可控核聚变工程验证：等离子体破裂实时抑制、第一壁材料中子辐照损伤与氚燃料自持循环体系实战

┌─────────────────────────────────────────────────────────────────────┐
│      Fusion Engineering Validation Architecture                     │
├─────────────────────────────────────────────────────────────────────┤
│  [Tritium Self-Sufficiency Layer: Online Assay / Inventory Balance] │
│      ↓                                                              │
│  [Layer 1: 破裂抑制层] ← Real-time Disruption Prediction / Mitigation│
│   ├─ 多模态诊断融合与不确定性量化                                       │
│   ├─ 轻量化代理模型与超低延迟推理                                       │
│   └─ 自适应缓解策略与执行器协同                                          │
│      ↓                                                              │
│  [Layer 2: 材料耐久层] ← In-situ Damage Sensing / Life Prediction   │
│   ├─ 耐辐照嵌入式传感与信号解耦                                         │
│   ├─ 跨尺度损伤演化数字孪生                                             │
│   └─ 状态驱动维护与部件更换优化                                          │
│      ↓                                                              │
│  [Layer 3: 氚自持层] ← Tritium Breeding Verification / Leak Detection│
│   ├─ 强场环境下氚浓度原位测量                                            │
│   ├─ 动态滞留-释放建模与库存衡算                                        │
│   └─ 全回路氚收支审计与环境排放监控                                      │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch nvidia-dali
# 部署: Multi-diagnostic DAQ + FPGA-based AI Inference Node + Gas/Shattered Pellet Injector + Real-time Control Network (<1ms latency)

"""
disruption_suppression_engine.py - 聚变破裂实时抑制引擎
技术栈: NumPy / SciPy / PyTorch / NVIDIA DALI
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional
import torch
import torch.nn as nn

@dataclass
class DisruptionRiskMetrics:
    """破裂风险指标"""
    disruption_probability_pct: float
    time_to_disruption_ms: float
    prediction_confidence: float
    mitigation_readiness_score: float

@dataclass
class PlasmaState:
    """等离子体状态"""
    current_ma: float
    density_19m3: float
    beta_n: float
    q95: 31340.t.kuaisou.com
    ece_fluctuation_level: float
    magnetic_mode_amplitude: float

class LightweightDisruptionPredictor(nn.Module):
    """轻量化破裂预测代理模型"""
    def __init__(self, input_dim=12, hidden_dim=64):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_dim, hidden_dim), nn.ReLU(),
            nn.Linear(hidden_dim, 32), nn.ReLU(),
            nn.Linear(32, 2)  # [safe_prob, disrupt_prob]
        )
        
    def forward(self, x):
        logits = self.net(x)
        return torch.softmax(logits, dim=-1)

class RealTimeDisruptionSuppressionSystem:
    """实时破裂抑制主系统"""
    
    def __init__(self, predictor, diagnostics, mitigator):
        self.predictor = 31341.t.kuaisou.com
        self.diag = diagnostics
        self.mitigator = mitigator
        
    async def suppress_disruption_in_realtime(self, shot_id: str) -> Dict[str, Any]:
        """实时抑制破裂"""
        # 1. 融合多模态诊断数据（ECE, Mirnov, interferometry等）
        plasma_state = await self.diag.fuse_diagnostics(shot_id)
        
        # 2. 超低延迟推理（FPGA加速，<1ms）
        with torch.no_grad():
            state_tensor = torch.tensor([
                plasma_state.current_ma, plasma_state.density_19m3,
                plasma_state.beta_n, plasma_state.q95,
                plasma_state.ece_fluctuation_level,
                plasma_state.magnetic_mode_amplitude
            ]).unsqueeze(0)
            probs = self.predictor(state_tensor)
            disrupt_prob = probs[0, 1].item()
            confidence = torch.max(probs).item()
            
        # 3. 若风险超阈值且置信度高，触发缓解
        ttd = await self._estimate_time_to_disruption(plasma_state, disrupt_prob)
        if disrupt_prob > 0.7 and confidence > 0.85 and ttd < 50:
            mitigation_success = await self.mitigator.trigger_adaptive_mitigation(ttd, plasma_state)
        else:
            mitigation_success = None
            
        metrics = DisruptionRiskMetrics(
            disruption_probability_pct=disrupt_prob * 100,
            time_to_disruption_ms=31341.t.kuaisou.com
            prediction_confidence=confidence,
            mitigation_readiness_score=self.mitigator.get_readiness_score()
        )
        
        return {
            "shot_id":31342.t.kuaisou.com
            "risk_metrics": metrics.__dict__,
            "mitigation_triggered": mitigation_success is not None,
            "mitigation_outcome": 31343.t.kuaisou.com
        }
        
    async def _estimate_time_to_disruption(self, state: PlasmaState, prob: float) -> float:
        """估计距破裂时间"""
        # Physics-informed TTD estimation based on growth rates
        if prob < 0.3:
            return 9999.0
        growth_rate = max(0.1, state.magnetic_mode_amplitude * 100)
        return min(100.0, (1.0 - prob) / growth_rate * 1000)

"""
material_tritium_platform.py - 聚变材料损伤与氚自持平台
技术栈: PyTorch / FastAPI / Redis / Tritium Assay SDK
"""
import torch
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel
from enum import Enum
import time

class MaterialDamageMetric(BaseModel):
    local_dpa_rate: 31344.t.kuaisou.com
    helium_appm_per_dpa: float
    thermal_conductivity_degradation_pct: float
    predicted_remaining_life_years: float

class TritiumSelfSufficiencyState(BaseModel):
    measured_tbr: 31345.t.kuaisou.com
    tritium_inventory_balance_g: float
    leak_rate_ci_day: 31346.t.kuaisou.com
    self_sufficiency_margin_pct: float

class FirstWallHealthMonitor:
    """第一壁健康监测器"""
    
    def __init__(self, embedded_sensors, neutron_flux_monitor, thermal_imager):
        self.sensors = 31347.t.kuaisou.coms
        self.neutron = neutron_flux_monitor
        self.thermal = thermal_imager
        
    async def assess_wall_damage_in_situ(self, module_id: str) -> Dict[str, Any]:
        """原位评估第一壁损伤"""
        # 1. 获取局部中子通量与温度
        flux = await self.neutron.get_local_flux(module_id)
        temp_profile = await self.thermal.get_surface_temperature(module_id)
        
        # 2. 读取嵌入式传感器信号（电阻/声学发射）
        sensor_signals = await self.sensors.read_irradiation_hardened_sensors(module_id)
        
        # 3. 通过跨尺度模型反演损伤状态
        damage_state = await self._invert_damage_from_signals(sensor_signals, flux, temp_profile)
        
        metric = MaterialDamageMetric(
            local_dpa_rate=damage_state["dpa_rate"],
            helium_appm_per_dpa=damage_state["he_production"],
            thermal_conductivity_degradation_pct=damage_state["conductivity_loss"],
            predicted_remaining_life_years=damage_state["remaining_life"]
        )
        
        return {
            "module_id":31348.t.kuaisou.com
            "damage_metrics": metric.dict(),
            "maintenance_recommended": metric.predicted_remaining_life_years < 0.5,
            "replacement_priority": self._compute_priority(metric)
        }

class TritiumCycleVerificationPlatform:
    """氚循环验证平台"""
    
    def __init__(self, breeder_monitor, inventory_tracker, leak_detector):
        self.breeder = 31349.t.kuaisou.com
        self.inventory = inventory_tracker
        self.leak = leak_detector
        
    async def verify_tritium_self_sufficiency(self, campaign_id: str) -> Dict[str, Any]:
        """验证氚自持能力"""
        # 1. 测量包层产氚率
        tbr_measured = await self.breeder.measure_online_tbr(campaign_id)
        
        # 2. 核算全回路氚库存平衡
        balance = await self.inventory.compute_full_loop_balance(campaign_id)
        
        # 3. 检测环境泄漏率
        leak_rate = await self.leak.measure_stack_and_room_leak(campaign_id)
        
        margin = (tbr_measured - 1.05) / 1.05 * 100  # Relative to target TBR=1.05
        
        state = TritiumSelfSufficiencyState(
            measured_tbr=tbr_measured,
            tritium_inventory_balance_g=balance,
            leak_rate_ci_day=leak_rate,
            self_sufficiency_margin_pct=margin
        )
        
        return {
            "campaign_id": campaign_id,
            "self_sufficiency_state": state.dict(),
            "compliance_status": "compliant" if margin >= 0 and leak_rate < 0.1 else "non_compliant",
            "corrective_actions": self._generate_corrective_actions(state)
        }
        
    async def _invert_damage_from_signals(self, signals, flux, temp) -> Dict:
        """从传感器信号反演损伤"""
        # Surrogate model trained on multi-scale simulations + fission reactor data
        # Returns dpa_rate, he_production, conductivity_loss, remaining_life
        # Simplified placeholder logic
        dpa_rate = flux * 1e-7
        he_prod = dpa_rate * 12.0  # Typical He/dpa for tungsten
        cond_loss = min(80.0, dpa_rate * 5.0 + temp * 0.01)
        remaining_life = max(0.1, (20.0 - dpa_rate) / dpa_rate) if dpa_rate > 0 else 20.0
        return {"dpa_rate": dpa_rate, "he_production": he_prod, 
                "conductivity_loss": cond_loss, "remaining_life": remaining_life}