大规模多相流模拟中用于灵活特征分析的原位数据摘要
In Situ Data Summaries for Flexible Feature Analysis in Large-Scale Multiphase Flow Simulations
AI总结：
针对大规模多相流模拟中气泡动力学分析面临的数据存储与I/O瓶颈，提出一种端到端可视化分析管线，利用统计技术进行原位气泡检测，并支持事后灵活交互探索，有效提升了专家分析效率。
摘要：
多相流的研究对于理解各种材料之间的复杂相互作用至关重要。特别是，在设计诸如流化床反应器（FBR）等化学反应器时，对流体动力学的详细理解对于优化反应器性能和稳定性至关重要。FBR允许专家进行涉及多相材料的不同类型的化学反应，特别是气体与固体之间的相互作用。在这种复杂的化学过程中，反应器中空隙区域的形成（通常称为气泡）是一种重要现象。对这些气泡的研究对于预测反应器的整体效率具有深远意义。但是，为了理解气泡动力学而进行的物理实验成本高昂且并非易事。因此，为了研究此类化学过程和气泡动力学，正在开发一种先进的大规模并行计算流体动力学离散元模型（CFD-DEM），即MFIX-Exa，用于模拟多相流。尽管MFIX-Exa在模拟气泡现象方面已被证明具有准确性，但输出数据的极大规模使得在存储和I/O时间方面都无法使用传统的事后分析功能。为了解决这些问题并使应用科学家能够高效且及时地探索气泡动力学，我们开发了一个端到端的可视化分析管线，该管线能够使用统计技术原位检测气泡，随后在事后分析阶段对气泡动力学进行灵活且交互式的可视化探索。来自专家的积极反馈表明，所提出的方法在探索极大规模多相流模拟中的气泡动力学方面具有有效性。
英文摘要：
The study of multiphase flow is essential for understanding the complex interactions of various materials. In particular, when designing chemical reactors such as fluidized bed reactors (FBR), a detailed understanding of the hydrodynamics is critical for optimizing reactor performance and stability. An FBR allows experts to conduct different types of chemical reactions involving multiphase materials, especially interaction between gas and solids. During such complex chemical processes, formation of void regions in the reactor, generally termed as bubbles, is an important phenomenon. Study of these bubbles has a deep implication in predicting the reactor's overall efficiency. But physical experiments needed to understand bubble dynamics are costly and non-trivial. Therefore, to study such chemical processes and bubble dynamics, a state-of-the-art massively parallel computational fluid dynamics discrete element model (CFD-DEM), MFIX-Exa is being developed for simulating multiphase flows. Despite the proven accuracy of MFIX-Exa in modeling bubbling phenomena, the very-large size of the output data prohibits the use of traditional post hoc analysis capabilities in both storage and I/O time. To address these issues and allow the application scientists to explore the bubble dynamics in an efficient and timely manner, we have developed an end-to-end visual analytics pipeline that enables in situ detection of bubbles using statistical techniques, followed by a flexible and interactive visual exploration of bubble dynamics in the post hoc analysis phase. Positive feedback from the experts has indicated the efficacy of the proposed approach for exploring bubble dynamics in very-large scale multiphase flow simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
