Radiation-induced attenuation in aluminum- and aluminum-thulium-doped optical fibers.

Gamma-ray-induced attenuation in Al-doped and Al/Tm-co-doped optical fibers is investigated in the visible and near-infrared domain up to 1 Gy. The behavior of radiation-induced attenuation (RIA) regarding dose and dose rate is discussed. Our results reveal high sensitivities for both types of fibers at low gamma ray doses and also reveal that Al/Tm fibers are very promising at original interrogation wavelengths for dosimetry applications.

Influence of temperature and dose rate on radiation-induced attenuation at 1542 nm in fluorine-doped fibers.

Radiation-induced attenuation (RIA) at 1542 nm of fluorine-doped fibers under gamma radiation source has been investigated for different dose rates and temperatures. Both the temperature and dose rate dependencies are unusual. First, the fiber presents an enhanced low dose rate sensitivity that is favored by increasing temperature. Furthermore, in certain conditions, RIA increases with irradiation temperature, which is a very rare phenomenon. We have built a phenomenological model that shows that these behaviors can be explained considering that two color centers previously identified in the literature are responsible for RIA: inherent and strain-assisted self-trapped holes.

Towards the intrahour forecasting of direct normal irradiance using sky-imaging data.

Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon [Formula: see text] is up to [Formula: see text] ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model (Ineichen and Perez, 2002) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models - either a persistence of DNI or a persistence of the clear-sky index - are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about [Formula: see text], with [Formula: see text], and [Formula: see text], with [Formula: see text].