高效准确的流线绘制一直是流场可视化的重要研究内容, 流线可以对流场的重要特征进行有效的稀疏表示, 但流线需要长期的粒子追踪过程及大量的积分计算, 在面向大规模流场可视化时时间效率较低, 需要高性能计算设备进行辅助计算. 本文通过设计一种基于深度学习的高精度流线生成算法, 将初始的低精度流线快速映射为稠密的高精度流线, 可以在较短的时间内快速生成可靠的流线可视化结果, 并在此基础上设计了交互式实时流场可视化系统, 涵盖了流场的特征检测, 属性关联分析, 信息论分析等, 帮助用户快速了解流场数据, 找到自己感兴趣的区域进行后续进一步深度分析, 避免了获取过多冗余数据, 同时优化了分析工作的效率, 满足用户对于流场结构, 特征属性等多维度进行关联分析的需求.
Streamline rendering has long remained as one of the most common techniques for flow visualization. The streamline is an effective sparse representation of the flow field, which can capture the flow behavior, but generating streamline needs long-term particle tracing and massive integral operations. Large-scale flow visualization takes considerable computation time, and the parallel computing algorithm and high-performance equipment are needed. In this study, a high-resolution streamline generation algorithm based on deep learning is designed. The initial sparse low-resolution streamline is quickly mapped into the dense high-resolution streamline to provide reliable streamline visualization results in a short time. On this basis, an interactive real-time flow visualization system is developed, which is capable of flow-field feature detection, attribute correlation analysis, information theory analysis, etc. It can help users quickly understand the flow field data and find their areas of interest for post-hoc analysis, avoiding redundant data and enhancing work efficiency. In addition, it can meet the users’ needs for multi-dimensional correlation analysis of flow field structures, features, and attributes.