# An Animated Visualization Method for Large-Scale Unstructured Unsteady Flow
## 2 Related Work
### Animation Inter-Frame Delay Optimization  
Typically, the method of reading and rendering while playing an unsteady animation is used to increase resource utilization. The rendering process can be accelerated by the use of multithreading, GPU encoding, and other techniques [15; 16]. [PERSON] et al. [17] implemented a CPU-GPU hybrid decompression that conducted LZO lossless decompression on the CPU, followed by variable-length coding decompression and differential decompression on the GPU, thereby decreasing the time overhead of data decompression. [PERSON] et al. [18] proposed a pipeline rendering method for unsteady data that alternates the execution of the data I/O process and the rendering and drawing process, thus concealing the data reading overhead. Nevertheless, the data I/O time will be much greater than the rendering time when a lot of data is processed in a single time step. Creating a pipeline is challenging due to the performance bottleneck in loading data from external memory, which necessitates the use of additional processors for parallel processing. [PERSON] et al. [19] designed a parallel architecture pipeline for unsteady data, which can be used for interactive visualization of large-scale seismic simulation data, but this parallel design requires the support of a supercomputer's multi-processor and high memory capacity and is therefore incompatible with ordinary computers. Therefore, when the size of the unsteady data is large, there is not yet a general data processing method that enables standard personal computers to play unsteady animation smoothly. To handle large-scale unstructured unsteady data, most of the current state-of-the-art commercial visualization software, for example, Tecplot 360 EX 2022 R1 and VisIt 3.3.0, employ high-performance computing methods [20]. They avoid using data compression techniques to render the animation, in order to preserve the data accuracy. By adopting a server-client architecture, they distribute visualization computing tasks to multiple nodes and leverage parallel computing to the fullest. This approach enables interactive animation of large-scale unstructured unsteady flow fields, but it is costly due to the high demand for hardware resources. Data compression methods based on tree structures and differential coding can save memory, but they are not easy to apply to irregular unstructured unsteady flow fields. Furthermore, these methods increase CPU loads on personal computers. They need extra data decompression or specific rendering algorithms for the compressed data, which lowers their generality. This paper proposes a data preprocessing method based on the open-source software ParaView5.10, which minimizes the data size for visualization. It avoids the introduction of additional computations in animation rendering and the simplified data can be visualized directly using existing visualization engines with a high degree of generality. The current mainstream CFD post-processing analysis software, ParaView, implements a general method for unsteady animation. It supports unstructured data with complex mesh structures. However, it only supports one simple, traditional animation mode. Data I/O and rendering calculations are required at every step of the animation to create an unsteady animation. This playback pattern is adequate for smaller data sizes. Large datasets, especially with large meshes, make the animation too slow to analyze the flow field. Therefore, this paper proposes two unstructured unsteady flow field data simplification techniques and uses them to support two new animation playback modes. The first method simply loads all the time steps first and then renders them for playback. This eliminates the overhead of inter-frame I/O. The second method is based on a flow field data simplification technique, which reads and simplifies all time steps before playing the animation. Data simplification is performed according to user needs and data consistency is maintained as much as possible.