import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import CubicSpline from scipy.integrate import simpson # ----------------------------- # 1. 材料级：PDMS光学性能计算（你的模型核心逻辑，修复变量定义错误） # ----------------------------- def make_strictly_increasing(wl, n, k): # 去除重复点并确保波长严格递增 unique_wl, indices = np.unique(wl, return_index=True) if len(unique_wl) != len(wl): print(f"Removed {len(wl) - len(unique_wl)} duplicate wavelength points") wl, n, k = wl[indices], n[indices], k[indices] # 确保严格递增 is_increasing = np.diff(wl) > 0 if not all(is_increasing): valid_indices = np.concatenate([[True], is_increasing]) wl, n, k = wl[valid_indices], n[valid_indices], k[valid_indices] return wl, n, k def read_split_data(file_path): with open(file_path, 'r', encoding='utf-8') as f: lines = [line.strip() for line in f if line.strip() and not line.startswith('#')] split_idx = None for i, line in enumerate(lines): if line == 'wl k': split_idx = i break if split_idx is None: raise ValueError("未找到'wl k'表头，请检查数据格式！") n_lines = lines[1:split_idx] wl_n, n_list = [], [] for line in n_lines: parts = line.split() if len(parts) != 2: continue # 跳过格式错误的行 wl, n_val = parts wl_n.append(float(wl)), n_list.append(float(n_val)) k_lines = lines[split_idx + 1:] wl_k, k_list = [], [] for line in k_lines: parts = line.split() if len(parts) != 2: continue # 跳过格式错误的行 wl, k_val = parts wl_k.append(float(wl)), k_list.append(float(k_val)) # 转换为numpy数组 wl_n, n_list = np.array(wl_n), np.array(n_list) wl_k, k_list = np.array(wl_k), np.array(k_list) # 确保n和k的波长完全一致 assert np.allclose(wl_n, wl_k), "n和k的波长列表不一致！" # 排序 sorted_idx = np.argsort(wl_n) return wl_n[sorted_idx], n_list[sorted_idx], k_list[sorted_idx] def fresnel_reflectance(n1, k1, n2, k2): m1, m2 = n1 + 1j * k1, n2 + 1j * k2 return np.abs((m1 - m2) / (m1 + m2)) ** 2 def thin_film_optical_properties(n_film, k_film, d, wl): """修复denominator未定义的错误，完整计算R_total和T_total""" R12 = fresnel_reflectance(1.0, 0.0, n_film, k_film) # 空气→薄膜 R23 = fresnel_reflectance(n_film, k_film, 1.0, 0.0) # 薄膜→空气 delta = 2 * np.pi * n_film * d / wl # 干涉相位差 alpha_abs = 4 * np.pi * k_film * d / wl # 吸收衰减系数 # 计算分母（关键修复：补充denominator的定义） denominator = 1 + R12 * R23 * np.exp(-alpha_abs) + 2 * np.sqrt(R12 * R23 * np.exp(-alpha_abs)) * np.cos(2 * delta) # 总反射率 numerator_R = R12 + R23 * np.exp(-alpha_abs) + 2 * np.sqrt(R12 * R23 * np.exp(-alpha_abs)) * np.cos(2 * delta) R_total = numerator_R / denominator # 总透射率（修复后） T_total = (1 - R12) * (1 - R23) * np.exp(-alpha_abs) / denominator # 吸收率=发射率（热平衡下） alpha_total = 1 - R_total - T_total return alpha_total, R_total, T_total # alpha_total即ε（发射率） # ----------------------------- # 2. 数据读取与预处理 # ----------------------------- # 替换为你的data.txt实际路径（确保正确） DATA_PATH = '/Users/spasolreisa/IdeaProjects/asiaMath/data.txt' wl_all, n_all, k_all = read_split_data(DATA_PATH) wl_all, n_all, k_all = make_strictly_increasing(wl_all, n_all, k_all) print(f"数据读取成功：波长范围 {wl_all.min():.2f}–{wl_all.max():.2f} μm，共{len(wl_all)}个数据点") # 三次样条插值 cs_n = CubicSpline(wl_all, n_all) cs_k = CubicSpline(wl_all, k_all) # 定义PDMS厚度和计算波长范围 thicknesses = [0.5, 1.0, 1.5, 2.0] wl_fine = np.linspace(wl_all.min(), wl_all.max(), 500) # 细化解析度 # ----------------------------- # 3. 预计算材料级关键参数（ε和α） # ----------------------------- # 存储平均发射率（8-13μm，黑体辐射加权）和平均太阳吸收率（0.3-2.5μm，太阳光谱加权） avg_eps_dict = {} # 平均发射率 ε_avg avg_alpha_dict = {} # 平均太阳吸收率 α_avg # 定义权重光谱（辐射冷却+太阳吸收关键波段） def planck_spectrum(wl, T): """普朗克黑体光谱（8-13μm波段权重）""" wl_m = wl * 1e-6 # 转换为米 c1 = 3.7418e8 # 第一辐射常数 (W·μm⁴/m²) c2 = 14388 # 第二辐射常数 (μm·K) return c1 / (wl_m ** 5 * (np.exp(c2 / (wl * T)) - 1)) def solar_spectrum_am15(wl): """AM1.5太阳光谱（0.3-2.5μm波段权重）""" spectrum = np.zeros_like(wl) mask = (wl >= 0.3) & (wl <= 2.5) wl_masked = wl[mask] # 经验拟合AM1.5标准光谱 spectrum[mask] = np.where( wl_masked < 0.5, 800 + 400 * wl_masked, np.where(wl_masked < 1.0, 1000 - 200 * (wl_masked - 0.5), np.where(wl_masked < 1.5, 900 - 100 * (wl_masked - 1.0), 750 - 200 * (wl_masked - 1.5))) ) return spectrum # 计算各厚度的平均ε和α for d in thicknesses: print(f"\n正在计算厚度 {d} μm 的光学性能...") # ----------------------------- # 计算平均发射率 ε_avg（8-13μm，黑体辐射加权） # ----------------------------- if wl_all.min() <= 13 and wl_all.max() >= 8: wl_rad = np.linspace(8, 13, 300) # 辐射冷却核心波段 n_rad = cs_n(wl_rad) k_rad = cs_k(wl_rad) eps_rad, _, _ = thin_film_optical_properties(n_rad, k_rad, d, wl_rad) planck_weight = planck_spectrum(wl_rad, T=298) # 25℃黑体光谱权重 # 加权平均 eps_avg = simpson(eps_rad * planck_weight, wl_rad) / simpson(planck_weight, wl_rad) else: print(f"警告：数据未覆盖8-13μm波段，使用全波段平均发射率替代") n_film = cs_n(wl_fine) k_film = cs_k(wl_fine) eps_full, _, _ = thin_film_optical_properties(n_film, k_film, d, wl_fine) eps_avg = np.mean(eps_full) avg_eps_dict[d] = eps_avg # ----------------------------- # 计算平均太阳吸收率 α_avg（0.3-2.5μm，太阳光谱加权） # ----------------------------- if wl_all.min() <= 2.5 and wl_all.max() >= 0.3: wl_solar = np.linspace(0.3, 2.5, 300) # 太阳光谱波段 n_solar = cs_n(wl_solar) k_solar = cs_k(wl_solar) alpha_solar, _, _ = thin_film_optical_properties(n_solar, k_solar, d, wl_solar) solar_weight = solar_spectrum_am15(wl_solar) # AM1.5太阳光谱权重 # 加权平均 alpha_avg = simpson(alpha_solar * solar_weight, wl_solar) / simpson(solar_weight, wl_solar) else: print(f"警告：数据未覆盖0.3-2.5μm太阳波段，使用PDMS典型值α=0.08") alpha_avg = 0.08 # PDMS在太阳波段的典型吸收率（低吸收） avg_alpha_dict[d] = alpha_avg # ----------------------------- # 4. 系统级：净冷却功率计算（解答思路核心逻辑） # ----------------------------- # 系统物理参数（可根据实际场景调整） sigma = 5.67e-8 # 斯特藩-玻尔兹曼常数 (W/m²·K⁴) G_sun_list = [500, 700, 900, 1100] # 不同太阳辐照强度（对应多云到晴天） T_amb_list = np.linspace(293, 318, 6) # 环境温度（20-45℃，转换为开尔文） v_wind = 1.5 # 风速 (m/s) h_conv = 5.6 + 3.1 * v_wind # 对流换热系数（经验公式，W/m²·K） def net_cooling_power(eps, alpha, T_s, T_amb, G_sun, h_conv, sigma): """净冷却功率公式：P_net = 辐射散热 - 太阳吸收热 - 对流换热损失""" # 辐射散热（向宇宙太空） rad散热 = eps * sigma * T_s ** 4 # 太阳吸收热（从太阳光获取的热量） solar吸热 = alpha * G_sun # 对流换热损失（向环境散热/吸热） conv损失 = h_conv * (T_s - T_amb) # 环境辐射吸收（从环境获取的辐射热） amb_rad吸热 = eps * sigma * T_amb ** 4 # 净冷却功率（正值表示主动冷却，负值表示吸热） return rad散热 - solar吸热 - conv损失 - amb_rad吸热 def solve_surface_temperature(eps, alpha, T_amb, G_sun, h_conv, sigma): """迭代求解PDMS薄膜表面温度T_s（净冷却功率=0时的热平衡温度）""" T_s_guess = T_amb - 5 # 初始猜测（比环境低5℃） tol = 1e-3 # 收敛精度 max_iter = 100 # 最大迭代次数 for _ in range(max_iter): P_net = net_cooling_power(eps, alpha, T_s_guess, T_amb, G_sun, h_conv, sigma) # 数值微分求导（牛顿迭代法，确保收敛） dP_dT = (net_cooling_power(eps, alpha, T_s_guess + 1e-4, T_amb, G_sun, h_conv, sigma) - net_cooling_power(eps, alpha, T_s_guess - 1e-4, T_amb, G_sun, h_conv, sigma)) / (2e-4) if abs(dP_dT) < 1e-6: break # 避免除以零 # 更新猜测值 T_s_new = T_s_guess - P_net / dP_dT # 限制温度范围（物理合理值） T_s_new = max(250, min(T_amb + 5, T_s_new)) # 检查收敛 if abs(T_s_new - T_s_guess) < tol: return T_s_new T_s_guess = T_s_new return T_s_guess # 若未收敛，返回最后一次猜测值 # ----------------------------- # 5. 全链条分析：材料性能→系统冷却性能 # ----------------------------- # 存储各厚度的系统级结果 system_results = {} for d in thicknesses: eps = avg_eps_dict[d] alpha = avg_alpha_dict[d] # 初始化结果矩阵（太阳辐照×环境温度） P_net_matrix = np.zeros((len(G_sun_list), len(T_amb_list))) T_s_matrix = np.zeros((len(G_sun_list), len(T_amb_list))) # 遍历所有太阳辐照和环境温度组合 for i, G_sun in enumerate(G_sun_list): for j, T_amb in enumerate(T_amb_list): # 求解表面温度 T_s = solve_surface_temperature(eps, alpha, T_amb, G_sun, h_conv, sigma) T_s_matrix[i, j] = T_s # 计算净冷却功率 P_net = net_cooling_power(eps, alpha, T_s, T_amb, G_sun, h_conv, sigma) P_net_matrix[i, j] = P_net # 存储结果 system_results[d] = { "eps_avg": eps, "alpha_avg": alpha, "P_net": P_net_matrix, "T_s": T_s_matrix } # ----------------------------- # 6. 结果可视化（全链条分析图表） # ----------------------------- plt.rcParams['font.sans-serif'] = ['Arial'] # 统一字体 plt.rcParams['axes.unicode_minus'] = False # 支持负号 # 图1：材料级性能（平均ε和α随厚度变化） fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) thicknesses_arr = np.array(thicknesses) # 平均发射率 ax1.bar(thicknesses_arr - 0.08, [system_results[d]["eps_avg"] for d in thicknesses], width=0.15, label='Avg Emissivity (8-13μm)', color='darkred', alpha=0.8) ax1.set_xlabel('PDMS Thickness (μm)', fontsize=12) ax1.set_ylabel('Emissivity', fontsize=12) ax1.set_title('Average Emissivity (Radiative Cooling Window)', fontsize=14, fontweight='bold') ax1.grid(True, alpha=0.3) ax1.set_ylim(0, 1.05) # 平均太阳吸收率 ax2.bar(thicknesses_arr - 0.08, [system_results[d]["alpha_avg"] for d in thicknesses], width=0.15, label='Avg Solar Absorptivity (0.3-2.5μm)', color='darkblue', alpha=0.8) ax2.set_xlabel('PDMS Thickness (μm)', fontsize=12) ax2.set_ylabel('Absorptivity', fontsize=12) ax2.set_title('Average Solar Absorptivity', fontsize=14, fontweight='bold') ax2.grid(True, alpha=0.3) ax2.set_ylim(0, 0.2) # 限制范围，更清晰 plt.tight_layout() plt.savefig('material_performance.png', dpi=300, bbox_inches='tight') # 图2：系统级性能（净冷却功率随环境温度变化，选最优厚度） # 最优厚度：ε/α比值最大（平衡高发射和低吸收） optimal_d = max(thicknesses, key=lambda x: system_results[x]["eps_avg"] / (system_results[x]["alpha_avg"] + 0.01)) print( f"\n最优厚度：{optimal_d} μm（ε={system_results[optimal_d]['eps_avg']:.4f}, α={system_results[optimal_d]['alpha_avg']:.4f}）") fig, ax = plt.subplots(figsize=(12, 6)) T_amb_c = T_amb_list - 273.15 # 转换为摄氏度 colors = ['red', 'orange', 'green', 'blue'] for i, G_sun in enumerate(G_sun_list): P_net = system_results[optimal_d]["P_net"][i, :] ax.plot(T_amb_c, P_net, marker='o', markersize=6, linewidth=2, color=colors[i], label=f'Solar Irradiance = {G_sun} W/m²') ax.set_xlabel('Ambient Temperature (°C)', fontsize=12) ax.set_ylabel('Net Cooling Power (W/m²)', fontsize=12) ax.set_title(f'Net Cooling Power vs Ambient Temperature (PDMS Thickness = {optimal_d} μm)', fontsize=14, fontweight='bold') ax.grid(True, alpha=0.3) ax.legend(fontsize=11) # 添加零线（区分冷却/吸热） ax.axhline(y=0, color='black', linestyle='--', alpha=0.5, label='Zero Cooling Power') plt.tight_layout() plt.savefig('net_cooling_power.png', dpi=300, bbox_inches='tight') # 图3：表面温度随环境温度变化 fig, ax = plt.subplots(figsize=(12, 6)) for i, G_sun in enumerate(G_sun_list): T_s = system_results[optimal_d]["T_s"][i, :] - 273.15 # 转换为摄氏度 ax.plot(T_amb_c, T_s, marker='s', markersize=6, linewidth=2, color=colors[i], label=f'Solar Irradiance = {G_sun} W/m²') ax.set_xlabel('Ambient Temperature (°C)', fontsize=12) ax.set_ylabel('PDMS Surface Temperature (°C)', fontsize=12) ax.set_title(f'Surface Temperature vs Ambient Temperature (PDMS Thickness = {optimal_d} μm)', fontsize=14, fontweight='bold') ax.grid(True, alpha=0.3) ax.legend(fontsize=11) # 添加环境温度参考线（y=x） ax.plot(T_amb_c, T_amb_c, color='black', linestyle='--', alpha=0.5, label='Ambient Temperature') plt.tight_layout() plt.savefig('surface_temperature.png', dpi=300, bbox_inches='tight') plt.show() # ----------------------------- # 7. 关键结果输出（量化分析） # ----------------------------- print("\n" + "=" * 60) print("材料-系统全链条关键结果") print("=" * 60) for d in thicknesses: print(f"\n厚度 {d} μm:") print(f" - 平均发射率（8-13μm）: {system_results[d]['eps_avg']:.4f}") print(f" - 平均太阳吸收率（0.3-2.5μm）: {system_results[d]['alpha_avg']:.4f}") print(f" - 最优工况净冷却功率（T_amb=30℃, G_sun=900 W/m²）: {system_results[d]['P_net'][2, 2]:.2f} W/m²") print(f" - 对应表面温度: {system_results[d]['T_s'][2, 2] - 273.15:.2f} ℃") print("\n" + "=" * 60) print("结论：PDMS薄膜的最优厚度为 {} μm，在典型工况下（30℃环境、900 W/m²太阳辐照）".format(optimal_d)) print("可实现 {:.2f} W/m² 的净冷却功率，表面温度比环境低 {:.2f} ℃".format( system_results[optimal_d]['P_net'][2, 2], 30 - (system_results[optimal_d]['T_s'][2, 2] - 273.15) )) print("=" * 60)