Engineering topics

Todosow, Michael

Publications and source records attributed to Todosow, Michael.

5 records

DOE OSTI2022

Subscale maturation of advanced reactor technologies (SMART): A path forward for nuclear thermal propulsion fuel and reactor development

Nuclear Thermal Propulsion (NTP) systems are actively being developed for future crewed missions to Mars. NTP systems excel in missions where both high thrust and high specific impulse are required, but modern NTP systems currently do not have a Technology Readiness Level (TRL) high enough for use in crewed space exploration. TRLs are used to demonstrate the level of rigor with which a component/system has been tested/demonstrated for its intended use. While space systems technology in general must be qualified as a unit, nuclear technology must be first demonstrated to meet qualification level requirements both at the fuel (component) level and the reactor (subsystem) level. Here, in this paper, historic NTP development programs are surveyed to identify a testing and development strategy that can be effectively implemented to allow for NTP reactor development. Based on this strategy, required facilities to enable such activities are identified. Current domestic experimental capabilities to support NTP qualification are limited to separate effects testing of individual components. Separate effects testing is found extensively in historic NTP development efforts but is not sufficient for full fuel and reactor qualification. Combined effects testing allows for an accurate assessment of fuel performance but is not achievable for NTP conditions in existing facilities. Assessment of historic development programs suggests that an intermediate, subscale test facility is necessary to advance NTP TRLs. A solution to meet this need is proposed, namely the Subscale Maturation of Advanced Reactor Technologies (SMART) facility. SMART will mitigate risk to NTP development by enabling performance and reactor physics demonstrations of NTP subsystems. A SMART facility could be built by modifying existing nuclear test facilities, which may potentially enable schedule and cost savings. To pursue reactor qualification beyond the subscale, a new ground test facility will be necessary. This ground test facility should be developed concurrently with SMART to allow for the facility to be operational in time for expedited NTP engine demonstration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Options for Subscale Maturation of Advanced Reactor Technologies Testing for Nuclear Thermal Propulsion

Several options could be implemented to establish an irradiation testing capability suitable for investigation of the performance of multiple nuclear thermal propulsion fuel elements at prototypic conditions. The prototypic conditions of interest are based on the current needs of the National Aeronautics and Space Administration’s Space Nuclear Power Program. The results of such testing are also intended to reduce the risks currently seen for any future subscale or full-scale ground testing of an engine-reactor system. The optimal solution is dependent upon several factors such as performance, cost, availability, schedule, technology readiness level, and plans for future testing in the SNP Program. Three options, based on different combinations of these factors, are considered in this report.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Using machine learning to improve efficiency and accuracy of burnup measurements at PBR reactors [Slides]

The outline of the slides include: Motivations of the work; Modeling and simulation; Machine learning model; Results and comparison study with linear regression; and Conclusions. This work was done to help PBR designers and operators understand the burnup measurement better. We look forward to discussing the results in detail with industrial collaborators.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

VERIFICATION OF TRISO FUEL BURNUP USING MACHINE LEARNING ALGORITHMS

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134Cs, 137Cs, 154Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2021

Verification of Triso Fuel Burnup Using Machine Learning Algorithms

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134 Cs, 137 Cs, 154 Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗