Abstract
Radioactive Particle Tracking (RPT) is a fluid measurement technique used in opaque ducts. To initiate the gauging process, RPT always requires the calibration of detectors. However, calibration poses a challenge for RPT deployment in industries. In most cases, experimental measurements are not a feasible option to calibrate RPT systems. To overcome this issue, Monte-Carlo simulations have been developed to imitate detector behavior and information recording. This study aimed to develop a user-friendly GEANT4 application for non-programmers to implement RPT in pipes with rectangular and cylindrical shapes. Additionally, the study used the application to evaluate which radiation detector arrangement can provide the best information for reconstructing particle position based on an Exploratory Data Analysis and the performance of machine learning algorithms. Two types of trajectories were simulated using the SAS GEANT4 application: straight and helicoidal. The generated results were normalized according to the activity of the radioactive source. The K-Nearest Neighbors algorithm and the Feedforward Neural Network were used to reconstruct the particle position. The detector arrangements were evaluated using Mean Euclidean Distance Error and Mean Absolute Error for each axis.
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