How does simscale normalize in convergence plot

Common normalization can not limit the result of data set to a certain range, however, simscale is able to normalize the convergence plot to 0~1. I’d like to know if any one know how simscale done that

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Convergence represents a trend of how the solution improves per iteration. Convergence is typically measured in terms of normalized residuals, where 1.0 indicates the initial error, and a residual close to 0 indicates that the solution has reached a high level of accuracy.

Further understanding of this will require you to delve into the numerical methods of the pressure-velocity coupling algorithms in CFD and discretization. However, I think this forum post gives a good basic explanation:

"The discretized equations (N-S, continuity, turbulence, etc.) are written in the form of a matrix A and vectors x and b , A x =b . During the solution of the equation system defined by the matrix and vectors, you perform several iterations (called inner iterations). After each iteration, the computed residuals measure imbalance in the conservation equations. This should be getting smaller after each iteration. So you get from an initial residual value to a final residual value. During this whole process, you only solve for the vector x and do not update the matrix A . When your final residual is low enough, you update the matrix and repeat the whole process I just described (called outer iteration). Voila, you get a new initial residual that has naturally a larger value than the previous final residual.

The residuals should gradually decrease as you move from the initial to the final residual. That’s the main requirement. But monitoring just residuals is not enough, you should monitor also forces, moments, integral quantities etc. You can find many discussions about this."