Astronomical phenomena are governed by processes on all spatial and temporal scales, ranging from days to the age of the Universe (13.8,Gyr) as well as from km size up to the size of the Universe. This enormous range in scales is contrived, but as lo
ng as there is a physical connection between the smallest and largest scales it is important to be able to resolve them all, and for the study of many astronomical phenomena this governance is present. Although covering all these scales is a challenge for numerical modelers, the most challenging aspect is the equally broad and complex range in physics, and the way in which these processes propagate through all scales. In our recent effort to cover all scales and all relevant physical processes on these scales we have designed the Astrophysics Multipurpose Software Environment (AMUSE). AMUSE is a Python-based framework with production quality community codes and provides a specialized environment to connect this plethora of solvers to a homogeneous problem solving environment.
We present the open source Astrophysical Multi-purpose Software Environment (AMUSE, www.amusecode.org), a component library for performing astrophysical simulations involving different physical domains and scales. It couples existing codes within a P
ython framework based on a communication layer using MPI. The interfaces are standardized for each domain and their implementation based on MPI guarantees that the whole framework is well-suited for distributed computation. It includes facilities for unit handling and data storage. Currently it includes codes for gravitational dynamics, stellar evolution, hydrodynamics and radiative transfer. Within each domain the interfaces to the codes are as similar as possible. We describe the design and implementation of AMUSE, as well as the main components and community codes currently supported and we discuss the code interactions facilitated by the framework. Additionally, we demonstrate how AMUSE can be used to resolve complex astrophysical problems by presenting example applications.
We study the evolution of embedded clusters. The equations of motion of the stars in the cluster are solved by direct N-body integration while taking the effects of stellar evolution and the hydrodynamics of the natal gas content into account. The gr
avity of the stars and the surrounding gas are coupled self consistently to allow the realistic dynamical evolution of the cluster. While the equations of motion are solved, a stellar evolution code keeps track of the changes in stellar mass, luminosity and radius. The gas liberated by the stellar winds and supernovae deposits mass and energy into the gas reservoir in which the cluster is embedded. We examine cluster models with 1000 stars, but we varied the star formation efficiency (between 0.05-0.5), cluster radius (0.1-1.0 parsec), the degree of virial support of the initial population of stars (0-100%) and the strength of the feedback. We find that an initial star fraction $M_star/M_{rm tot} > 0.05$ is necessary for cluster survival. Survival is more likely if gas is not blown out violently by a supernova and if the cluster has time to approach virial equilibrium during out-gassing. While the cluster is embedded, dynamical friction drives early and efficient mass segregation in the cluster. Stars of $m gtrsim 2,M_odot$ are preferentially retained, at the cost of the loss of less massive stars. We conclude that the degree of mass segregation in open clusters such as the Pleiades is not the result of secular evolution but a remnant of its embedded stage.
