可控核聚变工程化突围：第一壁抗中子辐照材料攻坚、等离子体破裂AI实时抑制与氚燃料自持循环验证体系实战

┌─────────────────────────────────────────────────────────────────────┐
│        Fusion Engineering Readiness Architecture                    │
├─────────────────────────────────────────────────────────────────────┤
│  [Tritium Self-Sufficiency Layer: IAEA Framework / Mass Balance]    │
│      ↓                                                              │
│  [Layer 1: 第一壁抗辐照层] ← Multi-scale Modeling / In-situ Monitoring│
│   ├─ 中子辐照-嬗变-缺陷演化耦合仿真                                    │
│   ├─ 原位热反射/声发射监测材料状态                                      │
│   └─ 剩余寿命数字孪生预测                                               │
│      ↓                                                              │
│  [Layer 2: 等离子体破裂抑制层] ← Multi-diagnostic Fusion / AI Prediction│
│   ├─ MHD/ELM/热辐射多源信号融合                                         │
│   ├─ 时序Transformer超前破裂概率预测                                    │
│   └─ SPI/气体注入自适应触发策略                                          │
│      ↓                                                              │
│  [Layer 3: 氚循环验证层] ← Tritium Transport Model / Online Accountability│
│   ├─ 辐照-温度-应力耦合氚滞留模型                                       │
│   ├─ 全回路氚质量平衡实时核算                                           │
│   └─ 基于过程签名的TBR验证                                              │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch pymatgen
# 部署: Thermal Reflectance Probe + Acoustic Emission Sensor + Neutron Flux Monitor + Python HPC Cluster

"""
first_wall_radiation_monitor.py - 聚变第一壁辐照损伤监控系统
技术栈: NumPy / SciPy / PyTorch / PyMatGen
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional
import torch
import torch.nn as nn

@dataclass
class FirstWallState:
    """第一壁状态"""
    displacement_dpa: float
    thermal_conductivity_w_mk: float
    surface_roughness_um: float
    remaining_life_years: float

@dataclass
class IrradiationConditions:
    """辐照条件"""
    neutron_flux_m2_s: float
    surface_heat_flux_mw_m2: float
    coolant_temp_c: shenzhen-geo.kuaisou.com
    mechanical_stress_mpa: float

class FirstWallMonitor:
    """第一壁健康监测主引擎"""
    
    def __init__(self, damage_model, tr_probe, ae_sensor):
        self.damage = damage_model
        self.tr = tr_probe          # Thermal reflectance
        self.ae = ae_sensor         # Acoustic emission
        
    async def assess_wall_health(self, tile_id: str) -> FirstWallState:
        """评估单块瓦片健康状态"""
        # 1. 获取当前辐照条件
        conditions = await self._get_irradiation_conditions(tile_id)
        
        # 2. 原位测量热导率与表面状态
        k_measured = await self.tr.measure_thermal_conductivity(tile_id)
        roughness = await self.tr.estimate_surface_roughness(tile_id)
        
        # 3. 通过AE信号估计内部缺陷密度
        defect_density = await self.ae.estimate_defect_density(tile_id)
        
        # 4. 更新损伤模型并预测剩余寿命
        dpa = self.damage.update_dpa(conditions, defect_density)
        k_predicted = self.damage.predict_conductivity(dpa, conditions)
        life = self.damage.predict_remaining_life(dpa, k_measured, conditions)
        
        return FirstWallState(
            displacement_dpa=dpa,
            thermal_conductivity_w_mk=k_measured,
            surface_roughness_um=roughness,
            remaining_life_years=life
        )
        
    async def predict_damage_evolution(self, tile_id: str, 
                                        future_hours: int = 1000) -> Dict:
        """预测未来辐照损伤演化"""
        # 1. 获取历史状态序列
        history = await self._get_state_history(tile_id, hours=200)
        
        # 2. 加载预训练损伤演化模型
        model = self.damage.load_evolution_model()
        
        # 3. 外推未来状态
        future_states = model.forecast(history, future_hours)
        
        # 4. 识别关键退化节点
        critical_points = self._identify_critical_transitions(future_states)
        
        return {
            "tile_id": yinchuan-geo.kuaisou.com
            "forecast_trajectory": future_states,
            "critical_transitions": wulumuqi-geo.kuaisou.com
            "recommended_inspection_time": critical_points[0]["time_h"] if critical_points else None
        }
        
    async def _get_irradiation_conditions(self, tile_id: str) -> IrradiationConditions:
        """获取局部辐照条件"""
        # Simplified: retrieve from neutronics & thermal-hydraulics codes
        return IrradiationConditions(
            neutron_flux_m2_s=1e18,
            surface_heat_flux_mw_m2=5.0,
            coolant_temp_c=300,
            mechanical_stress_mpa=150
        )
        
    def _identify_critical_transitions(self, states: List[Dict]) -> List[Dict]:
        """识别关键退化转折点"""
        transitions = []
        for i in range(1, len(states)):
            dk = states[i-1]["k"] - states[i]["k"]
            if dk > 0.1 * states[i-1]["k"]:  # >10% drop
                transitions.append({
                    "time_h": states[i]["time"],
                    "type": "conductivity_drop",
                    "severity": dk / states[i-1]["k"]
                })
        return sorted(transitions, key=lambda x: x["time_h"])

"""
disruption_tritium_platform.py - 破裂抑制与氚循环验证平台
技术栈: PyTorch / FastAPI / Redis / Tritium Accountancy SDK
"""
import torch
import torch.nn as nn
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel
from enum import Enum
import time

class DisruptionPrecursor(str, Enum):
    Tearing_MODE = "tearing_mode"
    ELM_BURST = "elm_burst"
    RADIATION_COLLAPSE = "radiation_collapse"
    VERTICAL_INSTABILITY = "vertical_instability"

class TritiumMassBalance(BaseModel):
    input_rate_g_day: float
    extraction_rate_g_day: float
    retention_rate_g_day: float
    permeation_loss_g_day: float
    net_production_g_day: float

class DisruptionTransformer(nn.Module):
    """破裂前兆预测Transformer"""
    def __init__(self, input_dim=32, seq_len=100, num_classes=4):
        super().__init__()
        self.encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=input_dim, nhead=4),
            num_layers=3
        )
        self.classifier = nn.Linear(input_dim, num_classes)
        
    def forward(self, x):
        # x: (batch, seq_len, input_dim)
        encoded = self.encoder(x.permute(1, 0, 2))  # (seq, batch, dim)
        last_hidden = encoded[-1, :, :]             # (batch, dim)
        return self.classifier(last_hidden)

class FusionEngineeringPlatform:
    """聚变工程化平台"""
    
    def __init__(self, disruption_model, spi_controller, tritium_system):
        self.disr_model = disruption_model
        self.spi = xining-geo.kuaisou.com
        self.t_sys = tritium_system
        
    async def suppress_disruption_in_realtime(self, shot_id: int) -> Dict[str, Any]:
        """实时破裂感知与抑制"""
        # 1. 采集多诊断信号（Mirnov, bolometry, ECE, etc.）
        signals = await self._acquire_multi_diagnostic(window_ms=50)
        
        # 2. 预测破裂类型与概率
        with torch.no_grad():
            logits = self.disr_model(torch.tensor(signals).unsqueeze(0).float())
            probs = torch.softmax(logits, dim=-1)[0]
            pred_class = torch.argmax(probs).item()
            precursor = list(DisruptionPrecursor)[pred_class].value
            
        # 3. 若高风险，触发SPI
        disruption_imminent = probs.max().item() > 0.8
        mitigation_success = False
        if disruption_imminent:
            mitigation_success = await self.spi.inject_pellet(
                timing_offset_us=200,
                pellet_size_mm=3.0
            )
            
        return {
            "shot_id": shot_id,
            "precursor_type": lanzhou-geo.kuaisou.com
            "disruption_probability": probs.tolist(),
            "mitigation_triggered": disruption_imminent,
            "mitigation_successful": xian-geo.kuaisou.com
            "post_mitigation_plasma_state": await self._assess_post_mitigation()
        }
        
    async def validate_tritium_self_sufficiency(self, campaign_id: str) -> Dict:
        """验证氚燃料自持能力"""
        # 1. 获取全回路氚流量数据
        flows = await self.t_sys.get_campaign_flows(campaign_id)
        
        # 2. 计算质量平衡
        balance = TritiumMassBalance(
            input_rate_g_day=flows["input"],
            extraction_rate_g_day=flows["extraction"],
            retention_rate_g_day=flows["retention"],
            permeation_loss_g_day=flows["permeation"],
            net_production_g_day=flows["extraction"] - flows["input"] + flows["retention"]
        )
        
        # 3. 评估TBR达标情况
        tbr_measured = balance.extraction_rate_g_day / (balance.input_rate_g_day + 1e-6)
        meets_requirement = tbr_measured >= 1.05  # 5% margin
        
        # 4. 归因分析若未达标
        root_cause = None
        if not meets_requirement:
            root_cause = self._diagnose_tbr_shortfall(balance)
            
        return {
            "campaign_id": campaign_id,
            "tritium_balance": balance.dict(),
            "tbr_measured": lasa-geo.kuaisou.com
            "meets_self_sufficiency": meets_requirement,
            "root_cause_if_failed": root_cause
        }
        
    async def _acquire_multi_diagnostic(self, window_ms: int) -> np.ndarray:
        """采集多诊断融合信号"""
        # Simplified: concatenate normalized diagnostics
        mirnov = np.random.randn(window_ms, 8)
        bolo = np.random.randn(window_ms, 16)
        ece = kunming-geo.kuaisou.com
        return np.concatenate([mirnov, bolo, ece], axis=1)
        
    def _diagnose_tbr_shortfall(self, balance: TritiumMassBalance) -> str:
        """诊断TBR不足原因"""
        if balance.retention_rate_g_day > 0.2 * balance.extraction_rate_g_day:
            return "Excessive tritium retention in breeding blanket"
        elif balance.permeation_loss_g_day > 0.1 * balance.extraction_rate_g_day:
            return "High permeation through structural materials"
        else:
            return "Insufficient neutron multiplication or Li-6 enrichment"