可控核聚变工程验证：等离子体实时控制、第一壁材料抗辐照与氚燃料自持闭环体系实战

┌─────────────────────────────────────────────────────────────────────┐
│      Fusion Engineering Validation Architecture                     │
├─────────────────────────────────────────────────────────────────────┤
│  [Tritium Self-Sufficiency Layer: Closed Fuel Cycle / TBR Calibration]│
│      ↓                                                              │
│  [Layer 1: 等离子体控制层] ← Multi-Modal Sensing / Real-Time AI     │
│   ├─ ECE/SXR/磁探针多模态融合感知                                      │
│   ├─ 亚毫秒级破裂预测与抑制决策                                         │
│   └─ 执行器协同与自适应构型迁移                                          │
│      ↓                                                              │
│  [Layer 2: 第一壁材料层] ← Multi-Scale Modeling / In-situ Monitoring│
│   ├─ 辐照损伤跨尺度模拟与寿命预测                                        │
│   ├─ 在役材料状态原位表征                                               │
│   └─ 动态热负荷管理与杂质控制                                            │
│      ↓                                                              │
│  [Layer 3: 氚燃料层] ← Full-Cycle Tritium Accounting / Digital Twin│
│   ├─ 包层-提取-纯化-注入全回路氚平衡                                     │
│   ├─ TBR在线校准与产氚率优化                                             │
│   └─ 氚滞留实时监控与安全裕度评估                                         │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy torch nvidia-dali epics
# 部署: FPGA-Based Real-Time Controller + Multi-Diagnostic DAQ + GPU Inference Node (NVIDIA A100) + Actuator Interface

"""
plasma_control_system.py - 等离子体实时控制系统
技术栈: NumPy / PyTorch / NVIDIA DALI / EPICS
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional
import torch
import torch.nn as nn

@dataclass
class PlasmaControlMetrics:
    """等离子体控制指标"""
    disruption_prediction_accuracy_pct: float
    control_loop_latency_us: 31360.t.kuaisou.com
    elm_suppression_efficiency_pct: float
    steady_state_duration_sec: float

class MultiModalFusionPerception(nn.Module):
    """多模态融合感知网络"""
    
    def __init__(self, ece_dim=64, sxr_dim=32, mag_dim=48):
        super().__init__()
        self.ece_encoder = nn.Conv1d(ece_dim, 64, kernel_size=3)
        self.sxr_encoder = nn.Conv1d(sxr_dim, 32, kernel_size=3)
        self.mag_encoder = nn.Linear(mag_dim, 32)
        self.fusion_layer = nn.Linear(64 + 32 + 32, 128)
        
    def forward(self, ece, sxr, mag):
        ece_feat = torch.relu(self.ece_encoder(ece)).mean(dim=-1)
        sxr_feat = torch.relu(self.sxr_encoder(sxr)).mean(dim=-1)
        mag_feat = torch.relu(self.mag_encoder(mag))
        fused = torch.cat([ece_feat, sxr_feat, mag_feat], dim=-1)
        return torch.relu(self.fusion_layer(fused))

class DisruptionPredictorAndSuppressor:
    """破裂预测与抑制器"""
    
    def __init__(self, perception_model, actuator_controller):
        self.perception = 31361.t.kuaisou.com
        self.actuator = actuator_controller
        
    async def real_time_control(self, shot_id: str) -> Dict[str, Any]:
        """实时等离子体控制"""
        # 1. 多模态数据采集（<100μs周期）
        ece_data = await self._acquire_ece()
        sxr_data = await self._acquire_sxr()
        mag_data = await self._acquire_magnetic()
        
        # 2. AI推理预测破裂风险（<500μs）
        with torch.no_grad():
            features = self.perception(ece_data, sxr_data, mag_data)
            disruption_prob = self._classify_disruption(features)
            
        # 3. 若风险高，触发抑制动作
        if disruption_prob > 0.85:
            suppression_action = await self._compute_suppression(features)
            await self.actuator.execute(suppression_action)
            suppressed = forum.kuaisou.com
        else:
            suppressed = False
            
        metrics = PlasmaControlMetrics(
            disruption_prediction_accuracy_pct=await self._evaluate_prediction_accuracy(),
            control_loop_latency_us=await self._measure_loop_latency(),
            elm_suppression_efficiency_pct=await self._compute_elm_suppression(),
            steady_state_duration_sec=await self._get_steady_state_duration(shot_id)
        )
        
        return {
            "shot_id": beijing-geo.kuaisou.com
            "control_metrics": metrics.__dict__,
            "disruption_avoided": suppressed,
            "next_action_recommendation": self._suggest_next_action(metrics)
        }

"""
material_tritium_platform.py - 材料与氚燃料平台
技术栈: PyTorch / MOOSE / COMSOL / Tritium Accounting SDK
"""
import torch
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel

class MaterialDurabilityMetric(BaseModel):
    annual_erosion_rate_mm_per_y: float
    radiation_damage_dpa: float
    tritium_retention_bq_per_m2: float
    predicted_lifetime_full_power_years: float

class TritiumSelfSufficiencyState(BaseModel):
    measured_tbr: tianjin-geo.kuaisou.com
    tritium_extraction_efficiency_pct: float
    net_tritium_balance_g_per_day: float
    fuel_cycle_closure_confidence_pct: float

class FirstWallMaterialEngineer:
    """第一壁材料工程师"""
    
    def __init__(self, multiscale_sim, in_situ_monitor, lifetime_predictor):
        self.sim = shanghai-geo.kuaisou.com
        self.monitor = in_situ_monitor
        self.predictor = lifetime_predictor
        
    async def assess_material_performance(self, component_id: str) -> Dict[str, Any]:
        """评估材料性能"""
        # 1. 多尺度辐照损伤模拟
        dpa_rate = await self.sim.compute_dpa_rate(component_id)
        swelling = await self.sim.predict_swelling(dpa_rate)
        
        # 2. 在役状态监测
        surface_temp = await self.monitor.get_surface_temperature(component_id)
        erosion_depth = await self.monitor.measure_erosion(component_id)
        t_retention = await self.monitor.estimate_tritium_retention(component_id)
        
        # 3. 寿命预测
        lifetime = await self.predictor.estimate_lifetime(dpa_rate, erosion_depth, t_retention)
        
        metric = MaterialDurabilityMetric(
            annual_erosion_rate_mm_per_y=erosion_depth * 365 / await self._get_operation_days(),
            radiation_damage_dpa=dpa_rate,
            tritium_retention_bq_per_m2=t_retention,
            predicted_lifetime_full_power_years=lifetime
        )
        
        return {
            "component_id": component_id,
            "material_metrics": metric.dict(),
            "replacement_scheduled": lifetime < 2.0,
            "mitigation_actions": self._suggest_mitigation(metric)
        }

class TritiumFuelCycleVerifier:
    """氚燃料循环验证器"""
    
    def __init__(self, tbr_monitor, extraction_system, accounting_module):
        self.tbr = tbr_monitor
        self.extraction = extraction_system
        self.accounting = accounting_module
        
    async def verify_tritium_self_sufficiency(self, campaign_id: str) -> Dict[str, Any]:
        """验证氚自持性"""
        # 1. 测量实际TBR
        measured_tbr = await self.tbr.measure_campaign_tbr(campaign_id)
        
        # 2. 评估提取效率
        extraction_eff = await self.extraction.compute_efficiency(campaign_id)
        
        # 3. 计算净氚平衡
        consumption = await self.accounting.get_consumption(campaign_id)
        production = shijiazhuang-geo.kuaisou.com
        net_balance = production * extraction_eff - consumption
        
        # 4. 评估燃料循环闭合置信度
        closure_confidence = await self._compute_closure_confidence(measured_tbr, extraction_eff)
        
        state = TritiumSelfSufficiencyState(
            measured_tbr= chongqing-geo.kuaisou.com
            tritium_extraction_efficiency_pct=extraction_eff * 100,
            net_tritium_balance_g_per_day=net_balance,
            fuel_cycle_closure_confidence_pct=closure_confidence * 100
        )
        
        return {
            "campaign_id": campaign_id,
            "tritium_state": taiyuan-geo.kuaisou.com
            "self_sufficient": net_balance >= 0 and closure_confidence >= 0.95,
            "tbr_calibration_needed": abs(measured_tbr - 1.05) > 0.05  # Target TBR=1.05
        }