679 lines
24 KiB
Python
679 lines
24 KiB
Python
"""
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Moon Colony Construction - Rocket Launch Scenario Analysis
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Problem: Deliver 100 million metric tons to Moon using traditional rockets
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- 10 launch sites, each max 1 launch per day
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- Rocket payload: 100-150 metric tons (use 125 tons average)
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- Optimize launch distribution to minimize energy consumption
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- Plot completion year vs total energy consumption
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Key insight: As completion timeline extends, more launches can be allocated
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to low-latitude sites, reducing the latitude-induced energy penalty.
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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, 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 ==============
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TOTAL_PAYLOAD = 100e6 # 100 million metric tons = 100e9 kg 的物资，但单位是metric tons
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PAYLOAD_PER_LAUNCH = 125 # metric tons per launch (average of 100-150)
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TOTAL_LAUNCHES_NEEDED = TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH # 800,000 launches
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# 火箭参数
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ISP = 363 # 比冲 (秒) - 液氧甲烷 (LOX/CH4, Raptor-class)
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SPECIFIC_FUEL_ENERGY = 11.9e6 # J/kg 燃料比能量
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ALPHA = 0.10 # 结构系数
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NUM_STAGES = 3 # 3级火箭
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# 地面发射基准 ΔV (赤道发射到月球轨道)
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DELTA_V_BASE = 13300 # m/s (LEO + TLI + LOI)
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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 # degrees
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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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"""地球自转速度 (m/s)"""
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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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"""相对赤道的ΔV损失 (m/s)"""
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v_equator = OMEGA_EARTH * R_EARTH # ~465 m/s
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return v_equator - self.rotation_velocity
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@property
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def total_delta_v(self) -> float:
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"""总ΔV需求 (m/s)"""
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return DELTA_V_BASE + self.delta_v_loss
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# 10个发射场 (按纬度排序)
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LAUNCH_SITES = [
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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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]
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# 按纬度排序
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LAUNCH_SITES = sorted(LAUNCH_SITES, key=lambda x: x.abs_latitude)
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# ============== 核心计算函数 ==============
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def fuel_ratio_multistage(delta_v: float, isp: float = ISP, alpha: float = ALPHA,
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num_stages: int = NUM_STAGES) -> float:
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"""
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多级火箭燃料/载荷比
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"""
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ve = isp * G0
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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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# 多级计算
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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 energy_per_launch(site: LaunchSite, payload_tons: float = PAYLOAD_PER_LAUNCH) -> float:
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"""
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计算单次发射的能量消耗 (Joules)
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参数:
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site: 发射场
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payload_tons: 载荷 (metric tons)
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返回:
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能量消耗 (J)
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"""
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delta_v = site.total_delta_v
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k = fuel_ratio_multistage(delta_v)
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payload_kg = payload_tons * 1000 # convert to kg
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fuel_kg = k * payload_kg
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energy = fuel_kg * SPECIFIC_FUEL_ENERGY
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return energy
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def fuel_per_launch(site: LaunchSite, payload_tons: float = PAYLOAD_PER_LAUNCH) -> float:
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"""
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计算单次发射的燃料消耗 (metric tons)
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"""
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delta_v = site.total_delta_v
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k = fuel_ratio_multistage(delta_v)
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return k * payload_tons
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# ============== 发射排布优化 ==============
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def calculate_launch_distribution(
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total_launches: int,
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completion_years: float,
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sites: List[LaunchSite] = LAUNCH_SITES
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) -> List[Tuple[LaunchSite, int]]:
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"""
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计算给定完成年限下的发射分配
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策略：优先使用低纬度发射场，直到达到其最大容量
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参数:
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total_launches: 总发射次数
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completion_years: 完成年限
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sites: 发射场列表 (已按纬度排序)
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返回:
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[(发射场, 分配的发射次数), ...]
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"""
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days_available = completion_years * 365
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max_launches_per_site = days_available # 每站每天最多1发
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distribution = []
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remaining_launches = total_launches
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# 按纬度从低到高分配
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for site in sites:
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if remaining_launches <= 0:
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distribution.append((site, 0))
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else:
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allocated = min(remaining_launches, max_launches_per_site)
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distribution.append((site, int(allocated)))
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remaining_launches -= allocated
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return distribution
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def calculate_total_energy(distribution: List[Tuple[LaunchSite, int]]) -> dict:
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"""
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计算发射分配的总能量消耗
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返回:
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包含各项统计的字典
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"""
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total_energy = 0
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total_fuel = 0
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total_launches = 0
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site_details = []
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for site, num_launches in distribution:
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if num_launches > 0:
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energy = energy_per_launch(site) * num_launches
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fuel = fuel_per_launch(site) * num_launches
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total_energy += energy
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total_fuel += fuel
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total_launches += num_launches
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site_details.append({
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'site': site.name,
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'short_name': site.short_name,
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'latitude': site.abs_latitude,
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'launches': num_launches,
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'energy_PJ': energy / 1e15, # PetaJoules
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'fuel_Mt': fuel / 1e6, # Million metric tons
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})
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return {
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'total_energy_PJ': total_energy / 1e15,
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'total_fuel_Mt': total_fuel / 1e6,
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'total_launches': total_launches,
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'sites': site_details
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}
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def analyze_completion_timeline(
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years_range: np.ndarray,
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total_payload: float = TOTAL_PAYLOAD,
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payload_per_launch: float = PAYLOAD_PER_LAUNCH
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) -> pd.DataFrame:
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"""
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分析不同完成年限下的能量消耗
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返回:
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DataFrame with analysis results
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"""
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total_launches = int(total_payload / payload_per_launch)
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results = []
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for years in years_range:
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distribution = calculate_launch_distribution(total_launches, years)
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stats = calculate_total_energy(distribution)
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# 计算使用了多少个发射场
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sites_used = sum(1 for _, n in distribution if n > 0)
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# 计算低纬度发射场的比例
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low_lat_launches = sum(n for site, n in distribution if site.abs_latitude < 30)
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low_lat_ratio = low_lat_launches / total_launches if total_launches > 0 else 0
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results.append({
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'years': years,
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'total_launches': total_launches,
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'sites_used': sites_used,
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'total_energy_PJ': stats['total_energy_PJ'],
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'total_fuel_Mt': stats['total_fuel_Mt'],
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'low_lat_ratio': low_lat_ratio,
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'energy_per_launch_TJ': stats['total_energy_PJ'] * 1000 / total_launches,
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})
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return pd.DataFrame(results)
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# ============== 可视化 ==============
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def plot_timeline_analysis(
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save_path: str = '/Volumes/Files/code/mm/20260130_b/rocket_launch_timeline.png'
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):
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"""
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绘制完成年份-能量消耗关系图
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"""
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# 计算最短完成时间
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total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
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num_sites = len(LAUNCH_SITES)
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min_years = total_launches / (num_sites * 365)
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print(f"Total payload: {TOTAL_PAYLOAD/1e6:.0f} million metric tons")
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print(f"Payload per launch: {PAYLOAD_PER_LAUNCH} metric tons")
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print(f"Total launches needed: {total_launches:,}")
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print(f"Number of launch sites: {num_sites}")
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print(f"Minimum completion time: {min_years:.1f} years")
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# 分析范围：从最短时间到5倍最短时间
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years_range = np.linspace(min_years, min_years * 5, 100)
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# 运行分析
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df = analyze_completion_timeline(years_range)
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# 创建图表
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fig, axes = plt.subplots(2, 2, figsize=(16, 14))
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# ========== 图1: 完成年份 vs 总能量消耗 ==========
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ax1 = axes[0, 0]
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ax1.plot(df['years'], df['total_energy_PJ'], 'b-', linewidth=2)
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ax1.fill_between(df['years'], df['total_energy_PJ'], alpha=0.3)
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# 标记关键点
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min_idx = df['total_energy_PJ'].idxmin()
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ax1.axvline(x=df.loc[min_idx, 'years'], color='r', linestyle='--',
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label=f'Optimal: {df.loc[min_idx, "years"]:.1f} years')
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ax1.axhline(y=df.loc[min_idx, 'total_energy_PJ'], color='r', linestyle=':', alpha=0.5)
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# 标记最短时间点
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ax1.axvline(x=min_years, color='g', linestyle='--',
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label=f'Minimum time: {min_years:.1f} years')
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ax1.set_xlabel('Completion Time (years)', fontsize=12)
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ax1.set_ylabel('Total Energy Consumption (PJ)', fontsize=12)
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ax1.set_title('Completion Timeline vs Total Energy\n(100 Million Metric Tons to Moon)', fontsize=14)
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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# ========== 图2: 使用的发射场数量 ==========
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ax2 = axes[0, 1]
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ax2.plot(df['years'], df['sites_used'], 'g-', linewidth=2, marker='o',
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markevery=10, markersize=6)
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ax2.set_xlabel('Completion Time (years)', fontsize=12)
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ax2.set_ylabel('Number of Launch Sites Used', fontsize=12)
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ax2.set_title('Launch Sites Required vs Timeline', fontsize=14)
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ax2.set_ylim(0, 11)
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ax2.grid(True, alpha=0.3)
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# 添加发射场名称标注
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for i, site in enumerate(LAUNCH_SITES):
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ax2.axhline(y=i+1, color='gray', linestyle=':', alpha=0.3)
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ax2.text(df['years'].max() * 0.98, i+1 + 0.1, site.short_name,
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fontsize=8, ha='right', va='bottom')
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# ========== 图3: 低纬度发射比例 ==========
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ax3 = axes[1, 0]
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ax3.plot(df['years'], df['low_lat_ratio'] * 100, 'm-', linewidth=2)
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ax3.fill_between(df['years'], df['low_lat_ratio'] * 100, alpha=0.3, color='magenta')
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ax3.set_xlabel('Completion Time (years)', fontsize=12)
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ax3.set_ylabel('Low Latitude (<30°) Launch Ratio (%)', fontsize=12)
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ax3.set_title('Proportion of Launches from Low-Latitude Sites', fontsize=14)
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ax3.set_ylim(0, 105)
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ax3.grid(True, alpha=0.3)
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# ========== 图4: 平均每发能量 ==========
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ax4 = axes[1, 1]
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ax4.plot(df['years'], df['energy_per_launch_TJ'], 'r-', linewidth=2)
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# 计算赤道和最高纬度的能量参考线
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energy_equator = energy_per_launch(LAUNCH_SITES[0]) / 1e12 # TJ
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energy_max_lat = energy_per_launch(LAUNCH_SITES[-1]) / 1e12 # TJ
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ax4.axhline(y=energy_equator, color='g', linestyle='--',
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label=f'Equator baseline: {energy_equator:.1f} TJ')
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ax4.axhline(y=energy_max_lat, color='orange', linestyle='--',
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label=f'Alaska (57°): {energy_max_lat:.1f} TJ')
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ax4.set_xlabel('Completion Time (years)', fontsize=12)
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ax4.set_ylabel('Average Energy per Launch (TJ)', fontsize=12)
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ax4.set_title('Energy Efficiency vs Timeline\n(Longer timeline → more low-lat launches)', fontsize=14)
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ax4.legend()
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ax4.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig(save_path, dpi=150, bbox_inches='tight')
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print(f"\n图表已保存至: {save_path}")
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return df, fig
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def plot_launch_distribution(
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years: float,
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save_path: str = '/Volumes/Files/code/mm/20260130_b/launch_distribution.png'
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):
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"""
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绘制特定年限下的发射分配详情
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"""
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total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
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distribution = calculate_launch_distribution(total_launches, years)
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stats = calculate_total_energy(distribution)
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fig, axes = plt.subplots(1, 2, figsize=(14, 6))
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# ========== 图1: 发射次数分配 ==========
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ax1 = axes[0]
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sites = [s.short_name for s, _ in distribution]
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launches = [n for _, n in distribution]
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latitudes = [s.abs_latitude for s, _ in distribution]
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colors = plt.cm.RdYlGn_r(np.array(latitudes) / 60)
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bars = ax1.barh(sites, launches, color=colors)
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ax1.set_xlabel('Number of Launches', fontsize=12)
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ax1.set_ylabel('Launch Site', fontsize=12)
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ax1.set_title(f'Launch Distribution ({years:.1f} years completion)\n'
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f'Total: {total_launches:,} launches', fontsize=13)
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ax1.grid(True, alpha=0.3, axis='x')
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# 添加数值标签
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for bar, n in zip(bars, launches):
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if n > 0:
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ax1.text(bar.get_width() + 1000, bar.get_y() + bar.get_height()/2,
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f'{n:,}', va='center', fontsize=9)
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# ========== 图2: 能量消耗分配 ==========
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ax2 = axes[1]
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site_names = [d['short_name'] for d in stats['sites']]
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energies = [d['energy_PJ'] for d in stats['sites']]
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colors = plt.cm.RdYlGn_r(np.array([d['latitude'] for d in stats['sites']]) / 60)
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bars = ax2.barh(site_names, energies, color=colors)
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ax2.set_xlabel('Energy Consumption (PJ)', fontsize=12)
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ax2.set_ylabel('Launch Site', fontsize=12)
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ax2.set_title(f'Energy Distribution\n'
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f'Total: {stats["total_energy_PJ"]:.1f} PJ', fontsize=13)
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ax2.grid(True, alpha=0.3, axis='x')
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# 添加数值标签
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for bar, e in zip(bars, energies):
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if e > 0:
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ax2.text(bar.get_width() + 0.5, bar.get_y() + bar.get_height()/2,
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f'{e:.1f}', va='center', fontsize=9)
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plt.tight_layout()
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plt.savefig(save_path, dpi=150, bbox_inches='tight')
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print(f"分配详情图已保存至: {save_path}")
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return distribution, stats
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def print_analysis_summary(df: pd.DataFrame):
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"""打印分析摘要"""
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print("\n" + "=" * 80)
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print("ROCKET LAUNCH SCENARIO ANALYSIS SUMMARY")
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print("=" * 80)
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total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
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print(f"\nMission Parameters:")
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print(f" - Total payload: {TOTAL_PAYLOAD/1e6:.0f} million metric tons")
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print(f" - Payload per launch: {PAYLOAD_PER_LAUNCH} metric tons")
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print(f" - Total launches required: {total_launches:,}")
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print(f" - Number of launch sites: {len(LAUNCH_SITES)}")
|
||
|
||
# 最短时间场景
|
||
min_years = df['years'].min()
|
||
min_time_row = df[df['years'] == min_years].iloc[0]
|
||
print(f"\nMinimum Time Scenario ({min_years:.1f} years):")
|
||
print(f" - All {int(min_time_row['sites_used'])} sites at full capacity")
|
||
print(f" - Total energy: {min_time_row['total_energy_PJ']:.1f} PJ")
|
||
print(f" - Total fuel: {min_time_row['total_fuel_Mt']:.1f} million metric tons")
|
||
print(f" - Low-latitude ratio: {min_time_row['low_lat_ratio']*100:.1f}%")
|
||
|
||
# 最优能量场景 (只使用最低纬度站点)
|
||
max_years = df['years'].max()
|
||
max_time_row = df[df['years'] == max_years].iloc[0]
|
||
print(f"\nExtended Timeline Scenario ({max_years:.1f} years):")
|
||
print(f" - Using {int(max_time_row['sites_used'])} lowest-latitude sites")
|
||
print(f" - Total energy: {max_time_row['total_energy_PJ']:.1f} PJ")
|
||
print(f" - Total fuel: {max_time_row['total_fuel_Mt']:.1f} million metric tons")
|
||
print(f" - Low-latitude ratio: {max_time_row['low_lat_ratio']*100:.1f}%")
|
||
|
||
# 能量节省
|
||
energy_saving = (1 - max_time_row['total_energy_PJ'] / min_time_row['total_energy_PJ']) * 100
|
||
print(f"\nEnergy Savings (Extended vs Minimum time):")
|
||
print(f" - Energy reduction: {energy_saving:.1f}%")
|
||
print(f" - Time trade-off: {max_years - min_years:.1f} additional years")
|
||
|
||
print("\n" + "=" * 80)
|
||
print("Launch Site Details:")
|
||
print("=" * 80)
|
||
print(f"\n{'Site':<25} {'Latitude':>10} {'ΔV Loss':>10} {'Fuel Ratio':>12} {'Energy/Launch':>14}")
|
||
print(f"{'':25} {'(°)':>10} {'(m/s)':>10} {'':>12} {'(TJ)':>14}")
|
||
print("-" * 80)
|
||
|
||
for site in LAUNCH_SITES:
|
||
k = fuel_ratio_multistage(site.total_delta_v)
|
||
e = energy_per_launch(site) / 1e12
|
||
print(f"{site.name:<25} {site.abs_latitude:>10.1f} {site.delta_v_loss:>10.0f} {k:>12.1f} {e:>14.1f}")
|
||
|
||
print("=" * 80)
|
||
|
||
|
||
def analyze_launch_frequency_scenarios(
|
||
save_path: str = '/Volumes/Files/code/mm/20260130_b/launch_frequency_analysis.png'
|
||
):
|
||
"""
|
||
分析不同发射频率场景下的完成时间和能量消耗
|
||
"""
|
||
total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
|
||
num_sites = len(LAUNCH_SITES)
|
||
|
||
# 不同发射频率场景
|
||
frequencies = [1, 2, 4, 8, 12, 24] # launches per day per site
|
||
|
||
results = []
|
||
for freq in frequencies:
|
||
# 最短完成时间
|
||
min_years = total_launches / (num_sites * freq * 365)
|
||
|
||
# 分析该时间下的能量（所有站点满负荷）
|
||
distribution = calculate_launch_distribution(total_launches, min_years)
|
||
stats = calculate_total_energy(distribution)
|
||
|
||
results.append({
|
||
'frequency': freq,
|
||
'min_years': min_years,
|
||
'total_energy_PJ': stats['total_energy_PJ'],
|
||
'total_fuel_Mt': stats['total_fuel_Mt'],
|
||
})
|
||
|
||
df = pd.DataFrame(results)
|
||
|
||
# 绘图
|
||
fig, axes = plt.subplots(1, 2, figsize=(14, 6))
|
||
|
||
# 图1: 发射频率 vs 完成时间
|
||
ax1 = axes[0]
|
||
ax1.bar(range(len(frequencies)), df['min_years'], color='steelblue', alpha=0.8)
|
||
ax1.set_xticks(range(len(frequencies)))
|
||
ax1.set_xticklabels([f'{f}/day' for f in frequencies])
|
||
ax1.set_xlabel('Launch Frequency (per site)', fontsize=12)
|
||
ax1.set_ylabel('Minimum Completion Time (years)', fontsize=12)
|
||
ax1.set_title('Completion Time vs Launch Frequency\n(10 sites, 800,000 launches)', fontsize=13)
|
||
ax1.grid(True, alpha=0.3, axis='y')
|
||
|
||
# 添加数值标签
|
||
for i, (freq, years) in enumerate(zip(frequencies, df['min_years'])):
|
||
ax1.text(i, years + 2, f'{years:.1f}y', ha='center', fontsize=10, fontweight='bold')
|
||
|
||
# 图2: 能量消耗比较
|
||
ax2 = axes[1]
|
||
ax2.bar(range(len(frequencies)), df['total_energy_PJ'], color='coral', alpha=0.8)
|
||
ax2.set_xticks(range(len(frequencies)))
|
||
ax2.set_xticklabels([f'{f}/day' for f in frequencies])
|
||
ax2.set_xlabel('Launch Frequency (per site)', fontsize=12)
|
||
ax2.set_ylabel('Total Energy (PJ)', fontsize=12)
|
||
ax2.set_title('Total Energy Consumption\n(Same for all frequencies at max capacity)', fontsize=13)
|
||
ax2.grid(True, alpha=0.3, axis='y')
|
||
|
||
plt.tight_layout()
|
||
plt.savefig(save_path, dpi=150, bbox_inches='tight')
|
||
print(f"\n发射频率分析图已保存至: {save_path}")
|
||
|
||
return df
|
||
|
||
|
||
def plot_comprehensive_analysis(
|
||
save_path: str = '/Volumes/Files/code/mm/20260130_b/rocket_comprehensive.png'
|
||
):
|
||
"""
|
||
综合分析图：不同时间线下的能量消耗和发射分配
|
||
"""
|
||
total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
|
||
num_sites = len(LAUNCH_SITES)
|
||
min_years = total_launches / (num_sites * 365)
|
||
|
||
# 选择几个关键时间点
|
||
key_years = [min_years, min_years*1.5, min_years*2, min_years*3, min_years*5]
|
||
|
||
fig, axes = plt.subplots(2, 3, figsize=(18, 12))
|
||
|
||
# 第一行：能量趋势
|
||
ax_main = axes[0, 0]
|
||
years_range = np.linspace(min_years, min_years * 5, 100)
|
||
df = analyze_completion_timeline(years_range)
|
||
|
||
ax_main.plot(df['years'], df['total_energy_PJ'], 'b-', linewidth=2)
|
||
ax_main.fill_between(df['years'], df['total_energy_PJ'], alpha=0.3)
|
||
|
||
# 标记关键点
|
||
for y in key_years[1:]:
|
||
ax_main.axvline(x=y, color='gray', linestyle='--', alpha=0.5)
|
||
|
||
ax_main.set_xlabel('Completion Time (years)', fontsize=11)
|
||
ax_main.set_ylabel('Total Energy (PJ)', fontsize=11)
|
||
ax_main.set_title('Energy vs Timeline\n(Longer time → Lower energy)', fontsize=12)
|
||
ax_main.grid(True, alpha=0.3)
|
||
|
||
# 能量效率
|
||
ax_eff = axes[0, 1]
|
||
ax_eff.plot(df['years'], df['energy_per_launch_TJ'], 'r-', linewidth=2)
|
||
ax_eff.set_xlabel('Completion Time (years)', fontsize=11)
|
||
ax_eff.set_ylabel('Energy per Launch (TJ)', fontsize=11)
|
||
ax_eff.set_title('Average Energy Efficiency', fontsize=12)
|
||
ax_eff.grid(True, alpha=0.3)
|
||
|
||
# 发射场使用数量
|
||
ax_sites = axes[0, 2]
|
||
ax_sites.plot(df['years'], df['sites_used'], 'g-', linewidth=2)
|
||
ax_sites.set_xlabel('Completion Time (years)', fontsize=11)
|
||
ax_sites.set_ylabel('Sites Used', fontsize=11)
|
||
ax_sites.set_title('Number of Active Sites', fontsize=12)
|
||
ax_sites.set_ylim(0, 11)
|
||
ax_sites.grid(True, alpha=0.3)
|
||
|
||
# 第二行：三个时间点的发射分配饼图
|
||
scenario_years = [min_years, min_years*2, min_years*5]
|
||
scenario_names = ['Minimum Time', '2x Minimum', '5x Minimum']
|
||
|
||
for idx, (years, name) in enumerate(zip(scenario_years, scenario_names)):
|
||
ax = axes[1, idx]
|
||
distribution = calculate_launch_distribution(total_launches, years)
|
||
|
||
# 只显示有发射的站点
|
||
labels = [s.short_name for s, n in distribution if n > 0]
|
||
sizes = [n for _, n in distribution if n > 0]
|
||
latitudes = [s.abs_latitude for s, n in distribution if n > 0]
|
||
|
||
colors = plt.cm.RdYlGn_r(np.array(latitudes) / 60)
|
||
|
||
wedges, texts, autotexts = ax.pie(sizes, labels=labels, autopct='%1.0f%%',
|
||
colors=colors, pctdistance=0.75)
|
||
|
||
# 计算该场景的能量
|
||
stats = calculate_total_energy(distribution)
|
||
|
||
ax.set_title(f'{name}\n({years:.0f} years, {stats["total_energy_PJ"]:.0f} PJ)',
|
||
fontsize=11)
|
||
|
||
plt.suptitle(f'Rocket Launch Scenario: {TOTAL_PAYLOAD/1e6:.0f}M tons to Moon\n'
|
||
f'({total_launches:,} launches needed)', fontsize=14, y=1.02)
|
||
|
||
plt.tight_layout()
|
||
plt.savefig(save_path, dpi=150, bbox_inches='tight')
|
||
print(f"\n综合分析图已保存至: {save_path}")
|
||
|
||
return fig
|
||
|
||
|
||
# ============== 主程序 ==============
|
||
|
||
if __name__ == "__main__":
|
||
print("=" * 80)
|
||
print("MOON COLONY ROCKET LAUNCH SCENARIO ANALYSIS")
|
||
print("Mission: Deliver 100 million metric tons to Moon")
|
||
print("Constraint: 10 launch sites, max 1 launch/day each")
|
||
print("=" * 80)
|
||
|
||
# 运行时间线分析
|
||
df, fig = plot_timeline_analysis()
|
||
|
||
# 打印摘要
|
||
print_analysis_summary(df)
|
||
|
||
# 绘制综合分析图
|
||
plot_comprehensive_analysis()
|
||
|
||
# 发射频率场景分析
|
||
print("\n" + "=" * 80)
|
||
print("LAUNCH FREQUENCY SENSITIVITY ANALYSIS")
|
||
print("=" * 80)
|
||
freq_df = analyze_launch_frequency_scenarios()
|
||
print("\nIf launch frequency can be increased:")
|
||
print(f"{'Frequency':>15} {'Min Years':>15} {'Energy (PJ)':>15}")
|
||
print("-" * 50)
|
||
for _, row in freq_df.iterrows():
|
||
print(f"{row['frequency']:>12}/day {row['min_years']:>15.1f} {row['total_energy_PJ']:>15.0f}")
|
||
|
||
# 关键结论
|
||
print("\n" + "=" * 80)
|
||
print("KEY FINDINGS")
|
||
print("=" * 80)
|
||
print(f"""
|
||
1. MINIMUM COMPLETION TIME: {df['years'].min():.0f} years
|
||
(All 10 sites at 1 launch/day capacity)
|
||
|
||
2. TOTAL ENERGY REQUIRED: ~{df['total_energy_PJ'].mean():.0f} PJ
|
||
(~{df['total_fuel_Mt'].mean():.0f} million metric tons of fuel)
|
||
|
||
3. LATITUDE EFFECT:
|
||
- Energy savings from using only low-lat sites: ~2-3%
|
||
- Trade-off: Significantly longer timeline
|
||
|
||
4. CONCLUSION:
|
||
Traditional rockets alone are IMPRACTICAL for this mission.
|
||
219+ years is far beyond reasonable project timeline.
|
||
Space Elevator System is essential for feasibility.
|
||
""")
|