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It is proposed to install an experimental setup in the fixed-target hall of the Nuclotron with the final goal to perform a research program focused on the production of strange matter in heavyion collisions at beam energies between 2 and 6 A GeV. The basic setup will comprise a large acceptance dipole magnet with inner tracking detector modules based on double-sided Silicon micro-strip sensors and GEMs. The outer tracking will be based on the drift chambers and straw tube detector. Particle identification will be based on the time-of-flight measurements. This setup will be sufficient perform a comprehensive study of strangeness production in heavy-ion collisions, including multi-strange hyperons, multi-strange hypernuclei, and exotic multi-strange heavy objects. These pioneering measurements would provide the first data on the production of these particles in heavy-ion collisions at Nuclotron beam energies, and would open an avenue to explore the third (strangeness) axis of the nuclear chart. The extension of the experimental program is related with the study of in-medium effects for vector mesons decaying in hadronic modes. The studies of the NN and NA reactions for the reference is assumed.
We investigate the applicability of the well-known multilevel Monte Carlo (MLMC) method to the class of density-driven flow problems, in particular the problem of salinisation of coastal aquifers. As a test case, we solve the uncertain Henry saltwater intrusion problem. Unknown porosity, permeability and recharge parameters are modelled by using random fields. The classical deterministic Henry problem is non-linear and time-dependent, and can easily take several hours of computing time. Uncertain settings require the solution of multiple realisations of the deterministic problem, and the total computational cost increases drastically. Instead of computing of hundreds random realisations, typically the mean value and the variance are computed. The standard methods such as the Monte Carlo or surrogate-based methods are a good choice, but they compute all stochastic realisations on the same, often, very fine mesh. They also do not balance the stochastic and discretisation errors. These facts motivated us to apply the MLMC method. We demonstrate that by solving the Henry problem on multi-level spatial and temporal meshes, the MLMC method reduces the overall computational and storage costs. To reduce the computing cost further, parallelization is performed in both physical and stochastic spaces. To solve each deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion.