Cosmological hydrodynamics with adaptive mesh refinement. A new high resolution code called RAMSES. A new N-body and hydrodynamical code, called RAMSES, is presented. It has been designed to study structure formation in the universe with high spatial resolution. The code is based on Adaptive Mesh Refinement (AMR) technique, with a tree-based data structure allowing recursive grid refinements on a cell-by-cell basis. The N-body solver is very similar to the one developed for the ART code [CITE], with minor differences in the exact implementation. The hydrodynamical solver is based on a second-order Godunov method, a modern shock-capturing scheme known to compute accurately the thermal history of the fluid component. The accuracy of the code is carefully estimated using various test cases, from pure gas dynamical tests to cosmological ones. The specific refinement strategy used in cosmological simulations is described, and potential spurious effects associated with shock waves propagation in the resulting AMR grid are discussed and found to be negligible. Results obtained in a large N-body and hydrodynamical simulation of structure formation in a low density $Lambda$CDM universe are reported, with 2563 particles and $4.1 imes 10^7$ cells in the AMR grid, reaching a formal resolution of 81923. A convergence analysis of different quantities, such as dark matter density power spectrum, gas pressure power spectrum and individual haloe temperature profiles, shows that numerical results are converging down to the actual resolution limit of the code, and are well reproduced by recent analytical predictions in the framework of the halo model.

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  1. Hernández-Aguayo, César; Ruan, Cheng-Zong; Li, Baojiu; Arnold, Christian; Baugh, Carlton M.; Klypin, Anatoly; Prada, Francisco: Fast full (N)-body simulations of generic modified gravity: derivative coupling models (2022)
  2. Becker, Christoph; Eggemeier, Alexander; Davies, Christopher T.; Li, Baojiu: Proca-stinated cosmology. II: Matter, halo, and lensing statistics in the vector Galileon (2021)
  3. Chernykh, Igor; Kulikov, Igor; Tutukov, Alexander: Hydrogen-helium chemical and nuclear galaxy collision: hydrodynamic simulations on AVX-512 supercomputers (2021)
  4. Jean Sexton, Zarija Lukic, Ann Almgren, Chris Daley, Brian Friesen, Andrew Myers, Weiqun Zhang: Nyx: A Massively Parallel AMR Code for Computational Cosmology (2021) not zbMATH
  5. Keppens, Rony; Teunissen, Jannis; Xia, Chun; Porth, Oliver: \textttMPI-AMRVAC: a parallel, grid-adaptive PDE toolkit (2021)
  6. Rubira, Henrique; Voivodic, Rodrigo: The effective field theory and perturbative analysis for log-density fields (2021)
  7. Eckmann, Jean-Pierre; Hassani, Farbod: The detection of relativistic corrections in cosmological (N)-body simulations (2020)
  8. James M. Stone, Kengo Tomida, Christopher J. White, Kyle G. Felker: The Athena++ Adaptive Mesh Refinement Framework: Design and Magnetohydrodynamic Solvers (2020) arXiv
  9. Reverberi, Lorenzo; Daverio, David: \textttfRevolution-- relativistic cosmological simulations in (f(R)) gravity. I: Methodology (2019)
  10. Schmidmayer, Kevin; Petitpas, Fabien; Daniel, Eric: Adaptive mesh refinement algorithm based on dual trees for cells and faces for multiphase compressible flows (2019)
  11. Tram, Thomas; Brandbyge, Jacob; Dakin, Jeppe; Hannestad, Steen: Fully relativistic treatment of light neutrinos in (N)-body simulations (2019)
  12. Deriaz, Erwan; Peirani, Sébastien: Six-dimensional adaptive simulation of the Vlasov equations using a hierarchical basis (2018)
  13. Dumbser, Michael; Fambri, Francesco; Tavelli, Maurizio; Bader, Michael; Weinzierl, Tobias: Efficient implementation of ADER discontinuous Galerkin schemes for a scalable hyperbolic PDE engine (2018)
  14. Gnedin, Nickolay Y.; Semenov, Vadim A.; Kravtsov, Andrey V.: Enforcing the Courant-Friedrichs-Lewy condition in explicitly conservative local time stepping schemes (2018)
  15. Kulikov, I. M.; Chernykh, I. G.; Glinskiy, B. M.; Protasov, V. A.: An efficient optimization of Hll method for the second generation of Intel Xeon Phi processor (2018)
  16. Kulikov, I. M.; Chernykh, I. G.; Tutukov, A. V.: A new parallel Intel Xeon Phi hydrodynamics code for massively parallel supercomputers (2018)
  17. Liu, Chao; Oliynyk, Todd A.: Cosmological Newtonian limits on large spacetime scales (2018)
  18. Papoutsakis, Andreas; Sazhin, Sergei S.; Begg, Steven; Danaila, Ionut; Luddens, Francky: An efficient adaptive mesh refinement (AMR) algorithm for the discontinuous Galerkin method: applications for the computation of compressible two-phase flows (2018)
  19. Bose, Sownak; Li, Baojiu; Barreira, Alexandre; He, Jian-hua; Hellwing, Wojciech A.; Koyama, Kazuya; Llinares, Claudio; Zhao, Gong-Bo: Speeding up (N)-body simulations of modified gravity: chameleon screening models (2017)
  20. Descombes, Stéphane; Duarte, Max; Dumont, Thierry; Guillet, Thomas; Louvet, Violaine; Massot, Marc: Task-based adaptive multiresolution for time-space multi-scale reaction-diffusion systems on multi-core architectures (2017)

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