CAREER: HiPer: A CFD solver for High-Performance Turbulent Flow Simulations on Massively Parallel Machines

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Evolution of physics-based simulations and Computational Fluid Dynamics (CFD) in particular has fundamentally reshaped the design and engineering process in the last several decades. However, in spite of noteworthy success, today's CFD still remains limited to a small design space. One of the grand challenge problems is the simulation of a full aircraft envelope. Today's computing platforms are based on massive parallelism and heterogeneous processor designs to deliver petascale performance (10^15 floating point operations per second). This research has the potential to effectively harness this level of performance and significantly address grand challenge problems through advances in numerical schemes, efficient parallel algorithms, and implementation strategies to enable underlying simulations that are currently infeasible. It also aims at transforming CFD simulation capabilities to dramatically reduce the cost and time needed to solve complex multi-physics problems. As such, this research fills a gap in the understanding that lies at the intersection of computational fluid dynamics and parallel computing. This interdisciplinary research also shapes the next-generation of students and researchers with a multi-faceted skill set required to solve challenging problems at the boundary of domain sciences, applied mathematics, and computer science to promote the progress of science and advance national prosperity and welfare as stated by NSF's mission.This research creates HiPer, a CFD solver for high-performance turbulent flow simulations on massively parallel machines. HiPer solves the Navier-Stokes equations on multi-block structured grids for complex geometries by combining the following key components: (i) a novel time-delayed implicit time-marching scheme tailored for heterogeneous architectures; (ii) parallelization strategies for shared- and distributed-memory systems aimed at reducing the synchronization and communication time; and a hybrid multi-block structured grid enhanced by geometric multigrid to increase convergence as well as reduce the number of grid cells. Applications that are drivers in the near- and long-term serve as benchmarks for continually measuring progress towards the grand challenge goals. In particular, two application case studies serve as drivers for the near-term: (a) the simulation of the complex unsteady flow through multi-stage compressors and turbines, and (b) noise generation and propagation in a high-speed turbulent jet. The intellectual merit of this work is the development of novel numerical techniques, parallelization strategies, and scalable software that enable turbulent-separated flow simulations that are computationally intractable today. To engage and inspire young generations to this approach, this project strives to (i) organize hands-on workshops at relevant conferences, (ii) design and develop an educational kit targeted to teaching CFD and high-performance computing (HPC) concepts, (iii) design lab-based courses on HPC for computational scientists, including hands-on labs using HiPer on large-scale systems for on-site students, and (iv) release videos and guest lectures through University of California's Early Academic Outreach Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.