Farah Raed Hussein Alsafadi

College of Engineering

Works (7)

Updated: February 28th, 2025 05:01

2025 journal article

Data-driven prediction and uncertainty quantification of PWR crud-induced power shift using convolutional neural networks

ENERGY, 316.

By: A. Furlong* , F. Alsafadi*, S. Palmtag* , A. Godfrey & X. Wu* 

author keywords: Crud-induced power shift; Convolutional neural network; Uncertainty quantification
Sources: Web Of Science, ORCID, NC State University Libraries
Added: January 11, 2025

2024 journal article

ARTISANS-Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology

NUCLEAR ENGINEERING AND DESIGN, 423.

By: A. Akins n, A. Furlong n , L. Kohler n, J. Clifford n, C. Brady  n, F. Alsafadi n, X. Wu n 

author keywords: Artificial intelligence; Machine learning; Nuclear fission technology; Advanced nuclear reactors
Sources: Web Of Science, ORCID, NC State University Libraries
Added: April 8, 2024

2024 article

Development of Whole System Digital Twins for Advanced Reactors: Leveraging Graph Neural Networks and SAM Simulations

Liu, Y., Alsafadi, F., Mui, T., O'Grady, D., & Hu, R. (2024, October 13). NUCLEAR TECHNOLOGY.

By: Y. Liu *, F. Alsafadi n, T. Mui *, D. O'Grady* & R. Hu *

author keywords: Digital twin; graph neural networks; System Analysis Module; EBR-II, gFHR
Source: Web Of Science
Added: October 28, 2024

2023 journal article

Deep generative modeling-based data augmentation with demonstration using the BFBT benchmark void fraction datasets

NUCLEAR ENGINEERING AND DESIGN, 415.

By: F. Alsafadi n & X. Wu n 

author keywords: Deep generative modeling; Generative adversarial networks; Normalizing flows; Variational autoencoders
TL;DR: The findings shows that DGMs have a great potential to augment scientific data in nuclear engineering, which proves effective for expanding the training dataset and enabling other DL models to be trained more accurately. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries, ORCID
Added: December 18, 2023

2022 journal article

Effect of mesh refinement on the solution of the inverse uncertainty quantification problem for transient physics

PROGRESS IN NUCLEAR ENERGY, 152.

By: R. Abu Saleem *, F. Alsafadi* & N. Al-Abidah

author keywords: Inverse uncertainty quantification; Maximum likelihood estimate; Maximum A posterior estimate; Sensitivity analysis; Mesh refinement; FEBA
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Source: Web Of Science
Added: January 3, 2023

2021 journal article

A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal-hydraulics codes

NUCLEAR ENGINEERING AND DESIGN, 384.

By: X. Wu n , Z. Xie n, F. Alsafadi n & T. Kozlowski *

author keywords: Inverse uncertainty quantification; Calibration; Physical model parameters; Frequentist; Bayesian; Empirical
TL;DR: This review paper aims to provide a comprehensive and comparative discussion of the major aspects of the IUQ methodologies that have been used on the physical models in system thermal-hydraulics codes, including solidity, complexity, accessibility, independence, flexibility, comprehensiveness, transparency, and tractability. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: November 1, 2021

2021 journal article

Towards improving the predictive capability of computer simulations by integrating inverse Uncertainty Quantification and quantitative validation with Bayesian hypothesis testing

NUCLEAR ENGINEERING AND DESIGN, 383.

By: Z. Xie n, F. Alsafadi n & X. Wu n 

author keywords: Inverse Uncertainty Quantification; Quantitative validation; Bayesian hypothesis testing; Bayes factor; ANS nuclear grand challenge
TL;DR: This paper proposes a comprehensive framework to integrate results from inverse UQ and quantitative validation to provide robust predictions to account for all available sources of uncertainties in a rigorous Uncertainty Quantification process. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: October 12, 2021

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