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Polygence Scholar2023
Teja Koripella's profile

Teja Koripella

Class of 2024Sammamish, WA

Project Portfolio

Developing and Simulating a Novel Radon Gas Leak Pinpointing Ackermann Drive Robot Using OpenMC, Geiger Counters, and ROS

Started May 25, 2023

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Abstract or project description

As determined by Princeton’s Science and Global Security Department, neutron sources can be reliably traced back to their source utilizing a Multi-Armed-Bandit (MAB) based reinforcement learning algorithm. In this paper I will discuss the construction, programming, simulation, and testing of a novel approach to pinpointing and classifying gamma sources autonomously and determine its viability for tracking sources of radon gas leaks. Radon gas is a dangerous and common alpha emitter, claiming the lives of 20,000 people yearly. Since the most common isotope of Radon, Rn-222, decays into the gamma-emitters Bi-214 and Pb-214 in 46.7 minutes via transient equilibrium, I utilize Bateman’s equation to model radon exposure levels from its progeny using OpenMC. The environment consisted of a 1.3 MeV isotropic source comprised of Rn-222’s progeny along with a 100 KeV directional source of Cs-137, 100 feet from 4 Geiger-Müller (GM) tubes representing the frame of the robot, where I ran the pulse-height and flux tallies to calculate the estimated confidence interval. The algorithm used to translate the robot throughout the simulation relied on RRT-SLAM and Thompson-sampling algorithm (Thompson-Sampling is a MAB solution) which utilized reward from CPS recorded from the GM. The results from the simulation from this study were especially promising regardless of its low 63.8% isotope confidence interval (confidence that the located isotope was Rn-222) due to a remarkable a prediction error of 1.36 feet (algorithm prediction error from true radon location).