The Vertical Distribution and Evolution of Slam Pressure on an Oscillating Wave Surge Converter

The accurate definition of the extreme wave loads which act on offshore structures represents a significant challenge for design engineers and even with decades of empirical data to base designs upon there are still failures attributed to wave loading. The environmental conditions which cause these loads are infrequent and highly non-linear which means that they are not well understood or simple to describe. If the structure is large enough to affect the incident wave significantly further non-linear effects can influence the loading. Moreover if the structure is floating and excited by the wave field then its responses, which are also likely to be highly non-linear, must be included in the analysis. This makes the description of the loading on such a structure difficult to determine and the design codes will often suggest employing various tools including small scale experiments, numerical and analytical methods, as well as empirical data if available.
Wave Energy Converters (WECs) are a new class of offshore structure which pose new design challenges, lacking the design codes and empirical data found in other industries. These machines are located in highly exposed and energetic sites, designed to be excited by the waves and will be expected to withstand extreme conditions over their 25 year design life. One such WEC is being developed by Aquamarine Power Ltd and is called Oyster. Oyster is a buoyant flap which is hinged close to the seabed, in water depths of 10 to 15m, piercing the water surface. The flap is driven back and forth by the action of the waves and this mechanical energy is then converted to electricity.
It has been identified in previous experiments that Oyster is not only subject to wave impacts but it occasionally slams into the water surface with high angular velocity. This slamming effect has been identified as an extreme load case and work is ongoing to describe it in terms of the pressure exerted on the outer skin and the transfer of this short duration impulsive load through various parts of the structure.
This paper describes a series of 40th scale experiments undertaken to investigate the pressure on the face of the flap during the slamming event. A vertical array of pressure sensors are used to measure the pressure exerted on the flap. Characteristics of the slam pressure such as the rise time, magnitude, spatial distribution and temporal evolution are revealed. Similarities are drawn between this slamming phenomenon and the classical water entry problems, such as ship hull slamming. With this similitude identified, common analytical tools are used to predict the slam pressure which is compared to that measured in the experiment.

The inferential monitoring of the screw disturbance torque to predict process fluctuations in polymer extrusion

Polymer extrusion is one of the major methods of processing polymer materials and advanced process monitoring is important to ensure good product quality. However, commonly used process monitoring devices, e.g. temperature and pressure sensors, are limited in providing information on process dynamics inside an extruder barrel. Screw load torque dynamics, which may occur due to changes in solids conveying, melting, mixing, melt conveying, etc., are believed to be a useful indicator of process fluctuations inside the extruder barrel. However, practical measurement of the screw load torque is difficult to achieve. In this work, inferential monitoring of the screw load torque signal in an extruder was shown to be possible by monitoring the motor current (armature and/or field) and simulation studies were used to check the accuracy of the proposed method. The ability of this signal to aid identification and diagnosis of process issues was explored through an experimental investigation. Power spectral density and wavelet frequency analysis were implemented together with a covariance analysis. It was shown that the torque signal is dominated by the solid friction in the extruder and hence it did not correlate well with melting fluctuations. However, it is useful for online identification of solids conveying issues.

Hierarchical Task Network Planning with Common-Sense Reasoning for Multiple-People Behaviour Analysis

Safety on public transport is a major concern for the relevant authorities. We
address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone.