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Register a new userHydrodynamical Adaptive Mesh Refinement Simulations of Disk Galaxies
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To date, fully cosmological hydrodynamic disk simulations to redshift zero have only been undertaken with particle-based codes, such as GADGET, Gasoline, or GCD+. In light of the (supposed) limitations of traditional implementations of smoothed particle hydrodynamics (SPH), or at the very least, their respective idiosyncrasies, it is important to explore complementary approaches to the SPH paradigm to galaxy formation. We present the first high-resolution cosmological disk simulations to redshift zero using an adaptive mesh refinement (AMR)-based hydrodynamical code, in this case, RAMSES. We analyse the temporal and spatial evolution of the simulated stellar disks vertical heating, velocity ellipsoids, stellar populations, vertical and radial abundance gradients (gas and stars), assembly/infall histories, warps/lopsideness, disk edges/truncations (gas and stars), ISM physics implementations, and compare and contrast these properties with our sample of cosmological SPH disks, generated with GCD+. These preliminary results are the first in our long-term Galactic Archaeology Simulation program.
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We have explored the evolution of gas distributions from cosmological simulations carried out using the RAMSES adaptive mesh refinement (AMR) code, to explore the effects of resolution on cosmological hydrodynamical simulations. It is vital to understand the effect of both the resolution of initial conditions and the final resolution of the simulation. Lower initial resolution simulations tend to produce smaller numbers of low mass structures. This will strongly affect the assembly history of objects, and has the same effect of simulating different cosmologies. The resolution of initial conditions is an important factor in simulations, even with a fixed maximum spatial resolution. The power spectrum of gas in simulations using AMR diverges strongly from the fixed grid approach - with more power on small scales in the AMR simulations - even at fixed physical resolution and also produces offsets in the star formation at specific epochs. This is because before certain times the upper grid levels are held back to maintain approximately fixed physical resolution, and to mimic the natural evolution of dark matter only simulations. Although the impact of hold back falls with increasing spatial and initial-condition resolutions, the offsets in the star formation remain down to a spatial resolution of 1 kpc. These offsets are of order of 10-20%, which is below the uncertainty in the implemented physics but are expected to affect the detailed properties of galaxies. We have implemented a new grid-hold-back approach to minimize the impact of hold back on the star formation rate.
Wildland fires are complex multi-physics problems that span wide spatial scale ranges. Capturing this complexity in computationally affordable numerical simulations for process studies and outer-loop techniques (e.g., optimization and uncertainty quantification) is a fundamental challenge in reacting flow research. Further complications arise for propagating fires where a priori knowledge of the fire spread rate and direction is typically not available. In such cases, static mesh refinement at all possible fire locations is a computationally inefficient approach to bridging the wide range of spatial scales relevant to wildland fire behavior. In the present study, we address this challenge by incorporating adaptive mesh refinement (AMR) in fireFoam, an OpenFOAM solver for simulations of complex fire phenomena. The AMR functionality in the extended solver, called wildFireFoam, allows us to dynamically track regions of interest and to avoid inefficient over-resolution of areas far from a propagating flame. We demonstrate the AMR capability for fire spread on vertical panels and for large-scale fire propagation on a variable-slope surface that is representative of real topography. We show that the AMR solver reproduces results obtained using much larger statically refined meshes, at a substantially reduced computational cost.
A numerical code for solving various Lyman alpha (Lya) radiative transfer (RT) problems is presented. The code is suitable for an arbitrary, three-dimensional distribution of Lya emissivity, gas temperature, density, and velocity field. Capable of handling Lya RT in an adaptively refined grid-based structure, it enables detailed investigation of the effects of clumpiness of the interstellar (or intergalactic) medium. The code is tested against various geometrically and physically idealized configurations for which analytical solutions exist, and subsequently applied to three Lyman-break galaxies, extracted from high-resolution cosmological simulations at redshift z = 3.6. Proper treatment of the Lya scattering reveals a diversity of surface brightness (SB) and line profiles. Specifically, for a given galaxy the maximum observed SB can vary by an order of magnitude, and the total flux by a factor of 3 - 6, depending on the viewing angle. This may provide an explanation for differences in observed properties of high-redshift galaxies, and in particular a possible physical link between Lyman-break galaxies and regular Lya emitters.
Large-scale finite element simulations of complex physical systems governed by partial differential equations crucially depend on adaptive mesh refinement (AMR) to allocate computational budget to regions where higher resolution is required. Existing scalable AMR methods make heuristic refinement decisions based on instantaneous error estimation and thus do not aim for long-term optimality over an entire simulation. We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning (RL) to train refinement policies directly from simulation. AMR poses a new problem for RL in that both the state dimension and available action set changes at every step, which we solve by proposing new policy architectures with differing generality and inductive bias. The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations. We demonstrate in comprehensive experiments on static function estimation and the advection of different fields that RL policies can be competitive with a widely-used error estimator and generalize to larger, more complex, and unseen test problems.
We present new results characterizing cosmological shocks within adaptive mesh refinement N-Body/hydrodynamic simulations that are used to predict non-thermal components of large-scale structure. This represents the first study of shocks using adaptive mesh refinement. We propose a modified algorithm for finding shocks from those used on unigrid simulations that reduces the shock frequency of low Mach number shocks by a factor of ~3. We then apply our new technique to a large, (512 Mpc/h)^3, cosmological volume and study the shock Mach number (M) distribution as a function of pre-shock temperature, density, and redshift. Because of the large volume of the simulation, we have superb statistics that results from having thousands of galaxy clusters. We find that the Mach number evolution can be interpreted as a method to visualize large-scale structure formation. Shocks with Mach<5 typically trace mergers and complex flows, while 5<Mach<20 and Mach>20 generally follow accretion onto filaments and galaxy clusters, respectively. By applying results from nonlinear diffusive shock acceleration models using the first-order Fermi process, we calculate the amount of kinetic energy that is converted into cosmic ray protons. The acceleration of cosmic ray protons is large enough that in order to use galaxy clusters as cosmological probes, the dynamic response of the gas to the cosmic rays must be included in future numerical simulations.
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