test: integrate pinned Re100 and jet control-volume validation

tests/validation/periodic.py

@@ -0,0 +1,130 @@
+"""CPU-only cycle extraction. All criteria frozen before any Re100 GPU result."""
+
+import numpy as np
+
+GATES = dict(
+    min_tstar=250.0,
+    cycles=5,
+    min_period=2.0,
+    max_period=5.0,
+    min_lift_amplitude=0.2,
+    period_relative_range=0.005,
+    extrema_absolute_range=0.005,
+    mass_relative_range=1e-4,
+    cycle_mean_flux_relative=0.005,
+)
+REFERENCE_GATES = dict(
+    cd_relative=0.02, cl_absolute=0.03, dp_relative=0.02, st_relative=0.02
+)
+
+
+def minima(t, y):
+    out = []
+    for i in range(1, len(y) - 1):
+        if y[i] < y[i - 1] and y[i] <= y[i + 1]:
+            # Quadratic sub-sample location; only separated physical cycles accepted.
+            coef = np.polyfit(t[i - 1 : i + 2] - t[i], y[i - 1 : i + 2], 2)
+            v = t[i] - coef[1] / (2 * coef[0]) if coef[0] > 0 else t[i]
+            if t[i - 1] <= v <= t[i + 1] and (not out or v - out[-1] > 1.0):
+                out.append(float(v))
+    return out
+
+
+def extract(rows, min_time=250.0):
+    if len(rows) < 3:
+        return []
+    t = np.array([r["tstar_force"] for r in rows])
+    cl = np.array([r["cl"] for r in rows])
+    ends = minima(t, cl)
+    cycles = []
+    for a, b in zip(ends, ends[1:]):
+        if a < min_time or not GATES["min_period"] < b - a < GATES["max_period"]:
+            continue
+        grid = np.linspace(a, b, 257)
+        c = dict(start=a, end=b, period=b - a, st=1 / (b - a))
+        for key in ("cd", "cl", "dp", "mass", "flux_in", "flux_out"):
+            times = np.array(
+                [r["tstar" if key not in ("cd", "cl") else "tstar_force"] for r in rows]
+            )
+            # No endpoint extrapolation: pressure/mass must cover the whole cycle.
+            if a < times[0] or b > times[-1]:
+                break
+            v = np.interp(grid, times, [r[key] for r in rows])
+            c[key + "_min"] = float(v.min())
+            c[key + "_max"] = float(v.max())
+            c[key + "_midrange"] = float((v.min() + v.max()) / 2)
+            c[key + "_amplitude"] = float(v.max() - v.min())
+            c[key + "_time_mean"] = float(
+                np.sum((v[1:] + v[:-1]) * np.diff(grid) / 2) / (b - a)
+            )
+            if key in ("cd", "cl", "dp"):
+                c[key + "_waveform"] = v.tolist()
+        else:
+            c["flux_relative"] = abs(
+                c["flux_in_time_mean"] - c["flux_out_time_mean"]
+            ) / max(abs(c["flux_in_time_mean"]), 1e-30)
+            cycles.append(c)
+    return cycles
+
+
+def assess(rows):
+    cycles = extract(rows, min_time=GATES["min_tstar"])
+    tail = cycles[-GATES["cycles"] :]
+    checks = {}
+    checks["enough_cycles"] = len(tail) == GATES["cycles"]
+    if checks["enough_cycles"]:
+        checks["consecutive"] = all(
+            abs(a["end"] - b["start"]) < 1e-8 for a, b in zip(tail, tail[1:])
+        )
+        checks["recent"] = (
+            0 <= rows[-1]["tstar_force"] - tail[-1]["end"] <= GATES["max_period"]
+        )
+        checks["shedding"] = (
+            min(c["cl_amplitude"] for c in tail) >= GATES["min_lift_amplitude"]
+        )
+        periods = [c["period"] for c in tail]
+        checks["period"] = (
+            np.ptp(periods) / np.mean(periods) <= GATES["period_relative_range"]
+        )
+        checks["extrema"] = all(
+            np.ptp([c[k + "_" + e] for c in tail]) <= GATES["extrema_absolute_range"]
+            for k in ("cd", "cl", "dp")
+            for e in ("min", "max")
+        )
+        mass = [c["mass_time_mean"] for c in tail]
+        checks["mass"] = (
+            np.ptp(mass) / abs(np.mean(mass)) <= GATES["mass_relative_range"]
+        )
+        checks["flux"] = (
+            max(c["flux_relative"] for c in tail) <= GATES["cycle_mean_flux_relative"]
+        )
+    return dict(
+        periodic=bool(checks["enough_cycles"] and all(checks.values())),
+        checks={k: bool(v) for k, v in checks.items()},
+        cycles=cycles,
+    )
+
+
+def compare(cycle, ref):
+    errors = {}
+    passed = True
+    for key in ("cd", "cl", "dp"):
+        for ext in ("min", "max"):
+            k = key + "_" + ext
+            e = abs(cycle[k] - ref[k])
+            e = e if key == "cl" else e / abs(ref[k])
+            errors[k] = e
+            passed &= (
+                e
+                <= REFERENCE_GATES[key + ("_absolute" if key == "cl" else "_relative")]
+            )
+        errors[key + "_waveform_rms"] = float(
+            np.sqrt(
+                np.mean(
+                    (np.array(cycle[key + "_waveform"]) - ref[key + "_waveform"]) ** 2
+                )
+            )
+        )
+    errors["st_relative"] = abs(cycle["st"] - ref["st"]) / ref["st"]
+    passed &= errors["st_relative"] <= REFERENCE_GATES["st_relative"]
+    return dict(reference_agreement=bool(passed), errors=errors)