A Survey of Ocean Simulation and Rendering Techniques in Computer Graphics

This paper presents a survey of ocean simulation and rendering methods in computer graphics. To model and animate the ocean's surface, these methods mainly rely on two main approaches: on the one hand, those which approximate ocean dynamics with parametric, spectral or hybrid models and use empirical laws from oceanographic research. We will see that this type of methods essentially allows the simulation of ocean scenes in the deep water domain, without breaking waves. On the other hand, physically-based methods use Navier-Stokes Equations (NSE) to represent breaking waves and more generally ocean surface near the shore. We also describe ocean rendering methods in computer graphics, with a special interest in the simulation of phenomena such as foam and spray, and light's interaction with the ocean surface

EvoL: The new Padova T-SPH parallel code for cosmological simulations - I. Basic code: gravity and hydrodynamics

We present EvoL, the new release of the Padova N-body code for cosmological simulations of galaxy formation and evolution. In this paper, the basic Tree + SPH code is presented and analysed, together with an overview on the software architectures. EvoL is a flexible parallel Fortran95 code, specifically designed for simulations of cosmological structure formation on cluster, galactic and sub-galactic scales. EvoL is a fully Lagrangian self-adaptive code, based on the classical Oct-tree and on the Smoothed Particle Hydrodynamics algorithm. It includes special features such as adaptive softening lengths with correcting extra-terms, and modern formulations of SPH and artificial viscosity. It is designed to be run in parallel on multiple CPUs to optimize the performance and save computational time. We describe the code in detail, and present the results of a number of standard hydrodynamical tests.Comment: 33 pages, 49 figures, accepted on A&

GIZMO: A New Class of Accurate, Mesh-Free Hydrodynamic Simulation Methods

We present two new Lagrangian methods for hydrodynamics, in a systematic comparison with moving-mesh, SPH, and stationary (non-moving) grid methods. The new methods are designed to simultaneously capture advantages of both smoothed-particle hydrodynamics (SPH) and grid-based/adaptive mesh refinement (AMR) schemes. They are based on a kernel discretization of the volume coupled to a high-order matrix gradient estimator and a Riemann solver acting over the volume 'overlap.' We implement and test a parallel, second-order version of the method with self-gravity & cosmological integration, in the code GIZMO: this maintains exact mass, energy and momentum conservation; exhibits superior angular momentum conservation compared to all other methods we study; does not require 'artificial diffusion' terms; and allows the fluid elements to move with the flow so resolution is automatically adaptive. We consider a large suite of test problems, and find that on all problems the new methods appear competitive with moving-mesh schemes, with some advantages (particularly in angular momentum conservation), at the cost of enhanced noise. The new methods have many advantages vs. SPH: proper convergence, good capturing of fluid-mixing instabilities, dramatically reduced 'particle noise' & numerical viscosity, more accurate sub-sonic flow evolution, & sharp shock-capturing. Advantages vs. non-moving meshes include: automatic adaptivity, dramatically reduced advection errors & numerical overmixing, velocity-independent errors, accurate coupling to gravity, good angular momentum conservation and elimination of 'grid alignment' effects. We can, for example, follow hundreds of orbits of gaseous disks, while AMR and SPH methods break down in a few orbits. However, fixed meshes minimize 'grid noise.' These differences are important for a range of astrophysical problems.Comment: 57 pages, 33 figures. MNRAS. A public version of the GIZMO code, user's guide, test problem setups, and movies are available at http://www.tapir.caltech.edu/~phopkins/Site/GIZMO.htm

The cosmological simulation code GADGET-2

We discuss the cosmological simulation code GADGET-2, a new massively parallel TreeSPH code, capable of following a collisionless fluid with the N-body method, and an ideal gas by means of smoothed particle hydrodynamics (SPH). Our implementation of SPH manifestly conserves energy and entropy in regions free of dissipation, while allowing for fully adaptive smoothing lengths. Gravitational forces are computed with a hierarchical multipole expansion, which can optionally be applied in the form of a TreePM algorithm, where only short-range forces are computed with the `tree'-method while long-range forces are determined with Fourier techniques. Time integration is based on a quasi-symplectic scheme where long-range and short-range forces can be integrated with different timesteps. Individual and adaptive short-range timesteps may also be employed. The domain decomposition used in the parallelisation algorithm is based on a space-filling curve, resulting in high flexibility and tree force errors that do not depend on the way the domains are cut. The code is efficient in terms of memory consumption and required communication bandwidth. It has been used to compute the first cosmological N-body simulation with more than 10^10 dark matter particles, reaching a homogeneous spatial dynamic range of 10^5 per dimension in a 3D box. It has also been used to carry out very large cosmological SPH simulations that account for radiative cooling and star formation, reaching total particle numbers of more than 250 million. We present the algorithms used by the code and discuss their accuracy and performance using a number of test problems. GADGET-2 is publicly released to the research community.Comment: submitted to MNRAS, 31 pages, 20 figures (reduced resolution), code available at http://www.mpa-garching.mpg.de/gadge

A general class of Lagrangian smoothed particle hydrodynamics methods and implications for fluid mixing problems

Various formulations of smoothed particle hydrodynamics (SPH) have been proposed, intended to resolve certain difficulties in the treatment of fluid mixing instabilities. Most have involved changes to the algorithm which either introduces artificial correction terms or violates what is arguably the greatest advantage of SPH over other methods: manifest conservation of energy, entropy, momentum and angular momentum. Here, we show how a class of alternative SPH equations of motion (EOM) can be derived self-consistently from a discrete particle Lagrangian – guaranteeing manifest conservation – in a manner which tremendously improves treatment of these instabilities and contact discontinuities. Saitoh & Makino recently noted that the volume element used to discretize the EOM does not need to explicitly invoke the mass density (as in the ‘standard’ approach); we show how this insight, and the resulting degree of freedom, can be incorporated into the rigorous Lagrangian formulation that retains ideal conservation properties and includes the ‘∇h’ terms that account for variable smoothing lengths. We derive a general EOM for any choice of volume element (particle ‘weights’) and method of determining smoothing lengths. We then specify this to a ‘pressure–entropy formulation’ which resolves problems in the traditional treatment of fluid interfaces. Implementing this in a new version of the GADGET code, we show it leads to good performance in mixing experiments (e.g. Kelvin–Helmholtz and ‘blob’ tests). And conservation is maintained even in strong shock/blastwave tests, where formulations without manifest conservation produce large errors. This also improves the treatment of subsonic turbulence and lessens the need for large kernel particle numbers. The code changes are trivial and entail no additional numerical expense. This provides a general framework for self-consistent derivation of different ‘flavours’ of SPH

Smart detectors for Monte Carlo radiative transfer

Many optimization techniques have been invented to reduce the noise that is inherent in Monte Carlo radiative transfer simulations. As the typical detectors used in Monte Carlo simulations do not take into account all the information contained in the impacting photon packages, there is still room to optimize this detection process and the corresponding estimate of the surface brightness distributions. We want to investigate how all the information contained in the distribution of impacting photon packages can be optimally used to decrease the noise in the surface brightness distributions and hence to increase the efficiency of Monte Carlo radiative transfer simulations. We demonstrate that the estimate of the surface brightness distribution in a Monte Carlo radiative transfer simulation is similar to the estimate of the density distribution in an SPH simulation. Based on this similarity, a recipe is constructed for smart detectors that take full advantage of the exact location of the impact of the photon packages. Several types of smart detectors, each corresponding to a different smoothing kernel, are presented. We show that smart detectors, while preserving the same effective resolution, reduce the noise in the surface brightness distributions compared to the classical detectors. The most efficient smart detector realizes a noise reduction of about 10%, which corresponds to a reduction of the required number of photon packages (i.e. a reduction of the simulation run time) of 20%. As the practical implementation of the smart detectors is straightforward and the additional computational cost is completely negligible, we recommend the use of smart detectors in Monte Carlo radiative transfer simulations.Comment: 7 pages, 5 figures, accepted for publication in MNRA