Off-shell N=(4,4) supersymmetry for new (2,2) vector multiplets

We discuss the conditions for extra supersymmetry of the N=(2,2) supersymmetric vector multiplets described in arXiv:0705.3201 [hep-th] and in arXiv:0808.1535 [hep-th]. We find (4,4) supersymmetry for the semichiral vector multiplet but not for the Large Vector Multiplet.Comment: 15 page

Loads on a point-absorber wave energy converter in regular and focused extreme wave events

Accurate modeling and prediction of extreme loads for survivability is of crucial importance if wave energy is to become commercially viable. The fundamental differences in scale and dynamics from traditional offshore structures, as well as the fact that wave energy has not converged around one or a few technologies, implies that it is still an open question how the extreme loads should be modeled. In recent years, several methods to model wave energy converters in extreme waves have been developed, but it is not yet clear how the different methods compare. The purpose of this work is the comparison of two widely used approaches when studying the response of a point-absorber wave energy converter in extreme waves, using the open-source CFD software OpenFOAM. The equivalent design-waves are generated both as equivalent regular waves and as focused waves defined using NewWave theory. Our results show that the different extreme wave modeling methods produce different dynamics and extreme forces acting on the system. It is concluded that for the investigation of point-absorber response in extreme wave conditions, the wave train dynamics and the motion history of the buoy are of high importance for the resulting buoy response and mooring forces

A Model Free Control Based on Machine Learning for Energy Converters in an Array

This paper introduces a machine learning based control strategy for energy converter arrays designed to work under realistic conditions where the optimal control parameter can not be obtained analytically. The control strategy neither relies on a mathematical model, nor does it need a priori information about the energy medium. Therefore several identical energy converters are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each situation. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converters (WECs) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data