Generalised high-order Eulerian Smoothed Particle Hydrodynamics for internal flows applied to flow-induced vibration and nuclear tube banks

适用于流激振动和核管束的内部流动的广义高阶欧拉平滑粒子流体动力学

基本信息

批准号：
EP/R005729/1
负责人：
Steven Lind
金额：
$ 88.07万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR005729%2F1
关键词：
Generalised order Eulerian Smoothed Particle

项目摘要

Computational fluid dynamics or CFD is mature with several general-purpose commercial codes available based on the finite-volume or finite-element (mesh-based) approaches with various options for turbulence modelling. The success of CFD in industrial design has however encouraged increasing demands to be made in terms of the resolution of the flow and finer grain physics, requiring ever increasing resources to be employed, both in terms of computation and manpower. Notable successes are in nuclear reactors, turbo-machinery, combustion chambers, heat exchangers, marine turbines, vehicle aerodynamics, aeronautics, offshore engineering amongst many others. Commercial enterprise tends to focus on two key aspects for improving the efficiency and accuracy of simulations in increasingly complex and demanding practical problems: performance on massively parallel computing and optimal mesh generation. State-of-the-art in commercial CFD suggests meshes may comprise several hundred million cells, over a billion for a nuclear reactor simulation (CD-Adapco, 2016), and runs with massively parallel computing (often with thousands of processors) taking days or weeks. Implementation of High-Order (HO) methods has received comparatively less attention, but can offer flexibility and gains in efficiency and accuracy beyond what can be achieved through optimal meshing and parallelisation alone. HO methods are known to be beneficial, even necessary, in unsteady vortex-dominated and turbulent flow modelling where many problems remain beyond the reach of state-of-the-art second-order CFD even on supercomputers. Important open-source codes from academia are making headway in increasing uptake of HO methods, but optimal implementation within complex 3-D geometries (that may contain arbitrarily moving boundaries) and adaptivity remain challenging problems in a high-order framework. We propose to address these problems through an alternative numerical method that is attractive in its simplicity, amenable to high-order spatial approximations in complex domains while retaining a natural affinity for parallelisation on emerging architectures. We provide this improvement in capability by abandoning the mesh and using particles, which, in Lagrangian form, have been used widely for the modelling of highly distorted flows involving interfaces and multi-physics. The investigators have been active in the development of smoothed particle hydrodynamics (SPH) particularly in divergence-free incompressible form and in developing algorithms for energy efficient hardware. Recently an Eulerian form has been tested by the investigators with high order Gaussian interpolating kernels (up to 6th order) demonstrating spatial convergence to machine accuracy in model periodic problems. In viscous transient flow with second-order time stepping, the accuracy obtained is similar to spectral methods. This new approach opens up considerable opportunities particularly for internal flows. One downside of this approach is that several billion particles will be required for complex systems, and the floating point operations per second (FLOPS) per particle in SPH are typically an order of magnitude greater than the finite volume/hp-element equivalent. This is compensated by the SPH formulation being ideally suited for parallel processing due to its locally interpolative (meshless) nature and ease of implementation on emerging hardware including most GPUs.

计算流体动力学或CFD是成熟的，基于有限体积或有限元（基于网格的）方法，具有多种通用商业代码，并具有各种用于湍流建模的选项。然而，CFD在工业设计中的成功鼓励了根据流量和更精细的谷物物理学的分辨率提出的需求，这需要在计算和人力方面越来越多地使用资源。著名的成功是核反应堆，涡轮机，燃烧室，热交换器，海洋涡轮机，车辆空气动力学，航空，航空，海上工程等许多其他工程。商业企业倾向于集中于两个关键方面，以提高日益复杂且苛刻的实用问题中模拟的效率和准确性：在大规模平行计算和最佳网格生成方面的性能。商业CFD的最先进表明网格可能包含数亿个细胞，超过十亿个核反应堆仿真（CD-ADAPCO，2016年），并且经过数天或几周的大规模平行计算（通常与数千个处理器）进行运行。高阶（HO）方法的实施已得到相对较少的关注，但仅仅通过最佳的网格划分和平行化就可以提供效率和准确性的灵活性和提高。已知HO方法在不稳定的涡旋主导和湍流模型中是有益的，甚至是必要的，即使在超级计算机上，许多问题仍然超出了最先进的二阶CFD。来自学术界的重要开源代码正在逐渐增加HO方法的吸收，但是在复杂的3-D几何形状（可能包含任意移动的界限）中的最佳实现，并且在高阶框架中适应性仍然具有挑战性的问题。我们建议通过一种替代的数值方法来解决这些问题，该方法在其简单性方面具有吸引力，可以适应复杂域中的高阶空间近似，同时保留对新兴架构的自然亲和力。我们通过放弃网格和使用颗粒来提供这种改进的能力，该粒子以拉格朗日形式以涉及接口和多物理学的高度变形流的建模。研究人员一直活跃于平滑颗粒流体动力学（SPH）的开发中，尤其是在无差异的不可压缩形式和开发用于节能硬件的算法方面。最近，具有高阶高斯插值内核（最高6阶）的研究人员已经对欧拉形式进行了测试，该核（最多6阶）证明了模型周期性问题中的空间收敛到机器的准确性。在随着二阶时间步进的粘性瞬态流动中，获得的精度与光谱方法相似。这种新方法为内部流动带来了相当大的机会。这种方法的一个缺点是，复杂系统需要数十亿个颗粒，而SPH中每个粒子每秒的浮点操作通常比有限体积/HP元素等效的数量级大。 SPH公式由于其本地插值（无网状）性质以及在包括大多数GPU（包括大多数GPU）（包括大多数GPU）的新兴硬件上实施的易于实现，因此可以通过SPH公式进行平行处理而获得补偿。