发表机构
Institute of Aircraft Production Technology (IFPT), Hamburg University of Technology (TUHH)（飞机生产技术研究所（IFPT），汉堡工业大学（TUHH））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究氢气系统组件手动泄漏检测问题，通过机器人引导测试台量化嗅探轨迹运动学对检测可靠性的影响，得出特定几何形状路径规则和信号损失模型，揭示标准操作程序风险，还提出概念验证软件管道。
AI 中文摘要
氢气基础设施的完整性依赖可靠的泄漏检测，在电解槽制造中几乎完全通过手动示踪气体嗅探进行。尽管有标准规定，但缺乏空间探测指导说明，检测可靠性完全取决于操作员执行，传感器信号延迟进一步影响检测。本研究量化了嗅探轨迹运动学如何影响小规模管道和配件的检测可靠性，这是宏观扩散研究基本忽略的近场情况。使用机器人引导测试台消除操作员差异，在标准泄漏率（氮气中5体积%氢气）和不同扫描速度下，获取代表性几何形状的静态浓度场和动态轨迹通过情况。结果表明，扫描速度和空间探测方向强烈决定可检测性。传统线性轨迹在动态条件下经常错过泄漏，导致严重误报。相反，特定几何形状的路径，如围绕密封点的圆周插入路径，保持高安全裕度。据此得出特定几何形状的路径规则和动态信号损失的折减因子模型。研究发现当前标准操作程序存在切实安全风险。为实施这些规则，提出概念验证软件管道，可直接从3D模型生成经过验证的轨迹，用于辅助系统可视化。
英文摘要
The integrity of hydrogen infrastructure relies on reliable leak detection, performed almost exclusively via manual tracer gas sniffing in electrolyzer manufacturing. Although mandated by standards, the lack of spatial probe guidance instructions leaves detection reliability entirely to operator execution, further compromised by sensor signal delays. This study quantifies how sniffer trajectory kinematics affect detection reliability at small-scale pipes and fittings, a near-field regime largely neglected by macroscopic dispersion research. Using a robotically guided test bench to eliminate operator variability, static concentration fields and dynamic trajectory passes were acquired across representative geometries under standardized leak rates (5 vol% hydrogen in nitrogen) and varying scanning velocities. Results demonstrate that scanning velocity and spatial probe orientation strongly dictate detectability. Conventional linear trajectories frequently miss leaks under dynamic conditions, causing severe false negatives. Conversely, geometry-specific routing, such as circumferential plunging paths around sealing points, maintains a high safety margin. From these observations, geometry-specific routing rules and a reduction-factor model for dynamic signal loss are derived. The findings reveal that current standard operating procedures pose a tangible safety risk. To operationalize these rules, a proof-of-concept software pipeline is presented, generating validated trajectories directly from 3D models for visualization in assistance systems.
CommentsPreliminary draft. Work in progress
