# An Animated Visualization Method for Large-Scale Unstructured Unsteady Flow
## 5 Conclusions and Future Work  
This paper examines the current techniques for animating unsteady flow fields. Personal computers face resource constraints to animate large-scale unstructured flow fields. Unsteady data compression methods using hierarchical structures and differential coding are efficient but not suitable for unstructured flow fields. Compressing unstructured flow field data with time and space dimensions requires designing fast decompression or customized visualization algorithms, which is difficult. This paper presents two data simplification algorithms using ParaView's animation method. The algorithms simplify the flow field data using variables and meshes, which are the main components, and enable direct memory reading of the simplified data. The two data simplification algorithms in this paper can lower the memory cost of complex datasets by over 90%, as shown by four experiments. On this basis, personal computers can load simplified data directly into memory, thus avoiding inter-frame delays caused by data I/O. By comparing the three playback methods, it is demonstrated that the method in this paper can visualize the animation of large-scale unstructured unsteady flow fields on a personal computer and can respond to user interactions within 100 ms in real time. The method in this paper can enhance the user's analysis of unsteady flow fields, unlike the original playback method in ParaView. The method does not use data compression techniques, so the simplified data can be rendered directly by using existing flow field visualization techniques, which is very versatile.  
However, the animations are rendered using geometry-based flow field visualization techniques, so the simplified data lacks information about the interior of the flow field. The simplified data cannot be used directly for volume rendering or isosurface extraction. The user can switch between the simplified and raw data anytime with the timestep widget in this paper. This enables the analysis of the unsteady flow field with other visualization methods, combining the benefits of the two playback methods. Feature tracking and animation visualization will be used in future work to extract and analyze the internal regions and time-varying features of large-scale unsteady flow fields. Thread parallelism rendering techniques will also be explored to leverage the multi-core performance of PCs and enhance the animation frame rate.  
Figure 11: Comparison of dataset 2 visualization (**a**) is based on original data; (**b**) is based on simplified data; (**c**) is an isosurface extracted from (**a**); and (**d**) is an isosurface extracted from (**b**).  
**Author Contributions:** Methodology, X.T. and Y.H.; Writing--review and editing, C.Y.; Visualization, C.Y.; Writing--original draft, X.T. and Z.H.; Software, Z.H. and Y.H.; Supervision, Y.W.; Validation, Y.H.; Project administration, Y.W.; Funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Numerical Windtunnel Project (NNW2019 ZT6-A17) and the National Defense Basic Research Project (JCKY2022404C001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[[https://nnu-vvdb.nssdc.ac.cn:8443/VVDB/doSSOLoginNew.rdm](https://nnu-vvdb.nssdc.ac.cn:8443/VVDB/doSSOLoginNew.rdm)]([https://nnu-vvdb.nssdc.ac.cn:8443/VVDB/doSSOLoginNew.rdm](https://nnu-vvdb.nssdc.ac.cn:8443/VVDB/doSSOLoginNew.rdm))].  
**Conflicts of Interest:** The authors declare no conflict of interest.