Performance of a single liquid column damper for the control of dynamic responses of a tension leg platform

Tuned liquid column dampers have been proved to be successful in mitigating the dynamic responses of civil infrastructure. There have been some recent applications of this concept on wind turbines and this passive control system can help to mitigate responses of offshore floating platforms and wave devices. The control of dynamic responses of these devices is important for reducing loads on structural elements and facilitating operations and maintenance (O&M) activities. This paper outlines the use of a tuned single liquid column damper for the control of a tension leg platform supported wind turbine. Theoretical studies were carried out and a scaled model was tested in a wave basin to assess the performance of the damper. The tests on the model presented in this paper correspond to a platform with a very low natural frequency for surge, sway and yaw motions. For practical purposes, it was not possible to tune the liquid damper exactly to this frequency. The consequent approach taken and the efficiency of such approach are presented in this paper. Responses to waves of a single frequency are investigated along with responses obtained from wave spectra characterising typical sea states. The extent of control is quantified using peak and root mean squared dynamic responses respectively. The tests present some guidelines and challenges for testing scaled devices in relation to including response control mechanisms. Additionally, the results provide a basis for dictating future research on tuned liquid column damper based control on floating platforms.

Sensemaking, safety, and situated communities in (con)temporary networks

This paper discusses the difficulties involved in managing knowledge-intensive, multinational, multiorganisational, and multifunctional project networks. The study is based on a 2-year quasi-ethnography of one such network engaged in the design and development of a complex new process control system for an existing pharmaceutical plant in Ireland. The case describes how, drawing upon the organisational heritage of the corporations involved and the logic implicit within their global partnership arrangements, the project was initially structured in an aspatial manner that underestimated the complexity of the development process and the social relations required to support it. Following dissatisfaction with initial progress, a number of critical management interventions were made, which appeared to contribute to a recasting of the network ontology that facilitated the cultivation and protection of more appropriate communicative spaces. The case emphasises the need to move away from rationalistic assumptions about communication processes within projects of this nature, towards a richer conceptualisation of such enterprises as involving collective sensemaking activities within and between situated ‘communities’ of actors. Contrary to much contemporary writing, the paper argues that space and location are of crucial importance to our understanding of network forms of organising.

Artificial neural network application in short-term prediction in an oscillating water column

Oscillating Water Column (OWC) is one type of promising wave energy devices due to its obvious advantage over many other wave energy converters: no moving component in sea water. Two types of OWCs (bottom-fixed and floating) have been widely investigated, and the bottom-fixed OWCs have been very successful in several practical applications. Recently, the proposal of massive wave energy production and the availability of wave energy have pushed OWC applications from near-shore to deeper water regions where floating OWCs are a better choice. For an OWC under sea waves, the air flow driving air turbine to generate electricity is a random process. In such a working condition, single design/operation point is nonexistent. To improve energy extraction, and to optimise the performance of the device, a system capable of controlling the air turbine rotation speed is desirable. To achieve that, this paper presents a short-term prediction of the random, process by an artificial neural network (ANN), which can provide near-future information for the control system. In this research, ANN is explored and tuned for a better prediction of the airflow (as well as the device motions for a wide application). It is found that, by carefully constructing ANN platform and optimizing the relevant parameters, ANN is capable of predicting the random process a few steps ahead of the real, time with a good accuracy. More importantly, the tuned ANN works for a large range of different types of random, process.