Integration of temporal and semantic components into the Geographic Information. Part II: Methodology

The overall objective of this research project is to enrich geographic data with temporal and semantic components in order to significantly improve spatio-temporal analysis of geographic phenomena. To achieve this goal, we intend to establish and incorporate three new layers (structures) into the core of the Geographic Information by using mark-up languages as well as defining a set of methods and tools for enriching the system to make it able to retrieve and exploit such layers (semantic-temporal, geosemantic, and incremental spatio-temporal). Besides these layers, we also propose a set of models (temporal and spatial) and two semantic engines that make the most of the enriched geographic data. The roots of the project and its definition have been previously presented in Siabato & Manso-Callejo 2011. In this new position paper, we extend such work by delineating clearly the methodology and the foundations on which we will base to define the main components of this research: the spatial model, the temporal model, the semantic layers, and the semantic engines. By putting together the former paper and this new work we try to present a comprehensive description of the whole process, from pinpointing the basic problem to describing and assessing the solution. In this new article we just mention the methods and the background to describe how we intend to define the components and integrate them into the GI.

Prediction of wake effects on wind farm power production using a RANS approach. Part II. Offshore: Case studies from the UPWIND project

The estimation of power losses due to wind turbine wakes is crucial to understanding overall wind farm economics. This is especially true for large offshore wind farms, as it represents the primary source of losses in available power, given the regular arrangement of rotors, their generally largerdiameter and the lower ambient turbulence level, all of which conspire to dramatically affect wake expansion and, consequently, the power deficit. Simulation of wake effects in offshore wind farms (in reasonable computational time) is currently feasible using CFD tools. An elliptic CFD model basedon the actuator disk method and various RANS turbulence closure schemes is tested and validated using power ratios extracted from Horns Rev and Nysted wind farms, collected as part of the EU-funded UPWIND project. The primary focus of the present work is on turbulence modeling, as turbulent mixing is the main mechanism for flow recovery inside wind farms. A higher-order approach, based on the anisotropic RSM model, is tested to better take into account the imbalance in the length scales inside and outside of the wake, not well reproduced by current two-equation closure schemes.