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Final Exam (Dissertation Defense) Roberto Fairhurst Agosta

Event Type
Other
Sponsor
Department of Nuclear, Plasma & Radiological Engineering
Location
Mia Za's, 629 E Green St #5701, Champaign, IL 61820
Virtual
wifi event
Date
Nov 9, 2023   9:00 - 11:00 am  
Views
8
Originating Calendar
NPRE Events

Roberto Fairhurst Agosta, Ph.D. Candidate

Dr. Tomasz Kozlowski, Director of Research

November 9, 2023 | 9:00am - 11:00am CST 

This final examination will be held in 101A Talbot Laboratory.

A Zoom link is provided below for those who could not attend in person

 Meeting ID: 927 171 4982                  Password:  614349 

ENHANCED METHOD FOR THE SUPPORT OF EXPERIMENT SAFETY ANALYSIS

ABSTRACT:  Nuclear research reactors enable a wide range of applications and support the development of multiple fields, including energy production, isotopic production, space discovery, among others. Research reactor safety analyses demonstrate the contribution of the operational procedures to the prevention of accidents, focusing mainly on the reactor core. These analyses rely on the assumption of heat being locally deposited in the core after shutdown. This work, however, is concerned with research reactor experiments, and it investigates more detailed methods targeting the specific reactor regions hosting the experiments.

Additionally, research reactor experiments require the development of individual experiment safety analyses, which due to the lack of a streamlined workflow becomes effort and time-consuming as well as error-prone. This thesis supports the development of safety analyses for research reactor experiments with the objective of streamlining the calculation procedure as well as accelerating it. The methodology presented here calculates the delayed heating in experiments, generates the volumetric heat source term for thermal-fluids calculations, solves the experiment temperature evolution during a channel draining event, and determines if the event leads to a radioactive material release. Moreover, this work examines methods to accelerate the calculation, which include the creation of a generic irradiation database and data-based modeling.

This thesis starts with a literature survey summarizing previous work relevant to delayed heating calculations, transport/depletion solvers, and machine learning applied to nuclear engineering. The conclusions of the survey guide the choice of methods for the delayed heating calculation and the choice of machine learning algorithms for the data-based modeling.

The following chapters focus on the calculation workflows. This thesis establishes a delayed heating calculation workflow based on the formal three-step method that builds on top of the MCNP-ORIGEN Activation Automation (MOAA) tool. This workflow is verified and demonstrated with several exercises, which also ease the workflow visualization. The workflow is also demonstrated in two full-size research reactors, for which the heat in the structures and an experiment are calculated.

A simple modification of the delayed heating calculation workflow generates a shutdown dose rate calculation workflow as a value-added. This calculation workflow helps guide the post-irradiation examination (PIE) of experiments and helps prevent the unnecessary exposure of personnel and surrounding equipment. The calculation workflow is demonstrated for a Tristructural Isotropic (TRISO)-fueled experiment which highlights the applicability of this workflow to more complex geometries, such as High-Temperature Gas-Cooled Reactors (HTGRs).

When focusing on the acceleration of the calculations, this thesis proposes the generic irradiation database method. This method relies on the delayed heating calculation workflow to obtain the heat produced in experiments of individual chemical elements and through their combination calculate the heat produced in experiments of arbitrary material composition. This thesis verifies this method by studying a vast range of materials that could be used in a research reactor experiment.

Moreover, this thesis investigates the applicability of data-based modeling to predict the outcome of the most thermally challenging accident in an experiment, a channel draining event. The data preparation process and several machine learning algorithms are described before studying a simplified model. This simplified model allows drawing several useful conclusions before scaling it up, with some minor modifications, to a full-size reactor experiment.

Finally, this thesis wraps up with the conclusions from all the previous calculations. Overall, the results presented in this thesis meet this thesis’ objectives and prove that the proposed methodology decreases the computational expense and enables quick calculations supporting research reactor experiments.

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