A Scalable, Parallel Algorithm for Adaptive Smoothed Particle Hydrodynamics and the Large-Scale Simulation of Galaxy Formation in Cosmology

该建议将并行化并应用一种新的创新方法，该方法是为宇宙学中的3D流体动力学和引力聚集的数值模拟而开发的。 我们的目标是通过模拟膨胀宇宙中引力、气体动力学和辐射传输的耦合效应来解释宇宙中星系和大尺度结构的形成。 正确的解必须解决引力不稳定性的多尺度非线性增长，这导致各向异性引力坍缩，高度超音速运动，强激波，辐射冷却和许多数量级的密度对比。 该方法是我们的平滑粒子流体动力学（SPH）方法，称为自适应SPH，或ASPH，和著名的粒子-粒子/粒子-网格（P DSG 1 3 M）引力N体方法，在宇宙膨胀，物质主导的弗里德曼宇宙的大幅改进版本的合并。 ASPH方法通过引入用于时间和空间变量、各向异性平滑和用于激波跟踪的新算法来彻底改变标准SPH方法，以消除由于远离激波的人工粘性而引起的虚假加热。 ASPH方法显著增加了通过数值流体动力学模拟可以实现的动态范围，并且是目前实现研究上述问题所需的分辨率的最佳方法。这个项目将开发和应用我们现有算法的新的并行计算机版本，这将大大减少每次计算的CPU时间。 DSG1

This proposal will parallelize and apply a new and innovative method that was developed for the numerical simulation of 3D hydrodynamics and gravitational clustering in cosmology. Our goal is to explain the formation of galaxies and large-scale structure in the universe by simulating the coupled effects of gravity, gas dynamics, and radiative transfer in an expanding cosmological universe. A correct solution must resolve the multi-scale, nonlinear growth of gravitational instability, which leads to anisotropic gravitational collapse, highly supersonic motions, strong shocks, radiative cooling, and many orders of magnitude of density contrast. The method is a merger of our substantially improved version of the Smoothed Particle Hydrodynamics (SPH) Method, called Adaptive SPH, or ASPH, and the well-known Particle-Particle/Particle-Mesh(P DSG1 3M) gravitational N-body method, in a cosmologically expanding, matter-dominated Friedmann universe. The ASPH method revolutionizes the standard SPH method by introducing new algorithms for time-and space-variable, anisotropic smoothing and for shock tracking to eliminate spurious heating due to artificial viscosity away from shocks. The ASPH method has significantly increased the dynamical range that can be achieved by numerical hydrodynamical simulations, and is currently the best method to achieve the resolution required to study the problems mentioned above. This project will develop and apply a new parallel computer version of our existing algorithm, that will significantly reduce the CPU time per calculation. DSG1