Simulations of Multiphase Interstellar Turbulence with AMR

AST-0607675 Kritsuk该研究项目研究多个热相如何影响星际介质（ISM）中的湍流级联，利用自适应网格细化（AMR）技术模拟超音速湍流的潜力。 AMR的高分辨率三维数值模拟有望为一些悬而未决的问题提供更可靠和全面的答案：在湍流多相星际介质中，热、磁和引力效应是如何耦合的？ 这种耦合的性质和表现是什么？ 多相ISM中的湍流是如何调节星星形成的速率的？恒星形成的反馈又是如何推动星际湍流的？ 将对包括磁场、辐射输运、非平衡化学、辐射冷却和加热以及热传导在内的模型进行系统的审查，并根据地面和空间的观测数据进行验证。 这些模拟将确定湍流的统计特性，分形气体密度分布的参数，以及自引力原恒星核的结构和动力学性质。 该项目将表征大动态范围内多相ISM中湍流的特性和特征。学生培训是本研究的一个组成部分，但主要影响将来自修改后的恩佐程序的可用性，以及原始和再处理的模拟数据和其他教程材料。 研究人员还与Sun Microsystems，Inc.提高了恩佐代码的可扩展性和可移植性。

AST-0607675KritsukThis research project, on how multiple thermal phases affect the turbulent cascade in the interstellar medium (ISM), exploits the potential of adaptive mesh refinement (AMR) techniques for simulating supersonic turbulent flows. High-resolution, three-dimensional numerical simulations with AMR are expected to give more reliable and comprehensive answers to some outstanding questions: How are thermal, magnetic, and gravitational effects coupled in the turbulent multiphase interstellar medium? What are the nature and manifestations of this coupling? How does turbulence in the multiphase ISM regulate the rate of star formation, and how does star-formation feedback force interstellar turbulence? Models that include magnetic fields, radiation transport, non-equilibrium chemistry, radiative cooling and heating, and thermal conduction, will be examined systematically and validated against observational data, both from the ground and from space. These simulations will determine the statistical characteristics of turbulent flows, the parameters for fractal gas density distributions, and the structural and dynamical properties of self-gravitating protostellar cores. This project will characterize the properties and signatures of turbulence in a multiphase ISM over a large dynamic range.Student training is an integral part of this research, but the main impact will come from the availability of the modified Enzo program, as well as raw and reprocessed simulation data, and other tutorial materials. The researchers also collaborate with Sun Microsystems, Inc. in advancing the scalability and portability of the Enzo code.