quantum_reactor_simulation.py

import numpy as np
from scipy.integrate import odeint
from enhanced_ecosystem_with_psi import EnhancedHumanQuantumEcosystem

class QuantumReactor(EnhancedHumanQuantumEcosystem):
    def __init__(self, num_particles=1000, subspace_dimensions=11, interaction_strength=0.1, base_frequency=1e15):
        super().__init__(num_groups=subspace_dimensions, interaction_strength=interaction_strength)
        self.num_particles = num_particles
        self.subspace_dimensions = subspace_dimensions
        self.particle_states = np.random.rand(num_particles, subspace_dimensions)
        self.fusion_threshold = 0.95
        self.fusion_energy = 17.6  # MeV for D-T fusion

    def subspace_excitation(self, t):
        return np.sin(2 * np.pi * self.base_frequency * t + self.psi)

    def quantum_tunneling(self, particle_states):
        tunneling_prob = np.exp(-1 / (particle_states + 1e-10))
        return np.random.rand(*particle_states.shape) < tunneling_prob

    def fusion_reaction(self, particle_states):
        fusion_prob = np.mean(particle_states, axis=1)
        fusion_events = np.random.rand(self.num_particles) < fusion_prob
        return fusion_events

    def calculate_energy_output(self, fusion_events):
        return np.sum(fusion_events) * self.fusion_energy

    def reactor_dynamics(self, t, state):
        particle_states = state.reshape((self.num_particles, self.subspace_dimensions))
        
        # Apply subspace excitation
        excitation = self.subspace_excitation(t)
        particle_states *= (1 + 0.1 * excitation)

        # Apply quantum tunneling
        tunneling = self.quantum_tunneling(particle_states)
        particle_states[tunneling] += 0.1

        # Apply harmonic balancer effect
        balancer_effect = 0.1 * np.tanh(particle_states)
        particle_states += balancer_effect

        # Calculate fusion events
        fusion_events = self.fusion_reaction(particle_states)
        
        # Reset fused particles
        particle_states[fusion_events] = np.random.rand(np.sum(fusion_events), self.subspace_dimensions)

        # Calculate energy output
        energy_output = self.calculate_energy_output(fusion_events)

        return particle_states.flatten(), energy_output

    def run_simulation(self, duration, time_steps):
        t = np.linspace(0, duration, time_steps)
        initial_state = self.particle_states.flatten()

        def ode_wrapper(state, t):
            new_state, _ = self.reactor_dynamics(t, state)
            return new_state

        states = odeint(ode_wrapper, initial_state, t)
        
        energy_outputs = []
        for i in range(len(t)):
            _, energy = self.reactor_dynamics(t[i], states[i])
            energy_outputs.append(energy)

        return t, states, energy_outputs

def run_quantum_reactor_simulation():
    reactor = QuantumReactor()
    duration = 1e-12  # 1 picosecond
    time_steps = 1000

    t, states, energy_outputs = reactor.run_simulation(duration, time_steps)

    total_energy = np.sum(energy_outputs)
    average_power = total_energy / duration

    print(f"Quantum Reactor Simulation Results:")
    print(f"Total Energy Output: {total_energy:.2f} MeV")
    print(f"Average Power Output: {average_power:.2f} MeV/s")
    print(f"Peak Power Output: {np.max(energy_outputs):.2f} MeV/s")

    return t, states, energy_outputs

if __name__ == "__main__":
    run_quantum_reactor_simulation()