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The effect of parameter errors and optimality tolerances in solution algorithms on solution errors are studied.
The dependence of the solution errors on the discretization errors is analyzed in certain flow cases.
It also shows that the model solution errors are similar for an optimality tolerance of within 4%.
During this time interval, the LSE may have a mismatch between measurement and model, which can create significant solution errors.
These results exhibit smaller solution errors and improved convergence when compared with similar approaches relying only on spatial interpolation.
The case study shows that the model solution errors increase as the scale of problem increases for the same range of parameter errors.
Similar(46)
This allows a reliable estimation of the solution error.
The equation relating the optimal solution error and the errors of the input data is used to construct an approximation of the optimal solution error covariance.
Assessing solution error continues to be a formidable task when numerically solving practical flow problems.
In Figure 2, we show the profiles of the exact solution u alongside the solution error.
It is shown that either way, solution error is second order in the mesh spacing.
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