Recently, there has been more and more research on the abundance of MPs (MPs) in oceans, seas, and rivers. A lot is still uncertain about the distribution of MPs, and whether they are mainly deposited in seas & oceans, or river sediments. As global models on MP transport through rivers have only used statistical methods, we present a global riverine MP transport model based on mechanical principles. The model incorporates particle advection, settling, entrainment, and input emissions from wastewater treatment plants. The model was run for a period of 5 years, on 8 MP mixes of 15 MPs each, with the same 24 uncertainty scenarios for each MP mix (totalling 192 runs). Exported (to seas and oceans) and sedimented MPs showed a linear increase over time, while MPs suspended in the river reached steady state, but showed heavy seasonal fluctuations. Under the modelled uncertainties, after 5 years of simulation time, 76% of MPs are exported to seas and oceans and 19 % of MPs are deposited in river sediment. 5% of MPs were suspended in the water column. Major contributing areas to global MP emissions are area’s with large population densities, like Europe, North America, China & South East Asia, and India. Our work contributes to the understanding of MP flows through rivers, and could be used as a starting point for a MP material flow analysis, or as the basis for MP impact assessments. Future iterations of the model should implement man-made barriers and reservoirs, which
were not considered in the current version of the model.
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In this report, a model is presented to alleviate some of the computational work that goes into the effort of finding the magnetic properties of magnetocaloric materials. The model utilizes an interior point optimization routine to solve for the minimal exchange energy configuration of a system, given the exchange interactions of the material. The model is tested against four materials (Ni, MnO, Fe\textsubscript{2}P and Mn\textsubscript{2}Sb). For Ni and MnO, the exchange interactions are also computed. Three iterations of the model are compared. The base model, which only considers exchange interactions inside a chosen supercell, the base model with the inclusion of boundary conditions, and the base model with boundary conditions and the addition of an algorithm to find optimal solutions. \\ The algorithm analyzes the found results by the optimization routine, and if the result is considered not properly symmetric, runs the optimization routine another time, from a symmetrical starting point obtained from the outcome of the previous run. \\ In all versions of the model, effectiveness (percent of runs that resulted in the optimal configuration) and average run times were recorded. Three initialization methods for the model were used, and also tested for their effectiveness. For the algorithm, a parameter $\gamma$ is introduced that changes the size of some of the moments for the new starting points. Six different values for $\gamma$ were tested for their effectiveness against a test set of suboptimal solutions.
The model with the addition of boundary conditions and the algorithm performed the best out of the three iterations of the model, with an effectiveness of 99.895\%, and an average run time ranging from 0.62 s for 2$\times$2$\times$2 Ni, to 94.64 s for 3$\times$3$\times$3 Fe\textsubscript{2}P, in the case of $\gamma = 0.3$. To conclude, the model with the inclusion of the boundary conditions and the algorithm proves to be a robust method to evaluate the magnetic configuration of a material, especially for smaller systems. ... Read more