Search NASA⌕ Search

DOE OSTI · 1805703

Data and Insights for the KRUSTY Nuclear-Powered Tests

Abstract

This document, and the associated files, describe all of the data from the KRUSTY warm criticals and the KRUSTY nuclear system test. The warm criticals were a group of tests where fission power heated the core and the feedback response was studied. There were 3 experiments, the 15-cent run (which was a true “free run”), followed by 30-cent and 60-cent runs, where reactivity was added to heat the core to higher temperatures. The nuclear system test (often referred to as the full-run or the final-run), engaged the power conversion system and evaluated system performance. These experiments are described in a pair of NUCLEAR TECHNOLOGY papers, which includes plots of the data that this document-set includes. Note that a lot of information in this document is based on my interpretation and could be wrong, but I’ve included it to potentially save some people a lot of time and headaches by trying to figure out which instrumentation to believe, but beware treating anything I say as fact. I’ve also included several post-irradiation photos, to show some thermocouple locations and indicate possible changes in emissivity and/or geometry (although no changes in geometry were indicated).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Poston, David Irvin. 2021-06-29. Data and Insights for the KRUSTY Nuclear-Powered Tests. https://doi.org/10.2172/1805703

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Pulsed-Neutron Experiments at the Inherently Safe Subcritical Assembly

The pulsed neutron technique is a powerful, dynamic method to assay the reactivity of a multiplying system. This work presents the novel application of the pulsed neutron technique to the Inherently Safe Subcritical Assembly, an experimental configuration accepted by the International Criticality Safety Benchmark Evaluation Project Handbook. The experiments were replicated with COG11.3, a continuous-energy Monte Carlo code. The pulsed neutron data were analyzed using the Sjöstrand and Gozani area-ratio methods and by extracting the prompt neutron decay constant. Subsequent static k-eigenvalue and 𝛼-eigenvalue simulations were also performed for the same configurations. Neutron detector dead-time effects from the experiments were corrected using the Backwards Extrapolation Method and shown to have a negligible impact on the estimated reactivities. The results highlight that capturing time-dependent effects like delayed neutron precursor buildup are essential to accurately reproduce experimental results. They also highlight the importance of shielding the detectors from generator source neutrons in deeply subcritical configurations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Machine Learning–Based Condition Monitoring of a Circulating Water System of a Canadian Nuclear Plant

With the need to maintain long-term reliable energy using nuclear power plants, there is an underlying demand to ensure that the maintenance of plant components and systems is also done in an efficient and cost-effective manner. One way to achieve this is by moving from time-based maintenance to condition-based maintenance. The research presented in this paper focuses on applying statistical and machine-learning-based methods to capture anomalies within data for fault detection to further develop into condition monitoring. This paper focuses on system data for a circulating water system (CWS) of a pressurized heavy-water reactor for detecting anomalies. The different methodologies used for detecting and capturing anomalies in the CWS data are matrix profile, density-based spatial clustering of applications with noise (DBSCAN), and support vector machines (SVMs). Matrix profile and DBSCAN are used to distinguish between normal data and anomalous data. This paper presents a hybrid method using DBSCAN and SVM when a portion of the data is used for DBSCAN to generate clusters. This portion of data is then used to train the SVM along with the clusters generated by DBSCAN as output. SVM is then tested on unseen data as a predictive tool, which can work in real time to categorize data points as either normal or anomalous. This paper presents results that show the high accuracies of DBSCAN and SVM in capturing anomalies within the data for a CWS for fault detection. Thus, the maintenance plan would be focused on component condition rather than a time-based schedule by switching to an automated system to identify and predict faults within a CWS.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

SCALE HTR-PROTEUS Benchmark Model

This dataset contains input and result files of computational simulations of HTR-PROTEUS benchmark with the latest version of SCALE code system. The simulations cover criticality control rod worth calculations as well as sensitivity analysis and uncertainty quantification. Users wanting to reproduce results from this dataset are required to obtain a license to the SCALE code system for which details on the distribution can be found here: https://www.ornl.gov/scale/releases

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗