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Fix bug in wave drag derivatives #432

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merged 18 commits into from
Jul 16, 2024
Merged

Fix bug in wave drag derivatives #432

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7fb3a70
Fix bug in wave drag derivatives
A-CGray Jul 15, 2024
3650849
black
A-CGray Jul 15, 2024
5bce62f
Ignore OpenMDAO reports folders
A-CGray Jul 15, 2024
c61ff5a
tests
A-CGray Jul 15, 2024
994e971
tweak scaneagle test
A-CGray Jul 15, 2024
a4eb11d
bump patch version
A-CGray Jul 15, 2024
5bf3e8e
Bump oldest supported versions of things
A-CGray Jul 15, 2024
a9a1966
Forgot to bump `OPENMDAO_VERSION_OLDEST`
A-CGray Jul 15, 2024
2bab81b
Need openmdao 3.22 to work with numpy 1.24
A-CGray Jul 15, 2024
9ef3db2
Try running tests on our docker images
A-CGray Jul 15, 2024
f228023
Revert "Try running tests on our docker images"
A-CGray Jul 15, 2024
1e9e60f
Put it all back how it was
A-CGray Jul 15, 2024
436b2e1
Use older pip for "oldest" test
A-CGray Jul 15, 2024
5bbf39f
Revert "Bump oldest supported versions of things"
A-CGray Jul 15, 2024
c107fd6
Add `PIP_VERSION_OLDEST` variable
A-CGray Jul 15, 2024
4ae8f5c
Change optimisation failure test to work with older OpenMDAO
A-CGray Jul 15, 2024
3b00aff
Modify scaneagle tolerance
A-CGray Jul 15, 2024
c1a3e2a
Remove commented out old code
A-CGray Jul 16, 2024
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44 changes: 29 additions & 15 deletions openaerostruct/aerodynamics/wave_drag.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,14 +67,15 @@ def compute(self, inputs, outputs):
chords = inputs["chords"]
CL = inputs["CL"]

mean_chords = (chords[:-1] + chords[1:]) / 2.0
panel_areas = mean_chords * widths
avg_cos_sweep = np.sum(cos_sweep * panel_areas) / np.sum(panel_areas) # weighted average of 1/4 chord sweep
avg_t_over_c = np.sum(t_over_c * panel_areas) / np.sum(panel_areas) # weighted average of streamwise t/c
panel_mid_chords = (chords[:-1] + chords[1:]) / 2.0
panel_areas = panel_mid_chords * widths
sum_panel_areas = np.sum(panel_areas)
avg_cos_sweep = np.sum(cos_sweep * panel_areas) / sum_panel_areas # weighted average of 1/4 chord sweep
avg_t_over_c = np.sum(t_over_c * panel_areas) / sum_panel_areas # weighted average of streamwise t/c
MDD = self.ka / avg_cos_sweep - avg_t_over_c / avg_cos_sweep**2 - CL / (10 * avg_cos_sweep**3)
Mcrit = MDD - (0.1 / 80.0) ** (1.0 / 3.0)

if M > Mcrit:
if np.real(M) > np.real(Mcrit):
outputs["CDw"] = 20 * (M - Mcrit) ** 4
else:
outputs["CDw"] = 0.0
Expand All @@ -86,6 +87,14 @@ def compute(self, inputs, outputs):

def compute_partials(self, inputs, partials):
"""Jacobian for wave drag."""
# Explicitly zero out the partials to begin, we can't assume the input partials arrays contain zeros already
partials["CDw", "CL"][:] = 0.0
partials["CDw", "lengths_spanwise"][:] = 0.0
partials["CDw", "widths"][:] = 0.0
partials["CDw", "Mach_number"][:] = 0.0
partials["CDw", "chords"][:] = 0.0
partials["CDw", "t_over_c"][:] = 0.0

if self.with_wave:
ny = self.surface["mesh"].shape[1]
t_over_c = inputs["t_over_c"]
Expand All @@ -96,16 +105,16 @@ def compute_partials(self, inputs, partials):
chords = inputs["chords"]
CL = inputs["CL"]

chords = (chords[:-1] + chords[1:]) / 2.0
panel_areas = chords * widths
panel_mid_chords = (chords[:-1] + chords[1:]) / 2.0
panel_areas = panel_mid_chords * widths
sum_panel_areas = np.sum(panel_areas)
avg_cos_sweep = np.sum(cos_sweep * panel_areas) / sum_panel_areas
avg_t_over_c = np.sum(t_over_c * panel_areas) / sum_panel_areas

MDD = 0.95 / avg_cos_sweep - avg_t_over_c / avg_cos_sweep**2 - CL / (10 * avg_cos_sweep**3)
MDD = self.ka / avg_cos_sweep - avg_t_over_c / avg_cos_sweep**2 - CL / (10 * avg_cos_sweep**3)
Mcrit = MDD - (0.1 / 80.0) ** (1.0 / 3.0)

if M > Mcrit:
if np.real(M) > np.real(Mcrit):
dCDwdMDD = -80 * (M - Mcrit) ** 3
dMDDdCL = -1.0 / (10 * avg_cos_sweep**3)
dMDDdavg = (-10 * self.ka * avg_cos_sweep**2 + 20 * avg_t_over_c * avg_cos_sweep + 3 * CL) / (
Expand All @@ -114,14 +123,19 @@ def compute_partials(self, inputs, partials):
dMDDdtoc = -1.0 / (avg_cos_sweep**2)
dtocavgdtoc = panel_areas / sum_panel_areas

ccos = np.sum(widths * chords)
ccos2w = np.sum(chords * widths**2 / lengths_spanwise)
ccos = np.sum(widths * panel_mid_chords)
ccos2w = np.sum(panel_mid_chords * widths**2 / lengths_spanwise)

davgdcos = 2 * chords * widths / lengths_spanwise / ccos - chords * ccos2w / ccos**2
dtocdcos = chords * t_over_c / ccos - chords * np.sum(chords * widths * t_over_c) / ccos**2
davgdw = -1 * chords * widths**2 / lengths_spanwise**2 / ccos
davgdcos = (
2 * panel_mid_chords * widths / lengths_spanwise / ccos - panel_mid_chords * ccos2w / ccos**2
)
dtocdcos = (
panel_mid_chords * t_over_c / ccos
- panel_mid_chords * np.sum(panel_mid_chords * widths * t_over_c) / ccos**2
)
davgdw = -1 * panel_mid_chords * widths**2 / lengths_spanwise**2 / ccos
davgdc = widths**2 / lengths_spanwise / ccos - widths * ccos2w / ccos**2
dtocdc = t_over_c * widths / ccos - widths * np.sum(chords * widths * t_over_c) / ccos**2
dtocdc = t_over_c * widths / ccos - widths * np.sum(panel_mid_chords * widths * t_over_c) / ccos**2

dcdchords = np.zeros((ny - 1, ny))
i, j = np.indices(dcdchords.shape)
Expand Down
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