Aerosol generation and transformation mechanisms during the fuel debris cutting at Fukushima Daiichi future dismantling
During Fukushima Daiichi nuclear reactor accident, several hundred tons of fuel debris (the mixture generated by the reactor core melting and its interaction with structural materials) have been formed. Japanese government plans to dismantle with 30 to 40 years Fukushima Daiichi nuclear power station, which implies recovering these fuel debris that are there. CEA is part to several projects aiming at mastering the risks due to aerosols generated during fuel debris cutting.
The post-doctoral work objective is to exploit the large experimental database created thanks to these projects in order to study the generation and transformation mechanisms of these cutting aerosols for both thermal and mechanical cutting. An important source of aerosol seems to be partial evaporation/condensation, close to fractional distillation. A thermodynamic modelling shall be proposed, coupled with some kinetic effects. For mechanical cutting, aerosol analyses shall be compared to fuel debris block microstructure to quantify a preferential release of some phases.
After a bibliographic study, a synthesis of the experimental results will be carried out and completed, where necessary, by chemical or crystallographic analyses. The aim will be to propose a modelling of these aerosol generation and transformation mechanisms.
The postdoctoral researcher will work within an experimental laboratory of about 20 staff within CEA IRESNE institute (Cadarache site, Southern France).
Modelling of valley winds by statistical downscaling
To model and monitor atmospheric emissions in an area with significant relief, it is essential to represent the winds at the scale of this relief. Cadarache's operational meteorological model only has a horizontal resolution of 1km, which does not allow it to resolve the orographic effects of the valley.
However, obtaining simulation results with a high resolution model requires calculation times that are still incompatible with the constraints of operational weather forecasting (6 hours of calculation on our servers for 1 hour of forecast for Cadarache in 2020). This constrains the horizontal resolution of the calculations and does not make it possible to resolve the orographic valley effects.
The object of the post-doc is therefore to develop a downscaling model applied to a 3D mesh of the valley, with a sufficient resolution to, at the same time, model the aerology of the valley and follow a pollution plume using an atmospheric dispersion model. It will be implemented through the use of an artificial neural network, the learning of which will be based on measurements of local climatology and aerology, supplemented by synthetic data using a local high-resolution model.
The candidate will work within a small, attentive and benevolent CEA team while remaining connected to university research via the Toulouse Aerology Laboratory. He will be able to both become a specialist in applied research in the meteorological field and acquire digital and scientific skills that can be used in business.
Development of processing by Artificial Intelligence of a measuring and forecasting station
This post-doctoral proposal is part of the French atomic commission (CEA) project "MultiMod'Air", which involves developing an « intelligent » prototype of air quality measurement and forecasting station within two years. The work proposal is to develop the bricks of Artificial Intelligence (AI) of the project: correction by ANN (Artificial Neuronal network) of the measurements obtained through low cost sensors, correction ANN of weather forecasts at the station level, which are simple treatments to implement. The actual research work will concern the development of a AI based pollution forecast at the station by learning from past events.
Development of Monte-Carlo methods for the simulation of radiative transfer: application to severe accidents
This post-doctoral subject concerns the development of Monte-Carlo ray-tracing methods for modeling radiation heat transfer in the context of severe accidents. Starting from a well-developed software framework for Monte Carlo simulation of particle transport in the context of reactor physics and radiation protection, we will seek to adapt existing methods to the problem of radiative heat transfer, in a high-performance computing framework. To do this, we will develop a hierarchy of approximations associated with radiative heat transfer that are intended to allow the validation of simplified models implemented in the context of the numerical simulation of severe accidents in nuclear reactors. Focusing on algorithm and simulation performance, this work is intended to be a "proof of principle" of the possible software mutualization around the Monte-Carlo method for particle transport on the one hand and radiative heat transfer on the other hand.