The Universe is homogeneous on scales larger than at least 100 Mpc. Therefore, any simulations intended to completely predict the development of structures ought to work with a dynamical range 10-100 Mpc. A disadvantage of this fact is that enormous catalogues of galaxies are needed for a comparison of models of large-scale structure versus observations. The first sufficient catalogue will be the SDSS (Gunn and Knapp 1993), to come within a few years. On the other hand, a great advantage of examining very large scale structures is that density contrasts on these scales are relatively low (i.e., weakly nonlinear, with delta_rho/rho less than 1), and various fields observed (convolved) with large windows can be worked on either with the use of analytical methods (like perturbation theory) or numerical simulations with simplified physics (e.g., effects of pressure, radiation, etc., can be neglected). Even in the weakly nonlinear regime there are many features which have not been examined with analytical methods, e.g., moments of projected velocities or redshift distortions. All of the above give a good reason for writing a new, fast code, able to follow the evolution of structure in various cosmological models.

CPPA -- a new hydrodynamical code for cosmological large-scale structure simulations
Kudlicki, A., Plewa, T. and Rozyczka, M., 1996, Acta Astron., 46, 297
paper source with figures

CPPA movies

• 128^3, n=-1, H_0=50, Omega_0=1
  density contrast

• 64^3, n=-1, H_0=50, Omega_0=1
  density contrast vs. divergence

  each movie shows indvidual values of density contrast and divergence before filtering (raw data); each model has been computed using different random number sequence to calculate initial distribution of phases and perturbation amplitudes were calculated strictly according to the power spectrum; frames were taken at equal interval of 0.05 in expansion factor; red squares show probability distribution function p(theta) in logarithmic scale ranging from -5 to 1 and has been normalized to unity at maximum (compare with Fig.8 of Bernardeau and Weygaert, 1996, MNRAS, 279, 693).

  model	raw	top-hat
  a	304	159
  b	315	175
  d	328	180
  f	278	163

  the numbers indicate size in kilobytes

  cubic fit between density contrast and divergence

  red a0   green a1   blue a2   yellow a3

  click on the image for enlargement

AP3M movies

• 64^3, n=-1, Omega=1
  particle distribution
  sample model

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