Jožef Stefan Institute

About: Jožef Stefan Institute is a facility organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Liquid crystal & Dielectric. The organization has 3828 authors who have published 12614 publications receiving 291025 citations.

An evaluation of biomass co-firing in Europe.

Abstract: Reduction of the emissions of greenhouses gases, increasing the share of renewable energy sources (RES) in the energy balance, increasing electricity production from renewable energy sources and decreasing energy dependency represent the main goals of all current strategies in Europe. Biomass co-firing in large coal-based thermal power plants provides a considerable opportunity to increase the share of RES in the primary energy balance and the share of electricity from RES in gross electricity consumption in a country. Biomass-coal co-firing means reducing CO 2 and SO 2 , emissions and it may also reduce NO x emissions, and also represents a near-term, low-risk, low-cost and sustainable energy development. Biomass-coal co-firing is the most effective measure to reduce CO 2 emissions, because it substitutes coal, which has the most intensive CO 2 emissions per kWh electricity production, by biomass, with a zero net emission of CO 2 . Biomass co-firing experience worldwide are reviewed in this paper. Biomass co-firing has been successfully demonstrated in over 150 installations worldwide for most combinations of fuels and boiler types in the range of 50–700 MWe, although a number of very small plants have also been involved. More than a hundred of these have been in Europe. A key indicator for the assessment of biomass co-firing is intrduced and used to evaluate all available biomass co-firing technologies.

Quasiparticle relaxation dynamics in superconductors with different gap structures: Theory and experiments on YBa 2 Cu 3 O 7 − δ

Abstract: Photoexcited quasiparticle relaxation dynamics are investigated in a ${\mathrm{YBa}}_{2}{\mathrm{Cu}}_{3}{\mathrm{O}}_{7\mathrm{\ensuremath{-}}\mathrm{\ensuremath{\delta}}}$ superconductor as a function of doping \ensuremath{\delta} and temperature $T$ using ultrafast time-resolved optical spectroscopy. A model calculation is presented that describes the temperature dependence of the photoinduced quasiparticle population ${n}_{\mathrm{pe}},$ photoinduced transmission $\ensuremath{\Delta}\mathcal{T}/\mathcal{T},$ and relaxation time \ensuremath{\tau} for three different superconducting gaps: (i) a temperature-dependent collective gap such that $\mathit{\ensuremath{\Delta}}(T)\ensuremath{\rightarrow}0$ as $\stackrel{\ensuremath{\rightarrow}}{T}{T}_{c},$ (ii) a temperature-independent gap, which might arise for the case of a superconductor with preformed pairs, and (iii) an anisotropic (e.g., $d$-wave) gap with nodes. Comparison of the theory with data of photoinduced transmission $|\ensuremath{\Delta}\mathcal{T}/\mathcal{T}|,$ reflection $|\ensuremath{\Delta}\mathcal{R}/\mathcal{R}|,$ and quasiparticle recombination time \ensuremath{\tau} in ${\mathrm{YBa}}_{2}{\mathrm{Cu}}_{3}{\mathrm{O}}_{7\mathrm{\ensuremath{-}}\mathrm{\ensuremath{\delta}}}$ over a very wide range of doping $(0.1l\ensuremath{\delta}l0.48)$ is found to give good quantitative agreement with a temperature-dependent BCS-like isotropic gap near optimum doping $(\ensuremath{\delta}l0.1)$ and a temperature-independent isotropic gap in underdoped ${\mathrm{YBa}}_{2}{\mathrm{Cu}}_{3}{\mathrm{O}}_{7\mathrm{\ensuremath{-}}\mathrm{\ensuremath{\delta}}}(0.15l\ensuremath{\delta}l0.48).$ A pure $d$-wave gap was found to be inconsistent with the data.

Dynamic systems identification with Gaussian processes

Abstract: This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) prior model. This model is an example of the use of a probabilistic non-parametric modelling approach. GPs are flexible models capable of modelling complex nonlinear systems. Also, an attractive feature of this model is that the variance associated with the model response is readily obtained, and it can be used to highlight areas of the input space where prediction quality is poor, owing to the lack of data or complexity (high variance). We illustrate the GP modelling technique on a simulated example of a nonlinear system.