RX-PSA

RESOURCE

Abstract

This code is built on top of the ML-PSA utility and implements the ability to implement a multi-fidelity parallel simulated annealing optimization for a range of engineering problems. RX-PSA provides specializations to interact with modern nuclear reactor codes such as VERA to perform assembly and core optimization in a robust way. RX-PSA also provides LWR specific objective functions and constraints to provide a straightforward user interface that reactor designers are familiar with.
Release Date:
2021-08-09
Project Type:
Closed Source
Software Type:
Scientific
Programming Languages:
Python 3
Sponsoring Org.:
Code ID:
95909
Site Accession Number:
8284
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Country of Origin:
United States

RESOURCE

Citation Formats

Collins, Benjamin S., Gurecky, William L., Kropaczek, David J., Laiu, Ming Tse P., and Pandya, Tara M. RX-PSA. Computer Software. USDOE. 09 Aug. 2021. Web. doi:10.11578/dc.20221117.2.
Collins, Benjamin S., Gurecky, William L., Kropaczek, David J., Laiu, Ming Tse P., & Pandya, Tara M. (2021, August 09). RX-PSA. [Computer software]. https://doi.org/10.11578/dc.20221117.2.
Collins, Benjamin S., Gurecky, William L., Kropaczek, David J., Laiu, Ming Tse P., and Pandya, Tara M. "RX-PSA." Computer software. August 09, 2021. https://doi.org/10.11578/dc.20221117.2.
@misc{ doecode_95909,
title = {RX-PSA},
author = {Collins, Benjamin S. and Gurecky, William L. and Kropaczek, David J. and Laiu, Ming Tse P. and Pandya, Tara M.},
abstractNote = {This code is built on top of the ML-PSA utility and implements the ability to implement a multi-fidelity parallel simulated annealing optimization for a range of engineering problems. RX-PSA provides specializations to interact with modern nuclear reactor codes such as VERA to perform assembly and core optimization in a robust way. RX-PSA also provides LWR specific objective functions and constraints to provide a straightforward user interface that reactor designers are familiar with.},
doi = {10.11578/dc.20221117.2},
url = {https://doi.org/10.11578/dc.20221117.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20221117.2}},
year = {2021},
month = {aug}
}