Transient Data, also called "Time Varying Data," is data that has a "time" component in addition to a spatial coordinate. This is common in many simulations where the result is not a steady-state solution but a series of datasets showing the response over time.
Working with Transient Data
Transient data is often visualized into movies to show the visualization evolve over time. With techniques like Isosurface, each time step can be analyzed and computed independently and the resulting images combined into the movie. Algorithms like Pathlines are dependent on the result of previous time steps, so special care must be taken to process the time steps in correct order.
When working with transient data, it is also very important to know the time step units. Computing derivatives between time steps is very dependent on the amount of time elapsed between each dataset, and can easily lead to incorrect results if not properly accounted for (e.g., attempting to compute an acceleration field from differences in the data's velocity field).
Examples of Transient Data include:
- Stress response of a building - Early time steps may show no stress at all, as nothing has happened. Later time steps will show significant stress.
- Weather Simulations - Many time steps showing weather patterns evolving over time