Advancing SOL simulations: avoiding the Boussinesq approximation and coupling closed and open magnetic flux surfaces

Plasma turbulence in the tokamak scrape-off layer (SOL) region, where magnetic field lines intersect the reactor inner walls, determines the heat load on the limiter or divertor targets. This is one of the most crucial issues on the way towards a fusion reactor. To tackle this problem we have improved the turbulent model in the GBS code, a new formulation of the vorticity equation that allow us to relax the Boussinesq approximation is implemented. The energy conservation properties of the new system of equations are evaluated. Also results of turbulent simulations in the SOL with and without the Boussinesq approximation are compared. In addition turbulent simulations across the last closed flux surface taking into account a cold and a hot ion regime are shown. These last simulations show a pressure gradient increase at the separatrix in the hot ion case at low resistivity

A methodology for the rigorous verification of plasma simulation codes

A methodology to perform a rigorous verification of Particle-in-Cell (PIC) simulations is presented, both for assessing the correct implementation of the model equations (code verification), and evaluating the numerical uncertainty affecting the simulation results (solution verification). The proposed code verification methodology is a generalization of the procedure developed for plasma simulation codes based on finite difference schemes that is described by Riva in [Riva et al, Physics of Plasmas, Volume 21, Issue 6, 2014, p.062301] and consists of an order of-accuracy test using the method of manufactured solutions. The generalization of the methodology for PIC codes consists of accounting for numerical schemes intrinsically affected by statistical noise and providing a suitable measure of the distance between continuous, analytical distribution functions and finite samples of computational particles. The solution verification consists of quantifying both the statistical and discretization uncertainties. The statistical uncertainty is estimated by repeating the simulation with different pseudorandom number generator seeds. For the discretization uncertainty, the Richardson extrapolation is used to provide an approximation of the analytical solution and the grid convergence index is used as an estimate of the relative discretization uncertainty. The code verification methodology is successfully applied to a PIC code that numerically solves the one-dimensional, electrostatic, collisionless Vlasov-Poisson system. The solution verification methodology is applied to quantify the numerical uncertainty affecting the two-stream instability growth rate, which is numerically evaluated thanks to a PIC simulation

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity