# An Animated Visualization Method for Large-Scale Unstructured Unsteady Flow
## 3 Methods
### Unsteady Animation Visualization Method
#### 3.2.1 Loading Parameter  
As shown in Figure 4, the I/O process of the CFD simulation data consists primarily of flow field mesh construction and flow field variable loading. Although flow field variables are used to record the physical quantities of vertices and cells, their reading process is relatively independent of the flow field mesh and these two types of data are typically loaded as separate data objects in the actual I/O process, then linked by the vertex or cell index. Since the flow field mesh has spatial connection relationships, it is necessary to obtain the complete mesh cell information to analyze and simplify it; therefore, the flow field mesh simplification cannot be conducted until the data I/O of each time step has been completed. However, there is no complex spatial relationship between flow field variables, and flow field variable simplification can be accomplished at the I/O stage by utilizing the variable description information contained within the data file.  
Multiple flow field variables, like fluid velocity, temperature, and pressure, are frequently included in scientific simulation data. Additionally, only one or a few variables are actually required to serve as the research object when analyzing a specific, time-varying scientific problem. Therefore, the user should be given the option to select the flow field variables that best suit their needs. The principle of selective data loading is to load only  
Figure 3: Unsteady flow animation visualization process.  
the flow field variables of interest to the user and to avoid loading redundant data. By doing this, the data size reduction goal is met while the animated visualizations are kept. As depicted in Figure 5, studying the attitude change in a missile after launch can use the pressure distribution on the upper and lower surfaces of the missile (involving the pressure variable) or the airflow direction around the missile (involving the velocity variable) and other variables become redundant and can be simplified at this time. To aid users in determining the necessary flow field variables, the first timestep of the unsteady flow field is loaded in the form of a steady flow field. Data preprocessing can be performed using common visualization techniques such as isosurfaces, cloud maps, streamlines, etc. to achieve the desired visualization effect and then determine the required flow field variables. To facilitate a more thorough analysis of unsteady data when determining the necessary variables, the proposed method enables unsteady animation using the conventional loading and drawing method. In this playback mode, only the current time step flow field data is loaded into memory. Although the animation latency is high, this mode allows users to investigate the temporal characteristics of unsteady data. If users are unable to determine the required flow field variables based on the first timestep flow field alone, they can switch to any time step and confirm the animation's required variables via a comprehensive analysis of the data from multiple time steps.  
After obtaining the user-required flow field variables through the preceding data preprocessing, these variable names are used as reading parameters to sequentially read the data files of all time steps of the unsteady flow field. The data loading algorithm filters all data according to the reading parameters, retaining only the necessary variables and avoiding the reading of redundant variables, thereby reducing memory consumption and accelerating data I/O.  
Figure 4: Process of data loading.  
Figure 5: Geometry\(-\)based visualization (**a**) plots the velocity vector arrows, and (**b**) displays the cloud map with pressure coefficient variable.