循环单一敏感性分析与单变量优化demo

examples/rc_design_sensitivity_demo.py

@@ -0,0 +1,136 @@
+# -*- coding: utf-8 -*-
+"""Demo: one-at-a-time design-parameter sensitivity for an RC Brayton cycle."""
+
+from pathlib import Path
+import csv
+import sys
+
+import matplotlib.pyplot as plt
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from brayton_cycle import local_rc_design_sensitivity  # noqa: E402
+
+
+DESIGN_VARIABLES = (
+    "T_low",
+    "T_high",
+    "p_low",
+    "p_high",
+    "ploss",
+    "x",
+)
+
+
+def build_base_case():
+    fixed_params = {
+        "T_high": 650 + 273.15,
+        "T_low": 42 + 273.15,
+        "p_high": 20.0e3,
+        "p_low": 9.09e3,
+        "ploss": 0.01,
+    }
+    params = {
+        "x": 0.279,
+        "compressor_eff": 0.9,
+        "recompressor_eff": 0.9,
+        "turbine_eff": 0.93,
+        "recuperator_eff": 0.94,
+        "highT_recuperator_eff": 0.96,
+    }
+    return fixed_params, params
+
+
+def sort_by_importance(rows):
+    return sorted(
+        rows,
+        key=lambda row: abs(row["normalized_sensitivity"] or 0.0),
+        reverse=True,
+    )
+
+
+def print_summary(rows):
+    print("RC design-parameter local sensitivity")
+    print("Efficiency values are decimals; 0.46 means 46%.")
+    print()
+    print(
+        f"{'variable':<10} {'base':>12} {'eff-':>12} "
+        f"{'eff+':>12} {'norm_sens':>14}"
+    )
+    print("-" * 66)
+    for row in sort_by_importance(rows):
+        sensitivity = row["normalized_sensitivity"]
+        sensitivity_text = "nan" if sensitivity is None else f"{sensitivity: .6f}"
+        print(
+            f"{row['variable']:<10} "
+            f"{row['base_value']:>12.6g} "
+            f"{row['minus_efficiency']:>12.6f} "
+            f"{row['plus_efficiency']:>12.6f} "
+            f"{sensitivity_text:>14}"
+        )
+
+
+def save_csv(rows, output_path):
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with output_path.open("w", newline="", encoding="utf-8") as file:
+        writer = csv.DictWriter(file, fieldnames=list(rows[0].keys()))
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def plot_sensitivity(rows, output_path=None, show=False):
+    sorted_rows = sort_by_importance(rows)
+    variables = [row["variable"] for row in sorted_rows]
+    sensitivities = [
+        row["normalized_sensitivity"] or 0.0
+        for row in sorted_rows
+    ]
+    colors = ["#1f77b4" if value >= 0 else "#d62728" for value in sensitivities]
+
+    fig, ax = plt.subplots(figsize=(8, 4.8), dpi=130)
+    ax.barh(variables, sensitivities, color=colors)
+    ax.axvline(0.0, color="black", linewidth=0.8)
+    ax.set_xlabel("Normalized sensitivity")
+    ax.set_title("RC design-parameter sensitivity")
+    ax.invert_yaxis()
+    fig.tight_layout()
+
+    if output_path is not None:
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        fig.savefig(output_path)
+    if show:
+        plt.show()
+
+    return fig, ax
+
+
+def main(save_outputs=True, show_plot=False):
+    fixed_params, params = build_base_case()
+    rows = local_rc_design_sensitivity(
+        fixed_params,
+        params,
+        variables=DESIGN_VARIABLES,
+        relative_step=0.01,
+    )
+
+    print_summary(rows)
+
+    if save_outputs:
+        output_dir = PROJECT_ROOT / "examples" / "output"
+        save_csv(rows, output_dir / "rc_design_sensitivity.csv")
+        plot_sensitivity(
+            rows,
+            output_path=output_dir / "rc_design_sensitivity.png",
+            show=show_plot,
+        )
+    elif show_plot:
+        plot_sensitivity(rows, show=True)
+
+    return rows
+
+
+if __name__ == "__main__":
+    main()