SlSeparator and Valve Scaling #1693
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dallan-keylogic
changed the title
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## scaling_toolbox #1693 +/- ##
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Coverage 77.39% 77.39%
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Files 395 395
Lines 64581 64592 +11
Branches 10851 10851
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+ Hits 49983 49993 +10
- Misses 12092 12093 +1
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| from idaes.core.solvers import get_solver | ||
| from pyomo.util.check_units import assert_units_consistent, assert_units_equivalent | ||
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| # from pyomo.util.check_units import assert_units_consistent, assert_units_equivalent |
| # Unscaled | ||
| jac, _ = get_jacobian(valve_model, scaled=False) | ||
| assert (jacobian_cond(jac=jac, scaled=False)) == pytest.approx( | ||
| 5.34657e5, rel=1e-3 | ||
| ) | ||
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| # Scaled | ||
| sm = TransformationFactory("core.scale_model").create_using( | ||
| valve_model, rename=False | ||
| ) | ||
| jac, _ = get_jacobian(sm, scaled=False) | ||
| assert (jacobian_cond(jac=jac, scaled=False)) == pytest.approx(27.270, rel=1e-3) |
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In the future, it might be nice wrap this in a helper function that just takes in (1) the model, (2) unscaled cond number, and (3) scaled cond number as required inputs. Tolerances for unscaled and scaled values could be optional, with default of 1e-3.
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Yeah. I want to rework get_jacobian to have separate options for 1) applying user scaling factors and 2) performing IPOPT's gradient-based autoscaling. Then we won't even need to do the scaling transformation (which is slow).
dallan-keylogic
requested review from
MAZamarripa,
aostace01,
avdudchenko,
rundxdi and
sufikaur
as code owners
4 participants
Summary/Motivation:
Adds scaler object for the
SLSeparatorand tests the existingPressureChangerScaleron theValveunit model.Depends on #1688.
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