使用基于事件的视觉传感器相机进行三维气泡流测量
Three-Dimensional Bubbly Flow Measurement Using Event-based Vision Sensor Cameras
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中文总结 AI 辅助
研究利用三相机EVS系统测量三维气泡流，通过多EVS配置解决深度信息缺失问题，开发计算框架处理数据重建轨迹，经多阶段验证表现良好，成功解析气泡相互作用，不过事件过饱和是硬件限制。
中文摘要 AI 辅助
采用三相机基于事件的视觉传感器（EVS）系统对气泡运动、形态及气泡间相互作用进行三维测量。多EVS配置缓解了基于二进制事件成像中缺乏直接深度信息的问题，同时保留了高时间分辨率等优势。实验配置包括八角形水箱等。开发了计算框架处理事件数据以重建三维运动轨迹，经多阶段严格验证，该框架在多种情况下表现良好，成功解析了密集气泡相互作用，证明此方法是高速体积跟踪和气泡流测量的强大框架，但事件过饱和仍是关键硬件限制。
英文摘要
A three-camera Event-Based Vision Sensor (EVS) system is employed to perform three-dimensional measurements of bubble motion, morphology, and bubbleÐbubble interactions. The multi-EVS configuration mitigates the absence of direct depth information inherent to binary event-based imaging while preserving key advantages, including high temporal resolution, low latency, and reduced data throughput. The experimental configuration consisted of an octagonal tank equipped with a controlled particle release mechanism and an air diffuser. Camera synchronization and pulsed LED illumination were achieved using a dedicated signal generator and driver circuitry, while calibration was performed using pulsed-illumination recordings of a target acquired at multiple depths. A comprehensive, inhouse computational framework was developed to process the event data for three-dimensional motion trajectory reconstruction. The validation of the developed tracking framework followed a rigorous multi-stage pipeline to ensure reconstruction fidelity. The framework was initially benchmarked against synthetic rendering cases of increasing kinematic complexity to evaluate 3D trajectory reconstruction accuracy under controlled conditions, achieving sub-millimeter global accuracy with root-mean-square error (RMSE) values ranging from 0.015 to 0.36 mm. Following numerical validation, physical baseline experiments were conducted using precisely manufactured particle releases through both a gated chamber and a single-particle claw opening mechanism. Subsequently, dynamic bubble plumes generated via multiple inlets across various compressed air flow rates were evaluated. The EVS framework successfully resolved dense bubble-bubble interactions, producing smooth, physically consistent three-dimensional trajectories across all tested conditions. Quantitative results showed strong agreement, yielding an overall velocity consistency exceeding 97% between conventional centroid tracking and independent velocity estimates derived from continuous-illumination event streaks. Overall, this methodology demonstrates a robust framework for high-speed volumetric tracking and bubble flow measurement. However, event oversaturation remains a key hardware limitation in ultra-dense regimes, as oversaturation in a single camera can cause system desynchronization.
