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

推进我们在广泛的科学和工程领域的理解需要在非常大的密度和物理尺度范围内的流体方程的解决方案。 这些问题，这是不解决当前一代的计算机，可以解决与新一代的大规模并行高性能计算机使用面向对象的语言，并可能形成这些机器的最重要的科学应用之一。 普林斯顿大学的MAE和天体物理学系处于研究数值算法和计算机技术发展的前沿，以利用下一代机器进行这项工作。 工程和科学目标是应用于空气动力学（飞机，汽车和船舶的设计）;湍流研究;和宇宙中的结构形成。 在这些不同的应用领域的进展需要一个持续的研究工作，对发展的算法服从并行处理，自动网格自适应，着色方案的区域分解的任意非结构化四面体网格，并实现有效的消息传递算法，以减少分布式存储器架构的通信成本。 这些问题是一般性的，而不是特定于特定应用领域。 人们认为，多学科的方法来研究这些问题将大大有利于学生的培训，并提高高性能计算在研究中的有效利用。 MAE和天体物理学系过去在这方面进行了合作，并将在未来继续这样做。 我们请求支持五名研究生接受培训，学习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，并将复杂的流体编码应用于从空气动力学中的湍流研究到宇宙结构演变的工程和物理问题。

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.