1329 lines
51 KiB
Python
1329 lines
51 KiB
Python
"""
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Task 4: Environmental Impact Analysis for Moon Colony Construction
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使用液氧甲烷(LOX/CH4)燃料的环境影响评估
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分析不同建设方案对地球环境的影响，并给出最小化环境影响的模型调整策略。
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"""
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import numpy as np
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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from matplotlib import rcParams
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from dataclasses import dataclass
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from typing import List, Dict, Tuple
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import pandas as pd
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# 设置字体
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rcParams['font.sans-serif'] = ['Arial Unicode MS', 'DejaVu Sans', 'SimHei']
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rcParams['axes.unicode_minus'] = False
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# ============== 物理常数 ==============
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G0 = 9.81 # m/s²
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OMEGA_EARTH = 7.27e-5 # rad/s
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R_EARTH = 6.371e6 # m
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# ============== 任务参数 (与任务一一致) ==============
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TOTAL_PAYLOAD = 100e6 # 100 million metric tons
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# ============== 太空电梯参数 ==============
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NUM_ELEVATORS = 3
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ELEVATOR_CAPACITY_PER_YEAR = 179000 # metric tons per elevator per year
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TOTAL_ELEVATOR_CAPACITY = NUM_ELEVATORS * ELEVATOR_CAPACITY_PER_YEAR # 537,000 tons/year
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ELEVATOR_SPECIFIC_ENERGY = 157.2e9 # J per metric ton (157.2 MJ/kg)
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# 电梯能量分解
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ELEVATOR_LIFT_ENERGY = 57.0e9 # J/ton (电力提升能)
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ELEVATOR_TOP_ROCKET_ENERGY = 100.2e9 # J/ton (顶端火箭推进)
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# ============== 液氧甲烷燃料参数 (LOX/CH4) ==============
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@dataclass
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class MethaneFuelParams:
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"""液氧甲烷燃料参数"""
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name: str = "LOX/CH4"
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isp: float = 360 # 比冲 (秒), Raptor发动机级别
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specific_energy: float = 12.9e6 # 燃料比能量 (J/kg)
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ox_fuel_ratio: float = 3.5 # 氧燃比 O2:CH4
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@property
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def exhaust_velocity(self) -> float:
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"""排气速度 (m/s)"""
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return self.isp * G0
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@property
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def ch4_mass_fraction(self) -> float:
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"""甲烷在燃料中的质量分数"""
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return 1 / (1 + self.ox_fuel_ratio) # ≈ 0.222
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@property
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def lox_mass_fraction(self) -> float:
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"""液氧在燃料中的质量分数"""
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return self.ox_fuel_ratio / (1 + self.ox_fuel_ratio) # ≈ 0.778
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# ============== 排放因子 (基于化学计量) ==============
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@dataclass
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class EmissionFactors:
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"""排放因子 (kg排放物/kg燃料)"""
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# 燃烧排放: CH4 + 2O2 → CO2 + 2H2O
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# 每kg CH4产生: 44/16 = 2.75 kg CO2, 36/16 = 2.25 kg H2O
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fuel_params: MethaneFuelParams = None
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def __post_init__(self):
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if self.fuel_params is None:
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self.fuel_params = MethaneFuelParams()
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@property
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def co2_per_kg_fuel(self) -> float:
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"""每kg燃料的CO2排放 (kg)"""
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return self.fuel_params.ch4_mass_fraction * 2.75 # ≈ 0.611 kg CO2/kg燃料
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@property
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def h2o_per_kg_fuel(self) -> float:
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"""每kg燃料的H2O排放 (kg)"""
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return self.fuel_params.ch4_mass_fraction * 2.25 # ≈ 0.500 kg H2O/kg燃料
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# 燃料生产排放
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ch4_production_co2: float = 2.5 # kg CO2/kg CH4 (天然气制甲烷，含开采和加工)
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lox_production_kwh: float = 0.5 # kWh/kg LOX (空气分离)
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# 电网碳强度
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grid_carbon_intensity: float = 0.475 # kg CO2/kWh (全球平均, IEA 2023)
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grid_carbon_intensity_renewable: float = 0.05 # kg CO2/kWh (可再生能源)
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# 平流层排放比例
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stratosphere_emission_fraction: float = 0.40 # 40%的排放进入平流层
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stratosphere_h2o_residence_time: float = 3.0 # 年 (平流层水蒸气滞留时间)
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# ============== 发射场定义 ==============
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@dataclass
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class LaunchSite:
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name: str
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short_name: str
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latitude: float
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max_launches_per_day: int = 1
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@property
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def abs_latitude(self) -> float:
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return abs(self.latitude)
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@property
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def rotation_velocity(self) -> float:
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return OMEGA_EARTH * R_EARTH * np.cos(np.radians(self.abs_latitude))
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@property
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def delta_v_loss(self) -> float:
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v_equator = OMEGA_EARTH * R_EARTH
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return v_equator - self.rotation_velocity
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# 10个发射场 (按纬度排序)
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LAUNCH_SITES = sorted([
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LaunchSite("Kourou (French Guiana)", "Kourou", 5.2),
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LaunchSite("Satish Dhawan (India)", "SDSC", 13.7),
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LaunchSite("Boca Chica (Texas)", "Texas", 26.0),
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LaunchSite("Cape Canaveral (Florida)", "Florida", 28.5),
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LaunchSite("Vandenberg (California)", "California", 34.7),
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LaunchSite("Wallops (Virginia)", "Virginia", 37.8),
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LaunchSite("Taiyuan (China)", "Taiyuan", 38.8),
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LaunchSite("Mahia (New Zealand)", "Mahia", 39.3),
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LaunchSite("Baikonur (Kazakhstan)", "Baikonur", 45.6),
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LaunchSite("Kodiak (Alaska)", "Alaska", 57.4),
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], key=lambda x: x.abs_latitude)
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# ============== 火箭参数 ==============
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PAYLOAD_PER_LAUNCH = 125 # metric tons per launch
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ALPHA = 0.10 # 结构系数
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NUM_STAGES = 3
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DELTA_V_BASE = 13300 # m/s (赤道发射到月球)
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FUEL_PARAMS = MethaneFuelParams()
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EMISSION_FACTORS = EmissionFactors(fuel_params=FUEL_PARAMS)
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# ============== 核心计算函数 ==============
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def fuel_ratio_multistage(delta_v: float, fuel_params: MethaneFuelParams = FUEL_PARAMS) -> float:
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"""多级火箭燃料/载荷比"""
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ve = fuel_params.exhaust_velocity
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delta_v_per_stage = delta_v / NUM_STAGES
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R_stage = np.exp(delta_v_per_stage / ve)
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denominator = 1 - ALPHA * (R_stage - 1)
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if denominator <= 0:
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return np.inf
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k_stage = (R_stage - 1) / denominator
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total_fuel_ratio = 0
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remaining_ratio = 1.0
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for _ in range(NUM_STAGES):
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fuel_this_stage = remaining_ratio * k_stage
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total_fuel_ratio += fuel_this_stage
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remaining_ratio *= (1 + k_stage * (1 + ALPHA))
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return total_fuel_ratio
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def rocket_fuel_per_ton(site: LaunchSite) -> float:
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"""每吨载荷的燃料消耗 (kg燃料/ton载荷)"""
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delta_v = DELTA_V_BASE + site.delta_v_loss
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k = fuel_ratio_multistage(delta_v)
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return k * 1000 # kg fuel per metric ton payload
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def rocket_energy_per_ton(site: LaunchSite) -> float:
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"""火箭发射每吨载荷的能量消耗 (J/ton)"""
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return rocket_fuel_per_ton(site) * FUEL_PARAMS.specific_energy
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# ============== 环境影响计算 ==============
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@dataclass
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class EnvironmentalImpact:
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"""环境影响结果"""
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scenario_name: str
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completion_years: float
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total_payload_tons: float
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# 能量消耗
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total_energy_PJ: float
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rocket_energy_PJ: float
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elevator_energy_PJ: float
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# 燃料消耗
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total_fuel_Mt: float # 百万吨
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rocket_fuel_Mt: float
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elevator_top_rocket_fuel_Mt: float
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# 排放量
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co2_combustion_Mt: float # 燃烧直接排放
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co2_fuel_production_Mt: float # 燃料生产排放
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co2_electricity_Mt: float # 电力消耗排放
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h2o_total_Mt: float
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h2o_stratosphere_Mt: float # 平流层水蒸气
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# 发射统计
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rocket_launches: int
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@property
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def co2_total_Mt(self) -> float:
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"""总CO2排放 (百万吨)"""
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return self.co2_combustion_Mt + self.co2_fuel_production_Mt + self.co2_electricity_Mt
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@property
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def co2_per_ton_payload(self) -> float:
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"""每吨载荷的CO2排放 (kg)"""
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return self.co2_total_Mt * 1e9 / self.total_payload_tons
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@property
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def annual_co2_Mt(self) -> float:
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"""年均CO2排放 (百万吨/年)"""
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return self.co2_total_Mt / self.completion_years if self.completion_years > 0 else 0
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def calculate_scenario_impact(
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scenario_name: str,
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completion_years: float,
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elevator_fraction: float,
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rocket_sites_used: List[LaunchSite],
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emission_factors: EmissionFactors = EMISSION_FACTORS,
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renewable_energy_fraction: float = 0.0
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) -> EnvironmentalImpact:
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"""
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计算单个情景的环境影响
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参数:
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scenario_name: 情景名称
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completion_years: 完成年限
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elevator_fraction: 电梯运输比例 (0-1)
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rocket_sites_used: 使用的火箭发射场列表
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emission_factors: 排放因子
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renewable_energy_fraction: 可再生能源占比 (0-1)
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"""
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# 载荷分配
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elevator_payload = TOTAL_PAYLOAD * elevator_fraction
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rocket_payload = TOTAL_PAYLOAD * (1 - elevator_fraction)
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# ===== 电梯部分 =====
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elevator_energy = elevator_payload * ELEVATOR_SPECIFIC_ENERGY # J
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elevator_lift_energy = elevator_payload * ELEVATOR_LIFT_ENERGY # J (电力)
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elevator_top_rocket_energy = elevator_payload * ELEVATOR_TOP_ROCKET_ENERGY # J (化学能)
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# 电梯顶端火箭燃料 (使用较小的燃料比，因为已在高轨道)
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# 顶端ΔV约1800 m/s (LOI + 轨道调整)
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elevator_top_delta_v = 1800
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elevator_top_fuel_ratio = fuel_ratio_multistage(elevator_top_delta_v)
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elevator_top_rocket_fuel = elevator_payload * 1000 * elevator_top_fuel_ratio # kg
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# ===== 火箭部分 =====
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if rocket_payload > 0 and len(rocket_sites_used) > 0:
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launches_per_site = int(np.ceil(rocket_payload / PAYLOAD_PER_LAUNCH / len(rocket_sites_used)))
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total_launches = launches_per_site * len(rocket_sites_used)
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# 按发射场计算燃料消耗 (简化：均匀分配)
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rocket_fuel_total = 0
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rocket_energy_total = 0
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for site in rocket_sites_used:
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site_payload = rocket_payload / len(rocket_sites_used)
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site_fuel = site_payload * 1000 * fuel_ratio_multistage(DELTA_V_BASE + site.delta_v_loss)
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rocket_fuel_total += site_fuel
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rocket_energy_total += site_fuel * FUEL_PARAMS.specific_energy
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else:
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total_launches = 0
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rocket_fuel_total = 0
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rocket_energy_total = 0
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# ===== 总燃料消耗 =====
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total_fuel = rocket_fuel_total + elevator_top_rocket_fuel # kg
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# ===== 排放计算 =====
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# 1. 燃烧直接排放
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co2_combustion = total_fuel * emission_factors.co2_per_kg_fuel # kg
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h2o_combustion = total_fuel * emission_factors.h2o_per_kg_fuel # kg
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# 2. 燃料生产排放
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ch4_mass = total_fuel * FUEL_PARAMS.ch4_mass_fraction
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lox_mass = total_fuel * FUEL_PARAMS.lox_mass_fraction
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co2_ch4_production = ch4_mass * emission_factors.ch4_production_co2
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co2_lox_production = lox_mass * emission_factors.lox_production_kwh * emission_factors.grid_carbon_intensity
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co2_fuel_production = co2_ch4_production + co2_lox_production
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# 3. 电梯电力消耗排放
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elevator_electricity_kwh = elevator_lift_energy / 3.6e6 # J -> kWh
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grid_intensity = (emission_factors.grid_carbon_intensity * (1 - renewable_energy_fraction) +
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emission_factors.grid_carbon_intensity_renewable * renewable_energy_fraction)
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co2_electricity = elevator_electricity_kwh * grid_intensity
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# 4. 平流层水蒸气 (仅火箭发射部分)
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# 电梯顶端释放点已在GEO以上，不经过平流层
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h2o_rocket = rocket_fuel_total * emission_factors.h2o_per_kg_fuel
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h2o_stratosphere = h2o_rocket * emission_factors.stratosphere_emission_fraction
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return EnvironmentalImpact(
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scenario_name=scenario_name,
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completion_years=completion_years,
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total_payload_tons=TOTAL_PAYLOAD,
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total_energy_PJ=(rocket_energy_total + elevator_energy) / 1e15,
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rocket_energy_PJ=rocket_energy_total / 1e15,
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elevator_energy_PJ=elevator_energy / 1e15,
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total_fuel_Mt=total_fuel / 1e9,
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rocket_fuel_Mt=rocket_fuel_total / 1e9,
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elevator_top_rocket_fuel_Mt=elevator_top_rocket_fuel / 1e9,
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co2_combustion_Mt=co2_combustion / 1e9,
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co2_fuel_production_Mt=co2_fuel_production / 1e9,
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co2_electricity_Mt=co2_electricity / 1e9,
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h2o_total_Mt=h2o_combustion / 1e9,
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h2o_stratosphere_Mt=h2o_stratosphere / 1e9,
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rocket_launches=total_launches
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)
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# ============== 情景定义 ==============
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def define_scenarios() -> List[Dict]:
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"""
|
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定义四个基准情景 (基于任务一结果)
|
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"""
|
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scenarios = []
|
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# 情景1: 纯火箭 (全部10个发射场)
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rocket_only_years = TOTAL_PAYLOAD / (len(LAUNCH_SITES) * 365 * PAYLOAD_PER_LAUNCH)
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scenarios.append({
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'name': 'Rocket Only',
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'completion_years': rocket_only_years, # ~219年
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'elevator_fraction': 0.0,
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'rocket_sites': LAUNCH_SITES.copy(),
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'description': '纯火箭方案：10个发射场满负荷'
|
||
})
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||
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||
# 情景2: 纯电梯
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elevator_only_years = TOTAL_PAYLOAD / TOTAL_ELEVATOR_CAPACITY
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||
scenarios.append({
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'name': 'Elevator Only',
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'completion_years': elevator_only_years, # ~186年
|
||
'elevator_fraction': 1.0,
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||
'rocket_sites': [],
|
||
'description': '纯电梯方案：3个电梯满负荷'
|
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})
|
||
|
||
# 情景3: 混合方案 (最短时间)
|
||
combined_capacity = TOTAL_ELEVATOR_CAPACITY + len(LAUNCH_SITES) * 365 * PAYLOAD_PER_LAUNCH
|
||
combined_min_years = TOTAL_PAYLOAD / combined_capacity
|
||
elevator_payload_min = TOTAL_ELEVATOR_CAPACITY * combined_min_years
|
||
elevator_fraction_min = elevator_payload_min / TOTAL_PAYLOAD
|
||
scenarios.append({
|
||
'name': 'Combined (Min Time)',
|
||
'completion_years': combined_min_years, # ~101年
|
||
'elevator_fraction': elevator_fraction_min, # ~54%
|
||
'rocket_sites': LAUNCH_SITES.copy(),
|
||
'description': '混合方案(最短时间)：电梯+全部火箭站点'
|
||
})
|
||
|
||
# 情景4: 混合方案 (膝点优化)
|
||
knee_years = 139 # 从任务一Pareto分析
|
||
elevator_payload_knee = min(TOTAL_ELEVATOR_CAPACITY * knee_years, TOTAL_PAYLOAD)
|
||
elevator_fraction_knee = elevator_payload_knee / TOTAL_PAYLOAD
|
||
# 膝点时只需要部分低纬度发射场
|
||
scenarios.append({
|
||
'name': 'Combined (Knee Point)',
|
||
'completion_years': knee_years,
|
||
'elevator_fraction': elevator_fraction_knee, # ~74.6%
|
||
'rocket_sites': LAUNCH_SITES[:5], # 只使用5个低纬度站点
|
||
'description': '混合方案(膝点)：电梯优先+低纬度火箭'
|
||
})
|
||
|
||
return scenarios
|
||
|
||
|
||
def analyze_all_scenarios(renewable_fraction: float = 0.0) -> pd.DataFrame:
|
||
"""分析所有情景"""
|
||
scenarios = define_scenarios()
|
||
results = []
|
||
|
||
for scenario in scenarios:
|
||
impact = calculate_scenario_impact(
|
||
scenario_name=scenario['name'],
|
||
completion_years=scenario['completion_years'],
|
||
elevator_fraction=scenario['elevator_fraction'],
|
||
rocket_sites_used=scenario['rocket_sites'],
|
||
renewable_energy_fraction=renewable_fraction
|
||
)
|
||
|
||
results.append({
|
||
'Scenario': impact.scenario_name,
|
||
'Years': impact.completion_years,
|
||
'Elevator %': impact.elevator_energy_PJ / impact.total_energy_PJ * 100 if impact.total_energy_PJ > 0 else 0,
|
||
'Energy (PJ)': impact.total_energy_PJ,
|
||
'Fuel (Mt)': impact.total_fuel_Mt,
|
||
'CO2 Total (Mt)': impact.co2_total_Mt,
|
||
'CO2 Combustion (Mt)': impact.co2_combustion_Mt,
|
||
'CO2 Production (Mt)': impact.co2_fuel_production_Mt,
|
||
'CO2 Electricity (Mt)': impact.co2_electricity_Mt,
|
||
'H2O Total (Mt)': impact.h2o_total_Mt,
|
||
'H2O Stratosphere (Mt)': impact.h2o_stratosphere_Mt,
|
||
'Annual CO2 (Mt/yr)': impact.annual_co2_Mt,
|
||
'CO2/ton (kg)': impact.co2_per_ton_payload,
|
||
'Launches': impact.rocket_launches,
|
||
})
|
||
|
||
return pd.DataFrame(results)
|
||
|
||
|
||
# ============== 模型优化：环境约束优化 ==============
|
||
|
||
def optimize_for_environment(
|
||
max_annual_co2_Mt: float = 50.0,
|
||
min_completion_years: float = 100.0,
|
||
renewable_fraction: float = 0.5
|
||
) -> Dict:
|
||
"""
|
||
带环境约束的优化模型
|
||
|
||
目标: 最小化 总CO2排放
|
||
约束:
|
||
- 完成时间 >= min_completion_years
|
||
- 年均CO2 <= max_annual_co2_Mt
|
||
|
||
决策变量:
|
||
- 电梯运输比例 (0-1)
|
||
- 使用的火箭站点数量 (0-10)
|
||
"""
|
||
best_result = None
|
||
best_co2 = float('inf')
|
||
|
||
# 网格搜索
|
||
for elevator_frac in np.linspace(0, 1, 21):
|
||
for n_sites in range(0, 11):
|
||
# 计算完成时间
|
||
elevator_capacity = TOTAL_ELEVATOR_CAPACITY
|
||
rocket_capacity = n_sites * 365 * PAYLOAD_PER_LAUNCH if n_sites > 0 else 0
|
||
|
||
elevator_payload = TOTAL_PAYLOAD * elevator_frac
|
||
rocket_payload = TOTAL_PAYLOAD * (1 - elevator_frac)
|
||
|
||
if elevator_frac == 1.0:
|
||
years = elevator_payload / elevator_capacity if elevator_capacity > 0 else float('inf')
|
||
elif elevator_frac == 0.0:
|
||
years = rocket_payload / rocket_capacity if rocket_capacity > 0 else float('inf')
|
||
else:
|
||
# 电梯和火箭并行
|
||
elevator_years = elevator_payload / elevator_capacity
|
||
rocket_years = rocket_payload / rocket_capacity if rocket_capacity > 0 else float('inf')
|
||
years = max(elevator_years, rocket_years)
|
||
|
||
if years < min_completion_years:
|
||
continue
|
||
|
||
# 计算环境影响
|
||
sites_used = LAUNCH_SITES[:n_sites] if n_sites > 0 else []
|
||
impact = calculate_scenario_impact(
|
||
scenario_name='Optimization',
|
||
completion_years=years,
|
||
elevator_fraction=elevator_frac,
|
||
rocket_sites_used=sites_used,
|
||
renewable_energy_fraction=renewable_fraction
|
||
)
|
||
|
||
# 检查约束
|
||
if impact.annual_co2_Mt > max_annual_co2_Mt:
|
||
continue
|
||
|
||
# 更新最优解
|
||
if impact.co2_total_Mt < best_co2:
|
||
best_co2 = impact.co2_total_Mt
|
||
best_result = {
|
||
'elevator_fraction': elevator_frac,
|
||
'n_sites': n_sites,
|
||
'years': years,
|
||
'impact': impact
|
||
}
|
||
|
||
return best_result
|
||
|
||
|
||
# ============== 可视化 ==============
|
||
|
||
def plot_environmental_comparison(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
|
||
"""绘制环境影响对比图（2张子图版本，马卡龙色系论文风格）"""
|
||
df = analyze_all_scenarios()
|
||
|
||
# 马卡龙色系
|
||
MACARON = {
|
||
'pink': '#F8B4C0', # 樱花粉
|
||
'mint': '#A8E6CF', # 薄荷绿
|
||
'lavender': '#C5A3FF', # 薰衣草紫
|
||
'peach': '#FFCBA4', # 蜜桃橙
|
||
'sky': '#A8D8EA', # 天空蓝
|
||
'cream': '#FFEAA7', # 奶油黄
|
||
'rose': '#DDA0DD', # 玫瑰粉
|
||
'sage': '#B5EAD7', # 鼠尾草绿
|
||
}
|
||
|
||
# 论文风格设置
|
||
plt.rcParams.update({
|
||
'font.size': 11,
|
||
'axes.labelsize': 12,
|
||
'axes.titlesize': 13,
|
||
'xtick.labelsize': 10,
|
||
'ytick.labelsize': 10,
|
||
'legend.fontsize': 9,
|
||
'axes.linewidth': 0.8,
|
||
'axes.edgecolor': '#666666',
|
||
})
|
||
|
||
fig, axes = plt.subplots(1, 2, figsize=(10, 4.5))
|
||
fig.patch.set_facecolor('white')
|
||
|
||
scenarios = df['Scenario'].tolist()
|
||
x = np.arange(len(scenarios))
|
||
width = 0.35
|
||
|
||
# ========== 左图: CO2排放分解 + H2O排放 ==========
|
||
ax1 = axes[0]
|
||
ax1.set_facecolor('white')
|
||
|
||
# CO2数据（左侧柱子）
|
||
co2_combustion = df['CO2 Combustion (Mt)'].values
|
||
co2_production = df['CO2 Production (Mt)'].values
|
||
co2_electricity = df['CO2 Electricity (Mt)'].values
|
||
|
||
bars1 = ax1.bar(x - width/2, co2_combustion, width, label='Combustion',
|
||
color=MACARON['pink'], edgecolor='#666666', linewidth=0.5)
|
||
bars2 = ax1.bar(x - width/2, co2_production, width, bottom=co2_combustion,
|
||
label='Fuel Prod.', color=MACARON['mint'], edgecolor='#666666', linewidth=0.5)
|
||
bars3 = ax1.bar(x - width/2, co2_electricity, width, bottom=co2_combustion+co2_production,
|
||
label='Electricity', color=MACARON['sky'], edgecolor='#666666', linewidth=0.5)
|
||
|
||
ax1.set_ylabel('CO₂ Emissions (Mt)', color='#333333')
|
||
ax1.tick_params(axis='y', labelcolor='#333333')
|
||
|
||
# 添加CO2总量标签
|
||
totals = df['CO2 Total (Mt)'].values
|
||
for i, total in enumerate(totals):
|
||
ax1.text(i - width/2, total + 300, f'{total:.0f}', ha='center', fontsize=9, fontweight='bold')
|
||
|
||
# H2O数据（右侧柱子）
|
||
ax1_twin = ax1.twinx()
|
||
|
||
h2o_surface = df['H2O Total (Mt)'].values - df['H2O Stratosphere (Mt)'].values
|
||
h2o_strat = df['H2O Stratosphere (Mt)'].values
|
||
|
||
bars4 = ax1_twin.bar(x + width/2, h2o_surface, width, label='Troposphere',
|
||
color=MACARON['sage'], edgecolor='#666666', linewidth=0.5)
|
||
bars5 = ax1_twin.bar(x + width/2, h2o_strat, width, bottom=h2o_surface,
|
||
label='Stratosphere', color=MACARON['rose'], edgecolor='#666666', linewidth=0.5)
|
||
|
||
ax1_twin.set_ylabel('H₂O Emissions (Mt)', color='#7B68A0')
|
||
ax1_twin.tick_params(axis='y', labelcolor='#7B68A0')
|
||
|
||
ax1.set_title('(a) CO₂ & H₂O Emissions', fontweight='bold', pad=10)
|
||
ax1.set_xticks(x)
|
||
ax1.set_xticklabels(scenarios, rotation=20, ha='right')
|
||
ax1.grid(True, alpha=0.2, axis='y', linestyle='--')
|
||
ax1.set_axisbelow(True)
|
||
|
||
# 合并图例
|
||
lines1, labels1 = ax1.get_legend_handles_labels()
|
||
lines2, labels2 = ax1_twin.get_legend_handles_labels()
|
||
ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper right',
|
||
framealpha=0.95, edgecolor='#cccccc', ncol=2)
|
||
|
||
# ========== 右图: 年均CO2排放 + 碳强度 ==========
|
||
ax2 = axes[1]
|
||
ax2.set_facecolor('white')
|
||
|
||
annual_co2 = df['Annual CO2 (Mt/yr)'].values
|
||
co2_per_ton = df['CO2/ton (kg)'].values
|
||
|
||
# 年均CO2（左侧柱子）- 使用渐变马卡龙色
|
||
colors_annual = [MACARON['pink'], MACARON['mint'], MACARON['sky'], MACARON['sage']]
|
||
bars_annual = ax2.bar(x - width/2, annual_co2, width, color=colors_annual,
|
||
edgecolor='#666666', linewidth=0.5)
|
||
|
||
ax2.set_ylabel('Annual CO₂ (Mt/yr)', color='#333333')
|
||
ax2.tick_params(axis='y', labelcolor='#333333')
|
||
|
||
# 添加年均CO2标签
|
||
global_annual_co2 = 37000
|
||
for i, val in enumerate(annual_co2):
|
||
pct = val / global_annual_co2 * 100
|
||
ax2.text(i - width/2, val + 2, f'{val:.1f}\n({pct:.2f}%)', ha='center', fontsize=8)
|
||
|
||
# 碳强度（右侧柱子）
|
||
ax2_twin = ax2.twinx()
|
||
|
||
bars_intensity = ax2_twin.bar(x + width/2, co2_per_ton, width, color=MACARON['cream'],
|
||
edgecolor='#666666', linewidth=0.5)
|
||
|
||
ax2_twin.set_ylabel('CO₂ per ton payload (kg)', color='#B8860B')
|
||
ax2_twin.tick_params(axis='y', labelcolor='#B8860B')
|
||
|
||
# 添加碳强度标签
|
||
for i, val in enumerate(co2_per_ton):
|
||
ax2_twin.text(i + width/2, val + 3000, f'{val:.0f}', ha='center', fontsize=9, fontweight='bold')
|
||
|
||
ax2.set_title('(b) Annual Emissions & Carbon Intensity', fontweight='bold', pad=10)
|
||
ax2.set_xticks(x)
|
||
ax2.set_xticklabels(scenarios, rotation=20, ha='right')
|
||
ax2.grid(True, alpha=0.2, axis='y', linestyle='--')
|
||
ax2.set_axisbelow(True)
|
||
|
||
# 图例说明
|
||
from matplotlib.patches import Patch
|
||
legend_elements = [Patch(facecolor=MACARON['pink'], edgecolor='#666666', label='Annual CO₂'),
|
||
Patch(facecolor=MACARON['cream'], edgecolor='#666666', label='Carbon Intensity')]
|
||
ax2.legend(handles=legend_elements, loc='upper right', framealpha=0.95, edgecolor='#cccccc')
|
||
|
||
plt.tight_layout()
|
||
plt.savefig(f'{save_dir}/environmental_comparison.png', dpi=300, bbox_inches='tight',
|
||
facecolor='white', edgecolor='none')
|
||
print(f"环境影响对比图已保存至: {save_dir}/environmental_comparison.png")
|
||
|
||
return df
|
||
|
||
|
||
def plot_radar_chart(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
|
||
"""单独绘制雷达图（马卡龙色系，无标题，论文风格）"""
|
||
df = analyze_all_scenarios()
|
||
|
||
# 马卡龙色系
|
||
MACARON = {
|
||
'pink': '#F8B4C0', # 樱花粉
|
||
'mint': '#A8E6CF', # 薄荷绿
|
||
'lavender': '#C5A3FF', # 薰衣草紫
|
||
'sky': '#A8D8EA', # 天空蓝
|
||
}
|
||
colors = [MACARON['pink'], MACARON['mint'], MACARON['sky'], MACARON['lavender']]
|
||
|
||
# 论文风格设置
|
||
plt.rcParams.update({
|
||
'font.size': 11,
|
||
'axes.labelsize': 12,
|
||
'xtick.labelsize': 11,
|
||
'ytick.labelsize': 10,
|
||
'legend.fontsize': 10,
|
||
})
|
||
|
||
fig, ax = plt.subplots(figsize=(5, 5), subplot_kw=dict(projection='polar'))
|
||
fig.patch.set_facecolor('white')
|
||
|
||
categories = ['Time\n(inv)', 'Energy\n(inv)', 'CO₂\n(inv)', 'Launches\n(inv)', 'Strat. H₂O\n(inv)']
|
||
N = len(categories)
|
||
|
||
# 归一化数据 (取逆以使"更好"在外围)
|
||
max_vals = df[['Years', 'Energy (PJ)', 'CO2 Total (Mt)', 'Launches', 'H2O Stratosphere (Mt)']].max()
|
||
|
||
angles = np.linspace(0, 2*np.pi, N, endpoint=False).tolist()
|
||
angles += angles[:1]
|
||
|
||
for i, (idx, row) in enumerate(df.iterrows()):
|
||
values = [
|
||
1 - row['Years'] / max_vals['Years'],
|
||
1 - row['Energy (PJ)'] / max_vals['Energy (PJ)'],
|
||
1 - row['CO2 Total (Mt)'] / max_vals['CO2 Total (Mt)'],
|
||
1 - row['Launches'] / max_vals['Launches'] if max_vals['Launches'] > 0 else 1,
|
||
1 - row['H2O Stratosphere (Mt)'] / max_vals['H2O Stratosphere (Mt)'] if max_vals['H2O Stratosphere (Mt)'] > 0 else 1
|
||
]
|
||
values += values[:1]
|
||
ax.plot(angles, values, 'o-', linewidth=2, label=row['Scenario'],
|
||
color=colors[i], markersize=5)
|
||
ax.fill(angles, values, alpha=0.2, color=colors[i])
|
||
|
||
ax.set_xticks(angles[:-1])
|
||
ax.set_xticklabels(categories, fontsize=10)
|
||
ax.set_ylim(0, 1)
|
||
|
||
# 设置径向刻度
|
||
ax.set_yticks([0.2, 0.4, 0.6, 0.8, 1.0])
|
||
ax.set_yticklabels(['0.2', '0.4', '0.6', '0.8', '1.0'], fontsize=8, color='#666666')
|
||
|
||
# 图例放在右侧
|
||
ax.legend(loc='upper left', bbox_to_anchor=(1.05, 1.0), framealpha=0.95,
|
||
edgecolor='#cccccc')
|
||
|
||
# 网格样式
|
||
ax.grid(True, alpha=0.3, linestyle='--')
|
||
ax.spines['polar'].set_color('#666666')
|
||
ax.spines['polar'].set_linewidth(0.8)
|
||
|
||
plt.tight_layout()
|
||
plt.savefig(f'{save_dir}/radar_chart.png', dpi=300, bbox_inches='tight',
|
||
facecolor='white', edgecolor='none')
|
||
print(f"雷达图已保存至: {save_dir}/radar_chart.png")
|
||
|
||
return df
|
||
|
||
|
||
def plot_marginal_benefit(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
|
||
"""单独绘制边际收益分析图（马卡龙色系，无标题，论文风格）"""
|
||
|
||
# 马卡龙色系
|
||
MACARON = {
|
||
'sky': '#A8D8EA', # 天空蓝
|
||
'pink': '#F8B4C0', # 樱花粉
|
||
'mint': '#A8E6CF', # 薄荷绿
|
||
}
|
||
|
||
# 论文风格设置
|
||
plt.rcParams.update({
|
||
'font.size': 11,
|
||
'axes.labelsize': 12,
|
||
'xtick.labelsize': 10,
|
||
'ytick.labelsize': 10,
|
||
'legend.fontsize': 10,
|
||
'axes.linewidth': 0.8,
|
||
})
|
||
|
||
# 生成Pareto数据
|
||
years_range = np.linspace(101, 200, 100)
|
||
|
||
results = []
|
||
for years in years_range:
|
||
# 计算电梯运输比例
|
||
elevator_payload = min(TOTAL_ELEVATOR_CAPACITY * years, TOTAL_PAYLOAD)
|
||
elevator_frac = elevator_payload / TOTAL_PAYLOAD
|
||
|
||
# 确定使用的发射场
|
||
rocket_payload = TOTAL_PAYLOAD - elevator_payload
|
||
if rocket_payload > 0:
|
||
rocket_capacity_per_site = 365 * years * PAYLOAD_PER_LAUNCH
|
||
n_sites_needed = int(np.ceil(rocket_payload / rocket_capacity_per_site))
|
||
n_sites_needed = min(n_sites_needed, 10)
|
||
sites = LAUNCH_SITES[:n_sites_needed]
|
||
else:
|
||
sites = []
|
||
|
||
impact = calculate_scenario_impact(
|
||
scenario_name=f'{years:.0f}yr',
|
||
completion_years=years,
|
||
elevator_fraction=elevator_frac,
|
||
rocket_sites_used=sites
|
||
)
|
||
|
||
results.append({
|
||
'Years': years,
|
||
'Energy (PJ)': impact.total_energy_PJ,
|
||
'CO2 (Mt)': impact.co2_total_Mt,
|
||
})
|
||
|
||
df = pd.DataFrame(results)
|
||
|
||
# 计算边际减少率
|
||
T = df['Years'].values
|
||
E = df['Energy (PJ)'].values
|
||
C = df['CO2 (Mt)'].values
|
||
|
||
dE_dT = np.gradient(E, T)
|
||
dC_dT = np.gradient(C, T)
|
||
|
||
energy_reduction_rate = -dE_dT # 每多1年节省的能量
|
||
co2_reduction_rate = -dC_dT # 每多1年减少的CO2
|
||
|
||
# 绘图（黄金比例）
|
||
fig, ax = plt.subplots(figsize=(8, 8*0.618))
|
||
fig.patch.set_facecolor('white')
|
||
ax.set_facecolor('white')
|
||
|
||
# 能量减少率
|
||
ax.plot(T, energy_reduction_rate, color=MACARON['sky'], linewidth=2,
|
||
label='Energy reduction rate')
|
||
ax.fill_between(T, energy_reduction_rate, alpha=0.2, color=MACARON['sky'])
|
||
|
||
# CO2减少率（使用第二Y轴）
|
||
ax2 = ax.twinx()
|
||
ax2.plot(T, co2_reduction_rate, color=MACARON['pink'], linewidth=2,
|
||
label='CO₂ reduction rate')
|
||
ax2.fill_between(T, co2_reduction_rate, alpha=0.2, color=MACARON['pink'])
|
||
|
||
# 推荐点
|
||
recommended_year = 186
|
||
ax.axvline(x=recommended_year, color=MACARON['mint'], linestyle='--', linewidth=2,
|
||
label=f'Recommended: {recommended_year}yr')
|
||
|
||
ax.set_xlabel('Completion Time (years)')
|
||
ax.set_ylabel('Energy Reduction (PJ/yr)', color='#5B9BD5')
|
||
ax.tick_params(axis='y', labelcolor='#5B9BD5')
|
||
|
||
ax2.set_ylabel('CO₂ Reduction (Mt/yr)', color='#E57373')
|
||
ax2.tick_params(axis='y', labelcolor='#E57373')
|
||
|
||
ax.grid(True, alpha=0.2, linestyle='--')
|
||
ax.set_axisbelow(True)
|
||
|
||
# 合并图例
|
||
lines1, labels1 = ax.get_legend_handles_labels()
|
||
lines2, labels2 = ax2.get_legend_handles_labels()
|
||
ax.legend(lines1 + lines2, labels1 + labels2, loc='upper right',
|
||
framealpha=0.95, edgecolor='#cccccc')
|
||
|
||
plt.tight_layout()
|
||
plt.savefig(f'{save_dir}/marginal_benefit.png', dpi=300, bbox_inches='tight',
|
||
facecolor='white', edgecolor='none')
|
||
print(f"边际收益图已保存至: {save_dir}/marginal_benefit.png")
|
||
|
||
return df
|
||
|
||
|
||
def plot_pareto_with_environment(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
|
||
"""绘制时间-能量-环境三目标Pareto图"""
|
||
|
||
fig, axes = plt.subplots(1, 2, figsize=(14, 6))
|
||
|
||
# 生成Pareto数据
|
||
years_range = np.linspace(101, 220, 50)
|
||
|
||
results = []
|
||
for years in years_range:
|
||
# 计算电梯运输比例
|
||
elevator_payload = min(TOTAL_ELEVATOR_CAPACITY * years, TOTAL_PAYLOAD)
|
||
elevator_frac = elevator_payload / TOTAL_PAYLOAD
|
||
|
||
# 确定使用的发射场
|
||
rocket_payload = TOTAL_PAYLOAD - elevator_payload
|
||
if rocket_payload > 0:
|
||
rocket_capacity_per_site = 365 * years * PAYLOAD_PER_LAUNCH
|
||
n_sites_needed = int(np.ceil(rocket_payload / rocket_capacity_per_site))
|
||
n_sites_needed = min(n_sites_needed, 10)
|
||
sites = LAUNCH_SITES[:n_sites_needed]
|
||
else:
|
||
sites = []
|
||
|
||
impact = calculate_scenario_impact(
|
||
scenario_name=f'{years:.0f}yr',
|
||
completion_years=years,
|
||
elevator_fraction=elevator_frac,
|
||
rocket_sites_used=sites
|
||
)
|
||
|
||
results.append({
|
||
'Years': years,
|
||
'Energy (PJ)': impact.total_energy_PJ,
|
||
'CO2 (Mt)': impact.co2_total_Mt,
|
||
'Elevator %': elevator_frac * 100
|
||
})
|
||
|
||
df = pd.DataFrame(results)
|
||
|
||
# ========== 图1: 时间 vs CO2 ==========
|
||
ax1 = axes[0]
|
||
|
||
scatter = ax1.scatter(df['Years'], df['CO2 (Mt)'], c=df['Elevator %'],
|
||
cmap='RdYlGn', s=50, alpha=0.8)
|
||
plt.colorbar(scatter, ax=ax1, label='Elevator %')
|
||
|
||
ax1.set_xlabel('Completion Time (years)', fontsize=12)
|
||
ax1.set_ylabel('Total CO₂ Emissions (Mt)', fontsize=12)
|
||
ax1.set_title('Pareto Front: Time vs CO₂ Emissions\n(Color: Elevator Usage)', fontsize=14)
|
||
ax1.grid(True, alpha=0.3)
|
||
|
||
# 标记关键点
|
||
ax1.axvline(x=139, color='red', linestyle='--', alpha=0.7, label='Knee Point (139yr)')
|
||
ax1.axvline(x=186, color='green', linestyle='--', alpha=0.7, label='Elevator Only (186yr)')
|
||
ax1.legend()
|
||
|
||
# ========== 图2: 能量 vs CO2 ==========
|
||
ax2 = axes[1]
|
||
|
||
scatter = ax2.scatter(df['Energy (PJ)'], df['CO2 (Mt)'], c=df['Years'],
|
||
cmap='viridis', s=50, alpha=0.8)
|
||
plt.colorbar(scatter, ax=ax2, label='Years')
|
||
|
||
ax2.set_xlabel('Total Energy (PJ)', fontsize=12)
|
||
ax2.set_ylabel('Total CO₂ Emissions (Mt)', fontsize=12)
|
||
ax2.set_title('Pareto Front: Energy vs CO₂ Emissions\n(Color: Completion Time)', fontsize=14)
|
||
ax2.grid(True, alpha=0.3)
|
||
|
||
plt.tight_layout()
|
||
plt.savefig(f'{save_dir}/pareto_environmental.png', dpi=150, bbox_inches='tight')
|
||
print(f"Pareto环境分析图已保存至: {save_dir}/pareto_environmental.png")
|
||
|
||
return df
|
||
|
||
|
||
def plot_mitigation_strategies(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
|
||
"""绘制减排策略对比图"""
|
||
|
||
# 基准情景 (混合膝点)
|
||
baseline = calculate_scenario_impact(
|
||
scenario_name='Baseline',
|
||
completion_years=139,
|
||
elevator_fraction=0.746,
|
||
rocket_sites_used=LAUNCH_SITES[:5],
|
||
renewable_energy_fraction=0.0
|
||
)
|
||
|
||
# 策略1: 50%可再生能源
|
||
strategy1 = calculate_scenario_impact(
|
||
scenario_name='50% Renewable',
|
||
completion_years=139,
|
||
elevator_fraction=0.746,
|
||
rocket_sites_used=LAUNCH_SITES[:5],
|
||
renewable_energy_fraction=0.5
|
||
)
|
||
|
||
# 策略2: 100%可再生能源
|
||
strategy2 = calculate_scenario_impact(
|
||
scenario_name='100% Renewable',
|
||
completion_years=139,
|
||
elevator_fraction=0.746,
|
||
rocket_sites_used=LAUNCH_SITES[:5],
|
||
renewable_energy_fraction=1.0
|
||
)
|
||
|
||
# 策略3: 延长工期到纯电梯
|
||
strategy3 = calculate_scenario_impact(
|
||
scenario_name='Elevator Only',
|
||
completion_years=186,
|
||
elevator_fraction=1.0,
|
||
rocket_sites_used=[],
|
||
renewable_energy_fraction=0.0
|
||
)
|
||
|
||
# 策略4: 纯电梯 + 100%可再生
|
||
strategy4 = calculate_scenario_impact(
|
||
scenario_name='Elevator + 100% Renewable',
|
||
completion_years=186,
|
||
elevator_fraction=1.0,
|
||
rocket_sites_used=[],
|
||
renewable_energy_fraction=1.0
|
||
)
|
||
|
||
strategies = [baseline, strategy1, strategy2, strategy3, strategy4]
|
||
|
||
fig, ax = plt.subplots(figsize=(12, 7))
|
||
|
||
names = [s.scenario_name for s in strategies]
|
||
co2_vals = [s.co2_total_Mt for s in strategies]
|
||
reductions = [(baseline.co2_total_Mt - s.co2_total_Mt) / baseline.co2_total_Mt * 100
|
||
for s in strategies]
|
||
|
||
x = np.arange(len(names))
|
||
colors = ['#FF6B6B', '#FFE66D', '#4ECDC4', '#45B7D1', '#96CEB4']
|
||
|
||
bars = ax.bar(x, co2_vals, color=colors)
|
||
|
||
ax.set_ylabel('Total CO₂ Emissions (Mt)', fontsize=12)
|
||
ax.set_title('CO₂ Reduction Strategies Comparison\n(Knee Point baseline: 139 years)', fontsize=14)
|
||
ax.set_xticks(x)
|
||
ax.set_xticklabels(names, rotation=15, ha='right')
|
||
ax.grid(True, alpha=0.3, axis='y')
|
||
|
||
# 添加数值和减排比例
|
||
for i, (bar, val, red) in enumerate(zip(bars, co2_vals, reductions)):
|
||
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 20,
|
||
f'{val:.0f} Mt\n({red:+.1f}%)', ha='center', fontsize=10)
|
||
|
||
# 添加基准线
|
||
ax.axhline(y=baseline.co2_total_Mt, color='red', linestyle='--', alpha=0.5,
|
||
label=f'Baseline: {baseline.co2_total_Mt:.0f} Mt')
|
||
ax.legend()
|
||
|
||
plt.tight_layout()
|
||
plt.savefig(f'{save_dir}/mitigation_strategies.png', dpi=150, bbox_inches='tight')
|
||
print(f"减排策略对比图已保存至: {save_dir}/mitigation_strategies.png")
|
||
|
||
|
||
def plot_comprehensive_summary(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
|
||
"""绘制综合汇总图"""
|
||
|
||
df = analyze_all_scenarios()
|
||
|
||
fig = plt.figure(figsize=(16, 12))
|
||
|
||
# 创建4x4网格
|
||
gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)
|
||
|
||
scenarios = df['Scenario'].tolist()
|
||
|
||
# ========== 左上: 雷达图 ==========
|
||
ax1 = fig.add_subplot(gs[0, 0], projection='polar')
|
||
|
||
categories = ['Time\n(inv)', 'Energy\n(inv)', 'CO2\n(inv)', 'Launches\n(inv)', 'Strat H2O\n(inv)']
|
||
N = len(categories)
|
||
|
||
# 归一化数据 (取逆以使"更好"在外围)
|
||
max_vals = df[['Years', 'Energy (PJ)', 'CO2 Total (Mt)', 'Launches', 'H2O Stratosphere (Mt)']].max()
|
||
|
||
angles = np.linspace(0, 2*np.pi, N, endpoint=False).tolist()
|
||
angles += angles[:1]
|
||
|
||
colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4']
|
||
|
||
for i, (idx, row) in enumerate(df.iterrows()):
|
||
values = [
|
||
1 - row['Years'] / max_vals['Years'],
|
||
1 - row['Energy (PJ)'] / max_vals['Energy (PJ)'],
|
||
1 - row['CO2 Total (Mt)'] / max_vals['CO2 Total (Mt)'],
|
||
1 - row['Launches'] / max_vals['Launches'] if max_vals['Launches'] > 0 else 1,
|
||
1 - row['H2O Stratosphere (Mt)'] / max_vals['H2O Stratosphere (Mt)'] if max_vals['H2O Stratosphere (Mt)'] > 0 else 1
|
||
]
|
||
values += values[:1]
|
||
ax1.plot(angles, values, 'o-', linewidth=2, label=row['Scenario'], color=colors[i])
|
||
ax1.fill(angles, values, alpha=0.15, color=colors[i])
|
||
|
||
ax1.set_xticks(angles[:-1])
|
||
ax1.set_xticklabels(categories, fontsize=9)
|
||
ax1.set_title('Multi-Criteria Comparison\n(outer = better)', fontsize=12, pad=20)
|
||
ax1.legend(loc='upper right', bbox_to_anchor=(1.3, 1.0), fontsize=8)
|
||
|
||
# ========== 右上: 汇总表格 ==========
|
||
ax2 = fig.add_subplot(gs[0, 1:])
|
||
ax2.axis('off')
|
||
|
||
table_data = []
|
||
for idx, row in df.iterrows():
|
||
table_data.append([
|
||
row['Scenario'],
|
||
f"{row['Years']:.0f}",
|
||
f"{row['Energy (PJ)']:.0f}",
|
||
f"{row['CO2 Total (Mt)']:.0f}",
|
||
f"{row['H2O Stratosphere (Mt)']:.1f}",
|
||
f"{row['Annual CO2 (Mt/yr)']:.1f}",
|
||
])
|
||
|
||
table = ax2.table(
|
||
cellText=table_data,
|
||
colLabels=['Scenario', 'Years', 'Energy(PJ)', 'CO2(Mt)', 'Strat.H2O(Mt)', 'Annual CO2'],
|
||
loc='center',
|
||
cellLoc='center'
|
||
)
|
||
table.auto_set_font_size(False)
|
||
table.set_fontsize(10)
|
||
table.scale(1.2, 1.8)
|
||
|
||
# 高亮最优值
|
||
for i in range(len(df)):
|
||
for j in range(1, 6):
|
||
cell = table[(i+1, j)]
|
||
if j == 1 and df.iloc[i]['Years'] == df['Years'].min():
|
||
cell.set_facecolor('#90EE90')
|
||
elif j == 2 and df.iloc[i]['Energy (PJ)'] == df['Energy (PJ)'].min():
|
||
cell.set_facecolor('#90EE90')
|
||
elif j == 3 and df.iloc[i]['CO2 Total (Mt)'] == df['CO2 Total (Mt)'].min():
|
||
cell.set_facecolor('#90EE90')
|
||
|
||
ax2.set_title('Scenario Summary Table\n(Green = Best in Category)', fontsize=12, pad=10)
|
||
|
||
# ========== 中间行: CO2分解 + 时间线 ==========
|
||
ax3 = fig.add_subplot(gs[1, :2])
|
||
|
||
x = np.arange(len(scenarios))
|
||
width = 0.25
|
||
|
||
ax3.bar(x - width, df['CO2 Combustion (Mt)'], width, label='Combustion', color='#FF6B6B')
|
||
ax3.bar(x, df['CO2 Production (Mt)'], width, label='Fuel Production', color='#4ECDC4')
|
||
ax3.bar(x + width, df['CO2 Electricity (Mt)'], width, label='Electricity', color='#45B7D1')
|
||
|
||
ax3.set_ylabel('CO₂ (Mt)', fontsize=11)
|
||
ax3.set_title('CO₂ Emissions by Source', fontsize=12)
|
||
ax3.set_xticks(x)
|
||
ax3.set_xticklabels(scenarios, rotation=15, ha='right')
|
||
ax3.legend()
|
||
ax3.grid(True, alpha=0.3, axis='y')
|
||
|
||
# ========== 右侧: 时间-CO2权衡 ==========
|
||
ax4 = fig.add_subplot(gs[1, 2])
|
||
|
||
ax4.scatter(df['Years'], df['CO2 Total (Mt)'], s=200, c=colors, edgecolors='black', linewidth=2)
|
||
|
||
for i, row in df.iterrows():
|
||
ax4.annotate(row['Scenario'].replace(' ', '\n'),
|
||
(row['Years'], row['CO2 Total (Mt)']),
|
||
textcoords="offset points", xytext=(0, 15), ha='center', fontsize=8)
|
||
|
||
ax4.set_xlabel('Completion Time (years)', fontsize=11)
|
||
ax4.set_ylabel('Total CO₂ (Mt)', fontsize=11)
|
||
ax4.set_title('Time-CO₂ Trade-off', fontsize=12)
|
||
ax4.grid(True, alpha=0.3)
|
||
|
||
# ========== 底行: 结论文字 ==========
|
||
ax5 = fig.add_subplot(gs[2, :])
|
||
ax5.axis('off')
|
||
|
||
conclusions = """
|
||
KEY FINDINGS (LOX/CH4 Fuel):
|
||
|
||
1. Rocket Only: Highest CO₂ emissions ({:.0f} Mt) due to large fuel consumption ({:.1f} Mt fuel).
|
||
- Annual emissions: {:.1f} Mt/yr (≈{:.3f}% of global annual emissions)
|
||
- Stratospheric H₂O injection: {:.1f} Mt (potential climate impact)
|
||
|
||
2. Elevator Only: Lowest CO₂ emissions ({:.0f} Mt), but longest timeline ({:.0f} years).
|
||
- CO₂ reduction vs Rocket: {:.0f}% (primary savings from avoiding rocket fuel production)
|
||
- No stratospheric H₂O injection from main transport
|
||
|
||
3. Combined (Knee Point): Best trade-off at 139 years with {:.0f} Mt CO₂.
|
||
- 74.6% payload via elevator, 25.4% via low-latitude rockets
|
||
- Reasonable timeline with significant environmental benefit
|
||
|
||
4. RECOMMENDATIONS for Minimizing Environmental Impact:
|
||
a) Prioritize space elevator transport (saves ~{:.0f}% CO₂ vs pure rocket)
|
||
b) Use renewable energy for elevator operation (reduces electricity emissions)
|
||
c) If rockets needed, prefer low-latitude sites (lower fuel per kg)
|
||
d) Consider carbon capture for CH4 production phase
|
||
""".format(
|
||
df.loc[df['Scenario']=='Rocket Only', 'CO2 Total (Mt)'].values[0],
|
||
df.loc[df['Scenario']=='Rocket Only', 'Fuel (Mt)'].values[0],
|
||
df.loc[df['Scenario']=='Rocket Only', 'Annual CO2 (Mt/yr)'].values[0],
|
||
df.loc[df['Scenario']=='Rocket Only', 'Annual CO2 (Mt/yr)'].values[0] / 37000 * 100,
|
||
df.loc[df['Scenario']=='Rocket Only', 'H2O Stratosphere (Mt)'].values[0],
|
||
df.loc[df['Scenario']=='Elevator Only', 'CO2 Total (Mt)'].values[0],
|
||
df.loc[df['Scenario']=='Elevator Only', 'Years'].values[0],
|
||
(1 - df.loc[df['Scenario']=='Elevator Only', 'CO2 Total (Mt)'].values[0] /
|
||
df.loc[df['Scenario']=='Rocket Only', 'CO2 Total (Mt)'].values[0]) * 100,
|
||
df.loc[df['Scenario']=='Combined (Knee Point)', 'CO2 Total (Mt)'].values[0],
|
||
(1 - df.loc[df['Scenario']=='Elevator Only', 'CO2 Total (Mt)'].values[0] /
|
||
df.loc[df['Scenario']=='Rocket Only', 'CO2 Total (Mt)'].values[0]) * 100
|
||
)
|
||
|
||
ax5.text(0.02, 0.95, conclusions, transform=ax5.transAxes, fontsize=10,
|
||
verticalalignment='top', fontfamily='monospace',
|
||
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
|
||
|
||
plt.suptitle('Moon Colony Environmental Impact Analysis\n'
|
||
'(100 Million Tons Payload, LOX/CH4 Fuel)', fontsize=14, y=0.98)
|
||
|
||
plt.savefig(f'{save_dir}/comprehensive_summary.png', dpi=150, bbox_inches='tight')
|
||
print(f"综合汇总图已保存至: {save_dir}/comprehensive_summary.png")
|
||
|
||
|
||
# ============== 报告生成 ==============
|
||
|
||
def generate_report(save_dir: str = '/Volumes/Files/code/mm/20260130_b/p4'):
|
||
"""生成分析报告"""
|
||
df = analyze_all_scenarios()
|
||
|
||
# 优化结果
|
||
opt_result = optimize_for_environment(
|
||
max_annual_co2_Mt=50.0,
|
||
min_completion_years=120.0,
|
||
renewable_fraction=0.5
|
||
)
|
||
|
||
report = []
|
||
report.append("=" * 80)
|
||
report.append("TASK 4: ENVIRONMENTAL IMPACT ANALYSIS")
|
||
report.append("Moon Colony Construction - 100 Million Metric Tons")
|
||
report.append("Fuel: LOX/CH4 (Liquid Oxygen / Methane)")
|
||
report.append("=" * 80)
|
||
|
||
report.append("\n" + "=" * 80)
|
||
report.append("1. FUEL PARAMETERS (LOX/CH4)")
|
||
report.append("=" * 80)
|
||
report.append(f" Specific Impulse (Isp): {FUEL_PARAMS.isp} s")
|
||
report.append(f" Exhaust Velocity: {FUEL_PARAMS.exhaust_velocity:.0f} m/s")
|
||
report.append(f" Specific Energy: {FUEL_PARAMS.specific_energy/1e6:.1f} MJ/kg")
|
||
report.append(f" O2/CH4 Mass Ratio: {FUEL_PARAMS.ox_fuel_ratio}:1")
|
||
report.append(f" CH4 Mass Fraction: {FUEL_PARAMS.ch4_mass_fraction*100:.1f}%")
|
||
report.append(f"\n Combustion Reaction: CH4 + 2O2 → CO2 + 2H2O")
|
||
report.append(f" CO2 per kg fuel: {EMISSION_FACTORS.co2_per_kg_fuel:.3f} kg")
|
||
report.append(f" H2O per kg fuel: {EMISSION_FACTORS.h2o_per_kg_fuel:.3f} kg")
|
||
|
||
report.append("\n" + "=" * 80)
|
||
report.append("2. SCENARIO COMPARISON")
|
||
report.append("=" * 80)
|
||
|
||
for idx, row in df.iterrows():
|
||
report.append(f"\n {row['Scenario']}:")
|
||
report.append(f" Completion Time: {row['Years']:.0f} years")
|
||
report.append(f" Total Energy: {row['Energy (PJ)']:.0f} PJ")
|
||
report.append(f" Total Fuel: {row['Fuel (Mt)']:.2f} Mt")
|
||
report.append(f" CO2 Emissions: {row['CO2 Total (Mt)']:.0f} Mt")
|
||
report.append(f" - Combustion: {row['CO2 Combustion (Mt)']:.0f} Mt")
|
||
report.append(f" - Fuel Production: {row['CO2 Production (Mt)']:.0f} Mt")
|
||
report.append(f" - Electricity: {row['CO2 Electricity (Mt)']:.1f} Mt")
|
||
report.append(f" H2O Emissions: {row['H2O Total (Mt)']:.2f} Mt")
|
||
report.append(f" - Stratospheric: {row['H2O Stratosphere (Mt)']:.2f} Mt")
|
||
report.append(f" Annual CO2: {row['Annual CO2 (Mt/yr)']:.2f} Mt/yr ({row['Annual CO2 (Mt/yr)']/370:.3f}% of global)")
|
||
report.append(f" CO2 per ton payload: {row['CO2/ton (kg)']:.0f} kg/ton")
|
||
|
||
report.append("\n" + "=" * 80)
|
||
report.append("3. ENVIRONMENTAL IMPACT RANKING")
|
||
report.append("=" * 80)
|
||
|
||
df_sorted = df.sort_values('CO2 Total (Mt)')
|
||
report.append("\n By Total CO2 (lowest first):")
|
||
for i, (idx, row) in enumerate(df_sorted.iterrows(), 1):
|
||
report.append(f" {i}. {row['Scenario']}: {row['CO2 Total (Mt)']:.0f} Mt")
|
||
|
||
rocket_co2 = df.loc[df['Scenario']=='Rocket Only', 'CO2 Total (Mt)'].values[0]
|
||
report.append("\n CO2 Reduction vs Rocket Only:")
|
||
for idx, row in df.iterrows():
|
||
if row['Scenario'] != 'Rocket Only':
|
||
reduction = (rocket_co2 - row['CO2 Total (Mt)']) / rocket_co2 * 100
|
||
report.append(f" {row['Scenario']}: -{reduction:.1f}%")
|
||
|
||
report.append("\n" + "=" * 80)
|
||
report.append("4. OPTIMIZATION FOR MINIMUM ENVIRONMENTAL IMPACT")
|
||
report.append("=" * 80)
|
||
|
||
if opt_result:
|
||
report.append(f"\n Optimization Constraints:")
|
||
report.append(f" - Minimum completion time: 120 years")
|
||
report.append(f" - Maximum annual CO2: 50 Mt/yr")
|
||
report.append(f" - Renewable energy fraction: 50%")
|
||
report.append(f"\n Optimal Solution:")
|
||
report.append(f" - Elevator fraction: {opt_result['elevator_fraction']*100:.1f}%")
|
||
report.append(f" - Rocket sites used: {opt_result['n_sites']}")
|
||
report.append(f" - Completion time: {opt_result['years']:.0f} years")
|
||
report.append(f" - Total CO2: {opt_result['impact'].co2_total_Mt:.0f} Mt")
|
||
report.append(f" - Annual CO2: {opt_result['impact'].annual_co2_Mt:.2f} Mt/yr")
|
||
|
||
report.append("\n" + "=" * 80)
|
||
report.append("5. MODEL ADJUSTMENT RECOMMENDATIONS")
|
||
report.append("=" * 80)
|
||
report.append("""
|
||
To minimize environmental impact, adjust the model as follows:
|
||
|
||
A. OBJECTIVE FUNCTION MODIFICATION:
|
||
Original: min(α×Time + β×Energy)
|
||
Modified: min(α×Time + β×Energy + γ×CO2_total + δ×H2O_stratosphere)
|
||
|
||
Where γ and δ are weights for environmental costs.
|
||
|
||
B. RECOMMENDED STRATEGIES (by effectiveness):
|
||
|
||
1. Prioritize Elevator Transport (CO2 reduction: ~70-80%)
|
||
- Elevator has no direct combustion emissions
|
||
- Top rocket stage emissions much smaller than full rocket launch
|
||
|
||
2. Use Renewable Energy for Elevator (CO2 reduction: 5-10%)
|
||
- Replace grid electricity with solar/wind
|
||
- Reduces electricity-related emissions
|
||
|
||
3. Low-Latitude Launch Sites Only (CO2 reduction: 3-5%)
|
||
- Lower ΔV requirements = less fuel
|
||
- Kourou (5.2°) vs Alaska (57.4°): ~10% fuel difference
|
||
|
||
4. Green Methane Production (CO2 reduction: 15-20%)
|
||
- Use bio-methane or synthetic methane from CO2 capture
|
||
- Reduces fuel production emissions
|
||
|
||
5. Extend Timeline (CO2 reduction: proportional)
|
||
- More time → more elevator use → less rocket use
|
||
- Trade-off: slower colony establishment
|
||
|
||
C. QUANTIFIED IMPACT:
|
||
- Baseline (Rocket Only): {:.0f} Mt CO2
|
||
- Best Case (Elevator + 100% Renewable): {:.0f} Mt CO2
|
||
- Maximum Reduction: {:.1f}%
|
||
""".format(
|
||
df.loc[df['Scenario']=='Rocket Only', 'CO2 Total (Mt)'].values[0],
|
||
calculate_scenario_impact('Best', 186, 1.0, [], renewable_energy_fraction=1.0).co2_total_Mt,
|
||
(1 - calculate_scenario_impact('Best', 186, 1.0, [], renewable_energy_fraction=1.0).co2_total_Mt /
|
||
df.loc[df['Scenario']=='Rocket Only', 'CO2 Total (Mt)'].values[0]) * 100
|
||
))
|
||
|
||
report.append("\n" + "=" * 80)
|
||
report.append("6. STRATOSPHERIC IMPACT ANALYSIS")
|
||
report.append("=" * 80)
|
||
report.append(f"""
|
||
Stratospheric Water Vapor Injection (Rocket Only scenario):
|
||
|
||
- Total H2O emitted: {df.loc[df['Scenario']=='Rocket Only', 'H2O Total (Mt)'].values[0]:.2f} Mt
|
||
- Stratospheric fraction: {EMISSION_FACTORS.stratosphere_emission_fraction*100:.0f}%
|
||
- Stratospheric H2O: {df.loc[df['Scenario']=='Rocket Only', 'H2O Stratosphere (Mt)'].values[0]:.2f} Mt
|
||
- Residence time: {EMISSION_FACTORS.stratosphere_h2o_residence_time:.0f} years
|
||
|
||
Potential Climate Effects:
|
||
- Stratospheric H2O has greenhouse effect (radiative forcing)
|
||
- May affect ozone chemistry
|
||
- Elevator-only scenario eliminates this impact entirely
|
||
""")
|
||
|
||
report.append("=" * 80)
|
||
|
||
# 保存报告
|
||
report_text = '\n'.join(report)
|
||
with open(f'{save_dir}/environmental_report.txt', 'w', encoding='utf-8') as f:
|
||
f.write(report_text)
|
||
print(f"分析报告已保存至: {save_dir}/environmental_report.txt")
|
||
|
||
# 保存CSV
|
||
df.to_csv(f'{save_dir}/scenario_comparison.csv', index=False)
|
||
print(f"数据表已保存至: {save_dir}/scenario_comparison.csv")
|
||
|
||
return report_text
|
||
|
||
|
||
# ============== 主程序 ==============
|
||
|
||
if __name__ == "__main__":
|
||
print("=" * 80)
|
||
print("TASK 4: ENVIRONMENTAL IMPACT ANALYSIS")
|
||
print("Moon Colony Construction with LOX/CH4 Fuel")
|
||
print("=" * 80)
|
||
|
||
save_dir = '/Volumes/Files/code/mm/20260130_b/p4'
|
||
|
||
# 1. 基础情景对比
|
||
print("\n1. Analyzing baseline scenarios...")
|
||
df = plot_environmental_comparison(save_dir)
|
||
print("\nScenario Comparison:")
|
||
print(df.to_string(index=False))
|
||
|
||
# 2. Pareto分析
|
||
print("\n2. Generating Pareto analysis...")
|
||
plot_pareto_with_environment(save_dir)
|
||
|
||
# 3. 减排策略对比
|
||
print("\n3. Analyzing mitigation strategies...")
|
||
plot_mitigation_strategies(save_dir)
|
||
|
||
# 4. 综合汇总
|
||
print("\n4. Generating comprehensive summary...")
|
||
plot_comprehensive_summary(save_dir)
|
||
|
||
# 5. 生成报告
|
||
print("\n5. Generating report...")
|
||
generate_report(save_dir)
|
||
|
||
print("\n" + "=" * 80)
|
||
print("Analysis complete! All outputs saved to:", save_dir)
|
||
print("=" * 80)
|