In vivo surface roughness evolution of a stressed metallic implant

Peer reviewed

Measurements of stress-optic coefficient and Young's modulus in PMMA fibers drawn under different conditions

We have systematically measured the differential stress-optic coefficient, ΔC, and Young's modulus, E, in a number of PMMA fibers drawn with different stress, ranging from 2 up to 27 MPa. Effect of temperature annealing on those parameters was also investigated. ΔC was determined in transverse illumination by measuring the dependence of birefringence on additional axial stress applied to the fiber. Our results show that ΔC in PMMA fibers has a negative sign and ranges from -4.5 to -1.5×10-12 Pa -1 depending on the drawing stress. Increase of the drawing stress results in greater initial fiber birefringence and lower ΔC. The dependence of ΔC and initial birefringence upon drawing stress is nonlinear and gradually saturates for higher drawing stress. Moreover, we find that ΔC is linearly proportional to initial fiber birefringence and that annealing the fiber has no impact on the slope of this dependence. On the other hand, no clear dependence was observed between the fiber drawing stress and the Young's modulus of the fibers as measured using microscopic digital image correlation with the fibers tensioned using an Instron tension tester. © 2010 SPIE.

Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods

Energy saving, reduction of greenhouse gasses and increased use of renewables are key policies to achieve the European 2020 targets. In particular, distributed renewable energy sources, integrated with spatial planning, require novel methods to optimise supply and demand. In contrast with large scale wind turbines, small and medium wind turbines (SMWTs) have a less extensive impact on the use of space and the power system, nevertheless, a significant spatial footprint is still present and the need for good spatial planning is a necessity. To optimise the location of SMWTs, detailed knowledge of the spatial distribution of the average wind speed is essential, hence, in this article, wind measurements and roughness maps were used to create a reliable annual mean wind speed map of Flanders at 10 m above the Earth’s surface. Via roughness transformation, the surface wind speed measurements were converted into meso- and macroscale wind data. The data were further processed by using seven different spatial interpolation methods in order to develop regional wind resource maps. Based on statistical analysis, it was found that the transformation into mesoscale wind, in combination with Simple Kriging, was the most adequate method to create reliable maps for decision-making on optimal production sites for SMWTs in Flanders (Belgium).