Vortexje - An Open-Source Panel Method for Co-Simulation

This paper discusses the use of the 3-dimensional panel method for dynamical system simulation. Specifically, the advantages and disadvantages of model exchange versus co-simulation of the aerodynamics and the dynamical system model are discussed. Based on a trade-off analysis, a set of recommendations for a panel method implementation and for a co-simulation environment is proposed. These recommendations are implemented in a C++ library, offered on-line under an open source license. This code is validated against XFLR5, and its suitability for co-simulation is demonstrated with an example of a tethered wing, i.e, a kite. The panel method implementation and the co-simulation environment are shown to be able to solve this stiff problem in a stable fashion.Comment: 13 pages, 8 figure

Tracking control with adaption of kites

A novel tracking paradigm for flying geometric trajectories using tethered kites is presented. It is shown how the differential-geometric notion of turning angle can be used as a one-dimensional representation of the kite trajectory, and how this leads to a single-input single-output (SISO) tracking problem. Based on this principle a Lyapunov-based nonlinear adaptive controller is developed that only needs control derivatives of the kite aerodynamic model. The resulting controller is validated using simulations with a point-mass kite model.Comment: 20 pages, 12 figure

A broad-coverage distributed connectionist model of visual word recognition

In this study we describe a distributed connectionist model of morphological processing, covering a realistically sized sample of the English language. The purpose of this model is to explore how effects of discrete, hierarchically structured morphological paradigms, can arise as a result of the statistical sub-regularities in the mapping between word forms and word meanings. We present a model that learns to produce at its output a realistic semantic representation of a word, on presentation of a distributed representation of its orthography. After training, in three experiments, we compare the outputs of the model with the lexical decision latencies for large sets of English nouns and verbs. We show that the model has developed detailed representations of morphological structure, giving rise to effects analogous to those observed in visual lexical decision experiments. In addition, we show how the association between word form and word meaning also give rise to recently reported differences between regular and irregular verbs, even in their completely regular present-tense forms. We interpret these results as underlining the key importance for lexical processing of the statistical regularities in the mappings between form and meaning