About the Research
Researchers in the mechanical engineering and aeronautics & astronautics departments at Stanford University are involved in several research programs that require large-scale massively parallel computing resources to carry out first-of-a-kind simulations. The cluster is being used to compute the details of the flow and acoustic fields created by helicopters in forward flight. For this purpose two major simulation codes, SUmb and CDP, are run simultaneously to compute the flow in separate areas of the domain: SUmb computes the flow in the region near the surface of the blades where compressibility and viscous effects are dominant, and CDP resolves the wake portion where the identification of the strength and location of the trailing vortices is of fundamental importance.
SUmb (Stanford University multiblock) and CDP (named for the late Charles David Pierce) are both massively parallel flow solvers developed at Stanford under the sponsorship of the Department of Energy’s ASC program. SUmb can be used to solve for the compressible flow in many applications including, but are not limited to, jet engines, subsonic and supersonic aircraft, helicopters, launch vehicles, space and re-entry vehicles, and a host of other research applications. SUmb uses a multi-block structured meshing approach. The mesh is decomposed into a number of pieces that are distributed to each of the processors in a calculation and the Message Passing Interface (MPI) standard is used to communicate between processors using a high-bandwidth, low-latency network (InfiniBand for most of our clusters).
CDP uses a fully unstructured grid approach to allow more flexibility in concentrating the grid points in regions of interest. The code was developed to simulate the multiphase reacting flow in jet engine combustors, but can be applied more generally to simulate a variety of flows where important flow structure persists for a relatively long time, such as the trailing vortices generated by the helicopter’s blade tips. For coupled simulations where SUmb needs to interact with CDP, the codes use a Python-based interface to simplify access to the data structures, while allowing the core portions of the solution to be carried out using highly optimized compiled languages. Both codes have been routinely run on thousands of processors and have been readied for computations on large parallel computers such as BlueGene/L, which is expected to reach a total of about 130,000 processors.
Other research groups design, analyze, develop, and validate mathematical models and computational methods for the high-performance simulation of multidisciplinary engineering and technological problems. They specialize in distributed computing and massively parallel processing. Recent efforts focused on and continue to address structural dynamics, contact problems, nonlinear aeroelasticity of fighter aircraft, fluid-structure interaction, underwater acoustics, inverse problems, and shape optimization. Current emphasis is on multiscale methods, dynamic data-driven systems, model reduction, near-real-time computing, and large-scale applications in aerospace, mechanical, naval, and marine engineering.