Das Projekt "Stochastic treatment of cloud related processes in nonhydrostatic weather prediction models" wird vom Umweltbundesamt gefördert und von Rheinische Friedrich-Wilhelms-Universität Bonn, Meteorologisches Institut durchgeführt. Cloud processes have a strong influence on the energy and moisture budget of the atmosphere. Since in numerical weather prediction (NWP) models cloud related processes usually are of subgrid scale, they have to be parameterized in a set of parameterization schemes describing e.g. grid scale clouds and precipitation, subgrid scale cumulus convection and atmospheric radiative transfer. The aim of the proposed research project is to develop a new stochastic physics approach (SPA) for the treatment of cloud related processes in nonhydrostatic NWP models. The stochastic approach shall represent unresolved subgrid scale variability and model parameter uncertainties. For this purpose, selected fixed model parameters will be replaced by appropriate stochastic processes.The application of the SPA shall increase the forecast skill of the hosting NWP model. In an ensemble prediction system (EPS) the ensemble spread shall grow to more realistic values and thus help to overcome the current problem of too small spread (underdispersion) when only considering uncertainties in the initial or boundary conditions. The algorithms of the stochastic approach and the EPS will be developed for use in any nonhydrostatic NWP model that includes prognostic microphysics/precipitation, a radiation scheme with fractional cloud cover and an arbitrary mass flux convection scheme.
Das Projekt "Optimisation and design of biomass combustion systems (OPTICOMB)" wird vom Umweltbundesamt gefördert und von Technische Universität Graz, Institut für Prozess- und Partikeltechnik durchgeführt. The major problems regarding biomass combustion are still the NOx and CO emissions, especially when the fuel becomes more diverse (high peaks during transients). The continuously changing fuel composition, the non-linearity of the process and the multi variability of the process makes it difficult to decrease the emissions further. Therefore classical control strategies are no longer effective. In order to improve the actual process control system, advanced control technologies based upon process models are needed. To achieve this goal static models have to be integrated with dynamic models. At present, no satisfying tools are available to describe the NOx formation in the fuel layer and the gas phase. Therefore, an extensive study on fuel layer and gas phase NOx formation mechanisms will be performed. The developed mechanisms will be integrated in a CFD combustion model and a static fuel layer model in order to be able to minimise the CO and NOx emission. Based upon experimental work and plant data, a new grate will be designed. A dynamic furnace model is developed for biomass combustion. Special measurements techniques will be used to gather actual plant data (2 plants, diverse fuels) to validate the models. The stochastic characteristics of the fuel will be revealed, which is used together with the dynamic model to investigate the disturbance rejection capacity of the plant. All information will be used to develop new control concepts and to design new combustion systems also from a dynamic point of view. These will be tested in an installation. The environmental survey of the influence of the proposed technology, a market analysis, information dissemination and exploitation strategies will be carried out.