MCNP TREAT Core Model with Big-BUSTER
This MCNP model contains the Big-BUSTER experiment containment system within the TREAT core in a half slot configuration.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
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Mercury Modeling of TREAT Big-BUSTER Free Field Characterization Experiments
This report summarizes modeling of Big-BUSTER free field characterization (FFC) experiments at the Idaho National Laboratory (INL) Transient Reactor Test Facility (TREAT) reactor performed from December 20th, 2023, to February 22nd, 2024. TREAT is an air-cooled graphite moderated research reactor that can be used to study material response to neutron irradiation. The BUSTER (Broad Use Specimen Transient Experiment Rig) refers to a containment module for placing experiments in the reactor core. The Big-BUSTER is an analogous platform for a modified core configuration with a larger experimental cavity.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Dry Heatsink Capsule and Large Irradiation Test Vehicle Design for TREAT
Review of design features and how fabrication will be completed for DRIFT Capsules, Big-BUSTER, and Moderator Assemblies for Big-BUSTER in TREAT.
42 ENGINEERING↗
NCERC-CL TREAT Free Field Characterization Foil Reaction Rate Results
This report summarizes the analysis and preliminary MCNP reaction rate modeling of free field characterization measurements performed in the Transient Reaction Test Facility (TREAT) at Idaho National Laboratory (INL) between 12/18/2023 and 2/22/2024. A series of seven irradiations were performed using the Big-BUSTER (Broad Use Specimen Transient Experiment Rig) core configuration. Foil sets including 19.5% LEU-Zr alloy wire, S, Au, Fe, Ni, Co, Ti, and Zr were deployed in each irradiation. Identical foil sets were supplied to Los Alamos National Laboratory (LANL), Lawrence Livermore National Laboratory (LLNL), and INL. All foils were supplied by INL. Additional details on the experiment can be found in [2]. The foil sets were shipped to the National Criticality Experiments Research Center Counting Laboratory (NCERC-CL) branch at LANL (NCERC-CL NISC) and were received on 3/25/2024. Additional details on receipt and counting are reported in [3]. All reactor dosimetry measurements were performed in adherence to the ASTM standards applicable to reactor dosimetry. Measured reaction rates from Au, Co, Fe, Ni, and Ti are reported. Select reaction rate ratios and simulated reaction rates and ratios are discussed. Reaction rates for all measured reaction products were modeled with MCNP 6.3.1 and a TREAT input deck supplied by Edward Lum. Neutron emission estimates calculated using the measured and simulated reaction rates show excellent agreement between the three fast-threshold n-p reactions with the percent differences being < 9% between the three reactions. The capture reactions had poorer, but still reasonable, agreement with < 20% percent differences between the neutron emission estimates of the three capture reactions. The percent difference between the fast-threshold and capture reactions is ∼ 100% for each reaction. This supports the TREAT model is not accurately modeling the neutron spectrum and additional measurements are required to characterize TREAT and match measurements to simulation.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files
This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.
15 GEOTHERMAL ENERGY↗
GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint
Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.
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