X-ray Imaging Analysis of Silo Flow Parameters Based on Trace Particles Using Targeted Crowdsourcing

Area of Science:

• Physics
• Engineering
• Data Science

Background:

• Silo flow analysis is crucial for industrial processes.
• Traditional methods struggle with complex imaging data from X-ray radiography.
• Automatic processing of imperfect tomographic data is challenging.

Purpose of the Study:

• To develop a novel crowdsourcing method for analyzing silo flow parameters.
• To improve the efficiency and accuracy of particle tracking in X-ray images.
• To enable quantitative description of material movement within silos.

Main Methods:

• Utilized X-ray radiography with traced particles to study gravitational silo flow.
• Implemented a "targeted crowdsourcing" approach for human-driven annotation of trace particles.
• Focused annotation on critical image areas to identify flow parameters.

Main Results:

• Demonstrated the feasibility of analyzing volumetric material movement from 2D radiography.
• Successfully derived quantitative flow parameters (velocity components) for different silo regions.
• Showed that targeted crowdsourcing speeds up pre-processing without compromising output quality.

Conclusions:

• Targeted crowdsourcing offers an efficient and effective solution for analyzing complex silo flow data.
• This method enhances the extraction of meaningful flow parameters from imperfect imaging.
• The approach is valuable for understanding material structure movement in bulk solids.