Abstract

We present a data-driven visual analysis approach for the in-depth exploration of large numbers of droplets. Understanding droplet dynamics in sprays is of interest across many scientific fields for both simulation scientists and engineers. In this paper, we analyze large-scale direct numerical simulation datasets of the two-phase flow of non-Newtonian jets. Our interactive visual analysis approach comprises various dedicated exploration modalities that are supplemented by directly linking to ParaView. This hybrid setup supports a detailed investigation of droplets, both in the spatial domain and in terms of physical quantities . Considering a large variety of extracted physical quantities for each droplet enables investigating different aspects of interest in our data. To get an overview of different types of characteristic behaviors, we cluster massive numbers of droplets to analyze different types of occurring behaviors via domain-specific pre-aggregation, as well as different methods and parameters. Extraordinary temporal patterns are of high interest, especially to investigate edge cases and detect potential simulation issues. For this, we use a neural network-based approach to predict the development of these physical quantities and identify irregularly advected droplets.

Graphic Abstract

中文翻译：

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大规模多相喷雾模拟中液滴动力学的可视化分析

摘要

我们提出了一种数据驱动的可视化分析方法，用于大量液滴的深入探索。对于仿真科学家和工程师来说，了解喷雾中的液滴动力学是许多科学领域的兴趣所在。在本文中，我们分析了非牛顿射流两相流的大规模直接数值模拟数据集。我们的交互式视觉分析方法包括各种专用的探索方式，通过直接链接到ParaView可以对其进行补充。这种混合设置支持在空间域和物理量方面对液滴进行详细研究。考虑每个液滴的多种提取的物理量，可以研究我们数据中感兴趣的不同方面。要概述不同类型的特征行为，我们将大量的液滴聚集起来，以通过特定于域的预聚集以及不同的方法和参数来分析不同类型的发生行为。非凡的时间模式引起了人们的极大兴趣，尤其是用于研究边缘情况并检测潜在的模拟问题。为此，我们使用基于神经网络的方法来预测这些物理量的发展并识别不规则平流的液滴。

图形摘要