Simulations and Experiments to Reach Numerical Multiphase Informations for Security Analysis on Large Volume Vacuum Systems Like Tokamaks

Improvement of the shadow tracking setup as a method to measure the velocities values of dark dust in order to reduce the risks of radioactive releases or explosions

The mobilisation of dust is a key security issue in nuclear and industrial plants because it can provoke both explosions (in particular temperature and pressure conditions) and dangerous radioactive releases. This work is focused on the tungsten dust resuspension inside small tank for aerosol removal and DUST (STARDUST)-Upgrade, an experimental facility used to reproduce different conditions typical of the loss of vacuum accidents. In this paper, the authors present the facility and the materials used to mount the experimental setup together with the methods and algorithms implemented to track the dust velocity vectors. Tungsten dust is also analyzed through scanning electron microscopy and X-ray diffraction to find some correlation between the variation of the dust morphology, due to the different experimental conditions, with the dust mobilisation paths. The materials and methods together with the experimental results are analyzed and discussed to demonstrate the implementation with the shadow tracking setup presented in the previous facility STARDUST.

A novel integrated approach for the hazardous radioactive dust source terms estimation in future nuclear fusion power plants

An open issue still under investigation by several international entities working on the safety and security field for the foreseen nuclear fusion reactors is the estimation of source terms that are a hazard for the operators and public, and for the machine itself in terms of efficiency and integrity in case of severe accident scenarios. Source term estimation is a crucial key safety issue to be addressed in the future reactors safety assessments, and the estimates available at the time are not sufficiently satisfactory. The lack of neutronic data along with the insufficiently accurate methodologies used until now, calls for an integrated methodology for source term estimation that can provide predictions with an adequate accuracy. This work proposes a complete methodology to estimate dust source terms starting from a broad information gathering. The wide number of parameters that can influence dust source term production is reduced with statistical tools using a combination of screening, sensitivity analysis, and uncertainty analysis. Finally, a preliminary and simplified methodology for dust source term production prediction for future devices is presented.

Image computing techniques to extrapolate data for dust tracking in case of an experimental accident simulation in a nuclear fusion plant

In this paper, a preliminary shadowgraph-based analysis of dust particles re-suspension due to loss of vacuum accident (LOVA) in ITER-like nuclear fusion reactors has been presented. Dust particles are produced through different mechanisms in nuclear fusion devices, one of the main issues is that dust particles are capable of being re-suspended in case of events such as LOVA. Shadowgraph is based on an expanded collimated beam of light emitted by a laser or a lamp that emits light transversely compared to the flow field direction. In the STARDUST facility, the dust moves in the flow, and it causes variations of refractive index that can be detected by using a CCD camera. The STARDUST fast camera setup allows to detect and to track dust particles moving in the vessel and then to obtain information about the velocity field of dust mobilized. In particular, the acquired images are processed such that per each frame the moving dust particles are detected by applying a background subtraction technique based on the mixture of Gaussian algorithm. The obtained foreground masks are eventually filtered with morphological operations. Finally, a multi-object tracking algorithm is used to track the detected particles along the experiment. For each particle, a Kalman filter-based tracker is applied; the particles dynamic is described by taking into account position, velocity, and acceleration as state variable. The results demonstrate that it is possible to obtain dust particles' velocity field during LOVA by automatically processing the data obtained with the shadowgraph approach.