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Support transient user demand return factor #1727

Merged
merged 10 commits into from
Aug 19, 2024
Merged

Support transient user demand return factor #1727

merged 10 commits into from
Aug 19, 2024

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Jingru923
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Fixes #1230

@Jingru923 Jingru923 linked an issue Aug 14, 2024 that may be closed by this pull request
Add support for transient UserDemand return factor #1230
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@Jingru923 Jingru923 marked this pull request as ready for review August 14, 2024 12:17
SouthEndMusic
SouthEndMusic reviewed Aug 14, 2024
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Looking good! You should test it by having a simple model without allocation with a transient return factor

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SouthEndMusic
SouthEndMusic reviewed Aug 14, 2024
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core/test/run_models_test.jl Outdated
@@ -395,8 +395,8 @@ end
@test only(model.integrator.sol(0day)) == 1000.0
# constant UserDemand withdraws to 0.9m/900m3
@test only(model.integrator.sol(150day)) ≈ 900 atol = 5
# dynamic UserDemand withdraws to 0.5m/509m3
@test only(model.integrator.sol(180day)) ≈ 509 atol = 1
# dynamic UserDemand withdraws to 0.5m/503m3
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This is still too coarse a test. You should have a look at DataFrame(Ribasim.flow_table(model)), and check whether the outflow to inflow proportion matches your transient return factor

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Timepoint at 179day show a return ratio of 0.197ish and return factor is 0.2

@Jingru923 Jingru923 mentioned this pull request Aug 15, 2024
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I made some modifications to the test model so that:

  • The different user demand nodes abstract flow at different times so that they can be tested individually
  • The interpolations are not so steep that averaging flows makes testing hard

SouthEndMusic
SouthEndMusic approved these changes Aug 19, 2024
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I had one last remark, other than that this is good to if you agree with my changes.

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@Jingru923 Jingru923 merged commit 0cf38a6 into main Aug 19, 2024
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@Jingru923 Jingru923 deleted the return-factor branch August 19, 2024 08:57
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@SouthEndMusic SouthEndMusic SouthEndMusic approved these changes

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Add support for transient UserDemand return factor
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@Jingru923 @SouthEndMusic