A GPU-accelerated, hybrid FVM-RANS methodology for modeling rotorcraft brownout
A GPU-accelerated, hybrid FVM-RANS methodology for modeling rotorcraft brownout
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2013
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通讯作者：
Sebastian Thomas
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作者：
Sebastian Thomas
论文题目：A GPU-加速，混合FVM-RANS方法建模旋翼机布朗塞巴斯蒂安托马斯博士哲学，2013年论文指导：教授詹姆斯D.为了更好地理解旋翼机欠压环境中遇到的复杂两相流场，已经开发了一种数值有效的混合欧拉-拉格朗日方法。当旋翼机靠近覆盖有松散颗粒（如沙子、灰尘或雪）的表面时，就会出现断电问题。这些颗粒可以被大量夹带到旋翼尾流中，导致飞行员能见度的潜在危险降低。人们相信，这种现象的计算效率模型，经过可用实验测量的验证，可以用作揭示旋翼机灯火管制的潜在物理原理的有价值的工具。目前的工作涉及的设计，开发和验证的混合求解器相结合的自由涡方法的数值效率与相对高保真度的三维，时间精确，雷诺平均，Navier-Stokes（RANS）求解器。对于双相模拟，这种混合方法可以单向耦合的沉积物跟踪算法来研究云的发展。为了探索GPU在RANS模拟中的应用，在CUDA-C中设计并开发了一个三维、时间精确、隐式、结构化、可压缩、粘性、湍流、有限体积RANS求解器。基于GPU的求解器的验证和验证进行了规范和现实的基准问题在各种GPU平台上。在这些测试用例中，GPU-RANS求解器的性能评估表明，它比等效的单CPU核心计算快一到两个数量级（最新平台上的细粒度计算高达50倍）。对于涉及隐式方法的模拟，使用了多粒度技术，该技术试图利用线并行方法家族中固有的中间粗粒度并行性，如交替方向隐式（ADI）方案与保守变量并行性相结合。然后，将经过验证的GPU-RANS求解器与基于GPU的freevortex和沉积物跟踪方法相结合，以模拟单相和双相、模型规模的欠压环境。定性和定量验证的方法进行了比较预测与现有的测量，包括流场测量和观测的颗粒传输机制，已与实验室规模的转子/喷气配置在地面效应。特别是，双相模拟能够解决分散相的关键运输现象，如蠕变，旋涡捕获和沉积物波的形成。此外，这些模拟被证明比传统CPU集群上的等效计算更有效，在单个GTX Titan上使用混合方法的模型规模的掉电模拟现在每转需要1.25小时，而在32个Intel Xeon内核上每转需要6小时。一种GPU加速的混合FVM-RANS方法用于旋翼机褐变建模
Title of dissertation: A GPU-ACCELERATED, HYBRID FVM-RANS METHODOLOGY FOR MODELING ROTORCRAFT BROWNOUT Sebastian Thomas Doctor of Philosophy, 2013 Dissertation directed by: Professor James D. Baeder Department of Aerospace Engineering 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 GPUbased 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 freevortex and sediment tracking methods to model single and dual-phase, modelscale 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. A GPU-ACCELERATED, HYBRID FVM-RANS METHODOLOGY FOR MODELING ROTORCRAFT BROWNOUT