Grant E. Hearn

Laboratory testing the Anaconda

Laboratory measurements of the performance of the Anaconda are presented, a wave energy converter comprising a submerged water-filled distensible tube aligned with the incident waves. Experiments were carried out at a scale of around 1:25 with a 250 mm diameter and 7 m long tube, constructed of rubber and fabric, terminating in a linear power take-off of adjustable impedance. The paper presents some basic theory that leads to predictions of distensibility and bulge wave speed in a pressurized compound rubber and fabric tube, including the effects of inelastic sectors in the circumference, longitudinal tension and the surrounding fluid. Results are shown to agree closely with measurements in still water. The theory is developed further to provide a model for the propagation of bulges and power conversion in the Anaconda. In the presence of external water waves, the theory identifies three distinct internal wave components and provides theoretical estimates of power capture. For the first time, these and other predictions of the behaviour of the Anaconda, a device unlike almost all other marine systems, are shown to be in remarkably close agreement with measurements.

Optimisation of a tubular linear machine with permanent magnets for wave energy extraction

Purpose – The purpose of this paper is to optimise the cost‐based performance of a tubular linear generator and to minimise cogging forces.Design/methodology/approach – Optimisation of a tubular linear generator with longitudinal flux topology has been undertaken using a finite element method. The computational models used have been verified experimentally.Findings – The use of an oversized stator linear generator design as opposed to an oversized translator design has the potential to increase the output electromotive force per unit material cost by 25 per cent for slotless iron core topologies and approximately 14 per cent for air core topologies. For cogging force minimisation, optimisation of the length of the stator core is an effective technique for both oversized stator and oversized translator constructions. Comparisons of magnet materials also indicate that the higher cost of rare earth magnets to ferrites is compensated by their superior specific performances.Originality/value – In this paper, a...

Modelling Tunnel Thrusters for Autonomous Underwater Vehicles

With 900 Autonomous Underwater Vehicles (AUVs) required over the next decade (Newman et al., 2007) existing survey-style AUVs need improved utilization factors. Additional control devices to extend operational capability need consideration together with the interchange between AUV control approaches. This paper considers supplementary through-body tunnel thruster control during the transition from survey operation to low-speed manoeuvring. Modified manoeuvring equations permit investigation of energy level demands as a positively buoyant AUV is slowed down from cruising speed to maintaining a stationary position. A suitable model of the selected thruster device is proposed following a literature review of tunnel thruster performance.

A ship based intelligent anti-collision decision-making support system utilizing trial manoeuvres

To provide a novel intelligent anti-collision decision-making support system it is necessary to facilitate a precise anti-collision information capability. In the reported research an innovative self-learning neurofuzzy network is proposed and applied to learn new information adaptively without forgetting old knowledge. To handle imprecise information a fuzzy set interpretation facility is incorporated into the network design. Additionally neural network architecture is used to train the parameters of the fuzzy inference system (FIS). The learning process is based on a hybrid learning algorithm and off-line training data. The training data is obtained from trial manoeuvres. This support system has been developed to help ship operators make a precise anti-collision decision, whilst simultaneously reducing the burden of bridge data processing.

A theoretical approach to facilitating transition phase motion in a positively buoyant autonomous underwater vehicle

Positively buoyant autonomous underwater vehicles (AUVs) operate at survey speeds with a pitch angle that is maintained through application of the control surfaces, sufficient to generate hydrodynamic forces to counteract the excess buoyancy. To facilitate lower forward speeds and the ability to hover requires some additional method of control. This paper reviews possible options and then indicates how control can be achieved using a single or pair of through-body tunnel thrusters. New equations appropriate to AUVs are proposed and experimental results are used to estimate the equation parameters. These equations are used within a simulation of the Autosub AUV to determine the response of the AUV during the transition between survey and low speed operation. The results obtained from the simulations are analysed in terms of the performance of the AUV and the demanded energy levels to assess the feasibility of using tunnel thrusters as a low speed control device.

A Modified Learning Algorithm for Backpropagation Network

A modified backpropagation (MBP) algorithm is proposed in this paper. The MBP algorithm is based on the idea that both the error Ep and its change ΔEP can be applied as information to update weights. It can be seen from Fig. 1 that when Ep curve is convex, the signs of Ep and ΔEp slopes are the same. When Ep curve becomes concave, the signs are opposite. Therefore, if ΔEp slope is added to or subtracted from Ep slope as appropriate, the network training is expected to speed up. This idea can be expressed as

An initial estimation of DP requirements for a non-moored operational FPSO

\Delta w_{ij}=-\eta \frac{\partial E_{p}}{\partial w_{ij}}+sign\left(\frac{\partial\Delta E_{p}}{\partial w_{ij}}\right).\xi.\frac{\partial \Delta E_{p}}{\partial w_{ij}}

Ship intelligent autopilot in narrow water

where ξ is a constant defined as an accelerator. For clarity, we define i, j and k as the unit in output, hidden and input layers. The derivatives of ΔEp with respect to wij and wjk are given as