Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
NH2.4 | Advances in experimental volcanology: closing the gap between simulations and the reality of sub-aerial volcanic hazards
EDI
Advances in experimental volcanology: closing the gap between simulations and the reality of sub-aerial volcanic hazards
Convener: Fabio Dioguardi | Co-convener: Audrey Michaud-Dubuy
Understanding, modelling and forecasting volcanic hazards such as pyroclastic density currents, volcanic plumes and clouds, gas emissions and dispersions, etc. can be very challenging. These phenomena, which can be theoretically described by computational fluid dynamics, are in fact very complex in their nature. Indeed, they are: multiphase (gas, solid and liquid phases), multispecies (e.g., different gas species), turbulent, from incompressible to compressible, polydisperse (the solid phase is often composed by particles of a wide range of size, shape and density), etc. A unified model capturing all these volcanic hazards is thus still to be elaborated. Meanwhile, many recent efforts are put into the investigation of these processes and how to quantitatively implement them in models that can be used for hazard quantification and mitigation purposes. These studies are often experimental, from the laboratory scale to a larger scale and with different ranges of complexity, because this approach allows controlling the parameters characterizing the physical processes under investigation. However, application of experimental results to large-scale natural processes is only possible if a thorough scaling analysis is carried out early on.
In this session we welcome experimental studies covering all the sub-aerial volcanic hazards, from eruptive clouds to gravity flows. In particular, we encourage contributions that focus on closing the gap between the laboratory and the natural scale (e.g., via a careful approach of the scaling issue), and potentially applying the knowledge acquired in the experiments to simulation tools (e.g., via new constitutive equations).