NERS Project #1: Atombot: Swarm Robotics and Collective Intelligence
Faculty Mentor: Y Z, [email protected]
Prerequisites:
- GPA>3.9; PCB and circuit design (Altium or Eagle) and fabrication
- fabrication skills (3D printing, laser cutter, milling machine, soldering, etc.)
- programming with C/C++/Python
- 3D CAD design (SolidWorks); basic algorithms.
Project Description: The understanding of collective phenomena is one of the major intellectual challenges in a wide range of fields, from materials science to life science. One fundamentally interesting and technologically impactful system is swarm robotics – a collection of robots that can self-organize and exhibit higher-level collective intelligence. Because of the low cost of individual units of the swarm, swarm robots may have a profound impact on a wide range of disciplines, such as information gathering, cooperative missions, and collective artificial intelligence. This Atombot project will build a controllable miniature swarm robot system, based on a prototype developed by the Z Lab, study the fundamental emergent behavior of many robot systems, and explore mission-critical applications in homeland security and safeguarding. The Atombot team seeks students with a broad range of backgrounds and interests to take roles in the technology, science, and commercialization sub-teams. Students will have the opportunity to develop skills in additive manufacturing, electronics, control and optimization, physics, and business development.
Research Mode: In Lab
NERS Project #2: Explore Generative AI Applications for Knowledge Management of Nuclear Engineering Fields
Faculty Mentor: Professor Todd Allen, [email protected]
Additional Mentor: Yugo Ashida, [email protected]
Prerequisites:
- Computer Science major or equivalent knowledge
Project Description: Natural Language Processing (NLP) tools are very helpful for users to search, discover, and understand the contents and interrelationships of scientific literature and/or technical documents in each specific engineering and technology field. Generative artificial intelligence (GenAI) tools, such as ChatGPT3.5, ChatGPT4.0, and Maizey, are now being used in the College of Engineering and university-wide. We need to understand the potential and capability of using these tools for nuclear engineering applications. We are recruiting a student to assist in the exploration of using GenAI for understanding nuclear engineering based on literature content. Additionally, the student will help with identifying industrial needs and possible methodologies/algorithms to improve the current GenAI technology.
Research Mode: In-person, remote, hybrid
NERS Project #3: Proton Irradiation of Chrome Coatings and Characterization
Faculty Mentor: Peng Wang, [email protected]
Prerequisites:
- N/A
Project Description: This work scope covers the initial phase of experiments testing Zircaloy specimens that have been cold sprayed with a chromium coating and exposed to proton irradiation. The adhesion and corrosion resistance of the coatings will be assessed following proton irradiation as well as potential interface interactions that may have occurred. These experiments are intended to mimic potential in-pile testing of other chrome-coated specimens and identify any critical technical concerns more expeditiously.
Research Mode: In Lab
NERS Project #4: Radiological Health Engineering
Faculty Mentor: Professor Kim Kearfott, [email protected]
Additional Mentor: Jordan D. Noey MS, [email protected]
Prerequisites:
- Students work as part of a team
- a student’s specific assignment will depend upon the background and interests of each student
Project Description: Radiological Health Engineering represents a quantitative approach to radiation safety. It concerns itself with protecting humans and the environment from ionizing radiation. Topics may include measuring small amounts of radiation in the environment, dosimeters for workers, reducing radiation dose, and public communications about the health effects of radiation dose. For this project, the student(s) will work with a nuclear engineering professor specializing in radiological health engineering on a topic of mutual interest while serving as a general assistant to that professor. Preference will be given to students interested in radiation physics or nuclear engineering.
Research Mode: In-person (some remote may be possible).
NERS Project #5: Environmental Monitoring: The Radiation Weather Station (RWS)
Faculty Mentor: Professor Kim Kearfott, [email protected]
Additional Mentor: Jordan D. Noey MS, [email protected]
Prerequisites:
- Students work as part of a team
- A student’s specific assignment will depend upon the background and interests of each student
Project Description: Knowing changes in the presence of radionuclides in the environment is important for detecting and responding to nuclear power plant accidents and radiological terrorist events. The Radiation Weather Station has sensors at several University of Michigan’s North Campus stations, with auxiliary stations planned elsewhere. Sensors include temperature, pressure, humidity, rainfall, wind speed and direction, solar radiation, solar flares, soil moisture, radon, and gamma rays. Naturally occurring, medical and nuclear power plant radionuclides may be detected, with information collected about the energy of their gamma rays. Monitoring is designed to identify radionuclides from natural, planned, and accidental releases while tracking indoor and outdoor environmental conditions. All data are shared through a continuously updated website. Various projects involve developing and improving the database, deploying new sensors, analyzing large data sets, developing websites, adding mobile phone-based radiation detectors, and preparing educational materials for the public and K-12 audiences. A small, affordable RWS featuring a smaller collection of more affordable sensors controlled by a Raspberry Pi computer is also undergoing development. Tasks and specific work are tailored to the student, depending upon their interests and capabilities. This may involve one (or possibly multiple) of the following: software usage, statistical analysis, coding, hardware interfacing, analysis of temporal data sets, machine learning, design and 3-D printing of mechanical parts and cases, design of circuits and printed circuit boards, historical research, and technical writing for the public.
Note: This project is *not* about climate change or weather prediction. It is about detecting and characterizing the type and source of ionizing radiation as it changes as a function of time at different locations.
Research Mode: In-person (some remote may be possible)
NERS Project #6: Smart Radiation Detectors and Adaptive Navigation for Radiation Surveys
Faculty Mentor: Professor Kim Kearfott, [email protected]
Additional Mentor: Jordan D. Noey MS, [email protected]
Prerequisites:
- Students work as part of a team
- A student’s specific assignment will depend upon the background and interests of each student
Project Description: Several software and hardware development projects are available, all related to improving and testing an intelligent radiation detector. The current design is a Geiger-Muller (GM) ionizing radiation detector controlled by Raspberry Pi computer. Enhanced designs may ultimately be possible using spectroscopic detectors. Individuals performing radiation surveys in smaller spaces, such as individual laboratories, could use smart radiation detectors. They could also be transported larger distances using ground-based or aerial platforms, ranging from firetrucks to unmanned aerial vehicles (UAVs) or drones. Software involving auditory or visual communications could alert surveyors of spots that have been missed in performing detailed surveys, such as lab benches, when searching for contamination. More complex software could be used to construct the distribution of radiation sources in an environment from limited measurements. That approximation could then inform the best locations for addition measurements and optimize a measurement path to improve knowledge of the sources further. Such approaches would be invaluable for first responders to radiological events and those involved in the cleanup of legacy radioactive wastes or the decommissioning of nuclear facilities. Experienced students seek to develop computer programs and mobile device Apps to fully enable their appropriate functioning, display of data, and intelligent navigation of such detectors. Students should provide transparent information about their experience and proficiency in software, hardware, radiation detection, and mechanical design so that their role on a diverse team working on this project could be identified. A heavy-lift drone capable of an 8 kg payload is available for use.
Note: The algorithms in this project may also be applied to the location of WiFi emitters and other non-ionizing radiation sources (such as radio waves). Some projects are available with those, “substitutes” for ionizing radiation.
Research Mode: In-person (some remote may be possible). Some experiments may be performed outside.
NERS Project #7: Radon Gas—An Indoor Radioactive Hazard Useful for the Detection of Nuclear Weapons and Earthquakes
Faculty Mentor: Professor Kim Kearfott, [email protected]
Additional Mentor: Jordan D. Noey MS, [email protected]
Prerequisites:
- Students work as part of a team
- A student’s specific assignment will depend upon the background and interests of each student
Project Description: Radon gas is a ubiquitous naturally occurring radioactive material throughout the environment and in all buildings, at least in small amounts. It can be readily detected but presents health hazards when in high concentrations. Radon gas levels change as a function of local weather conditions and the heating or cooling situation within a building. Radon has also been observed to change many days before significant earthquakes. This project involves the study of radon gas as a function of time indoors and outside. State-of-the-art equipment is deployed to measure radon gas and track local weather and other conditions such as solar and background radiation from other sources. Students may participate in experimental data collection and analysis of large data sets. Discrimination of airborne transuranic radionuclides from naturally occurring radon gas is particularly important for worker protection during commercial nuclear power plant outages and dose control during emergencies. The topic is especially suitable for students interested in homeland security/treaty verification, nuclear power plant operations, and/or radiation protection. Students with appropriate backgrounds may be able to apply machine learning techniques to analyze existing data sets.
Research Mode: In-person (some remote may be possible)
NERS Project #8: Thermoluminescent and Optically Stimulated Luminescent Dosimetry– Radiation Dose Measurements for Workers, Patients, and Environmental Monitoring
Faculty Mentor: Professor Kim Kearfott, [email protected]
Additional Mentor: Jordan D. Noey MS, [email protected]
Prerequisites:
- Students work as part of a team
- A student’s specific assignment will depend upon the background and interests of each student
Project Description: Dosimeters are passive, integrating materials used to monitor the radiation exposure of workers in nuclear facilities. Although all workers receive dosimeters, there are different types, and they have different performance characteristics. New dosimeter types and ways of calibrating and deploying them are being developed. Dosimetry systems are also used for medical applications, including radiation therapy, diagnostic radiology, and nuclear medicine. The limitations of different dosimeters are being actively compared and characterized for medical applications. Advanced software is also being developed to automatically analyze thermoluminescent dosimeter glow curves for research projects and routine analysis. A dosimetry calibration source is also being carefully characterized using quality control experiments undergoing development. Students may be engaged in performing experiments, data analysis, and/or software design. This project is especially suitable for students interested in homeland security/treaty verification, medical physics, nuclear power plant operations, and/or radiation protection.
Research Mode: In-person (some remote may be possible)
NERS Project #9: Radiation Spectroscopy for Radionuclide Identification
Faculty Mentor: Professor Kim Kearfott, [email protected]
Additional Mentor: Jordan D. Noey MS, [email protected]
Prerequisites:
- Students should have strong skills, experience, or interest in computer programming or radiation detection
Project description: The applications of this project are the protection of the public from environmental radiation, nuclear accident dose reconstruction, and nuclear weapons treaty verification. Energy spectroscopy involves the determination of the energy of particular types of radiation, which are characteristic of the source of radiation. Alpha, gamma, and neutron spectroscopic devices are calibrated and deployed to solve real-world problems involving radiation sources. Students may become involved in nuanced calibrations, data interpretation, and specific measurement campaigns involving a variety of state-of-the-art and newly developed instruments used for radiation spectroscopy. Applications of an imaging spectrometer to the medical environment as well as for naturally occurring radioactivity may also be explored. The topic is especially suitable for students ultimately interested in homeland security/treaty verification, medical physics, nuclear power plant operations, and/or radiation protection.
Research Mode: In-person (some remote may be possible)
NERS Project #10: Extended Reality Training and Virtual Reality Games for Radiation Protection (DoseBusters Team)
Faculty Mentor: Professor Kim Kearfott, [email protected]
Additional Mentor: Jordan D. Noey MS, [email protected]
Prerequisites:
- Students work as part of a team
- A student’s specific assignment will depend upon the background and interests of each student.
- Students unfamiliar with the software being used will be afforded the opportunity to learn.
Project Description: Work with a team producing a game or other extended reality experience to teach the principles of radiation protection or attract interest to the nuclear sciences. Students may become involved in the overall game or experience design, artwork, rendering of nuclear-specific objects, implementation of realistic radiation source and detector physics, or creation of competitive aspects to the software. Unity and Blender are currently being used for the VR game, which is implemented on an Oculus Quest. Uptail and 3-D cameras are employed for the separate extended reality training experience.
Research Mode: In-person, hybrid (some remote may be possible)
NERS Project #11: Design of an Intelligent Radiation Awareness Drone (iRAD Team)
Faculty Mentor: Professor Kim Kearfott, [email protected]
Prerequisites:
- Students work as part of a team
- A student’s specific assignment will depend upon the background and interests of each student.
- Students unfamiliar with any software being used will be afforded the opportunity to learn.
Project description: An affordable drone capable of carrying a radiation detector, and having its movements controlled based on collected information, is being designed and constructed from parts. The Intelligent Radiation Awareness Drone (iRAD) is ultimately to be provided to high schools to interest students in both nuclear sciences and robotics. Before that can occur, however, technical problems involving aircraft design (motors, layout), collision avoidance (visible camera), terrain holding (LIDAR), payload interfacing (radiation and other detectors), environmental hardening, and navigational control need solved. This undergraduate team is assigned to work together on those tasks. A heavy-lift drone capable of an 8 kg payload is available for use in this project: work with this drone will include software and sensor development to implement real-time mapping of hazards.
Research Mode: In-person
NERS Project #12: Development of Therapeutic Radiation Dose Prediction Methods based on Diagnostic Imaging
Faculty Mentor: Martha Matuszak, [email protected]
Additional Mentor: Daniel Polan, [email protected]
Prerequisites:
- Basic programming knowledge (Python, MATLAB, or C#)
Project Description: Prior to CT simulation and treatment planning, it is difficult for physicians to quantitatively access the potential toxicity profile for a patient referred for external beam radiation therapy. Given differences in disease extent, location, and proximity to organs, treatment-associated toxicity risk may vary significantly between patients and may not be fully realized until after the initial patient consult and plan optimization. Recently, deep-learning-based radiation dose prediction methods have been demonstrated to accurately predict 3D dose distributions based directly on patient anatomy. However, published methods have focused on the use of CT simulation imaging for prediction, rather than pre-consult diagnostic imaging. This project focuses on extending these methods to diagnostic imaging to provide physicians with individualized dose predictions prior to consultation, which may assist in discussing and managing patient treatments. The student will work as part of a multidisciplinary team to develop, implement, and test these methods. During the project, students will develop an understanding of radiation therapy treatment planning, machine learning, imaging processing, and data analysis techniques. Specific project tasks can be tailored to the interests of the student.
Research Mode: In-person, or hybrid
NERS Project #13: Developing and testing methods and tools for the socially engaged design of nuclear energy technologies
Faculty Mentor: Prof. Aditi Verma, Nuclear Engineering and Radiological Sciences
Additional Mentor: Dr. Katie Snyder, Program in Technical Communication
Prerequisites:
- Students work as part of a team
- A student’s specific assignment will depend upon the background and interests of each student
Project Description: As all energy technologies, including nuclear, become smaller and distributed, it has become vital that technology designers engage with potential host communities during the technology design and development process. Across the country, communities in Alaska, at sites of present and former nuclear research facilities, and coal sites are expressing an interest in nuclear energy and calling for a seat at the table as key decisions about technology and facility design are made. Failure to incorporate these perspectives in the design of nuclear as well as other energy technologies could significantly slow down the transition to a low-carbon energy system. In Fall 2023, with our ENGR100 course, we piloted a novel socially engaged process for designing energy technologies, making use of several tools, including virtual reality models of reactor systems, generative AI tools for prototyping, and interactive workshops to support socially engaged design.
Students working on this project will analyze the qualitative and quantitative data collected during that pilot, carry out interviews and surveys, refine and test socially engaged design tools, and work with the faculty mentors to write up the research findings. Students working on this project will have the opportunity to continue working with us in Fall 2024 semester as peer mentors for our ENGR100 course where we will again run a socially engaged design process.
Research Mode: Hybrid with availability to meet in Ann Arbor. In-person all-team meetings will take place at the start of the project, after which students may carry out hybrid or remote work.
NERS project #14: Analysis of the demographic impacts of nuclear fuel cycle facilities
Faculty mentor: Prof Aditi Verma
Additional mentors: Gabrielle Hoelzle and Prof. Todd Allen
Prerequisites:
- A strong interest in the technology-policy intersection and interest in interdisciplinary research is ideal.
- Familiarity with programming and statistical analysis is desirable but not required.
- Students in any year of study are encouraged to apply.
Project description: The Fastest Path to Zero initiative is seeking a motivated undergraduate research assistant to build on an ongoing analysis of the demographic impacts of nuclear fuel cycle facilities in the US. Preliminary analysis carried out by the Fastest Path team shows the differential impacts of extractive facilities (uranium mills and mines) on host communities of these facilities. For example, this analysis shows that extractive facilities have typically been sited in counties that have high percentages of Native American, Latino, and Hispanic populations at the time of siting and the percentage of white populations in these communities has fallen in the decades following facility siting, while the percentages of Native American, Hispanic, Latino, and Black populations have increased over time in these same counties. This trend, as well as others, revealed through this analysis, indicate that the siting of both extractive and energy generation facilities may have heightened existing inequities along racial and socioeconomic lines within and across communities over time. We aim to build on this initial analysis to include waste management facilities, research facilities such as the national labs, as well as cleanup sites.
Nuclear reactor developers as well as policymakers are actively considering where and how to site future nuclear energy and fuel cycle facilities, and the results of such an analysis can not only inform siting policy decisions in this pivotal moment for the nuclear energy sector but also identify and repair existing inequities created by the nuclear energy sector over its trajectory of development. Further, the results may also guide renewable energy technology companies that are embarking on a major energy transition with significant extraction requirements.
Research Mode: Hyrbid
NERS Project #15: Fairness and Sarcasm Analysis of Large Language Models in Sentiment Analysis
Faculty Mentor: Majdi I. Radaideh (AIMS group – Artificial Intelligence and Multiphysics Simulations group), [email protected]
Prerequisites:
- Python Programming (required), Natural Language Processing and Large Language Models are a plus
Project Description: As large language models become increasingly pervasive, concerns about fairness and the ability to accurately interpret nuanced expressions, such as sarcasm, have gained prominence. This project delves into the intricate interplay between fairness considerations and the effective identification of sarcastic sentiments within the context of sentiment analysis using large language models. The project addresses critical issues of fairness in sentiment analysis models, exploring potential biases that may arise due to imbalances in training data or inherent biases present in the models themselves. In addition, we plan to assess the fairness of sentiment predictions across various demographic groups, ensuring that the deployment of these models does not inadvertently perpetuate or exacerbate societal disparities. The students will get access to more than 1M tweets collected from the social media platform “X” about the sentiment of the US public regarding nuclear power. The students are expected to train models such as BERT, GPT, and LLaMA and identify any potential biases in the training and model predictions of the sentiment by these models. This project is part of AIMS ongoing efforts to use large language models to determine the level of the US public support of nuclear power in a fair and accurate way.
Research Mode: In-person, hybrid
NERS Project #16: Scalable Reinforcement Learning for Physics-informed Nuclear Reactor Control.
Faculty Mentor: Majdi I. Radaideh (AIMS group – Artificial Intelligence and Multiphysics Simulations group), [email protected]
Prerequisites:
- Python programming (required), deep reinforcement learning, and control theory are a plus
Project Description: Nuclear microreactors are an innovation in nuclear power that provide modular and small-scale power reactors for electricity generation in remote areas. Due to their small size, microreactors have unconventional ways of operation and control, which drive new research questions about the autonomous control of these reactors. This project explores the potential of deep reinforcement learning algorithms in offering robust and safe operation of microreactors. Students will focus on implementing and testing various deep reinforcement learning algorithms for a microreactor design developed in-house. More importantly, students will contribute to the development of a scalable reinforcement learning toolkit that allows training and tuning reinforcement learning algorithms at large scales leveraging parallel CPU and GPU resources. The conclusions of this project can open the door for a new control paradigm based on machine learning that can complement existing approaches such as PID and model predictive control. This project is part of AIMS ongoing efforts to develop interpretable model-based intelligent control algorithms for the control of complex systems.
Research Mode: In-person, hybrid
NERS Project #17: Efficient Uncertainty Quantification Methods to Identify Prediction Intervals for Neural Networks
Faculty Mentor: Majdi I. Radaideh (AIMS group – Artificial Intelligence and Multiphysics Simulations group), [email protected]
Prerequisites:
- Python programming (required), machine learning and uncertainty quantification are a plus.
- US citizenship or permanent residency is required.
Project Description: Neural networks have exhibited remarkable success across diverse applications, yet their inherent uncertainty poses challenges in decision-making processes, especially when extrapolating beyond their training data regime. Methods such as Bayesian Neural Networks (BNNs) and Monte Carlo Dropout have been developed in the past as potential methods for uncertainty quantification of neural networks. However, these methods have certain deficiencies such as being expensive and hard to train for BNN and the possibility for uncertainty underestimation, and the requirement of dropout layers for Monte Carlo dropout. In recent years, the concept of “deep ensembles” has started to emerge as a computationally efficient and yet accurate uncertainty predictor for neural networks. Deep ensembles involve training multiple neural network instances independently on the same dataset, each with different initializations or architectures. During inference, predictions from these diverse models are aggregated to provide not only point estimates but also a measure of uncertainty. In this project, students will implement two variants of deep ensemble methods and benchmark them against BNN and Monte Carlo dropout for a nuclear engineering application (a dataset will be provided by the mentor). This project is part of AIMS ongoing efforts to increase the confidence in neural network predictions for sensitive applications like nuclear power plants.
Research Mode: In-person, hybrid
NERS Project #18: Model-agnostic Explainability Methods for Black-box AI Models in Nuclear Power Applications
Faculty Mentor: Majdi I. Radaideh (AIMS group – Artificial Intelligence and Multiphysics Simulations group), [email protected]
Prerequisites:
- Python programming (required), machine learning and nuclear power knowledge are a plus.
- US citizenship or permanent residency is required.
Project Description: Artificial intelligence (AI) and machine learning (ML) applications in the nuclear industry are continuously growing in the areas of component monitoring, predictive maintenance, digital twins, surrogate model development, cybersecurity, and more. While neural networks can offer high accuracy in complex applications and are considered among the most powerful AI/ML models, they lack interpretability, which is crucial in the nuclear industry. To address this issue, explainable AI (XAI) methods are proposed, that allow end-users to understand the reasons behind a model’s predictions by answering four primary questions: Why does the model make certain predictions? Should I trust this model? Is the model or data biased? When is this model going to be unreliable? In this project, students will apply various traditional model explainability methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). More importantly, students will implement and test state-of-the-art approaches like “Integrated Gradients” and their variants. Integrated gradients attribute a prediction to each feature by integrating the gradients of the model’s output with respect to the input along a path from a baseline to the input. Integrated gradients have a clear theoretical foundation, linking the attribution to the model’s gradients and ensuring a principled approach to explaining predictions. Students will compare these XAI methods on nuclear power applications related to critical heat flux predictions and nuclear reactor control. Two students from computer science and nuclear engineering might be selected to collaborate on the two aspects of this project. This project is the first step of a new initiative AIMS laboratory is working on to develop an XAI framework for nuclear power applications to address licensing challenges.
Research Mode: In-person, hybrid
NERS Project #19: Radiation Detection Methods for Photon Active Interrogation
Faculty Mentor: Sara Pozzi, [email protected]
Graduate Student Mentor: Caryanne Wilson, [email protected]
Prerequisites:
- Ability to work independently, inquisitive/questioning attitude.
- Programming experience (e.g., matlab, python, C++) is preferred
Project Description: Photon active interrogation techniques improve detection capabilities for shielded special nuclear material (SNM), such as highly enriched uranium. Photon active interrogation can improve detection capabilities because an intense, high-energy photon beam can penetrate shielding materials and induce photonuclear reactions in SNM, greatly increasing radiation emissions. Neutron active interrogation techniques can be used to detect explosives and narcotics in cargo containers. When interrogated with neutrons, explosives and narcotics will emit radiation signatures that can be used to identify their type and quantity. The University of Michigan is developing economical photon and neutron active interrogation techniques for homeland security applications using a 9-MeV linear accelerator (linac), a DT neutron generator, and new detection technologies. Students will participate in experiments, develop simulations, analyze data, and learn underlying nuclear engineering concepts.
Research Mode: Hybrid
NERS Project #20: Fission Chain Reaction Noise for Nuclear Nonproliferation and Safeguards
Faculty Mentor: Sara Pozzi, [email protected]
Graduate Student Mentor: Flynn Darby, [email protected]
Prerequisites:
- Willingness to learn, experience with coding (Python, MATLAB, or C++) is a bonus, python proficiency is preferred
Project Description: Fission events occur when a large atom splits into two smaller atoms. Immediately after a fission event, the two fragments are considerably energetic and emit several prompt neutrons and gamma rays. The neutrons from one fission event can induce fission via radiative capture in adjacent atoms, inducing a chain reaction. When a large amount of fissionable material is arranged in close geometry, outside sources can cause fission chain reactions. These chain reactions may be quantified with radiation measurements. We use organic scintillation radiation detectors to detect both neutron and photon radiation from fission events. The excellent timing precision and pulse shape discrimination capabilities of these detectors make organic scintillators the preferred detector. By analyzing the signal from the detector, we can characterize complex facility experiments and even nuclear research reactors. The characterization may serve as validation data for simulation code, corroboration of reaction and measurement theory, and a means of verification for inspection purposes. Students will help setup and analyze basic experiments, analyze large datasets, and learn and apply the analysis methods required to quantify fission chain reactions via radiation measurements.
Research Mode: Hybrid
NERS Project #21: Neutron and Gamma-Ray Imaging using Augmented Reality
Faculty Mentor: Sara Pozzi, [email protected]
Graduate Student Mentor: Ricardo Lopez, [email protected]
Prerequisites:
- Experience with Unity and C#. Python is necessary and knowledge of Rust code is desired.
Project Description: Radiation imaging systems are used to locate sources in applications such as nuclear nonproliferation, nuclear safeguards, and emergency response. Our handheld dual particle imaging system combines organic and inorganic scintillation detectors with arrays of silicon photomultipliers into a detection system capable of detecting and imaging source of neutrons and gamma rays. This system is compact and produces images and energy spectra of radiation sources in its field of view. In order to improve the interface for future operators, we are developing an augmented reality output to display the radiation images on a real-world overlay. The current version of the system interfaces with the Microsoft HoloLens2 platform for augmented reality display of pre-acquired or live radiation imaging data. Students will have the opportunity to collect radiation detection data and further develop the augmented reality interface and algorithm.
Research Mode: In Lab (Preferred), Hybrid