Engineering topics

Gribok, Andrei V.

Publications and source records attributed to Gribok, Andrei V..

8 records

DOE OSTI2024

Qualification and Quantification of Porosity at the Top of the Fuel Pins in Metallic Fuels Using Image Processing

Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize fuel behavior, such as fuel axial expansion. While several fuel expansion results obtained from neutron radiography imaging have been published, the analysis of neutron radiography for the purpose of describing statistical properties of porous matter formed on top of the fuel pins, also referred to as fluff in previous publications, is significantly less represented in the literature with just a single paper so far. This study aims to validate and augment results reported in previous publications using automated image processing. The paper describes the statistical properties of the porous matter in terms of nine parameters derived from radiography images and correlates those parameters with such fuel properties as composition, expansion, temperature, and burnup. The reported results are based on 1097 fuel pins of eight different fuel compositions. For three major fuel types, U-10Zr, U-8Pu-10Zr, and U-19Pu-10Zr, a clear negative correlation is found between the Pu content and five parameters describing the amount of porous matter generated. The parameters describing granularity properties, however, showed either negative correlation or nonlinear dependency from fuel composition. The parameters describing the amount showed a positive correlation with fuel axial expansion, while granularity parameters showed a negative correlation with axial expansion. The dependency on cladding temperature was found to be weak. A positive correlation is demonstrated for volume parameters and fuel burnup. In general, reported results confirm and validate findings published in previous studies using a much larger number of pins and automated processing techniques, which easily lend themselves to reproducibility, thus avoiding subjective bias.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system

To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. Here this work presents how anticipatory control strategies can be implemented via model predictive control (MPC) of a single heat pipe’s temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that nonparametric models such as the ANN and ARX result in more fluctuated control actions compared to the MPC applied to the linear response surface model. However, when the cyber incidents are of a large magnitude, the linear response surface model produces smaller feasible regions than the nonparametric models under identical constraints.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Data Architecture and Analytics Requirements for Artificial Intelligence and Machine Learning Applications to Achieve Condition-Based Maintenance

This report identified some of the important requirements that needs to be taken into consideration as part of the data evolution for the CBM application of a CWS in a NPP. In the data evolution process, the information is converted into insight leading into actions using advancements in AI/ML technologies. A notion of RESET AI: design, development, deployment, and operation principals are introduced to lifecycle of AI technologies. Towards the end of the report, we discussed how this CBM can be realized in a SDE. As path forward, this report lays the foundation for developing a more detailed industry guidance supporting data evolution for other plant applications like operations and plant support. These would be developed as part of ongoing research in the fiscal year 2023.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Technical Basis for Advanced Artificial Intelligence and Machine Learning Adoption in Nuclear Power Plants

The research and development reported here is part of the Technology Enabled Risk-Informed Maintenance Strategy project sponsored by the U.S. Department of Energy’s Light Water Reactor Sustainability program. The primary objective of the research presented in this report is to produce a technical basis for developing explainable and trustable artificial intelligence (AI) and machine learning (ML) technologies. The technical basis will lay the foundation for addressing the technical and regulatory adoption challenges of AI/ML technologies across plant assets and the nuclear industry at scale and to achieve seamless cost-effective automation without compromising plant safety and reliability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Data Mining – Image Analysis of Radiography for Zr Redistribution

The research effort described in this report represents a first attempt to investigate the radial redistribution of Zr in ternary fuel alloys U-xPu-10Zr (x = 0, 8, 19) irradiated in the in-reactor fuel experiments during the operation of the U.S. Department of Energy’s (DOE’s) Experimental Breeder Reactor II (EBR-II) at Idaho National Laboratory (INL) using methods of image processing on post-irradiation neutron radiographs. Approximately 130,000 metal fuel pins were irradiated in EBR-II during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed historically now allowing application of modern image analysis techniques to characterize fuel behavior, such as fuel swelling, fluff formation, and now, fuel alloy constituent redistribution. The redistribution of fuel components depends on the temperature field, radially, within the fuel. Specifically, Zr is expected to redistribute radially towards the center of the pin, as well as towards the outer zones. Currently, direct imaging of a pin cross-section through optical methods or scanning electron microscopy (SEM) is used to study the constituents’ redistribution, which is very time-consuming and can only be applied to a limited number of pins. An automated image processing technique allowing for the investigation of fuel radial redistribution zones would significantly accelerate data analysis. In general, if the fuel temperature is hot enough, three redistribution zones are expected, corresponding to the main fuel components (e.g., U, Pu, Zr). While more assessment will be performed in fiscal year (FY)-2023, it seems possible to differentiate the pins according to their fuel composition using image analysis techniques on neutron radiographs of metallic fuel pins based on the analysis to date.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

An analysis of fluff formation in metallic fuel via data analyzes from EBR-II experiments and BISON fuel code modeling

During the operation of EBR-II, it was found that a highly porous structure (over 40% area fraction) formed at the top of several fuel columns. Previous work has shown that this structure, designated fluff in this paper, contains a significant fraction of fuel elements (e.g., U and Pu) which could potentially impact neutronics. This work aims in analyzing the formation mechanism of this microstructure so its impact can be incorporated into future metallic fuel modeling codes and algorithms. This paper details a preliminary examination into the formation mechanisms of fluff by performing qualitative and statistical analysis of EBR-II experimental data. Additionally, the operating conditions that have the greatest impact on fluff formation were determined based on this data set. Also, BISON fuel code simulations were used to help postulate potential fluff formation mechanisms. From this analysis it was found that the largest contributors to fluff formation were fuel burnup and composition, with fluff formation exhibiting a roughly linear positive correlation with increasing burnup and a negative correlation with increasing Pu content. It was also found that higher pin operating temperatures decreased fluff formation but only for U-10Zr fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

The primary objective of the research presented in this report is to develop scalable technologies that are deployable across plant assets and across the nuclear fleet to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). Over the years, the nuclear fleet has relied on labor-intensive and time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. A well-constructed risk-informed PdM approach for an identified plant asset has been developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), physics-informed modeling, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology driven PdM program, eliminating unnecessary O&M costs. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report (1) reflects the results of work by LWRS Program researchers with PSEG, Nuclear LLC-owned Salem and Hope Creek Nuclear Power Plants; (2) presents utilization of circulating water system (CWS) heterogeneous data and fault modes from both the Salem and Hope Creek nuclear power plant sites to develop salient fault signatures associated with each fault mode; (3) describes the integration of component-level predictive models into a robust system-level model enabled by the federated-transfer learning; (4) describes the development of physics-informed model of circulating water pump and motor; (5) develops a scalable risk and economic model; and (6) outlines the development of a user-centric visualization application. The outcomes presented in this report lays the foundation and provides a much-needed technical basis to focus on explainability and trustworthiness of ML and AI-based technologies, as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Automatic information extraction from neutron radiography imaging to estimate axial fuel expansion in $\mathrm{EBR-II}$

Approximately 130,000 metal fuel pins were irradiated in the Experimental Breeder Reactor II (EBR-II) during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed to characterize fuel behavior, like fuel swelling. However, due to the lack of technology or resources, many of the images have not been processed or were processed manually through visual examination. This paper represents first-attempt to develop an image processing algorithm capable of automatically extracting information regarding the degree of fuel swelling from neutron radiography imaging. The algorithm was applied to 120 images of three different metallic fuel pin compositions—U-10Zr, U-8Pu-10Zr, and U-19Pu-10Zr. The algorithm performs operations of image intensity adjustment, image binarization, region finding, and labeling to extract information about fuel swelling. The average growth for U-10Zr was found to be 8.49% with 95% Confidence Interval (CI) [8.33 –8.66%], for U-8Pu-10Zr — 7.50% with 95% CI [7.23 – 7.78%], and for U-19Pu-10Zr — 3.15%, with 95% CI [2.40 – 3.91%]. The results obtained by applying this automatic image processing algorithm are consistent with previously reported studies of the same types of fuels. The automatic image processing algorithm will be expanded to include thousands of available neutron radiography images and different types of fuels to investigate empirical dependencies of fuel swelling, which can be subsequently applied to advanced fuel modeling. Results from this study can later be compared to BISON simulations to further benchmark modeling efforts and develop assessment cases.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗