Make adaptive mesh refinement method more intuitive. Curvature-based adaptive mesh refinement is not intuitive and buggy. Also, making a good guess on how large the table is going to be a-priori is very difficult, resulting in unintentionally large tables. Proposed methods:
- Automated method based on regression error: Construct KD-tree algorithm with table nodes -> evaluate flamelet manifold data -> interpolate errors onto table levels -> apply refinement in areas with highest errors.
- User-based: apply refinement based on (relative) values of thermochemical state variables, e.g. "apply refinement where the temperature is between 50% and 100% of the maximum value on each table level"
- User-defined number of table nodes: Mesh resolution is adapted according to a specified approximate number of nodes per table level to avoid unintentionally large tables.
Make adaptive mesh refinement method more intuitive. Curvature-based adaptive mesh refinement is not intuitive and buggy. Also, making a good guess on how large the table is going to be a-priori is very difficult, resulting in unintentionally large tables. Proposed methods: