Lattice-physics (PWR fuel assembly neutronics simulation results)
Donated on 12/11/2024
This dataset encompasses lattice-physics parameters—the infinite multiplication factor (k-inf) and the pin power peaking factor (PPPF)—modeled as functions of variations in fuel pin enrichments for the NuScale US600 fuel assembly type C-01 (NFAC-01) [NuScale FSAR]. These critical parameters were computed using the MCNP6 code, a Monte Carlo-based tool for nuclear reactor criticality simulations. Fuel pin enrichments were uniformly sampled within the range of 0.7–5.0 weight percent (w/o) U-235 to generate the dataset. The dataset contains 39 features, each representing the enrichment of a specific fuel rod in a one-eighth symmetry of the NFAC assembly. The outputs of interest are the k-inf and PPPF values associated with these enrichments.
Dataset Characteristics
Tabular, Multivariate
Subject Area
Physics and Chemistry
Associated Tasks
Regression
Feature Type
Real
# Instances
24000
# Features
39
Dataset Information
Has Missing Values?
No
Introductory Paper
By Nguyen Huu Tiep, Hae-Yong Jeong, Kyung-Doo Kim, Nguyen Xuan Mung, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, Nguyen Ngoc Anh, Mai The Vu.. 2024
Published in Institute for Nuclear Science and Technology (INST), Vietnam Atomic Energy Institute (VINATOM), 179 Hoang Quoc Viet, Cau Giay, Hanoi 100000, Vietnam
Dataset Files
| File | Size |
|---|---|
| raw.csv | 11.3 MB |
| test.csv | 173.4 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset lattice_physics_pwr_fuel_assembly_neutronics_simulation_results = fetch_ucirepo(id=1091) # data (as pandas dataframes) X = lattice_physics_pwr_fuel_assembly_neutronics_simulation_results.data.features y = lattice_physics_pwr_fuel_assembly_neutronics_simulation_results.data.targets # metadata print(lattice_physics_pwr_fuel_assembly_neutronics_simulation_results.metadata) # variable information print(lattice_physics_pwr_fuel_assembly_neutronics_simulation_results.variables)
Huu Tiep, N. (2024). Lattice-physics (PWR fuel assembly neutronics simulation results) [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5BK64.
Creators
Nguyen Huu Tiep
tiepngh@gmail.com; tiepngh@sejong.ac.kr
Department of Quantum and Nuclear Engineering, Sejong University, Republic of Korea; Institute for Nuclear Science and Technology (INST), Vietnam Atomic Energy Institute (VINATOM);
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.