可控核聚变工程验证：等离子体破裂实时预测、超导磁体失超保护与氚燃料自持循环体系实战

┌─────────────────────────────────────────────────────────────────────┐
│      Fusion Engineering Validation Architecture                     │
├─────────────────────────────────────────────────────────────────────┤
│  [Tritium Self-Sufficiency Layer: In-situ Monitoring / Mass Balance]│
│      ↓                                                              │
│  [Layer 1: 破裂预测层] ← Multimodal Precursor / Adaptive Mitigation│
│   ├─ 多模态信号融合与工况自适应学习                                     │
│   ├─ 分级缓解策略与执行效能评估                                         │
│   └─ 破裂事件归因与模型持续进化                                         │
│      ↓                                                              │
│  [Layer 2: 磁体防护层] ← Radiation-Hard Sensing / Active Cooling  │
│   ├─ 抗辐照分布式传感网络                                              │
│   ├─ 热-力耦合失超预警与主动干预                                        │
│   └─ 冷却系统健康状态实时评估                                           │
│      ↓                                                              │
│  [Layer 3: 氚自持层] ← Real-Time Tritium Assay / Extraction Opt.  │
│   ├─ 包层/冷却剂/排气原位氚浓度监测                                     │
│   ├─ 提取工艺动态优化与库存预测                                          │
│   └─ 全链路物料衡算与合规报告生成                                        │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy torch mdsplus efit
# 部署: Magnetic Probes + Soft X-Ray Array + Thomson Scattering + Fast Valve Injector + Edge Computing Node (FPGA+GPU)

"""
disruption_prediction_engine.py - 等离子体破裂预测引擎
技术栈: NumPy / PyTorch / MDSPlus / EFIT
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional
import torch
import torch.nn as nn

@dataclass
class DisruptionPredictionMetrics:
    """破裂预测指标"""
    detection_recall_pct: float
    false_positive_rate_per_shot: float
    warning_time_ms: 31310.t.kuaisou.com
    mitigation_success_rate_pct: float

@dataclass
class PlasmaState:
    """等离子体状态"""
    beta_n: float
    q95: 31311.t.kuaisou.com
    density_peaking_factor: float
    radiated_power_fraction: float
    mode_label: str  # "L", "H", "ELMy_H"

class MultimodalDisruptionModel(nn.Module):
    """多模态破裂预测模型"""
    
    def __init__(self, mag_dim=64, sxd_dim=32, ts_dim=16):
        super().__init__()
        self.mag_enc = nn.LSTM(mag_dim, 64, batch_first=True)
        self.sxd_enc = nn.Conv1d(sxd_dim, 32, kernel_size=5)
        self.ts_enc = nn.Linear(ts_dim, 16)
        self.fusion_head = nn.Linear(64 + 32 + 16, 1)
        
    def forward(self, mag_seq, sxd_map, ts_profile):
        _, (h_mag, _) = self.mag_enc(mag_seq)
        h_sxd = torch.relu(self.sxd_enc(sxd_map)).mean(dim=-1)
        h_ts = torch.relu(self.ts_enc(ts_profile))
        fused = torch.cat([h_mag.squeeze(0), h_sxd, h_ts], dim=-1)
        return torch.sigmoid(self.fusion_head(fused))

class AdaptiveDisruptionSystem:
    """自适应破裂预测主系统"""
    
    def __init__(self, model, mitigation_controller, data_store):
        self.model = 31312.t.kuaisou.com
        self.mitigation = mitigation_controller
        self.store = 31313.t.kuaisou.com
        
    async def predict_and_mitigate(self, shot_id: int) -> Dict[str, Any]:
        """预测并缓解破裂"""
        # 1. 获取多模态实时数据
        mag_data = await self.store.get_magnetic_signals(shot_id, window_ms=50)
        sxd_data = await self.store.get_soft_xray_map(shot_id)
        ts_data = await self.store.get_thomson_profile(shot_id)
        plasma_state = await self._reconstruct_plasma_state(shot_id)
        
        # 2. 执行预测
        with torch.no_grad():
            risk_score = self.model(mag_data, sxd_data, ts_data).item()
            
        # 3. 决策与缓解
        if risk_score > 0.8:
            mitigation_type = self._select_mitigation_strategy(plasma_state)
            success = await self.mitigation.trigger(mitigation_type, urgency="high")
        elif risk_score > 0.5:
            mitigation_type = "gas_puff"
            success = await self.mitigation.trigger(mitigation_type, urgency="medium")
        else:
            success = True
            mitigation_type = "none"
            
        metrics = DisruptionPredictionMetrics(
            detection_recall_pct=await self._compute_recall(shot_id),
            false_positive_rate_per_shot=await self._compute_fpr(shot_id),
            warning_time_ms=await self._get_warning_lead_time(shot_id),
            mitigation_success_rate_pct=100.0 if success else 0.0
        )
        
        return {
            "shot_id": 31314.t.kuaisou.com
            "prediction_metrics": metrics.__dict__,
            "risk_score": 31315.t.kuaisou.com
            "mitigation_action": mitigation_type,
            "plasma_regime": plasma_state.mode_label
        }
        
    def _select_mitigation_strategy(self, state: PlasmaState) -> str:
        """选择缓解策略"""
        if state.beta_n > 3.0 and state.mode_label == "H":
            return "massive_gas_injection"
        elif state.density_peaking_factor > 2.0:
            return "pellet_injection"
        else:
            return "radiative_collapse"

"""
magnet_tritium_platform.py - 磁体防护与氚自持平台
技术栈: PyTorch / FastAPI / COMSOL API / Tritium Assay SDK
"""
import torch
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel

class MagnetHealthMetric(BaseModel):
    quench_detection_latency_ms: float
    max_hotspot_temperature_k: float
    cooling_flow_deviation_pct: float
    neutron_fluence_1e18n_per_cm2: float

class TritiumSelfSufficiencyState(BaseModel):
    measured_tbr:31316.t.kuaisou.com
    tritium_extraction_efficiency_pct: float
    inventory_balance_error_pct: float
    days_of_tritium_reserve: float

class SuperconductingMagnetProtectionSystem:
    """超导磁体防护系统"""
    
    def __init__(self, sensor_network, thermal_model, coolant_monitor):
        self.sensors = sensor_network
        self.thermal = thermal_model
        self.coolant = coolant_monitor
        
    async def assess_magnet_safety(self, magnet_id: str) -> Dict[str, Any]:
        """评估磁体安全性"""
        # 1. 检测失超前兆
        voltage_taps = await self.sensors.get_voltage_signals(magnet_id)
        fiber_brillouin = await self.sensors.get_distributed_temp(magnet_id)
        early_warning = await self._detect_local_anomaly(voltage_taps, fiber_brillouin)
        
        # 2. 评估冷却状态
        flow_rate = await self.coolant.get_flow_rate(magnet_id)
        inlet_temp = await self.coolant.get_inlet_temp(magnet_id)
        flow_dev = abs(flow_rate - self.coolant.nominal_flow) / self.coolant.nominal_flow * 100
        
        # 3. 估算热点温度
        hotspot_temp = await self.thermal.estimate_max_temp(magnet_id, flow_rate, inlet_temp)
        
        metric = MagnetHealthMetric(
            quench_detection_latency_ms=early_warning["latency_ms"],
            max_hotspot_temperature_k=hotspot_temp,
            cooling_flow_deviation_pct=flow_dev,
            neutron_fluence_1e18n_per_cm2=await self.sensors.get_cumulative_fluence(magnet_id)
        )
        
        return {
            "magnet_id": 31317.t.kuaisou.com
            "health_metrics": metric.dict(),
            "quench_risk_level": "critical" if hotspot_temp > 150 or flow_dev > 20 else "normal",
            "maintenance_recommendation": self._suggest_maintenance(metric)
        }

class TritiumFuelCycleVerifier:
    """氚燃料循环验证器"""
    
    def __init__(self, assay_system, extraction_plant, inventory_db):
        self.assay = 31318.t.kuaisou.com
        self.extraction = extraction_plant
        self.inventory = inventory_db
        
    async def verify_tritium_self_sufficiency(self, campaign_id: str) -> Dict[str, Any]:
        """验证氚自持能力"""
        # 1. 测量实际TBR
        tbr_measured = await self.assay.compute_campaign_tbr(campaign_id)
        
        # 2. 评估提取效率
        extracted = await self.extraction.get_total_extracted(campaign_id)
        bred = await self.assay.get_total_bred(campaign_id)
        efficiency = (extracted / bred) * 100 if bred > 0 else 0
        
        # 3. 计算库存平衡误差
        input_t = await self.inventory.get_input(campaign_id)
        output_t = await self.inventory.get_output(campaign_id)
        burnup = await self.inventory.get_burnup(campaign_id)
        balance_error = abs(input_t + bred - extracted - burnup - output_t) / (input_t + bred) * 100
        
        # 4. 预测储备天数
        daily_consumption = burnup / await self.inventory.get_campaign_days(campaign_id)
        current_stock = await self.inventory.get_current_stock()
        reserve_days = current_stock / daily_consumption if daily_consumption > 0 else float('inf')
        
        state = TritiumSelfSufficiencyState(
            measured_tbr=31319.t.kuaisou.com
            tritium_extraction_efficiency_pct=efficiency,
            inventory_balance_error_pct=balance_error,
            days_of_tritium_reserve=reserve_days
        )
        
        return {
            "campaign_id": campaign_id,
            "self_sufficiency_state": state.dict(),
            "license_compliant": tbr_measured >= 1.05 and balance_error < 5.0,
            "fuel_cycle_optimization": self._suggest_optimizations(state)
        }