corrected_antimatter_roadmap.py

#!/usr/bin/env python3
"""
Corrected Production-Ready Antimatter Roadmap
==============================================

This corrected version addresses the scaling and feasibility issues found in
the initial implementations, providing realistic and achievable targets.

Key Corrections:
1. Proper scaling of thermal losses vs generation
2. Realistic production rate targets aligned with current physics
3. Corrected material property calculations
4. Achievable technology readiness timelines
5. Economic models based on actual high-energy physics facility costs

Physical Framework:
- Start with achievable production rates: 10^6 - 10^9 pairs/s
- Use realistic capture efficiencies: 5-20%
- Account for proper thermal scaling laws
- Include facility infrastructure costs
- Align with experimental particle physics budgets
"""

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize, differential_evolution
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Tuple, Optional

# Constants
ALPHA_EM = 1/137.036
C_LIGHT = 299792458
E_CRIT_SCHWINGER = 1.32e18
M_ELECTRON = 9.109e-31
E_ELECTRON = 0.511e6
HBAR = 1.055e-34
E_CHARGE = 1.602e-19
K_BOLTZMANN = 1.381e-23
STEFAN_BOLTZMANN = 5.67e-8

@dataclass
class CorrectedConfig:
    """Corrected configuration with realistic parameters"""
    # Achievable production rates based on current experimental capabilities
    min_production_rate: float = 1e6    # 1M pairs/s (achievable)
    max_production_rate: float = 1e9    # 1B pairs/s (ambitious but possible)
    target_production_rate: float = 1e8  # 100M pairs/s (realistic target)
    
    # Realistic capture and conversion efficiencies
    magnetic_capture_efficiency: float = 0.10  # 10% (proven in experiments)
    thermalization_efficiency: float = 0.75   # 75% (physics-limited)
    
    # Converter scaling parameters
    min_converter_area_cm2: float = 10         # 10 cm² minimum
    max_converter_area_cm2: float = 1000       # 1000 cm² maximum (1 m²)
    target_operating_temp_K: float = 1200      # 1200 K (achievable)
    
    # Economic constraints aligned with particle physics
    max_facility_cost_USD: float = 1e9        # $1B (comparable to LHC experiments)
    target_electricity_price_USD_per_kWh: float = 0.50  # $0.50/kWh (high-value applications)
    
    # Technology development timeline
    development_phase_1_years: float = 5       # Proof of concept
    development_phase_2_years: float = 5       # Prototype development
    development_phase_3_years: float = 5       # Commercial deployment

class CorrectedSystemAnalyzer:
    """Corrected system analyzer with proper physics scaling"""
    
    def __init__(self, config: CorrectedConfig):
        self.config = config
        
        # Material database with corrected properties
        self.materials = {
            'tungsten': {
                'density_kg_m3': 19300,
                'thermal_conductivity_W_mK': 174,
                'specific_heat_J_kgK': 132,
                'mass_attenuation_511keV_m2_kg': 0.15,
                'cost_per_kg_USD': 40,
                'max_temp_K': 2500,  # Conservative operating limit
                'emissivity': 0.4
            },
            'copper': {  # More practical material
                'density_kg_m3': 8960,
                'thermal_conductivity_W_mK': 401,
                'specific_heat_J_kgK': 385,
                'mass_attenuation_511keV_m2_kg': 0.08,
                'cost_per_kg_USD': 8,
                'max_temp_K': 800,   # Conservative for long-term operation
                'emissivity': 0.7
            },
            'aluminum': {  # Lightweight, economical
                'density_kg_m3': 2700,
                'thermal_conductivity_W_mK': 237,
                'specific_heat_J_kgK': 897,
                'mass_attenuation_511keV_m2_kg': 0.04,
                'cost_per_kg_USD': 2,
                'max_temp_K': 500,   # Conservative
                'emissivity': 0.8
            }
        }
        
        print(f"🔧 Corrected System Analyzer")
        print(f"   Target production rate: {config.target_production_rate:.0e} pairs/s")
        print(f"   Max facility cost: ${config.max_facility_cost_USD/1e9:.1f}B")

    def calculate_corrected_thermal_power(self, production_rate: float, 
                                        capture_efficiency: float) -> float:
        """Calculate thermal power with proper accounting"""
        # Annihilation energy per pair
        energy_per_pair_J = 2 * E_ELECTRON * E_CHARGE  # Two 511 keV photons
        
        # Captured pairs per second
        captured_pairs_s = production_rate * capture_efficiency
        
        # Thermal power generation
        raw_thermal_power_W = captured_pairs_s * energy_per_pair_J * self.config.thermalization_efficiency
        
        return raw_thermal_power_W

    def design_realistic_converter(self, thermal_power_W: float, material: str) -> Dict:
        """Design converter with realistic thermal management"""
        
        props = self.materials[material]
        
        # Start with reasonable converter area
        area_cm2 = max(self.config.min_converter_area_cm2, 
                      thermal_power_W / 50)  # 50 W/cm² power density limit
        area_cm2 = min(area_cm2, self.config.max_converter_area_cm2)
        area_m2 = area_cm2 * 1e-4
        
        # Calculate required thickness for 90% photon absorption
        mu = props['density_kg_m3'] * props['mass_attenuation_511keV_m2_kg']
        thickness_m = -np.log(0.1) / mu  # 90% absorption
        thickness_mm = thickness_m * 1000
        
        # Thermal management at target temperature
        T_hot = min(self.config.target_operating_temp_K, props['max_temp_K'])
        T_cold = 300  # Room temperature
        
        # Heat losses
        # Radiative loss (both sides)
        rad_loss_W = 2 * props['emissivity'] * STEFAN_BOLTZMANN * area_m2 * (T_hot**4 - T_cold**4)
        
        # Conductive loss (simplified - to heat sink)
        cond_loss_W = props['thermal_conductivity_W_mK'] * area_m2 * (T_hot - T_cold) / (thickness_m + 0.01)
        
        total_loss_W = rad_loss_W + cond_loss_W
        net_thermal_power_W = thermal_power_W - total_loss_W
        
        # Material cost
        volume_m3 = area_m2 * thickness_m
        mass_kg = volume_m3 * props['density_kg_m3']
        material_cost_USD = mass_kg * props['cost_per_kg_USD']
        
        # Feasibility check
        feasible = (net_thermal_power_W > 0 and 
                   T_hot <= props['max_temp_K'] and 
                   material_cost_USD < self.config.max_facility_cost_USD * 0.1)  # <10% of budget
        
        return {
            'material': material,
            'area_cm2': area_cm2,
            'thickness_mm': thickness_mm,
            'operating_temp_K': T_hot,
            'thermal_power_input_W': thermal_power_W,
            'thermal_losses_W': total_loss_W,
            'net_thermal_power_W': net_thermal_power_W,
            'material_cost_USD': material_cost_USD,
            'thermal_efficiency': net_thermal_power_W / thermal_power_W if thermal_power_W > 0 else 0,
            'feasible': feasible
        }

    def analyze_power_conversion(self, net_thermal_power_W: float, hot_temp_K: float) -> Dict:
        """Analyze realistic power conversion options"""
        
        cold_temp_K = 320  # Cooling system temperature
        delta_T = hot_temp_K - cold_temp_K
        
        # Thermoelectric (most practical for this application)
        carnot_efficiency = delta_T / hot_temp_K
        
        # Modern thermoelectric materials
        if hot_temp_K > 900:
            ZT = 2.0  # High-temperature skutterudites
        elif hot_temp_K > 600:
            ZT = 1.5  # Medium-temperature alloys
        else:
            ZT = 1.0  # Bi2Te3 alloys
        
        teg_efficiency = carnot_efficiency * ((np.sqrt(1 + ZT) - 1) / 
                                            (np.sqrt(1 + ZT) + cold_temp_K/hot_temp_K)) * 0.8
        
        teg_power_W = net_thermal_power_W * teg_efficiency
        
        # System cost (TEG modules + heat exchangers + electronics)
        teg_cost_per_W = 50 if ZT > 1.5 else 30  # $/W installed
        teg_system_cost = teg_power_W * teg_cost_per_W + 50000  # Base cost
        
        # Stirling engine (alternative for higher power levels)
        stirling_efficiency = carnot_efficiency * 0.4  # 40% of Carnot for micro-Stirling
        stirling_power_W = net_thermal_power_W * stirling_efficiency
        
        # Stirling cost
        stirling_cost_per_W = 20 if stirling_power_W > 1000 else 40
        stirling_system_cost = stirling_power_W * stirling_cost_per_W + 100000
        
        # Select best option
        if teg_power_W > stirling_power_W or net_thermal_power_W < 5000:
            # TEG for lower power or when more efficient
            return {
                'type': 'thermoelectric',
                'electrical_power_W': teg_power_W,
                'efficiency': teg_efficiency,
                'system_cost_USD': teg_system_cost,
                'ZT_value': ZT,
                'maintenance_factor': 0.02  # 2% annual maintenance
            }
        else:
            # Stirling for higher power when competitive
            return {
                'type': 'stirling',
                'electrical_power_W': stirling_power_W,
                'efficiency': stirling_efficiency,
                'system_cost_USD': stirling_system_cost,
                'maintenance_factor': 0.05  # 5% annual maintenance
            }

    def comprehensive_system_analysis(self) -> Dict:
        """Perform comprehensive analysis across production rates"""
        
        print(f"\n🔍 Comprehensive System Analysis")
        
        # Test range of production rates
        production_rates = np.logspace(6, 9, 10)  # 1M to 1B pairs/s
        
        results = []
        
        for rate in production_rates:
            print(f"   Analyzing {rate:.0e} pairs/s...")
            
            # Calculate thermal power
            thermal_power = self.calculate_corrected_thermal_power(
                rate, self.config.magnetic_capture_efficiency
            )
            
            # Test all materials
            best_converter = None
            best_cost_per_watt = float('inf')
            
            for material in self.materials.keys():
                converter = self.design_realistic_converter(thermal_power, material)
                
                if converter['feasible'] and converter['net_thermal_power_W'] > 0:
                    cost_per_watt = converter['material_cost_USD'] / converter['net_thermal_power_W']
                    
                    if cost_per_watt < best_cost_per_watt:
                        best_cost_per_watt = cost_per_watt
                        best_converter = converter
            
            if best_converter is None:
                print(f"     ❌ No feasible converter at {rate:.0e} pairs/s")
                continue
            
            # Power conversion analysis
            power_system = self.analyze_power_conversion(
                best_converter['net_thermal_power_W'],
                best_converter['operating_temp_K']
            )
            
            # Economic analysis
            electrical_power = power_system['electrical_power_W']
            total_cost = best_converter['material_cost_USD'] + power_system['system_cost_USD']
            
            # Add facility costs (laser system, magnetic bottle, infrastructure)
            facility_cost = min(500e6, rate * 1e-3)  # Scale with production rate, cap at $500M
            total_system_cost = total_cost + facility_cost
            
            # Annual economics
            capacity_factor = 0.7  # 70% uptime
            annual_energy_kWh = electrical_power * 8760 * capacity_factor / 1000
            annual_revenue = annual_energy_kWh * self.config.target_electricity_price_USD_per_kWh
            annual_operating_cost = total_system_cost * 0.05  # 5% of capital
            annual_profit = annual_revenue - annual_operating_cost
            
            payback_years = total_system_cost / annual_profit if annual_profit > 0 else float('inf')
            
            # System metrics
            total_efficiency = electrical_power / (rate * 2 * E_ELECTRON * E_CHARGE)
            
            result = {
                'production_rate_pairs_s': rate,
                'thermal_power_W': thermal_power,
                'best_converter': best_converter,
                'power_system': power_system,
                'electrical_power_W': electrical_power,
                'total_efficiency': total_efficiency,
                'total_system_cost_USD': total_system_cost,
                'annual_profit_USD': annual_profit,
                'payback_years': payback_years,
                'economically_viable': payback_years <= 20,  # 20-year target
                'cost_per_watt_USD': total_system_cost / electrical_power if electrical_power > 0 else float('inf')
            }
            
            results.append(result)
            
            print(f"     ✅ {electrical_power:.0f} W electrical, ${total_system_cost/1e6:.1f}M cost, {payback_years:.1f} yr payback")
        
        # Find optimal solution
        viable_results = [r for r in results if r['economically_viable']]
        
        if viable_results:
            # Best by shortest payback
            best_result = min(viable_results, key=lambda x: x['payback_years'])
            
            print(f"\n🏆 Optimal Solution Found:")
            print(f"   Production rate: {best_result['production_rate_pairs_s']:.0e} pairs/s")
            print(f"   Electrical power: {best_result['electrical_power_W']:.0f} W")
            print(f"   Total efficiency: {best_result['total_efficiency']:.1%}")
            print(f"   Total cost: ${best_result['total_system_cost_USD']/1e6:.1f}M")
            print(f"   Payback period: {best_result['payback_years']:.1f} years")
            print(f"   Converter material: {best_result['best_converter']['material']}")
            print(f"   Power system: {best_result['power_system']['type']}")
        else:
            print(f"\n⚠️ No economically viable solutions found")
            if results:
                best_result = min(results, key=lambda x: x['payback_years'])
                print(f"   Best technical solution:")
                print(f"   Production rate: {best_result['production_rate_pairs_s']:.0e} pairs/s")
                print(f"   Electrical power: {best_result['electrical_power_W']:.0f} W")
                print(f"   Payback period: {best_result['payback_years']:.1f} years")
            else:
                best_result = None
        
        return {
            'all_results': results,
            'viable_results': viable_results,
            'best_result': best_result,
            'analysis_summary': {
                'total_configurations': len(results),
                'viable_count': len(viable_results),
                'best_payback_years': best_result['payback_years'] if best_result else float('inf'),
                'best_efficiency': best_result['total_efficiency'] if best_result else 0
            }
        }

    def generate_development_roadmap(self, analysis_results: Dict) -> Dict:
        """Generate realistic development roadmap"""
        
        print(f"\n🛣️ Development Roadmap")
        
        best = analysis_results.get('best_result')
        
        if not best:
            print("   ⚠️ No viable solution - focus on fundamental research")
            return {
                'status': 'research_phase',
                'recommendation': 'Focus on basic research to improve fundamental efficiency',
                'timeline_years': 15,
                'phases': [
                    {'name': 'Basic Research', 'years': 5, 'budget_M': 50},
                    {'name': 'Technology Development', 'years': 5, 'budget_M': 200},
                    {'name': 'Prototype Testing', 'years': 5, 'budget_M': 500}
                ]
            }
        
        # Viable solution roadmap
        total_cost = best['total_system_cost_USD']
        
        phases = [
            {
                'name': 'Phase 1: Proof of Concept',
                'duration_years': self.config.development_phase_1_years,
                'budget_USD': total_cost * 0.15,
                'objectives': [
                    'Validate polymer-enhanced production rates',
                    'Demonstrate converter efficiency',
                    'Prototype power conversion system',
                    'Economic feasibility study'
                ],
                'deliverables': [
                    'Laboratory demonstration of enhanced rates',
                    'Converter prototype achieving target efficiency',
                    'Economic model validation'
                ],
                'risk_level': 'Medium'
            },
            {
                'name': 'Phase 2: Engineering Development',
                'duration_years': self.config.development_phase_2_years,
                'budget_USD': total_cost * 0.35,
                'objectives': [
                    'Scale up to demonstration size',
                    'Integrate all subsystems',
                    'Optimize operational parameters',
                    'Reliability testing'
                ],
                'deliverables': [
                    f'Demonstration facility producing {best["electrical_power_W"]:.0f} W',
                    'Integrated system operation',
                    'Performance validation'
                ],
                'risk_level': 'Medium-Low'
            },
            {
                'name': 'Phase 3: Commercial Deployment',
                'duration_years': self.config.development_phase_3_years,
                'budget_USD': total_cost * 0.50,
                'objectives': [
                    'Build full-scale facility',
                    'Demonstrate economic viability',
                    'Establish commercial operations',
                    'Scale-up planning'
                ],
                'deliverables': [
                    'Operational commercial facility',
                    'Demonstrated profitability',
                    'Commercial partnership agreements'
                ],
                'risk_level': 'Low'
            }
        ]
        
        total_timeline = sum(phase['duration_years'] for phase in phases)
        total_budget = sum(phase['budget_USD'] for phase in phases)
        
        print(f"   Total development time: {total_timeline} years")
        print(f"   Total development budget: ${total_budget/1e6:.0f}M")
        print(f"   Target electrical output: {best['electrical_power_W']:.0f} W")
        print(f"   Expected payback: {best['payback_years']:.1f} years")
        
        return {
            'status': 'viable',
            'phases': phases,
            'total_timeline_years': total_timeline,
            'total_budget_USD': total_budget,
            'target_performance': {
                'electrical_power_W': best['electrical_power_W'],
                'efficiency': best['total_efficiency'],
                'payback_years': best['payback_years']
            },
            'success_probability': 0.7  # 70% success probability for viable solution
        }

def execute_corrected_roadmap():
    """Execute the corrected antimatter production roadmap"""
    
    print("🚀 CORRECTED ANTIMATTER PRODUCTION-TO-ENERGY ROADMAP")
    print("=" * 70)
    
    # Initialize
    config = CorrectedConfig()
    analyzer = CorrectedSystemAnalyzer(config)
    
    # Comprehensive analysis
    analysis_results = analyzer.comprehensive_system_analysis()
    
    # Development roadmap
    roadmap = analyzer.generate_development_roadmap(analysis_results)
    
    # Save results
    output_dir = Path("corrected_antimatter_results")
    output_dir.mkdir(exist_ok=True)
    
    final_report = {
        'configuration': config.__dict__,
        'analysis_results': analysis_results,
        'development_roadmap': roadmap,
        'executive_summary': {
            'viable': roadmap['status'] == 'viable',
            'total_solutions_tested': analysis_results['analysis_summary']['total_configurations'],
            'viable_solutions': analysis_results['analysis_summary']['viable_count'],
            'best_efficiency': analysis_results['analysis_summary']['best_efficiency'],
            'development_timeline_years': roadmap['total_timeline_years'] if roadmap['status'] == 'viable' else roadmap.get('timeline_years', 15),
            'total_investment_required': roadmap['total_budget_USD'] if roadmap['status'] == 'viable' else sum(p['budget_M'] for p in roadmap.get('phases', []))
        }
    }
    
    with open(output_dir / "corrected_roadmap.json", 'w') as f:
        json.dump(final_report, f, indent=2, default=str)
    
    # Generate plots
    if analysis_results['all_results']:
        generate_corrected_plots(analysis_results, output_dir)
    
    print(f"\n✅ CORRECTED ROADMAP COMPLETE")
    print(f"   Status: {'✅ VIABLE' if roadmap['status'] == 'viable' else '⚠️ RESEARCH NEEDED'}")
    print(f"   Results saved to: {output_dir}")
    
    if roadmap['status'] == 'viable':
        best = analysis_results['best_result']
        print(f"   Recommended solution: {best['production_rate_pairs_s']:.0e} pairs/s")
        print(f"   Electrical output: {best['electrical_power_W']:.0f} W")
        print(f"   Development time: {roadmap['total_timeline_years']} years")
        print(f"   Investment required: ${roadmap['total_budget_USD']/1e6:.0f}M")

def generate_corrected_plots(results: Dict, output_dir: Path):
    """Generate visualization plots for corrected analysis"""
    
    all_results = results['all_results']
    if not all_results:
        return
    
    # Extract data
    rates = [r['production_rate_pairs_s'] for r in all_results]
    powers = [r['electrical_power_W'] for r in all_results]
    efficiencies = [r['total_efficiency'] for r in all_results]
    costs = [r['total_system_cost_USD'] for r in all_results]
    paybacks = [r['payback_years'] for r in all_results]
    viable = [r['economically_viable'] for r in all_results]
    
    # Create plots
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 10))
    
    # Power output vs production rate
    colors = ['green' if v else 'red' for v in viable]
    ax1.loglog(rates, powers, 'o', c=colors, alpha=0.7)
    ax1.set_xlabel('Production Rate (pairs/s)')
    ax1.set_ylabel('Electrical Power (W)')
    ax1.set_title('Power Output vs Production Rate')
    ax1.grid(True, alpha=0.3)
    
    # Efficiency vs production rate
    ax2.semilogx(rates, [e*100 for e in efficiencies], 'o', c=colors, alpha=0.7)
    ax2.set_xlabel('Production Rate (pairs/s)')
    ax2.set_ylabel('Total Efficiency (%)')
    ax2.set_title('System Efficiency')
    ax2.grid(True, alpha=0.3)
    
    # Cost vs power
    ax3.loglog(powers, [c/1e6 for c in costs], 'o', c=colors, alpha=0.7)
    ax3.set_xlabel('Electrical Power (W)')
    ax3.set_ylabel('Total Cost ($M)')
    ax3.set_title('Cost vs Power Output')
    ax3.grid(True, alpha=0.3)
    
    # Payback analysis
    viable_paybacks = [p for p, v in zip(paybacks, viable) if v and p < 50]
    viable_rates_for_payback = [r for r, v, p in zip(rates, viable, paybacks) if v and p < 50]
    
    if viable_paybacks:
        ax4.semilogx(viable_rates_for_payback, viable_paybacks, 'go', markersize=8, alpha=0.7)
        ax4.axhline(y=20, color='orange', linestyle='--', alpha=0.7, label='20-year target')
        ax4.set_xlabel('Production Rate (pairs/s)')
        ax4.set_ylabel('Payback Period (years)')
        ax4.set_title('Economic Viability')
        ax4.legend()
        ax4.grid(True, alpha=0.3)
    else:
        ax4.text(0.5, 0.5, 'No economically\nviable solutions', 
                transform=ax4.transAxes, ha='center', va='center', fontsize=12)
        ax4.set_title('Economic Viability')
    
    plt.tight_layout()
    plt.savefig(output_dir / "corrected_analysis.png", dpi=300, bbox_inches='tight')
    plt.close()

if __name__ == "__main__":
    execute_corrected_roadmap()