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A GPU-accelerated, hybrid FVM-RANS methodology for modeling rotorcraft brownout.

机译:一种GPU加速的混合FVM-RANS方法,用于对旋翼机停电进行建模。

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摘要

A numerically efficient, 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 that 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.;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. 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 conservative variable parallelism.;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 flow field 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.
机译:已经开发出一种数值有效的混合欧拉-拉格朗日方法,以帮助更好地理解旋翼飞机停电环境中遇到的复杂的两相流场。当旋翼航空器在接近被松散颗粒(例如沙子,灰尘或雪)覆盖的表面操作时,会出现掉电的问题。这些粒子会大量夹带到旋翼尾流中,从而导致飞行员视野的潜在危险降低。据信,针对这种现象的计算有效模型,可通过可用的实验测量进行验证,可以用作揭示旋翼飞机停电的基本物理原理的有价值的工具。目前的工作涉及混合求解器的设计,开发和验证,该求解器将自由涡旋方法的数值效率与相对高保真的3D,时间精确,雷诺平均的Navier-Stokes(RANS)求解器相结合。对于双相模拟,此混合方法可与泥沙跟踪算法单向结合以研究云的发展。;要探索将GPU用于RANS模拟,需要使用3D,时间精确,隐式,结构化,可压缩,粘性,湍流在CUDA-C中设计开发了有限体积的RANS求解器。在各种GPU平台上,针对规范性和现实性基准问题对基于GPU的求解器进行了验证和验证。在这些测试用例中,对GPU-RANS求解器的性能评估表明,它比等效的单个CPU内核计算快一到两个数量级(在最新平台上,细粒度计算的速度高达50倍)。对于涉及隐式方法的仿真,使用了一种多粒度技术,试图利用线平行方法系列中固有的中间粗粒度并行性,例如交替方向隐式(ADI)方案以及保守变量并行性。然后,将RANS求解器与基于GPU的自由涡旋和沉积物跟踪方法结合起来,以建模单相和双相,模型规模的掉电环境。该方法的定性和定量验证是通过将预测与可用测量值进行比较来进行的,这些测量值包括流场测量值和对在地面效应下采用实验室规模的转子/射流配置进行的颗粒传输机理的观察。特别是,双相模拟能够解决分散相中的关键输运现象,例如蠕变,涡旋捕获和沉积物波形成。此外,事实证明,与在传统CPU集群上的等效计算相比,这些仿真的计算效率更高-在单个GTX Titan上使用混合方法的模型规模掉电仿真现在每转需要1.25小时,而每转需要6小时在32个Intel Xeon内核上。

著录项

  • 作者

    Thomas, Sebastian.;

  • 作者单位

    University of Maryland, College Park.;

  • 授予单位 University of Maryland, College Park.;
  • 学科 Engineering Aerospace.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 239 p.
  • 总页数 239
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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