A GPU-ACCELERATED, HYBRID FVM-RANS METHODOLOGY FOR MODELING ROTORCRAFT BROWNOUT

Abstract

A numerically effecient, hybrid Eulerian- Lagrangian methodology has been developed to help better understand the complicated two- phase flowfield encountered in rotorcraft brownout environments. The problem of brownout occurs when rotorcraft operate close to surfaces covered with loose particles such as sand, dust or snow. These particles can get entrained, in large quantities, into the rotor wake leading to a potentially hazardous degradation of the pilots visibility. It is believed that a computationally efficient model of this phenomena, validated against available experimental measurements, can be a used as a valuable tool to reveal the underlying physics of rotorcraft brownout. The present work involved the design, development and validation of a hybrid solver for the purpose of modeling brownout-like environments. The proposed methodology combines the numerical efficiency of a free-vortex method with the relatively high-fidelity of a 3D, time-accurate, Reynolds- averaged, Navier-Stokes (RANS) solver. For dual-phase simulations, this hybrid method can be unidirectionally coupled with a sediment tracking algorithm to study cloud development. In the past, large clusters of CPUs have been the standard approach for large simulations involving the numerical solution of PDEs. In recent years, however, an emerging trend is the use of Graphics Processing Units (GPUs), once used only for graphics rendering, to perform scientific computing. These platforms deliver superior computing power and memory bandwidth compared to traditional CPUs and their prowess continues to grow rapidly with each passing generation. CFD simulations have been ported successfully onto GPU platforms in the past. However, the nature of GPU architecture has restricted the set of algorithms that exhibit significant speedups on these platforms - GPUs are optimized for operations where a massively large number of threads, relative to the problem size, are working in parallel, executing identical instructions on disparate datasets. For this reason, most implementations in the scientific literature involve the use of explicit algorithms for time-stepping, reconstruction, etc. To overcome the difficulty associated with implicit methods, the current work proposes a multi-granular approach to reduce performance penalties typically encountered with such schemes. To explore the use of GPUs for RANS simulations, a 3D, time- accurate, implicit, structured, compressible, viscous, turbulent, finite-volume RANS solver was designed and developed in CUDA-C. During the development phase, various strategies for performance optimization were used to make the implementation better suited to the GPU architecture. Validation and verification of the GPU-based solver was performed for both canonical and realistic bench-mark problems on a variety of GPU platforms. In these test- cases, a performance assessment of the GPU-RANS solver indicated that it was between one and two orders of magnitude faster than equivalent single CPU core computations ( as high as 50X for fine-grain computations on the latest platforms). For simulations involving implicit methods, a multi-granular technique was used that sought to exploit the intermediate coarse- grain parallelism inherent in families of line- parallel methods like Alternating Direction Implicit (ADI) schemes coupled with con- servative variable parallelism. This approach had the dual effect of reducing memory bandwidth usage as well as increasing GPU occupancy leading to significant performance gains. The multi-granular approach for implicit methods used in this work has demonstrated speedups that are close to 50% of those expected with purely explicit methods. The validated GPU-RANS solver was then coupled with GPU-based free-vortex and sediment tracking methods to model single and dual-phase, model- scale brownout environments. A qualitative and quantitative validation of the methodology was performed by comparing predictions with available measurements, including flowfield measurements and observations of particle transport mechanisms that have been made with laboratory-scale rotor/jet configurations in ground effect. In particular, dual-phase simulations were able to resolve key transport phenomena in the dispersed phase such as creep, vortex trapping and sediment wave formation. Furthermore, these simulations were demonstrated to be computationally more efficient than equivalent computations on a cluster of traditional CPUs - a model-scale brownout simulation using the hybrid approach on a single GTX Titan now takes 1.25 hours per revolution compared to 6 hours per revolution on 32 Intel Xeon cores.