# An Animated Visualization Method for Large-Scale Unstructured Unsteady Flow
## 4 Experiment
### Animation Playback Delay  
Table 5 records the average inter-frame delay of the experimental data animation playback using three different methods. Method (1) takes the raw flow field data as the input data. At each time step, it performs the data loading and image rendering processes. Consequently, most of the delay in method (1) results from data I/O. ParaView 5.10 currently employs this technique. All the time-step data is read into memory via Method (2). The flow field data is acquired directly to render the animation. This method cannot be applied when the data exceeds the memory capacity. Method (3) expands on Method (2) by using data simplification to reduce the amount of data. Only a smaller portion of the data required for visualization will be loaded into memory using selective loading and mesh simplification. Mesh simplification contributes significantly to the decrease in animation latency and variable loading has little effect on visualization calculations. Among the datasets, dataset 3 and dataset 4 are too large in scale, and method (2) cannot directly load all the time steps into memory, so the first 20 time steps are used for research. To reduce the impact of random factors, the experiment records the total time spent playing the dataset animation multiple times and calculates the average inter-frame delay by dividing it by the total number of time steps. Compared with method (1), method (2) eliminates the inter-frame data I/O time overhead. But, when the data scale is larger (dataset 2, dataset 3, and dataset 4), the rendering and drawing time of each time step flow field increases significantly, which causes a decrease in the smoothness and real-time interactivity of the animation. Method (3) uses the simplified unsteady data as the visualization input, which can reduce the graphics' rendering computation based on eliminating the inter-frame I/O time overhead, thereby further reducing the animation playback delay. As can be seen from Table 5, the simplified experimental dataset was used as the visual input via the proposed method, which achieved an average inter-frame delay of animation rendering less than 100 ms. This enabled real-time response to the user's visual interaction. The data simplification improved the animations' frame rates but also affected the visualizations. The selective reading of variables had minimal impact, as the required variables were usually known. Other variables could be quickly read using the file's variable description information. However, the mesh simplification, which retained only the externally visible part of the flow field, altered some of the visualization results. Figure 11 illustrates the results of cloud map coloring with velocity at the sixth time step of dataset 2. They had slightly different color mappings because of the internally invisible points and cells and the variables distributed over them, which were removed. Thus, the range of values of the variables differed. The velocity in Figure 11a has a maximum value of 1.5183, whereas the maximum velocity in Figure 11b is 1.475. Interactions requiring inner data from the flow field, such as isosurface extraction, may produce different results. The complete isosurface of the turbulent region Figure 11c can be extracted from the flow field of Figure 11a. Only contour lines can be extracted for the data in Figure 11b with the same settings. Nonetheless, it offered an overall overview of the unsteady flow field and the visible part of the mesh was unchanged. The proposed unsteady animation visualization method enabled a global overview of the unsteady flow field using simplified data. Raw data could be used for a more comprehensive visual analysis when needed.