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GPU-based Real-Time Snow Avalanche Simulations

机译:基于GPU的实时雪崩模拟

摘要

Snow is a physical phenomenon that is hard to simulate due to the wide range of behaviors that can be found. Snow avalanches are of interest due to their complex physical properties and because they can have a very high cost, both in terms of economic impact and lives lost. In this thesis, we investigate the possibility of using fluid dynamics to model snow avalanches at real-time speeds. Real-time simulations allow for interactivity which again may accelerate the speed of development processes. It also allows for the simulations to be used as an interactive educational tool.Although modern GPUs (Graphics Processing Units) are primarily designed to accelerate graphics calculations in computer games, their computational power can now also be harnessed by several other applications. This thesis builds on our previous work on a simple Smoothing Particle Hydrodynamics (SPH) fluid dynamics model accelerated by GPUs. We extend this work by developing a more complex SPH model and integrate these two models in our novel framework for GPU simulations. The simple SPH model is suitable for interactive simulations of low-viscosity Newtonian fluids, whereas our more complex SPH model includes support for Non-Newtonian fluids through the use of rheological models. By using a rheological model that describes the Non-Newtonian flow characteristics of snow avalanches, we can reproduce the flowing behavior of dense flowing snow avalanches at interactive speeds. Our work shows that using the GPU can lead to very large performance improvements that make it possible to do real-time simulations which previously required costly specialized hardware or took minutes or hours to run. Using our highly optimized framework, we demonstrate a large improvement in performance for the simple model, and achieve generally very high performance for both implementations compared to other state-of-the-art implementations. Our recent results on the new NVIDIA Geforce GTX 470 Fermi-based card, achieves 215.4 IPS (iterations per seconds) for 64K particles using our simple model, and 122.2 IPS and 64.9IPS for 128K and 256K particles, respectively. For the complex model, 69.6 IPS at 64K particles, 37.4 IPS at 128K particles and 19.1 IPS at 256K particles were achieved. Real-time simulations enabling interactivity for the simple model is achieved for up to 256K particles, and up to 32/64K particles for the complex model.Many other features, such as additional snow and fluid effects, could extend this work. A list of this and other possible avenues for future work is also included.
机译:下雪是一种物理现象,由于可以发现多种行为,因此很难模拟。雪崩由于其复杂的物理特性以及在经济影响和生命损失方面可能具有很高的成本而备受关注。在本文中,我们研究了使用流体动力学以实时速度模拟雪崩的可能性。实时仿真允许交互性,这又可以加快开发过程的速度。尽管现代GPU(图形处理单元)的主要目的是加速计算机游戏中的图形计算,但它们的计算能力现在也可以被其他几个应用程序利用。本文基于我们先前在GPU加速的简单“平滑粒子流体动力学(SPH)”流体动力学模型上所做的工作。我们通过开发更复杂的SPH模型来扩展这项工作,并将这两个模型集成到我们新颖的GPU仿真框架中。简单的SPH模型适用于低粘度牛顿流体的交互式仿真,而我们更复杂的SPH模型包括通过使用流变模型对非牛顿流体的支持。通过使用描述雪崩非牛顿流特征的流变模型,我们可以以交互速度重现稠密流动雪崩的流动行为。我们的工作表明,使用GPU可以带来很大的性能改进,从而使实时仿真成为可能,而以前需要昂贵的专用硬件或者要花几分钟或几小时才能运行。使用我们高度优化的框架,我们证明了简单模型的性能有了很大的提高,并且与其他最新的实现相比,这两种实现通常都具有非常高的性能。我们在新的基于NVIDIA Geforce GTX 470 Fermi的显卡上获得的最新结果,使用我们的简单模型,对于64K粒子达到215.4 IPS(每秒迭代),对于128K和256K粒子分别达到122.2 IPS和64.9IPS。对于复杂模型,在64K颗粒下达到69.6 IPS,在128K颗粒下达到37.4 IPS,在256K颗粒下达到19.1 IPS。实时仿真可实现简单模型的交互性,最多可包含256K粒子,而复杂模型最多可包含32 / 64K粒子。许多其他功能(例如额外的积雪和流体效应)可以扩展这项工作。还列出了此方法以及其他可能的未来工作方法。

著录项

  • 作者

    Krog Øystein Eklund;

  • 作者单位
  • 年度 2010
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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