A new latching control technology for improving wave energy conversion

Extracting wave energy from seas has been proven to be very difficult although various technologies have been developed since 1970s. Among the proposed technologies, only few of them have been actually progressed to the advanced stages such as sea trials or pre-commercial sea trial and engineering. One critical question may be how we can design an efficient wave energy converter or how the efficiency of a wave energy converter can be improved using optimal and control technologies, because higher energy conversion efficiency for a wave energy converter is always pursued and it mainly decides the cost of the wave energy production. In this first part of the investigation, some conventional optimal and control technologies for improving wave energy conversion are examined in a form of more physical meanings, rather than the purely complex mathematical expressions, in which it is hoped to clarify some confusions in the development and the terminologies of the technologies and to help to understand the physics behind the optimal and control technologies. As a result of the understanding of the physics and the principles of the optima, a new latching technology is proposed, in which the latching duration is simply calculated from the wave period, rather than based on the future information/prediction, hence the technology could remove one of the technical barriers in implementing this control technology. From the examples given in the context, this new latching control technology can achieve a phase optimum in regular waves, and hence significantly improve wave energy conversion. Further development on this latching control technologies can be found in the second part of the investigation.

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.

Investigation of electrically active defects at the interface of high-k dielectrics and compound semiconductors

As silicon based devices in integrated circuits reach the fundamental limits of dimensional scaling there is growing research interest in the use of high electron mobility channel materials, such as indium gallium arsenide (InGaAs), in conjunction with high dielectric constant (high-k) gate oxides, for Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) based devices. The motivation for employing high mobility channel materials is to reduce power dissipation in integrated circuits while also providing improved performance. One of the primary challenges to date in the field of III-V semiconductors has been the observation of high levels of defect densities at the high-k/III-V interface, which prevents surface inversion of the semiconductor. The work presented in this PhD thesis details the characterization of MOS devices incorporating high-k dielectrics on III-V semiconductors. The analysis examines the effect of modifying the semiconductor bandgap in MOS structures incorporating InxGa1-xAs (x: 0, 0.15. 0.3, 0.53) layers, the optimization of device passivation procedures designed to reduce interface defect densities, and analysis of such electrically active interface defect states for the high-k/InGaAs system. Devices are characterized primarily through capacitance-voltage (CV) and conductance-voltage (GV) measurements of MOS structures both as a function of frequency and temperature. In particular, the density of electrically active interface states was reduced to the level which allowed the observation of true surface inversion behavior in the In0.53Ga0.47As MOS system. This was achieved by developing an optimized (NH4)2S passivation, minimized air exposure, and atomic layer deposition of an Al2O3 gate oxide. An extraction of activation energies allows discrimination of the mechanisms responsible for the inversion response. Finally a new approach is described to determine the minority carrier generation lifetime and the oxide capacitance in MOS structures. The method is demonstrated for an In0.53Ga0.47As system, but is generally applicable to any MOS structure exhibiting a minority carrier response in inversion.