Integrating High Performance Computing into Research: Computational Fluid Dynamics in Engineering and Astrophysics

在广泛的科学和工程领域推进我们的理解需要在很大范围的密度和物理尺度上解决流体方程。这些在当前计算机上无法解决的问题，可以通过使用面向对象语言的新一代大规模并行高性能计算机来解决，并可能成为这些机器最重要的科学应用之一。普林斯顿大学的MAE和天体物理系处于数值算法和计算机技术发展的研究前沿，以利用下一代机器进行这项工作。工程和科学目标是空气动力学的应用（飞机、汽车和船舶的设计）；对湍流的研究；以及宇宙的结构形成。这些不同应用领域的进展需要持续的研究努力，以开发适用于并行处理的算法，自动网格自适应，任意非结构化四面体网格的域分解的着色方案，以及实现有效的消息传递算法以降低分布式内存架构上的通信成本。这些问题是通用的，而不是特定于某个特定的应用领域。我们认为，采用多学科的方法来研究这些问题，将大大有利于培养学生，并提高高性能计算在研究中的有效应用。MAE和天体物理系过去曾在这项工作中合作过，未来也将继续这样做。我们要求支持五名研究生在使用大规模并行计算机平台设计偏微分方程数值解的算法以及将复杂的氢码应用于从空气动力学湍流研究到宇宙结构演化的工程和物理问题方面进行培训。

Advancing our understanding in a wide range of scientific and engineering fields require the solution of the fluid equations over a very large range of densities and physical scales. These problems, which are not soluble on the current generation of computers, can be solved with the new generation of massively parallel high performance computers using object oriented languages and are likely to form one of the most important scientific applications of these machines. The MAE and Astrophysics departments at Princeton are at the forefront of research into the development of numerical algorithms and computer technology to utilize the next generation of machines in this effort. The engineering and scientific goals are the applications to aerodynamics (the design of airplanes, cars and ships); to turbulence research; and to structure formation in the universe. Progress in these different application areas requires a continuing research effort toward the development of algorithms amenable to parallel processing, automatic grid adaption, coloring schemes for domain decomposition of an arbitrary unstructured tetrahedral grid, and implementation of efficient message passing algorithms to reduce the communication costs on distributed memory architectures. These problems are generic, and not specific to a particular field of application. It is felt that a multi-disciplinary approach to the study of such problems would greatly benefit the training of students and enhance the effective use of high performance computing in research. The MAE and Astrophysics departments have collaborated in this effort in the past, and will continue to do so in the future. We request support for five graduate students to be trained in the design of algorithms for the numerical solution of partial differential equations using massively parallel computer platforms and the application of complex hydrocodes to engineering and physics problems ranging from turbulence research in aerodyna mics to the evolution of cosmological structure.