可控核聚变工程验证：等离子体破裂实时预测、第一壁材料抗辐照损伤评估与氚燃料闭环计量体系实战

┌─────────────────────────────────────────────────────────────────────┐
│      Fusion Engineering Validation Architecture                     │
├─────────────────────────────────────────────────────────────────────┤
│  [Tritium Accountability Layer: Closed-loop Metrology / Containment] │
│      ↓                                                              │
│  [Layer 1: 破裂预警层] ← Multi-modal AI / Physics-Informed Fusion   │
│   ├─ 电磁+光学+粒子诊断时空融合                                        │
│   ├─ 物理约束神经网络与在线不确定性量化                                  │
│   └─ 分级响应与缓解执行                                                 │
│      ↓                                                              │
│  [Layer 2: 材料验证层] ← Cross-scale Simulation / In-situ Characterization│
│   ├─ 聚变谱辐照损伤等效验证                                             │
│   ├─ 微观结构-宏观性能关联建模                                          │
│   └─ 原位力学/热学性能监测                                              │
│      ↓                                                              │
│  [Layer 3: 氚管理层层] ← Dynamic Balance Model / Real-time Leak Detection│
│   ├─ 全厂氚流动态衡算                                                   │
│   ├─ β衰变自加热补偿计量                                                │
│   └─ 渗透监测与应急捕集联动                                             │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch xarray
# 部署: EIT/Magnetic Probes + IR Camera + Neutral Beam Diagnostics + FPGA-based Real-time Controller

"""
disruption_predictor.py - 聚变等离子体破裂实时预警系统
技术栈: NumPy / SciPy / PyTorch / XArray
"""
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"""
    probability: float          # 0-1
    time_to_disruption_ms: float
    dominant_mode: str          # e.g., "locked_mode", "thermal_quench"
    uncertainty: float          # 0-1

@dataclass
class DiagnosticSnapshot:
    """诊断快照"""
    magnetic_signals: np.ndarray   # (n_channels, n_timepoints)
    ir_images: np.ndarray          # (height, width)
    particle_flux: np.ndarray      # (n_detectors,)
    timestamp_us: 31277.t.kuaisou.com

class PhysicsInformedDisruptionModel(nn.Module):
    """物理约束破裂预测模型"""
    def __init__(self, n_mag=64, img_size=(128,128), n_part=16):
        super().__init__()
        # Magnetic branch with MHD stability constraints
        self.mag_encoder = nn.Sequential(
            nn.Conv1d(n_mag, 32, kernel_size=5),
            nn.ReLU(),
            nn.AdaptiveAvgPool1d(10)
        )
        # IR branch for thermal precursor detection
        self.ir_encoder = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=3),
            nn.ReLU(),
            nn.Flatten()
        )
        # Particle branch for edge transport anomalies
        self.part_encoder = nn.Linear(n_part, 32)
        
        # Fusion head with physics prior
        self.fusion_net = nn.Sequential(
            nn.Linear(32*10 + 32*126*126 + 32, 256),
            nn. 31278.t.kuaisou.com
            nn.Linear(256, 2)  # [prob, ttd]
        )
        self.uncertainty_head = nn.Linear(256, 1)
        
    def forward(self, mag, ir, part, return_unc=False):
        h_mag = self.mag_encoder(mag).flatten(start_dim=1)
        h_ir = self.ir_encoder(ir.unsqueeze(1))
        h_part = self.part_encoder(part)
        h = torch.cat([h_mag, h_ir, h_part], dim=-1)
        out = 31278.t.kuaisou.com
        prob = torch.sigmoid(out[:, 0])
        ttd = torch.relu(out[:, 1]) * 100  # ms
        if return_unc:
            unc = torch.sigmoid(self.uncertainty_head(h))
            return prob, ttd, unc
        return prob, ttd

class FusionControlSystem:
    """聚变控制系统"""
    
    def __init__(self, model, diagnostics, mitigation_system):
        self.model =31280.t.kuaisou.com
        self.diag = diagnostics
        self.mitigation = mitigation_system
        
    async def predict_disruption_realtime(self, shot_id: str) -> Dict[str, Any]:
        """实时破裂预测"""
        # 1. 获取同步诊断数据
        snap = await self.diag.acquire_snapshot(shot_id)
        
        # 2. 推理风险指标
        with torch.no_grad():
            prob, ttd, unc = self.model(
                torch.tensor(snap.magnetic_signals).unsqueeze(0),
                torch.tensor(snap.ir_images).float().unsqueeze(0),
                torch.tensor(snap.particle_flux).unsqueeze(0),
                return_unc=True
            )
            
        metrics = DisruptionRiskMetrics(
            probability=prob.item(),
            time_to_disruption_ms=ttd.item(),
            dominant_mode=self._classify_mode(snap),
            uncertainty=unc.item()
        )
        
        # 3. 分级响应决策
        action = self._decide_response(metrics)
        if action != "none":
            await self.mitigation.execute(action, shot_id)
            
        return {
            "shot_id": shot_id,
            "risk_metrics": metrics.__dict__,
            "response_action": 31279.t.kuaisou.com
            "diagnostic_timestamp_us": snap.timestamp_us
        }
        
    def _classify_mode(self, snap: DiagnosticSnapshot) -> str:
        """识别主导破裂模式"""
        # Simplified rule-based classification using physics priors
        if np.max(snap.ir_images) > 0.8 and np.std(snap.magnetic_signals) < 0.1:
            return "thermal_quench_precursor"
        elif np.any(np.abs(snap.magnetic_signals[-10:]) > 0.5):
            return "locked_mode"
        else:
            return "unknown"
            
    def _decide_response(self, metrics: DisruptionRiskMetrics) -> str:
        """分级响应决策"""
        if metrics.probability > 0.9 and metrics.time_to_disruption_ms < 5:
            return "emergency_shutdown"
        elif metrics.probability > 0.7 and metrics.uncertainty < 0.3:
            return "gas_puff_mitigation"
        elif metrics.probability > 0.5:
            return "reduce_heating_power"
        else:
            return "none"

"""
material_tritium_platform.py - 聚变材料验证与氚管理平台
技术栈: PyTorch / FastAPI / Redis / Tritium Monitoring 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 IrradiationDamageMetric(BaseModel):
    dpa_value: float
    helium_appm: float
    swelling_pct: float
    ductility_loss_pct: float

class TritiumAccountancyState(BaseModel):
    total_inventory_g: float
    unaccounted_loss_g: float
    permeation_rate_ci_day: float
    balance_closure_pct: float

class MaterialValidationEngine:
    """材料辐照损伤验证引擎"""
    
    def __init__(self, multiscale_sim, in_situ_sensor, ifmif_data):
        self.sim = 31281.t.kuaisou.com
        self.sensor = in_situ_sensor
        self.ifmif = ifmif_data  # IFMIF-DONES reference data
        
    async def validate_first_wall_material(self, sample_id: str) -> Dict[str, Any]:
        """验证第一壁材料抗辐照性能"""
        # 1. 获取原位力学/热学性能
        mech_props = await self.sensor.get_mechanical_properties(sample_id)
        thermal_props = await self.sensor.get_thermal_conductivity(sample_id)
        
        # 2. 运行多尺度模拟预测损伤
        sim_result = await self.sim.predict_damage(sample_id)
        
        # 3. 与IFMIF聚变谱数据交叉验证
        validation_gap = self._compute_validation_gap(sim_result, self.ifmif.get_reference(sample_id))
        
        metric = IrradiationDamageMetric(
            dpa_value=sim_result["dpa"],
            helium_appm=sim_result["he_concentration"],
            swelling_pct=sim_result["swelling"],
            ductility_loss_pct=mech_props["elongation_loss"]
        )
        
        return {
            "sample_id": 31282.t.kuaisou.com
            "damage_metrics": metric.dict(),
            "validation_gap": validation_gap,
            "qualification_status": "qualified" if validation_gap < 0.2 else "needs_review",
            "recommended_max_dpa": self._estimate_safe_limit(metric)
        }
        
    def _compute_validation_gap(self, sim: Dict, ref: Dict) -> float:
        """计算模拟与聚变谱参考数据的差距"""
        keys = ["swelling", "hardness", "thermal_conductivity"]
        errors = [abs(sim[k] - ref[k]) / ref[k] for k in keys if k in ref]
        return float(np.mean(errors)) if errors else 1.0

class TritiumManagementPlatform:
    """氚燃料管理平台"""
    
    def __init__(self, flow_meters, mass_spec, containment_monitors):
        self.flow = flow_meters
        self.ms = 31283.t.kuaisou.com
        self.contain = containment_monitors
        
    async def perform_tritium_accountancy(self, plant_zone: str) -> Dict[str, Any]:
        """执行区域氚衡算"""
        # 1. 采集所有进出流量与库存测量
        inflows = await self.flow.get_inflows(plant_zone)
        outflows = await self.flow.get_outflows(plant_zone)
        inventory = await self.ms.measure_inventory(plant_zone)
        
        # 2. 计算未计入损失
        total_in = sum(f["rate_g_h"] * f["duration_h"] for f in inflows)
        total_out = sum(f["rate_g_h"] * f["duration_h"] for f in outflows)
        unaccounted = total_in - total_out - inventory["delta_g"]
        
        # 3. 评估渗透率与闭合度
        perm_rate = await self.contain.get_permeation_rate(plant_zone)
        closure = 100.0 * (1.0 - abs(unaccounted) / max(total_in, 1e-6))
        
        state = TritiumAccountancyState(
            total_inventory_g=inventory["current_g"],
            unaccounted_loss_g=unaccounted,
            permeation_rate_ci_day=perm_rate,
            balance_closure_pct=closure
        )
        
        return {
            "plant_zone": 31284.t.kuaisou.com
            "accountancy_state": state.dict(),
            "compliance_status": "compliant" if closure > 95 and unaccounted < 0.1 else "investigate",
            "leak_localization": await self._localize_leaks(plant_zone) if unaccounted > 0.05 else None
        }
        
    async def _localize_leaks(self, zone: str) -> List[Dict]:
        """定位氚泄漏点"""
        # Use distributed sensor network to triangulate source
        sensors = await self.contain.get_all_readings(zone)
        hotspots = [s for s in sensors if s["concentration_ci_m3"] > 1e-6]
        return [{"location": h["position"], "severity": h["concentration_ci_m3"]} for h in hotspots]