Helicopters can experience brownout when flying close to a dusty surface. The uplifting of dust in the air can remarkably restrict the pilot’s visibility area. Consequently, a brownout can disorient the pilot and lead to the helicopter collision against the ground. Given its risks, brownout has become a high-priority problem for civil and military operations. Proper helicopter design is thus critical, as it has a strong influence over the shape and density of the cloud of dust that forms when brownout occurs. A way forward to improve aircraft design against brownout is the use of particle simulations. For simulations to be accurate and comparable to the real phenomenon, billions of particles are required. However, using a large number of particles, serial simulations can be slow and too computationally expensive to be performed. In this work, we investigate an message passing interface (MPI) + graphics processing unit (multi-GPU) approach to simulate brownout. In specific, we use a semi-implicit Euler method to consider the particle dynamics in a Lagrangian way, and we adopt a precomputed aerodynamic field. Here, we do not include particle–particle collisions in the model; this allows for independent trajectories and effective model parallelization. To support our methodology, we provide a speedup analysis of the parallelization concerning the serial and pure-MPI simulations. The results show (i) very high speedups of the MPI + multi-GPU implementation with respect to the serial and pure-MPI ones, (ii) excellent weak and strong scalability properties of the implemented time-integration algorithm, and (iii) the possibility to run realistic simulations of brownout with billions of particles at a relatively small computational cost. This work paves the way toward more realistic brownout simulations, and it highlights the potential of high-performance computing for aiding and advancing aircraft design for brownout mitigation.

中文翻译：

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掉电的 HPC 模拟：非相互作用的粒子动态模型

直升机在靠近布满灰尘的表面飞行时可能会出现电量不足的情况。空气中扬起的灰尘会显着限制飞行员的能见度区域。因此，电力不足会使飞行员迷失方向并导致直升机与地面相撞。鉴于其风险，停电已成为民用和军事行动的高度优先问题。因此，适当的直升机设计至关重要，因为它对发生电力不足时形成的灰尘云的形状和密度有很大影响。改进飞机设计以防止掉电的一种方法是使用粒子模拟。为了使模拟准确并与真实现象相当，需要数十亿个粒子。然而，使用大量粒子，串行模拟可能会很慢，而且计算成本太高而无法执行。在这项工作中，我们研究了一种消息传递接口 (MPI) + 图形处理单元 (multi-GPU) 方法来模拟掉电。具体而言，我们使用半隐式欧拉方法以拉格朗日方式考虑粒子动力学，并采用预先计算的气动场。在这里，我们不包括模型中的粒子-粒子碰撞；这允许独立的轨迹和有效的模型并行化。为了支持我们的方法，我们提供了关于串行和纯 MPI 模拟的并行化加速分析。结果显示 (i) MPI + 多 GPU 实现相对于串行和纯 MPI 实现非常高的加速，(ii) 实现的时间积分算法具有出色的弱和强可扩展性特性，(iii) 以相对较小的计算成本运行数十亿个粒子的掉电现实模拟的可能性。这项工作为实现更逼真的停电模拟铺平了道路，并突出了高性能计算在帮助和推进飞机设计以缓解停电方面的潜力。