IEEE Trans Vis Comput Graph. 2017 Jan;23(1):811-820. doi: 10.1109/TVCG.2016.2598604.
Study of flow instability in turbine engine compressors is crucial to understand the inception and evolution of engine stall. Aerodynamics experts have been working on detecting the early signs of stall in order to devise novel stall suppression technologies. A state-of-the-art Navier-Stokes based, time-accurate computational fluid dynamics simulator, TURBO, has been developed in NASA to enhance the understanding of flow phenomena undergoing rotating stall. Despite the proven high modeling accuracy of TURBO, the excessive simulation data prohibits post-hoc analysis in both storage and I/O time. To address these issues and allow the expert to perform scalable stall analysis, we have designed an in situ distribution guided stall analysis technique. Our method summarizes statistics of important properties of the simulation data in situ using a probabilistic data modeling scheme. This data summarization enables statistical anomaly detection for flow instability in post analysis, which reveals the spatiotemporal trends of rotating stall for the expert to conceive new hypotheses. Furthermore, the verification of the hypotheses and exploratory visualization using the summarized data are realized using probabilistic visualization techniques such as uncertain isocontouring. Positive feedback from the domain scientist has indicated the efficacy of our system in exploratory stall analysis.
研究涡轮发动机压气机中的流动不稳定性对于理解发动机失速的产生和发展至关重要。空气动力学专家一直在努力检测失速的早期迹象,以便设计新的失速抑制技术。美国宇航局(NASA)开发了一种基于纳维-斯托克斯方程的、时精确的计算流体动力学模拟器 TURBO,以增强对旋转失速过程中流动现象的理解。尽管 TURBO 已经证明了其高度的建模准确性,但过多的模拟数据在存储和 I/O 时间上都禁止了后期分析。为了解决这些问题,并允许专家进行可扩展的失速分析,我们设计了一种原位分布引导的失速分析技术。我们的方法使用概率数据建模方案在原位总结模拟数据的重要属性的统计信息。这种数据汇总使得可以在后期分析中进行流动不稳定性的统计异常检测,从而揭示旋转失速的时空趋势,供专家提出新的假设。此外,使用概率可视化技术(如不确定等轮廓线)对假设进行验证和使用汇总数据进行探索性可视化,已经实现了。来自领域科学家的积极反馈表明了我们的系统在探索性失速分析中的有效性。