research: Dütsch driver — continuous settling, domain criteria, profile coverage

common_window requires every pair from each case's own K_case to K+2;
a-priori domain criteria against the 60 D reference (separate from Table 1);
bilinear sampling returns the original fluid corner weight sum; analysis
time-averages bin samples, reports coverage/missing points and fails closed
on an empty common profile set; cases run to the end of cycle 11.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

tests/unit/test_duetsch_protocol.py

@@ -38,19 +38,22 @@ def test_settling_and_common_window():
     assert p.common_window([7, 10], [passes, passes]) is None    # K + 2 = 12 beyond the cap
     assert p.common_window([7, None], [passes, passes]) is None
     assert p.common_window([7, 7], [passes, dict(passes, **{8: False})]) is None  # must stay settled
+    # a case settled early must keep passing while waiting for the common K
+    assert p.common_window([7, 9], [dict(passes, **{8: False}), passes]) is None
 
 
 def test_solid_aware_bilinear():
     field = np.arange(16.0).reshape(4, 4)
     fluid = np.ones((4, 4), bool)
-    value, used = p.bilinear_fluid(field, fluid, 1.25, 1.5)
-    assert value == pytest.approx(0.75 * 0.5 * (5 + 9) + 0.25 * 0.5 * (6 + 10))
+    value, used, total = p.bilinear_fluid(field, fluid, 1.25, 1.5)
+    assert total == pytest.approx(1.0) and value == pytest.approx(0.75 * 0.5 * (5 + 9) + 0.25 * 0.5 * (6 + 10))
     fluid[1, 1] = False                                           # one solid corner
-    value, used = p.bilinear_fluid(field, fluid, 1.25, 1.5)
+    value, used, total = p.bilinear_fluid(field, fluid, 1.25, 1.5)
+    assert total == pytest.approx(1.0 - 0.75 * 0.5)
     assert all(not (i == 1 and j == 1) for i, j, _ in used)
     assert sum(w for *_, w in used) == pytest.approx(1.0)
     fluid[:] = False
-    assert p.bilinear_fluid(field, fluid, 1.25, 1.5) == (None, ())
+    assert p.bilinear_fluid(field, fluid, 1.25, 1.5) == (None, (), 0.0)
 
 
 def test_interval_bases_and_morison_fit_recover_known_coefficients():