Solution of physically-complex flows using parallel high-order finite-volume methods and hydrid solution-adaptive meshes

点击翻译按钮获取中文摘要

Computational fluid dynamics (CFD) has proven to be an important enabling technology in many areas of science and engineering. In spite of the relative maturity and widespread successes of CFD, there remain a variety of physically-complex flows, which are still not well understood and have proven to be very challenging to predict by numerical methods (e.g., turbulent and reactive flows and non-equilibrium micro-scale flows). Along with CFD algorithm development, the rapid increase in high-performance computing systems in the last 10-15 years has lead to terascale and, very recently, petascale parallel systems, ranging in size from a few thousand to hundreds of thousands of cores. These advances in computing hardware are, in turn, creating significant opportunities for CFD of physically-complex flows. Nevertheless, significant advances in numerical methods are required to fully exploit current and future computing platforms and thereby enable the more routine solution of physically-complex flows for practical engineering applications. The proposed research will focus on the development of novel parallel high-order finite-volume and hybrid adaptive mesh refinement (AMR) schemes for predicting physically-complex flows using parallel and new emerging computational architectures. Key elements of the research will include: (i) development of AMR and embedded mesh strategies for treatment of complex geometries and interfaces using hybrid (structure and unstructured) multi-block meshes; (ii) development of anisotropic mesh refinement techniques based on dual-weighted reconstruction and residual error estimates; (iii) design of efficient and scalable, two-level, coarse-fine-grain parallel methods for effective use of multi-core systems and floating-point accelerators; (iv) development of improved parallel implicit time-marching methods; and (v) enhancements of high-order finite-volume spatial discretization procedures for improved solution accuracy. The potential and performance of the proposed methodology will be demonstrated through application to the prediction of turbulent reactive flows, as well as non-equilibrium micro-channel flows.