hydrodynamic forces acting only on the hand when the model was rotated and accelerated in a swimming flume. The effect of acceleration on hydrodynamic forces on the hand was described by comparing the difference between accelerating and non-accelerating hands in different flow conditions. Hydrodynamic forces on the accelerating hand varied between 1.9 and 10 times greater than for the non-accelerating hand in angular motion and varied between 1.7 and 25 times greater than for the non-accelerating hand in general motion. These large increases occurred not only during positive acceleration phases but also during negative acceleration phases, and may be due to the added mass effect and a vortex formed on the dorsal side of the hand. This study provides new evidence for enhanced stroke techniques in swimming to generate increased propulsion by changing hand velocity during a stroke.

Influence of surface penetration on measured fluid force on a hand model.

The purpose of this study was to quantify the effect of wave drag due to surface penetration on drag and lift forces (C(d) and C(l)) acting on a hand model. The values of C(d) and C(l) had been acquired to gain the hydrodynamic characteristics of the swimmer's hand and predict force on the swimmer's hand. These values have also been used to benchmark computational fluid dynamics analysis. Because the previous studies used a hand/forearm model which penetrated the water's surface, the values of C(d) and C(l) include the effect of the surface wave on the model. Wave formation causes pressure differences between the frontal and rear sides of a surface-penetrating model as a result of depressions and elevations in the water's surface. This may be considered as wave drag due to surface penetration. Fluid forces due to wave drag on the forearm should not be included in the measured C(d) and C(l) of a swimmer's hand that does not sweep near the water's surface. Two hand/forearm models are compared, one with the hand rigidly connected to the forearm. The other model was constructed to isolate the fluid forces acting on the hand from the influence of wave drag on the forearm. The measurements showed that the effect of wave drag on the hand model caused large increases in the values of C(d), up to 46-98% with lesser increases in C(l) of 2-12% depending on the hand orientation. The present study provides an improved method to determine the values of C(d) and C(l) that eliminates the effect of wave drag on a hand/forearm model by isolating the measurement of fluid forces on the forearm of the hand/forearm model in order to separately acquire the forces on the hand.

Prediction of fluid forces acting on a hand model in unsteady flow conditions.

The aim of this study was to develop a method to predict fluid forces acting on the human hand in unsteady flow swimming conditions. A mechanical system consisting of a pulley and chain mechanism and load cell was constructed to rotate a hand model in fluid flows. To measure the angular displacement of the hand model a potentiometer was attached to the axis of the rotation. The hand model was then fixed at various angles about the longitudinal axis of the hand model and rotated at different flow velocities in a swimming flume for 258 different trials to approximate a swimmer's stroke in unsteady flow conditions. Pressures were taken from 12 transducers embedded in the hand model at a sampling frequency of 200Hz. The resultant fluid force acting on the hand model was then determined on the basis of the kinetic and kinematic data taken from the mechanical system at the frequency of 200Hz. A stepwise regression analysis was applied to acquire higher order polynomial equations that predict the fluid force acting on the accelerating hand model from the 12 pressure values. The root mean square (RMS) difference between the resultant fluid force measured and that predicted from the single best-fit polynomial equation across all trials was 5N. The method developed in the present study accurately predicted the fluid forces acting on the hand model.

Wave drag on human swimmers.

Drag measurements from a towed mannequin show total drag at the surface is up to 2.4 times the drag when fully immersed. This additional drag is due to the energy required to form waves in the wake behind the mannequin. The measurements show that passive wave drag is the largest drag, comprising up to 50-60% of the total at 1.7 m s(-1), much higher than any previous estimates. Comprehensive measurements spanning human swimming speeds and tow depths up to 1.0m demonstrate that wave drag on the mannequin is less than 5% of total drag for tows deeper than 0.5 m at 1 m s(-1) and 0.7 m at 2 m s(-1). Wave drag sharply increases above these depths to a maximum of up to 60% of the mannequin's 100 N total drag when towed at the surface at 1.7 m s(-1). The measurements show that to avoid significant wave drag during the underwater sections of starts and turns, swimmers must streamline at depths greater than 1.8 chest depths below the surface at Froude number (Fr)=0.2, and 2.8 chest depths at Fr=0.42. This corresponds to speeds of 0.9 and 2.0 m s(-1), respectively, for a chest depth of 0.25 m and toe to finger length of 2.34 m.