Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 23042852

The Optimized Parallel MOC module in Code cosLATC

Li, Shuo·Zhu, Chenglin·Yu, Hui·Yan, Yuhang·Chen, Yixue
View original source ↗Export metadata ↓

Abstract

A new neutron transport lattice physics code, cosLATC, which is an essential part in COSINE (Core and System Integrated Engine) code package, has been developed by SPICRI. It is mainly used for 2-D transport calculations on Pressurized Water Reactor (PWR) assemblies or pin cells. cosLATC generates few group constants for the core physics code cosCORE in COSINE package. With regards to cross-section data, WIMS-D 69 and 281 energy groups library is employed currently. cosLATC also has capability of resonance treatment, depletion and fuel temperature calculation. cosLATC uses modular tracking and parallelization technique in MOC transport calculation, which can save lots of memory and running time. In this paper, MOC module in cosLATC are introduced, the accuracy and efficiency of the module are verified by C5G7 benchmark and a PWR typical assembly. (authors)

Explore related subjects

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS73 NUCLEAR PHYSICS AND RADIATION PHYSICSBENCHMARKSGROUP CONSTANTSNEUTRON TRANSPORTNUCLEAR FUELSPWR TYPE REACTORSRESONANCE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Li, Shuo, Zhu, Chenglin, Yu, Hui, Yan, Yuhang, Chen, Yixue. 2016-07-01. The Optimized Parallel MOC module in Code cosLATC. https://www.osti.gov/biblio/23042852

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

KEEP EXPLORING

Related reports

DOE OSTI2026

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↗
DOE OSTI2026

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↗
DOE OSTI2026

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↗