louis
cfeedcf887
- 前馈冻结改用可配置的 3 秒流量绝对误差滑窗，修复采样抖动造成的重复解冻，并保留下游扰动后的自动更新 - 支持非单调阀特性反解、最小可测面积以下直接全关，以及闭阀端精细扫点与辨识开关 - 调整双压力判稳、最长等待时间、在线入口全开收尾和闭环四联图 - 更新配置、README、阀模型及离线测试，归档本轮实验数据与诊断产物
275 lines
8.7 KiB
Python
275 lines
8.7 KiB
Python
"""valve_model / identify_valve 离线自测（不碰硬件）。
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运行：
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python test_valve_model.py
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"""
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import csv
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import math
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import tempfile
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from pathlib import Path
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from valve_model import (
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P_ATM,
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CRITICAL_RATIO,
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ValveModel,
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abs_pressure,
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f_ratio,
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)
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def _close(a, b, rel=1e-6):
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return abs(a - b) <= rel * max(1.0, abs(a), abs(b))
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def _write_sweep_csv(path, *, scale=1.0, visit_openings=(100.0, 50.0, 0.0)):
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"""构造合成扫点 CSV：每个 step 30 个采样点，全程稳态。
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``visit_openings`` 为开度的访问顺序（模拟 open_loop.py 的打乱顺序）；
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A_eff 真值 = scale * (1000 - x) / 400，Q_ss 按阻塞流（F=1）反推。
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"""
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rows = []
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for step, opening in enumerate(visit_openings):
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x = 1000.0 - opening / 100.0 * 200.0 # 与 flow_control 的映射一致
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a_eff = scale * (1000.0 - x) / 400.0 # 单调递减的真值
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q_ss = a_eff * (400.0 + P_ATM) # 阻塞流，F=1
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for i in range(30):
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rows.append({
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"time_s": f"{step * 10 + i * 0.1:.6f}",
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"step_index": step,
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"opening_pct": opening,
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"motor_position": x,
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"flow_before_slm": "",
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"flow_after_slm": q_ss,
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"pressure_before_kpa": 400.0,
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"pressure_after_kpa": 0.0,
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"P_abs_ratio": "",
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"is_ratio_smaller_than_0.528": "Y",
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})
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with path.open("w", newline="", encoding="utf-8-sig") as f:
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writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
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writer.writeheader()
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writer.writerows(rows)
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def test_f_ratio():
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assert f_ratio(0.4) == 1.0
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assert f_ratio(CRITICAL_RATIO) == 1.0
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assert f_ratio(1.0) == 0.0
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assert f_ratio(1.1) == 0.0
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expected = math.sqrt(
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1.0 - ((0.7 - CRITICAL_RATIO) / (1.0 - CRITICAL_RATIO)) ** 2
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)
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assert _close(f_ratio(0.7), expected)
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def test_abs_pressure():
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assert _close(abs_pressure(0.0), P_ATM)
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assert _close(abs_pressure(300.0), 300.0 + P_ATM)
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def test_valve_model_roundtrip():
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# A_eff 随行程线性递减（x 越大开度越小、面积越小）。
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table = [(800.0, 0.5), (900.0, 0.25), (1000.0, 0.0)]
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model = ValveModel(table, motor_open=800.0, motor_closed=1000.0)
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assert _close(model.area_from_stroke(900.0), 0.25)
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assert _close(model.area_from_stroke(850.0), 0.375)
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assert _close(model.stroke_from_area(0.25), 900.0)
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# 阻塞流（P2=0 表压 → r≈0.2 < 0.528），flow_ss 与 feedforward_stroke 互逆。
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for x in (800.0, 850.0, 900.0, 1000.0):
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q = model.flow_ss(x, 400.0, 0.0)
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x_back = model.feedforward_stroke(q, 400.0, 0.0)
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assert _close(x, x_back, rel=0.01), (x, q, x_back)
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# 亚声速（P2=300 表压 → r≈0.8）。
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q = model.flow_ss(900.0, 400.0, 300.0)
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assert _close(model.feedforward_stroke(q, 400.0, 300.0), 900.0, rel=0.01)
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def test_area_below_measured_minimum_returns_fully_closed_stroke():
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model = ValveModel(
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[(800.0, 0.7358347), (980.0, 0.002094)],
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motor_open=800.0,
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motor_closed=1000.0,
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)
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assert model.stroke_from_area(0.002094) == 980.0
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assert model.stroke_from_area(0.002093) == 1000.0
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assert model.stroke_from_area(0.0) == 1000.0
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def test_non_monotonic_raises():
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table = [(800.0, 0.5), (900.0, 0.1), (1000.0, 0.3)]
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try:
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ValveModel(table, motor_open=800.0, motor_closed=1000.0)
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except ValueError:
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return
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raise AssertionError("非单调表应抛 ValueError")
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def test_monotonic_check_can_be_disabled():
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import identify_valve
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p1 = 400.0
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denom = p1 + P_ATM # p2=0 时为阻塞流，F=1。
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steady = [
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(800.0, 0.5 * denom, p1, 0.0, None),
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(900.0, 0.1 * denom, p1, 0.0, None),
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(1000.0, 0.2 * denom, p1, 0.0, None),
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]
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checked, dropped, _, _ = identify_valve.compute_area_table(
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steady,
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check_monotonic=True,
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)
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unchecked, unchecked_dropped, _, _ = identify_valve.compute_area_table(
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steady,
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check_monotonic=False,
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)
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assert len(checked) == 2
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assert len(dropped) == 1
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assert len(unchecked) == 3
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assert not unchecked_dropped
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model = ValveModel(
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unchecked,
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motor_open=800.0,
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motor_closed=1000.0,
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enforce_monotonic=False,
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)
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assert not model.is_monotonic
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# A_eff=0.15 有两个交点，选择更接近关闭端的 x=950。
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assert _close(model.stroke_from_area(0.15), 950.0)
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with tempfile.TemporaryDirectory() as tmp:
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path = Path(tmp) / "nonmonotonic.json"
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model.save(path)
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loaded = ValveModel.load(path, 800.0, 1000.0)
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assert not loaded.enforce_monotonic
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assert _close(loaded.stroke_from_area(0.15), 950.0)
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def test_identify_end_to_end():
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import identify_valve
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with tempfile.TemporaryDirectory() as tmp:
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csv_path = Path(tmp) / "sweep.csv"
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out_path = Path(tmp) / "model.json"
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_write_sweep_csv(csv_path)
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model, steady, table, dropped, stats, area_points = identify_valve.identify(
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[csv_path], tail=20, out_path=str(out_path)
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)
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assert len(table) == 3, table
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assert stats["choked"] == 3
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assert not dropped
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for x, a in table:
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assert _close(a, (1000.0 - x) / 400.0, rel=0.01), (x, a)
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assert len(area_points) == 3
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loaded = ValveModel.load(str(out_path), 800.0, 1000.0)
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assert _close(loaded.area_from_stroke(900.0), 0.25, rel=0.01)
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def test_merge_multiple_csvs():
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import identify_valve
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with tempfile.TemporaryDirectory() as tmp:
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csv_a = Path(tmp) / "run_a.csv"
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csv_b = Path(tmp) / "run_b.csv"
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_write_sweep_csv(csv_a, scale=1.0)
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_write_sweep_csv(csv_b, scale=1.1)
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model, steady, table, dropped, stats, _ = identify_valve.identify(
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[csv_a, csv_b], tail=20
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)
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assert len(steady) == 6, steady
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assert stats["choked"] == 6
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assert not dropped
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# 同一行程的两个点先平均：A_eff = 1.05 * 真值
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for x, a in table:
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assert _close(a, 1.05 * (1000.0 - x) / 400.0, rel=0.01), (x, a)
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def test_direction_tagging():
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import identify_valve
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with tempfile.TemporaryDirectory() as tmp:
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csv_path = Path(tmp) / "shuffled.csv"
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_write_sweep_csv(csv_path, visit_openings=(0.0, 100.0, 50.0))
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points = identify_valve.load_steady_points(csv_path, tail=20)
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directions = [p[4] for p in points]
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assert directions == [None, "up", "down"], directions
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def test_resolve_csv_paths_glob():
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import identify_valve
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with tempfile.TemporaryDirectory() as tmp:
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base = Path(tmp)
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(base / "b.csv").write_text("", encoding="utf-8")
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(base / "a.csv").write_text("", encoding="utf-8")
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(base / "c.txt").write_text("", encoding="utf-8")
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# glob 展开并按名称排序，只匹配 CSV
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paths = identify_valve.resolve_csv_paths([str(base / "*.csv")])
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assert [Path(p).name for p in paths] == ["a.csv", "b.csv"], paths
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# 显式路径 + glob 混用时去重
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paths = identify_valve.resolve_csv_paths(
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[str(base / "*.csv"), str(base / "a.csv")]
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)
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assert [Path(p).name for p in paths] == ["a.csv", "b.csv"], paths
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def test_open_loop_appends_fine_closed_end_strokes():
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import config
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import open_loop
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points = open_loop.generate_scan_points(0.0, 100.0, 50.0, seed=42)
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fine_strokes = open_loop.generate_fine_strokes(
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config.OPEN_LOOP_FINE_STROKE_START,
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config.MOTOR_CLOSED_POSITION,
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config.OPEN_LOOP_FINE_STROKE_STEP,
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)
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assert open_loop.generate_fine_strokes(975.0, 1000.0, 5.0) == [
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975.0,
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980.0,
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985.0,
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990.0,
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995.0,
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1000.0,
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]
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fine_points = points[-len(fine_strokes):]
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assert [position for _, position in fine_points] == fine_strokes
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for opening, position in fine_points:
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assert _close(
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opening,
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open_loop.motor_position_to_opening(
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position,
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config.MOTOR_OPEN_POSITION,
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config.MOTOR_CLOSED_POSITION,
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),
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)
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if __name__ == "__main__":
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tests = [
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test_f_ratio,
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test_abs_pressure,
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test_valve_model_roundtrip,
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test_area_below_measured_minimum_returns_fully_closed_stroke,
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test_non_monotonic_raises,
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test_monotonic_check_can_be_disabled,
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test_identify_end_to_end,
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test_merge_multiple_csvs,
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test_direction_tagging,
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test_resolve_csv_paths_glob,
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test_open_loop_appends_fine_closed_end_strokes,
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]
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for test in tests:
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test()
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print(f"PASS {test.__name__}")
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print("全部通过")
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