With the ultimate goal to predict plasmas heat and particle fluxes in ITER operation, more efforts are required to deal with realistic magnetic configurations and tokamak geometries. In an attempt to achieve this goal, we propose an adaptive mesh refinement method added to a fluid solver based on a high‐order hybrid discontinuous Galerkin (HDG) method. Based on unstructured meshes, this magnetic equilibrium free numerical scheme has shown promising and encouraging features to solve 2D/3D transport reduced Braginski fluid equations. To improve its numerical efficiency, a mesh refinement based on h‐adpativity is investigated. We describe here an adaptive refinement strategy on a reduced edge particle transport model based on electron density and parallel momentum. This strategy is illustrated in realistic tokamak wall geometry. Computations performed show potential gains in the required number of degrees of freedom against benchmark computations with uniform meshes, along with the potential to give an automated, goal‐oriented, mesh generation technique for edge transport simulations in 2D.

The simulation of fusion plasmas in realistic magnetic configurations and tokamak geometries still requires the development of advanced numerical algorithms owing to the complexity of the problem. In this context, we propose a Hybrid Discontinuous Galerkin (HDG) method to solve 2D transport fluid equations in realistic magnetic and tokamak wall geometries. This high-order solver can handle magnetic equilibrium free structured and unstructured meshes allowing a much more accurate discretization of the plasma facing components than current solvers based on magnetic field aligned methods associated with finite-differences (volumes) discretization. In addition, the method allows for handling realistic magnetic equilibrium, eventually non steady, a critical point in the modeling of full discharges including ramp up and ramp down phases. In this paper, we introduce the HDG algorithm with a special focus on recent developments related to the treatment of the cross-field diffusive terms, and to an adaptive mesh refinement technique improving the numerical efficiency and robustness of the scheme. The updated solver is verified with a manufactured solution method, and numerical tests are provided to illustrate the new capabilities of the code.

<p><strong>Dam reoperation for controlling water-related diseases: the potential of floating solar for compensating hydropower losses. </strong></p><p>Giacomo Piraccini<sup>1</sup>, Alessandro Amaranto<sup>1</sup>, Federica Bertoni<sup>1</sup>, Andrea Castelletti<sup>1</sup></p><p><sup>1</sup>Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy</p><p>Malaria is one of the leading causes of death in Sub-Saharan Africa, affecting around 200 million people in the region each year. In the proximity of hydropower reservoirs, the presence of large areas with stagnant water creates greater reproduction opportunities for Anopheles mosquitoes, and the number of disease cases is usually higher.  In this context, a soft mitigation strategy which is gaining much attention in recent years is controlling the water level in the lake to expose the Anopheles eggs right after laying. However, this operation strategy usually leads to both losses and fluctuation in hydropower production.</p><p>In this study, we evaluate the capability of floating solar technology to effectively compensate the loss in energy production occurring when avoiding the spread of malaria becomes an important factor in reservoir management. To do so, we implement a modelling framework where the floating solar plant size and the dam operation are jointly optimized with the objective of minimizing energy deficit, costs and malaria spread. As a demonstration, we study the Zambezi River, where the Kariba dam (shared between Zambia and Zambezi) is mainly operated for hydropower production. Here, we explore the potential tradeoffs between power generation and malaria spread by solving a joint planning (solar plant capacity)-management (dam operations) optimization problem using Evolutionary Multi-Objective Direct Policy Search (EMODPS). Numerical results show how a doubling in power generation can be obtained by covering about 1% of Kariba lake with floating solar panels. This highlights the potential of floating solar penetration in tropical climates, and the key role that the technology can play in both controlling water-related diseases and compensating hydropower production, especially in dry seasons.</p>