发表机构
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对封闭环境氢气泄漏检测问题，整合CFD、GA与DeepSets替代模型优化传感器布置，其检测性能优于传统方法，可降低计算成本，为相关系统集成提供基础。
AI 中文摘要
燃料电池车辆停车场等封闭环境中的氢气基础设施，因氢气点火能量低、可燃范围宽，带来重大安全挑战。当前监测系统多为被动式，仅在危险浓度形成后才检测泄漏。本研究开发了一种主动式传感器布置优化的计算框架，整合计算流体动力学（CFD）、遗传算法（GA）优化及DeepSets神经替代模型。针对50m×30m×3m的典型车库，生成了包含180种场景的CFD数据库，涵盖多种泄漏位置、泄漏速率（1-150g/s）及通风条件（ACH=3-10次/小时）。采用多目标GA优化传感器布置，并与均匀布置、随机布置及替代模型辅助方法对比。GA在60秒内达到96.1%的检测率，将盲区降至0.12%，相较于均匀基准的综合适应度提升约5%。DeepSets替代模型复现的近最优配置适应度差距低于0.01，同时减少了89%的CFD评估量，计算时间降低两个数量级，检测性能与空间覆盖仍与GA相当，表明替代模型辅助优化可在保持解质量的同时实现快速设计迭代。总体而言，结果显示基于CFD的优化相较于传统布局提升了检测有效性并减少了传感器需求，所提框架支持规模化部署，为将优化传感器网络与数字孪生系统集成以实现实时监测和风险评估奠定了基础。
英文摘要
Hydrogen infrastructure in enclosed environments, such as parking facilities for fuel cell vehicles, presents significant safety challenges due to hydrogen's low ignition energy and wide flammability range. Current monitoring systems are largely reactive, detecting leaks only after hazardous concentrations have formed. This study develops a computational framework for proactive sensor placement optimization by integrating computational fluid dynamics (CFD), genetic algorithm (GA) optimization, and a DeepSets neural surrogate. A CFD database of 180 scenarios was generated for a representative 50 m x 30 m x 3 m garage, covering multiple leak positions, rates (1-150 g/s), and ventilation conditions (ACH = 3-10 per hour). Sensor placement was optimized using a multi-objective GA and compared with uniform, random, and surrogate-assisted approaches. The GA achieved a detection rate of 96.1% within 60 s and reduced blind areas to 0.12%, corresponding to an approximately 5% improvement in composite fitness over a uniform baseline. The DeepSets surrogate reproduced near-optimal configurations with a fitness gap below 0.01 while reducing CFD evaluations by 89% and computational time by two orders of magnitude. Detection performance and spatial coverage remained comparable to the GA, demonstrating that surrogate-assisted optimization can retain solution quality while enabling rapid design iteration. Overall, the results show that CFD-informed optimization improves detection effectiveness and reduces sensor requirements compared to conventional layouts. The proposed framework supports scalable deployment and provides a foundation for integrating optimized sensor networks with digital twin systems for real-time monitoring and risk assessment.
