Improved initial guess for Newton solver in data-driven fluid model

[EvertBunschoten]

Proposed Changes

The data-driven fluid model uses a Newton algorithm to calculate thermodynamic states for thermodynamic pairs different from density + internal energy. The initial guess for the Newton algorithm was determined through additional settings in the configuration file or by approximation with the ideal gas law.

However, these methods do not always provide an initial guess sufficiently close to the target to get the Newton algorithm to converge, especially for non-reflective boundary conditions. I have added two features which address this problem:

1. When configuring the data-driven fluid model, a small, five-by-five thermodynamic table is generated in the density-static energy space. Pressure and temperature are stored on the nodes of this table. The initial guess for the Newton algorithm is now set according to the nearest-neighbor of the table, which brings the initial guess much closer to the target.
2. When the thermodynamic state approaches the limits of the look-up algorithm, additional relaxation is applied such that the Newton algorithm does not go out of bounds. This improves robustness for boundary calculations, especially in dilute conditions.

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[pcarruscag]

Are there no tests using the data-driven models? I was expecting this change to trigger some regression changes.
Maybe the Newton solves had no issues with the regressions?

[EvertBunschoten]

Are there no tests using the data-driven models? I was expecting this change to trigger some regression changes. Maybe the Newton solves had no issues with the regressions?

Yes the existing data-driven regression test happened to run just fine with the previous initialization method. However, I experienced issues when running some more challenging turbomachinery cases that use the Giles BC.