Influence of exposure conditions on helium transport and bubble growth in tungsten.

Helium diffusion, clustering and bubble nucleation and growth is modelled using the finite element method. The existing model from Faney et al. (Model Simul Mater Sci Eng 22:065010, 2018; Nucl Fusion 55:013014, 2015) is implemented with FEniCS and simplified in order to greatly reduce the number of equations. A parametric study is performed to investigate the influence of exposure conditions on helium inventory, bubbles density and size. Temperature is varied from 120 K to 1200 K and the implanted flux of 100 eV He is varied from [Formula: see text] to [Formula: see text]. Bubble mean size increases as a power law of time whereas the bubble density reaches a maximum. The maximum He content in bubbles was approximately [Formula: see text] He at [Formula: see text]. After 1 h of exposure, the helium inventory varies from [Formula: see text] at low flux and high temperature to [Formula: see text] at high flux and low temperature. The bubbles inventory varies from [Formula: see text] bubbles m[Formula: see text] to [Formula: see text] bubbles m[Formula: see text]. Comparison with experimental measurements is performed. The bubble density simulated by the model is in quantitative agreement with experiments.

The Paramak: automated parametric geometry construction for fusion reactor designs.

During the conceptual design process of fusion reactors it is useful to rapidly prototype different design concepts and assess their suitability against a range of high level requirements. Rapid prototyping allows the 'fail early' mantra of other fields to be applied to engineering design. Furthermore, the rapid generation of low fidelity analysis allows fast exploration of design space, which enables better decisions to be made during concept selection and the detailed design phase. The Paramak is an open-source tool that aims to provide automated parameter driven 3D CAD models for fusion reactor components and magnetic fusion reactors. The geometry produced is compatible with several analysis workflows and this allows iterative automated model building and analysis to help steer the design concept optimisation process. The Paramak uses CadQuery 2 to create the 3D CAD model. The Paramak framework is used to create a few example reactor configurations including: a spherical reactor, a regular large radius tokamak and a compact submersion tank reactor. Input parameters for the various reactors that the Paramak can generate generally fall into three categories: continuous ranges such as blanket thickness, integer ranges such as number of toroidal field coils and categorical parameters such as type of divertor. The Paramak facilitates parameter studies where users can investigate the impact of input design parameters on the reactor performance. The use of modern software practices allows the geometry to be continuously tested in analysis workflows to ensure it is fit for purpose. The generation of output metrics from input parameters lends itself to the use of data science and machine learning approaches in order to steer the design. The Paramak provides rapid construction of analysis ready CAD in a manner that allows the designer to save time when exploring the design space for design studies and facilitate automated generative design.

Parametric study of hydrogenic inventory in the ITER divertor based on machine learning.

A parametric study is performed with the 2D FESTIM code for the ITER monoblock geometry. The influence of the monoblock surface temperature, the incident ion energy and particle flux on the monoblock hydrogen inventory is investigated. The simulated data is analysed with a Gaussian regression process and an inventory map as a function of ion energy and incident flux is given. Using this inventory map, the hydrogen inventory in the divertor is easily derived for any type of scenario. Here, the case of a detached ITER scenario with inputs from the SOLPS code is presented. For this scenario, the hydrogen inventory per monoblock is highly dependent of surface temperature and ranges from [Formula: see text] to [Formula: see text] H after a [Formula: see text] s exposure. The inventory evolves as a power law of time and is lower at strike points where the surface temperature is high. Hydrogen inventory in the whole divertor after a [Formula: see text] s exposure is estimated at approximately 8 g.