整合

org/other/question1_pdms_emissivity.py

@@ -0,0 +1,151 @@
+import os
+from dataclasses import dataclass
+from typing import Dict, List
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+AIR_INDEX = 1.0  # normal incidence, non-absorbing ambient
+WAVELENGTH_MIN = 0.4  # µm
+WAVELENGTH_MAX = 25.0  # µm
+
+
+def cauchy_index(wavelength_um: np.ndarray) -> np.ndarray:
+    """
+    Dispersion model for PDMS based on Cauchy equation fitted to literature data:
+        n(λ) = A + B / λ^2 + C / λ^4
+    The coefficients (A=1.385, B=0.0125, C=0.00045) match n≈1.43 @ 0.55 µm
+    and n≈1.40 beyond 2 µm.
+    """
+    A = 1.385
+    B = 0.0125
+    C = 0.00045
+    lam2 = wavelength_um ** 2
+    return A + B / lam2 + C / (lam2 * wavelength_um ** 2)
+
+
+def extinction_coeff(wavelength_um: np.ndarray) -> np.ndarray:
+    """
+    Construct an approximate extinction coefficient profile by superposing
+    Gaussians centered at the known vibrational bands of PDMS in the mid-IR.
+    Peaks taken from FTIR measurements (Si-O-Si at ~9.2 µm, Si-CH3 rocking at
+    12.7 µm, CH stretches at 3.4 µm, etc.).
+    """
+    peaks = [
+        # (center µm, amplitude, width µm)
+        (3.4, 0.06, 0.25),
+        (8.0, 0.30, 0.50),
+        (9.2, 0.65, 0.60),
+        (10.6, 0.35, 0.45),
+        (12.7, 0.45, 0.35),
+        (14.5, 0.25, 0.60),
+    ]
+    k = np.full_like(wavelength_um, 5e-4)
+    for center, amp, width in peaks:
+        k += amp * np.exp(-0.5 * ((wavelength_um - center) / width) ** 2)
+    # enforce plausible upper bound
+    return np.clip(k, 0, 2.0)
+
+
+def thin_film_emissivity(
+    wavelength_um: np.ndarray, thickness_um: float, n_complex: np.ndarray
+) -> np.ndarray:
+    """
+    Normal-incidence emissivity for a PDMS slab in air computed using
+    the transfer-matrix solution for a single absorbing layer.
+    """
+    n0 = AIR_INDEX
+    ns = AIR_INDEX
+    thickness_m = thickness_um * 1e-6
+    # vacuum wavenumber
+    beta = 2 * np.pi / (wavelength_um * 1e-6)
+    delta = beta * n_complex * thickness_m
+
+    r01 = (n0 - n_complex) / (n0 + n_complex)
+    r12 = (n_complex - ns) / (n_complex + ns)
+    exp_term = np.exp(2j * delta)
+    numerator_r = r01 + r12 * exp_term
+    denominator = 1 + r01 * r12 * exp_term
+    r = numerator_r / denominator
+
+    t01 = 2 * n0 / (n0 + n_complex)
+    t12 = 2 * n_complex / (n_complex + ns)
+    exp_half = np.exp(1j * delta)
+    t = (t01 * t12 * exp_half) / denominator
+
+    R = np.abs(r) ** 2
+    T = (ns.real / n0) * np.abs(t) ** 2
+    A = 1 - R - T
+    return np.clip(A.real, 0, 1)
+
+
+@dataclass
+class EmissivityResult:
+    wavelengths: np.ndarray
+    emissivity_map: Dict[float, np.ndarray]
+    window_avg: Dict[float, float]
+
+
+def compute_emissivity(thicknesses_um: List[float]) -> EmissivityResult:
+    wavelengths = np.linspace(WAVELENGTH_MIN, WAVELENGTH_MAX, 1200)
+    n = cauchy_index(wavelengths)
+    k = extinction_coeff(wavelengths)
+    n_complex = n + 1j * k
+
+    emissivity_map = {}
+    window_avg = {}
+    window_mask = (wavelengths >= 8) & (wavelengths <= 13)
+
+    for d in thicknesses_um:
+        eps = thin_film_emissivity(wavelengths, d, n_complex)
+        emissivity_map[d] = eps
+        window_avg[d] = float(np.trapz(eps[window_mask], wavelengths[window_mask]) /
+                              np.trapz(np.ones_like(wavelengths[window_mask]),
+                                       wavelengths[window_mask]))
+
+    return EmissivityResult(wavelengths, emissivity_map, window_avg)
+
+
+def plot_emissivity(result: EmissivityResult, outdir: str) -> str:
+    plt.figure(figsize=(9, 5))
+    for d, eps in result.emissivity_map.items():
+        label = f"{d:.0f} µm (ε̄₈₋₁₃={result.window_avg[d]:.2f})"
+        plt.plot(result.wavelengths, eps, label=label)
+
+    plt.axvspan(8, 13, color="tab:gray", alpha=0.15, label="Atmospheric window")
+    plt.xlabel("Wavelength (µm)")
+    plt.ylabel("Spectral emissivity")
+    plt.title("PDMS Thin-Film Emissivity vs. Wavelength")
+    plt.ylim(0, 1.05)
+    plt.xlim(WAVELENGTH_MIN, WAVELENGTH_MAX)
+    plt.legend()
+    plt.grid(alpha=0.3)
+
+    os.makedirs(outdir, exist_ok=True)
+    output_path = os.path.join(outdir, "question1_emissivity.png")
+    plt.tight_layout()
+    plt.savefig(output_path, dpi=300)
+    plt.close()
+    return output_path
+
+
+def main():
+    thicknesses = [1, 5, 10, 25, 50, 100]
+    result = compute_emissivity(thicknesses)
+    outdir = os.path.join(os.path.dirname(__file__), "outputs")
+    figure_path = plot_emissivity(result, outdir)
+
+    summary_lines = ["thickness_um,avg_emissivity_8_13um"]
+    for d in thicknesses:
+        summary_lines.append(f"{d},{result.window_avg[d]:.4f}")
+    csv_path = os.path.join(outdir, "question1_emissivity_summary.csv")
+    with open(csv_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(summary_lines))
+
+    print(f"Figure saved to: {figure_path}")
+    print(f"Summary saved to: {csv_path}")
+
+
+if __name__ == "__main__":
+    main()
+

org/other/question2_pdms_cooling.py

@@ -0,0 +1,157 @@
+import importlib.util
+import math
+import os
+from dataclasses import dataclass
+from typing import Dict, List, Tuple
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["DejaVu Sans", "Arial", "Helvetica"]
+plt.rcParams["axes.unicode_minus"] = False
+
+SIGMA = 5.670374419e-8
+
+THICKNESSES = [1, 5, 10, 25, 50, 100]  # µm
+T_AMB = 300.0  # K (≈27 ℃)
+T_SKY = 280.0  # Clear dry sky equivalent radiation temperature
+SOLAR_IRR = 900.0  # W/m^2, clear sky noon
+H_CONV = 8.0  # W/m^2/K, natural convection + light wind
+
+
+def load_emissivity_module():
+    here = os.path.dirname(os.path.abspath(__file__))
+    target = os.path.join(here, "question1_pdms_emissivity.py")
+    spec = importlib.util.spec_from_file_location("q1", target)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)  # type: ignore[arg-type]
+    return module
+
+
+def solar_absorptance(thickness_um: float) -> float:
+    """
+    Empirical assumption: In PDMS silver-coated system, visible light absorption
+    is determined by thin film scattering and internal losses.
+    At 1 µm, α≈0.05, absorption increases by 0.01 for every 10 µm increase in thickness, capped at 0.15.
+    """
+    return min(0.05 + 0.001 * thickness_um, 0.15)
+
+
+@dataclass
+class CoolingResult:
+    thickness_um: float
+    eps_window: float
+    alpha_solar: float
+    net_power_at_amb: float
+    eq_temp_K: float
+
+    @property
+    def eq_temp_C(self) -> float:
+        return self.eq_temp_K - 273.15
+
+    @property
+    def delta_T(self) -> float:
+        return self.eq_temp_K - T_AMB
+
+
+def net_cooling_power(temp_K: float, emissivity: float, alpha_s: float) -> float:
+    radiative = emissivity * SIGMA * (temp_K**4 - T_SKY**4)
+    solar_gain = alpha_s * SOLAR_IRR
+    convective = H_CONV * (temp_K - T_AMB)
+    return -solar_gain - convective + radiative
+
+
+def solve_equilibrium(emissivity: float, alpha_s: float) -> float:
+    low, high = 250.0, 330.0
+    f_low = net_cooling_power(low, emissivity, alpha_s)
+    f_high = net_cooling_power(high, emissivity, alpha_s)
+
+    if f_low * f_high > 0:
+        # No sign change, return endpoint with smaller energy consumption
+        return low if abs(f_low) < abs(f_high) else high
+
+    for _ in range(80):
+        mid = 0.5 * (low + high)
+        f_mid = net_cooling_power(mid, emissivity, alpha_s)
+        if abs(f_mid) < 1e-4:
+            return mid
+        if f_low * f_mid <= 0:
+            high, f_high = mid, f_mid
+        else:
+            low, f_low = mid, f_mid
+    return 0.5 * (low + high)
+
+
+def evaluate() -> Tuple[List[CoolingResult], str]:
+    q1 = load_emissivity_module()
+    emissivity_data = q1.compute_emissivity(THICKNESSES)
+
+    results: List[CoolingResult] = []
+    for d in THICKNESSES:
+        eps_window = emissivity_data.window_avg[d]
+        alpha_s = solar_absorptance(d)
+        net_amb = net_cooling_power(T_AMB, eps_window, alpha_s)
+        eq_temp = solve_equilibrium(eps_window, alpha_s)
+        results.append(
+            CoolingResult(
+                thickness_um=d,
+                eps_window=eps_window,
+                alpha_solar=alpha_s,
+                net_power_at_amb=net_amb,
+                eq_temp_K=eq_temp,
+            )
+        )
+
+    outdir = os.path.join(os.path.dirname(__file__), "outputs")
+    os.makedirs(outdir, exist_ok=True)
+    csv_path = os.path.join(outdir, "question2_cooling_summary.csv")
+    with open(csv_path, "w", encoding="utf-8") as f:
+        f.write("thickness_um,eps_8_13,alpha_solar,net_power_amb_Wm2,eq_temp_C,delta_T_C\n")
+        for res in results:
+            f.write(
+                f"{res.thickness_um},{res.eps_window:.4f},{res.alpha_solar:.3f},"
+                f"{res.net_power_at_amb:.2f},{res.eq_temp_C:.2f},{res.delta_T:.2f}\n"
+            )
+
+    fig_path = os.path.join(outdir, "question2_cooling_results.png")
+    plot_results(results, fig_path)
+
+    return results, csv_path
+
+
+def plot_results(results: List[CoolingResult], fig_path: str) -> None:
+    thickness = [r.thickness_um for r in results]
+    net_power = [r.net_power_at_amb for r in results]
+    delta_T = [r.delta_T for r in results]
+
+    fig, ax1 = plt.subplots(figsize=(9, 5))
+    ax1.bar(thickness, net_power, width=4, alpha=0.6, label="Net Cooling Power @T_amb")
+    ax1.set_xlabel("PDMS Film Thickness (µm)")
+    ax1.set_ylabel("Net Cooling Power (W/m²)")
+    ax1.axhline(0, color="black", linewidth=0.8)
+
+    ax2 = ax1.twinx()
+    ax2.plot(thickness, delta_T, color="tab:red", marker="o", label="Equilibrium Temperature Difference")
+    ax2.set_ylabel("Equilibrium Temperature Difference (K)")
+
+    lines, labels = ax1.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    ax1.legend(lines + lines2, labels + labels2, loc="upper right")
+    fig.tight_layout()
+    plt.savefig(fig_path, dpi=300)
+    plt.close(fig)
+
+
+def main():
+    results, csv_path = evaluate()
+    print(f"Results written to: {csv_path}")
+    for r in results:
+        print(
+            f"d={r.thickness_um:>3} um, eps_8_13={r.eps_window:.2f}, alpha_sol={r.alpha_solar:.2f}, "
+            f"q_net(T_amb)={r.net_power_at_amb:.1f} W/m2, T_eq={r.eq_temp_C:.1f} °C "
+            f"(DeltaT={r.delta_T:.1f} K)"
+        )
+
+
+if __name__ == "__main__":
+    main()

org/other/question3_multilayer_optimization.py

@@ -0,0 +1,236 @@
+import itertools
+import math
+import os
+import random
+from dataclasses import dataclass
+from typing import Dict, List, Sequence, Tuple
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+plt.rcParams["font.sans-serif"] = ["DejaVu Sans", "Arial", "Helvetica"]
+plt.rcParams["axes.unicode_minus"] = False
+
+
+def load_q1_module():
+    here = os.path.dirname(os.path.abspath(__file__))
+    target = os.path.join(here, "question1_pdms_emissivity.py")
+    import importlib.util
+
+    spec = importlib.util.spec_from_file_location("q1", target)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)  # type: ignore[arg-type]
+    return module
+
+
+def planck_weight(wavelength_um: np.ndarray, temperature: float = 300.0) -> np.ndarray:
+    wl_m = wavelength_um * 1e-6
+    c1 = 3.7418e-16
+    c2 = 1.4388e-2
+    spectral = c1 / (wl_m**5 * (np.exp(c2 / (wl_m * temperature)) - 1))
+    return spectral
+
+
+def solar_weight(wavelength_um: np.ndarray) -> np.ndarray:
+    center1, width1 = 0.6, 0.35
+    center2, width2 = 1.6, 0.45
+    return np.exp(-((wavelength_um - center1) / width1) ** 2) + 0.35 * np.exp(
+        -((wavelength_um - center2) / width2) ** 2
+    )
+
+
+@dataclass
+class Material:
+    name: str
+    n_const: float
+    k_const: float
+
+    def nk(self, wavelength_um: np.ndarray) -> np.ndarray:
+        n = np.full_like(wavelength_um, self.n_const, dtype=np.complex128)
+        k = np.full_like(wavelength_um, self.k_const, dtype=np.complex128)
+        return n - 1j * k
+
+
+def pdms_index(wavelength_um: np.ndarray) -> np.ndarray:
+    q1 = load_q1_module()
+    n = q1.cauchy_index(wavelength_um)
+    k = q1.extinction_coeff(wavelength_um)
+    return n - 1j * k
+
+
+def ag_index(wavelength_um: np.ndarray) -> np.ndarray:
+    n = 0.15 + 0.6 * np.exp(-((wavelength_um - 0.5) / 0.4) ** 2)
+    k = 4.5 + 3.5 * np.exp(-((wavelength_um - 10) / 6) ** 2)
+    return n - 1j * k
+
+
+def transfer_matrix_stack(
+    wavelength_um: np.ndarray,
+    layer_nk: Sequence[np.ndarray],
+    thickness_um: Sequence[float],
+    substrate_nk: np.ndarray,
+    n0: float = 1.0,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    beta = 2 * np.pi / (wavelength_um * 1e-6)
+    q0 = n0
+    qs = substrate_nk
+
+    R = np.zeros_like(wavelength_um)
+    T = np.zeros_like(wavelength_um)
+
+    for idx, wl in enumerate(wavelength_um):
+        M = np.identity(2, dtype=complex)
+        for nk, d in zip(layer_nk, thickness_um):
+            n_layer = nk[idx]
+            delta = beta[idx] * n_layer * d * 1e-6
+            cos = np.cos(delta)
+            sin = 1j * np.sin(delta)
+            q = n_layer
+            Mj = np.array([[cos, sin / q], [q * sin, cos]], dtype=complex)
+            M = M @ Mj
+
+        numerator = (
+            q0 * M[0, 0]
+            + q0 * qs[idx] * M[0, 1]
+            - M[1, 0]
+            - qs[idx] * M[1, 1]
+        )
+        denominator = (
+            q0 * M[0, 0]
+            + q0 * qs[idx] * M[0, 1]
+            + M[1, 0]
+            + qs[idx] * M[1, 1]
+        )
+        r = numerator / denominator
+        t = 2 * q0 / denominator
+        R[idx] = np.abs(r) ** 2
+        T[idx] = np.real(qs[idx] / q0) * np.abs(t) ** 2
+
+    A = np.clip(1 - R - T, 0, 1)
+    return R, T, A
+
+
+def evaluate_stack(design: Dict) -> Dict:
+    solar_wl = np.linspace(0.35, 2.5, 120)
+    ir_wl = np.linspace(8, 13, 200)
+    solar_w = solar_weight(solar_wl)
+    ir_w = planck_weight(ir_wl)
+
+    substrate = ag_index
+
+    layer_funcs = []
+    thickness = []
+    for layer in design["layers"]:
+        material = layer["material"]
+        thickness.append(layer["thickness"])
+        if material == "PDMS":
+            layer_funcs.append(pdms_index)
+        else:
+            mat = MATERIAL_LIBRARY[material]
+            layer_funcs.append(lambda wl, m=mat: m.nk(wl))
+
+    solar_nk = [func(solar_wl) for func in layer_funcs]
+    ir_nk = [func(ir_wl) for func in layer_funcs]
+
+    solar_R, _, solar_A = transfer_matrix_stack(
+        solar_wl, solar_nk, thickness, substrate(solar_wl)
+    )
+    ir_R, _, ir_A = transfer_matrix_stack(ir_wl, ir_nk, thickness, substrate(ir_wl))
+
+    alpha = float(np.trapz(solar_A * solar_w, solar_wl) / np.trapz(solar_w, solar_wl))
+    epsilon = float(np.trapz(ir_A * ir_w, ir_wl) / np.trapz(ir_w, ir_wl))
+
+    score = epsilon - 0.3 * alpha
+    return {"alpha": alpha, "epsilon": epsilon, "score": score}
+
+
+MATERIAL_LIBRARY: Dict[str, Material] = {
+    "SiO2": Material("SiO2", 1.45, 1e-4),
+    "Al2O3": Material("Al2O3", 1.76, 1.5e-3),
+    "TiO2": Material("TiO2", 2.40, 5e-3),
+    "Si3N4": Material("Si3N4", 2.05, 2e-3),
+    "HfO2": Material("HfO2", 1.9, 2e-3),
+}
+
+
+def random_design() -> Dict:
+    num_layers = random.choice([2, 3])
+    middle_materials = random.sample(list(MATERIAL_LIBRARY.keys()), num_layers)
+    layers = [{"material": "PDMS", "thickness": random.uniform(10, 50)}]
+    for mat in middle_materials:
+        layers.append(
+            {
+                "material": mat,
+                "thickness": random.uniform(0.05, 2.0),
+            }
+        )
+    return {"layers": layers}
+
+
+def optimize(iterations: int = 800) -> List[Dict]:
+    best_designs: List[Dict] = []
+    for _ in range(iterations):
+        design = random_design()
+        metrics = evaluate_stack(design)
+        design.update(metrics)
+        best_designs.append(design)
+
+    best_designs.sort(key=lambda x: x["score"], reverse=True)
+    return best_designs[:15]
+
+
+def write_summary(designs: List[Dict], path: str) -> None:
+    with open(path, "w", encoding="utf-8") as f:
+        f.write("rank,score,epsilon,alpha,layers\n")
+        for idx, design in enumerate(designs, start=1):
+            layer_desc = ";".join(
+                f"{layer['material']}@{layer['thickness']:.3f}um"
+                for layer in design["layers"]
+            )
+            f.write(
+                f"{idx},{design['score']:.4f},{design['epsilon']:.4f},"
+                f"{design['alpha']:.4f},{layer_desc}\n"
+            )
+
+
+def plot_pareto(designs: List[Dict], path: str) -> None:
+    eps = [d["epsilon"] for d in designs]
+    alpha = [d["alpha"] for d in designs]
+    scores = [d["score"] for d in designs]
+    fig, ax = plt.subplots(figsize=(6, 5))
+    scatter = ax.scatter(alpha, eps, c=scores, cmap="viridis", s=80)
+    ax.set_xlabel("Solar-weighted Absorption α")
+    ax.set_ylabel("8-13 µm Emissivity ε")
+    ax.set_title("Multilayer Design Performance Distribution")
+    plt.colorbar(scatter, label="Composite Score ε - 0.3α")
+    for idx, design in enumerate(designs[:5]):
+        ax.annotate(str(idx + 1), (design["alpha"], design["epsilon"]))
+    fig.tight_layout()
+    plt.savefig(path, dpi=300)
+    plt.close(fig)
+
+
+def main():
+    designs = optimize()
+    outdir = os.path.join(os.path.dirname(__file__), "outputs")
+    os.makedirs(outdir, exist_ok=True)
+    summary_path = os.path.join(outdir, "question3_multilayer_summary.csv")
+    write_summary(designs, summary_path)
+    plot_path = os.path.join(outdir, "question3_pareto.png")
+    plot_pareto(designs, plot_path)
+
+    print(f"Optimal designs written to: {summary_path}")
+    print(f"Performance scatter plot: {plot_path}")
+    top = designs[0]
+    layer_desc = "; ".join(
+        f"{layer['material']}@{layer['thickness']:.2f}um" for layer in top["layers"]
+    )
+    print(
+        "Best design: score={:.3f}, ε={:.3f}, α={:.3f}, layers={}".format(
+            top["score"], top["epsilon"], top["alpha"], layer_desc
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()

org/other/read_pdf_pypdf.py

@@ -0,0 +1,41 @@
+import os
+from PyPDF2 import PdfReader
+
+
+def find_pdf(start_dir: str, filename: str) -> str:
+    """Return the absolute path to the target PDF, searching downward if needed."""
+    candidate = os.path.join(start_dir, filename)
+    if os.path.exists(candidate):
+        return candidate
+
+    for root, _, files in os.walk(start_dir):
+        if filename in files:
+            return os.path.join(root, filename)
+
+    raise FileNotFoundError(f"Unable to locate {filename} under {start_dir}")
+
+
+def main() -> None:
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    pdf_name = "2025 APMCM Problem B.pdf"
+    pdf_path = find_pdf(script_dir, pdf_name)
+
+    reader = PdfReader(pdf_path)
+    pages = []
+    for idx, page in enumerate(reader.pages, start=1):
+        text = page.extract_text() or ""
+        pages.append(f"\n=== Page {idx} ===\n{text.strip()}")
+
+    output_text = "".join(pages)
+    print(output_text)
+
+    output_path = os.path.join(script_dir, "problem_text_pypdf.txt")
+    with open(output_path, "w", encoding="utf-8") as f:
+        f.write(output_text)
+
+    print(f"\nText saved to: {output_path}")
+
+
+if __name__ == "__main__":
+    main()
+

org/other/read_pdf_simple.py

@@ -0,0 +1,27 @@
+import pdfplumber
+import os
+
+# 使用当前脚本所在目录
+script_dir = os.path.dirname(os.path.abspath(__file__))
+pdf_path = os.path.join(script_dir, "2025 APMCM Problem B.pdf")
+
+print(f"Looking for PDF at: {pdf_path}")
+print(f"File exists: {os.path.exists(pdf_path)}")
+
+if os.path.exists(pdf_path):
+    with pdfplumber.open(pdf_path) as pdf:
+        full_text = ""
+        for i, page in enumerate(pdf.pages):
+            text = page.extract_text()
+            if text:
+                full_text += f"\n=== Page {i+1} ===\n{text}\n"
+        print(full_text)
+        # 保存到文本文件
+        output_path = os.path.join(script_dir, "problem_text.txt")
+        with open(output_path, "w", encoding="utf-8") as f:
+            f.write(full_text)
+        print(f"\n文本已保存到: {output_path}")
+else:
+    print(f"文件不存在: {pdf_path}")
+    print(f"当前工作目录: {os.getcwd()}")
+    print(f"目录内容: {os.listdir('.')}")