PhD of Nuclear Engineering - North Carolina State University, USA
Reactor Design Calculations, Design Optimization, Uncertainty Quantification, Sensitivity Analysis, Inverse Problem, Reduced Order Modeling, Machine Learning Methods, Subspace Methods, Targeted Accuracy Assessment. Dissertation Title: (Scalable Methods for Uncertainty Quantification, Data Assimilation and Target Accuracy Assessment in Multi-Physics Advanced Simulation of Light Water Reactors)
RESEARCH, TEACHING, or OTHER INTERESTS
Nuclear Energy and Engineering, Nuclear Energy and Engineering
72
Scopus Publications
463
Scholar Citations
13
Scholar h-index
17
Scholar i10-index
Scopus Publications
Establishing benchmarks for large language models in nuclear engineering: A NucBench evaluation of GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.5 Pro Bassam A. Khuwaileh, Polina Matesha Annals of Nuclear Energy, 2026 This study presents NucBench, an open benchmark suite for assessing multimodal Large Language Models (LLMs) in nuclear engineering. NucBench includes curated quantitative, qualitative, and image-based tasks focused on pressurized and boiling water reactors, using standardized textual and visual inputs. As a community-driven resource, it supports consistent evaluation and development of reliable, domain-aware AI tools for reactor design and safety. Three advanced LLMs: GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.5 Pro, were tested on undergraduate and reactor operator exams and two-phase flow image classification. GPT-4.1 achieved the highest quantitative accuracy (up to 95%) under deterministic settings, while Claude 3.7 Sonnet showed the greatest consistency in open-ended tasks. Performance declined and variability increased with higher sampling temperatures, especially for Gemini 2.5 Pro. Image classification accuracy peaked for Churn regimes but fell for complex flows. Results emphasize model tuning, explainability, and benchmark expansion for safety–critical applications.
Mapping radionuclide concentrations in the UAE using a Gaussian process Machine learning approach Bassam A. Khuwaileh, Belal Almomani, Samar El-Sayed, Rahaf Ajaj, Yumna Akram Annals of Nuclear Energy, 2025 This study introduces a machine learning approach for mapping soil radionuclide concentrations in the UAE using Gaussian Process (GP) regression. Aimed at enhancing environmental monitoring and public health, the approach utilizes soil samples from across the UAE to create a radiation map (covering Ra-226, Th-232, and K-40 isotopes). GP regression, known for its proficiency in spatial data interpolation, predicts radionuclide levels in areas without direct testing. This method is adept at managing the non-linear spatial complexities inherent in geographic data, offering both a qualitative and quantitative understanding beneficial for decision-making and further sampling strategies. The results reveal the GP model’s capacity to accurately reflect geographic variances in isotopic concentrations, with RMSE values of 13%, 14%, and 22% for Ra-226, Th-232, and K-40, respectively. The model’s success in learning the geographical variations of the concentrations showcases its potential to guide future research by identifying areas of increased radioactivity risk.
A once-through artificial neural network approach for used nuclear fuel inverse depletion analysis: A comparative study Bassam A. Khuwaileh, Belal Almomani Annals of Nuclear Energy, 2024 This work addresses the challenge of identifying the origins and burnup history of used nuclear fuel, which is crucial for safeguarding and non-proliferation analysis. It presents a comparative evaluation of two distinct computational approaches to bundles at the same in-core position: the first entails building a forward Artificial Neural Networks (ANN) surrogate model for fuel inventory prediction followed by solving the inverse depletion problem using an optimization solver. The second approach is to build an inverse ANN model that relates the used nuclear fuel inventory to its initial and burnup conditions (a once-through approach), eliminating the need for the inverse optimization solver step which results in better accuracy, less computational costs, and renders the application of such approach practical. KENO VI depletion model is utilized to deplete a VVER assembly model generating the necessary datasets for the ANN inverse model training and validation. The inverse model is then employed to estimate the initial composition and the burnup for several test cases. The results are then compared to the known initial and burnup conditions as well as to the estimations made by benchmark results from an existing ANN forward surrogate model combined with the Particle Swarm Optimization (PSO) solver. Comparative analyses across eight scenarios consistently reveal that the once-through ANN inverse model has superior accuracy and consistency in terms of estimating the actual burnup and initial fuel enrichment with a relative root mean squared error (RMSE) of 1.20 × 10 - 2 compared to the benchmark approach (ANN-PSO) which yielded a relative RMSE of 2.18 × 10 - 2 . Moreover, the once-through inverse approach has a significantly lower computational cost as it requires 1 inverse model run compared to the 9,450,000 model runs required by the benchmark approach (ANN-PSO). Overall, the results demonstrate that the proposed once-through approach simplifies and enhances the efficiency of solving the inverse fuel depletion problem while outperforming the benchmark approach in terms of predictive accuracy computational cost.
A Comparative Study: Fuzzy Logic and ANN in Addressing Inverse Depletion Bassam Khuwaileh, Mohammad A. AlShabi, Polina Matesha Proceedings of SPIE the International Society for Optical Engineering, 2024 This study delves into a comprehensive comparison between Fuzzy Logic (FL) and Artificial Neural Networks (ANN) in the context of the Inverse Depletion problem. Both methodologies, recognized for their distinct capabilities in handling complex problems, are assessed for their efficacy, accuracy, and computational efficiency. Initial observations highlighted the inherent flexibility of FL in managing uncertainty and the adaptive nature of ANN in recognizing patterns from intricate datasets. A series of benchmark scenarios were established to gauge the performance of each model. Results indicate that while FL offers more interpretable solutions, ANNs often outpace in terms of prediction accuracy. However, the choice between the two largely hinges on the specific requirements of the problem at hand, including the available data quality and the desired output precision. This research underscores the importance of understanding the nuances of each method and provides insights to practitioners on selecting the optimal approach for tackling the Inverse Depletion problem in the field of nuclear forensics.
The Application of Sliding Innovation Filter as Estimation Strategy Applied to a UAV Mohammad A. AlShabi, Andrew A. Gadsden, Bassam Khuwaileh, Khaled Obaideen Proceedings of SPIE the International Society for Optical Engineering, 2024 The unmanned aerial vehicle (UAV) and unmanned aerial system (UAS) are popular in nowadays applications including military, industry, weather casting, monitoring, and many other applications. According to several research, the system must be controlled in precise way to make sure that the UAV and UAS are moving in the desired trajectories. However, this task is not an easy task in real life due to the presence of disturbances and noise in feedback measurements. To overcome this issue, researchers either developed more stable controllers, i.e. active disturbance rejection control (ADRC), or they improved the measured signals using filters with more accurate/stable performance. This work belongs to the second category, where a newly developed filter, which is referred to as sliding innovation filter (SIF), is used to estimate the states of a UAV system while it is tracking a target at the same height to improve the quality of the controller.
Uncertainty and Sensitivity Analyses for Postulated Severe Accidents of Reference PWRs and SMRs in the Frame of the IAEA CRP and Relevant Insights Kwang-Il Ahn, Federico Mezio, Shaukat Ali, Xuewu Cao, Yong Jin Cho, Bassam Khuwaileh, Victor Martinez-Quiroga, Fulvio Mascari, Fabrizio Gabrielli, David Luxat, Tatjana Jevremovic Nuclear Technology, 2024 In 2019, the International Atomic Energy Agency (IAEA) launched the 5-year Cooperative Research Project (CRP) I31033 to advance the understanding and characterization of sources of uncertainty and to investigate their effects on the key figures of merit (FOMs: response parameters of interest) of the severe accident code predictions for water-cooled reactors. Twenty-two institutions from 18 member states participated in the CRP, and as a result, TECDOCs are being developed for the relevant benchmark exercises. The respective TECDOCs address a specific exercise and outline relevant research results pointing to best practices for the uncertainty and sensitivity analyses of the currently available severe accident codes.For the uncertainty analysis exercise, the CRP participants defined their own analysis scope and framework, including target plant, severe accident code, accident scenario, and relevant FOMs. According to each respective framework, participants independently carried out their own exercise, including plant modeling and nodalization, simulation of reference cases, and relevant uncertainty and sensitivity analyses.Finally, general conclusions were made based on an analysis of the results in view of the best-practice application of the uncertainty and sensitivity methods. Among them, this paper summarizes the main results of the uncertainty and sensitivity exercise performed for both pressurized water reactors and integral light water small modular reactors in the frame of the IAEA CRP with relevant insights.
The two-pass sliding innovation smoother Mohammad Al-Shabi, S. Andrew Gadsden, Mamdouh El Haj Assad, Bassam Khuwaileh Proceedings of SPIE the International Society for Optical Engineering, 2021
A multiple model-based sliding innovation filter Mohammad Al-Shabi, S. Andrew Gadsden, Mamdouh El Haj Assad, Bassam Khuwaileh Proceedings of SPIE the International Society for Optical Engineering, 2021
Geothermal energy as power producer Mamdouh El Haj Assad, Sara Zubayda, Bassam Khuwaileh, Abir Hmida, Mohammad Al-Shabi Proceedings of SPIE the International Society for Optical Engineering, 2021
Analysis of hybrid geo-solar power plant Mamdouh El Haj Assad, Mohammad Al-Shabi, A. Sahlolbei, Bassam Khuwaileh Proceedings of SPIE the International Society for Optical Engineering, 2020
Geothermal energy use in seawater desalination Mamdouh Elhaj Assad, Mohammad Al-Shabi, Atefeh Sahlolbei, A. Hamida, Bassam Khuwaileh Proceedings of SPIE the International Society for Optical Engineering, 2020
Bayesian calibration of cross-sections using plant operating data International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering M and C 2019, 2019
Uncertainty contribution estimation for Monte Carlo uncertainty quantification via parameter space analysis Transactions of the American Nuclear Society, 2017
Romuse: Reduced order modeling based uncertainty/sensitivity estimator for reactor core simulators Transactions of the American Nuclear Society, 2017
Scalable algorithms for uncertainty quantification of multi-physics light water reactor problems with feedback effect Physics of Reactors 2016 Physor 2016 Unifying Theory and Experiments in the 21st Century, 2016
A new importance measure for reduced order modeling Transactions of the American Nuclear Society, 2014
Further developments of inverse sensitivity/uncertainty quantification for high dimensional-large scale constrained problems Transactions of the American Nuclear Society, 2014
An ultra compact high efficiency thermo-photovoltaic system for electricity generation International Journal of Renewable Energy Research, 2014
An improved method for inverse uncertainty quantification for nuclear data assessment Proceedings of the International Conference on Physics of Reactors Physor 2014, 2014
Subspace methods for multi-physics reduced order modeling in nuclear engineering applications Transactions of the American Nuclear Society, 2014
Subspace methods for multi-physics reduced order modeling in nuclear engineering applications Proceedings of the International Conference on Physics of Reactors Physor 2014, 2014
Efficient subspace construction for reduced order modeling in reactor analysis Proceedings of the International Conference on Physics of Reactors Physor 2014, 2014
Flexible uncertainty analysis of computer models with alchemy Transactions of the American Nuclear Society, 2014
Alternative approach for importance ranking of nuclear data Transactions of the American Nuclear Society, 2014
Employing non-converged iterates for reduced order modeling basis construction Transactions of the American Nuclear Society, 2014
Insure: An inverse sensitivity uncertainty quantification toolkit Transactions of the American Nuclear Society, 2014
Exploratory development of multi-physics reduced order modeling Transactions of the American Nuclear Society, 2013
Exploratory development of multi-physics reduced order modeling II Transactions of the American Nuclear Society, 2013
The effect of implicit self-shielding on the inverse sensitivity/ uncertainty quantification method for thermal reactors Transactions of the American Nuclear Society, 2013
Integral benchmark experiments in the inverse sensitivity/uncertainty computations Transactions of the American Nuclear Society, 2013
Establishing benchmarks for large language models in nuclear engineering: A NucBench evaluation of GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.5 Pro BA Khuwaileh, P Matesha Annals of Nuclear Energy 233, 112223 , 2026 2026 Citations: 1
Machine learning-based classification and regression approach for early detection of large break loss-of-coolant accident conditions B Almomani, BA Khuwaileh, SB Alam Arabian Journal for Science and Engineering 50 (22), 18971-18991 , 2025 2025 Citations: 3
Mapping radionuclide concentrations in the UAE using a Gaussian process Machine learning approach BA Khuwaileh, B Almomani, S El-Sayed, R Ajaj, Y Akram Annals of Nuclear Energy 217, 111335 , 2025 2025 Citations: 2
Uncertainty and sensitivity analyses for postulated severe accidents of reference PWRs and SMRs in the frame of the IAEA CRP and relevant insights KI Ahn, F Mezio, S Ali, X Cao, YJ Cho, B Khuwaileh, V Martinez-Quiroga, ... Nuclear Technology, 1-27 , 2024 2024 Citations: 5
A once-through artificial neural network approach for used nuclear fuel inverse depletion analysis: A comparative study BA Khuwaileh, B Almomani Annals of Nuclear Energy 205, 110598 , 2024 2024 Citations: 4
Validation of TRACE V5. 0 for intermediate break in the cold leg of the reactor with passive emergency core cooling system via the ATLAS facility P Matesha, Y Akram, B Khuwaileh, N Alyammahi, HJ Yoon Nuclear Engineering and Design 426, 113336 , 2024 2024 Citations: 3
IAEA CRP I31033: Severe Accident Uncertainty and Sensitivity Analyses for Reactors of PWR and SMR Types and Relevant Insights KI Ahn, D Luxat, F Gabrielli, F Mascari, V Martinez-Quiroga, R Pericas, ... Proceedings of the Canadian Nuclear Society , 2024 2024
The application of sliding innovation filter as estimation strategy applied to a UAV M AlShabi, SA Gadsden, B Khuwaileh, K Obaideen Unmanned Systems Technology XXVI 13055, 292-300 , 2024 2024
A comparative study: fuzzy logic and ANN in addressing inverse depletion BA Khuwaileh, M Alshabi, P Matesha Artificial Intelligence and Machine Learning for Multi-Domain Operations … , 2024 2024 Citations: 1
Validation of TRACE 5.0 for steam line break with passive auxiliary feedwater system via the ATLAS facility Y Akram, P Matesha, B Khuwaileh, N Alyammahi, HJ Yoon Nuclear Engineering and Design 422, 113115 , 2024 2024 Citations: 5
Theoretical Foundations and Applications of Computational Fluid Dynamics in Nuclear Engineering C Batra, D Bestion, N Bojan, E Frederix, A Gerschenfeld, G Giustini, ... International Atomic Energy Agency , 2023 2023
Applications of autonomous rover for radiometric surveillance Y Faroukh, M AlShabi, B Khuwaileh Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing … , 2023 2023 Citations: 3
Technology options and cost estimates of nuclear powered desalination in the United Arab Emirates BA Khuwaileh, FE Alzaabi, B Almomani, M Ali Journal of Nuclear Science and Technology 60 (3), 223-237 , 2023 2023 Citations: 17
Energy storage in nuclear desalination plants B Khuwaileh, A Ishag Energy Storage for Multigeneration, 143-173 , 2023 2023 Citations: 2
Nuclear design of an integrated small modular reactor based on the APR-1400 for RO desalination purposes: RR Alnuaimi et al. RR Alnuaimi, B Khuwaileh, M Zubair, D Hartanto Nuclear Science and Techniques 33 (8), 95 , 2022 2022 Citations: 8
Uncertainty and Sensitivity Analysis of the PWR and SMR by Means of Severe Accident Codes in the Framework of the IAEA CRP I31033 KI Ahn, JM García, S Ali International Conference on Topical Issues in Nuclear Installation Safety … , 2022 2022 Citations: 2
Differential parameters uncertainty estimation via a PCA-based monte carlo sampling approach: IRT-4M fuel type as a case study B A. Khuwaileh, Z Said Journal of Nuclear Science and Technology 58 (9), 984-991 , 2021 2021 Citations: 2
Artificial Neural Network based Particle Swarm Optimization solution approach for the inverse depletion of used nuclear fuel BA Khuwaileh, M Al-Shabi, MEH Assad Annals of Nuclear Energy 157, 108256 , 2021 2021 Citations: 22
An extended thermosyphon cooling system for apr-1400 reactor design SY Alhammadi, AA Alktebi, AE Eldemiery, V Gillette, MEH Assad, ... Case Studies in Thermal Engineering 25, 100894 , 2021 2021 Citations: 8
Geothermal energy as power producer MEH Assad, SRM Zubayda, B Khuwaileh, A Hmida, MA AlShabi Energy Harvesting and Storage: Materials, Devices, and Applications XI 11722 … , 2021 2021 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
Gaussian process approach for dose mapping in radiation fields BA Khuwaileh, WA Metwally Nuclear Engineering and Technology 52 (8), 1807-1816 , 2020 2020 Citations: 39
Gradient-free construction of active subspaces for dimension reduction in complex models with applications to neutronics KD Coleman, A Lewis, RC Smith, B Williams, M Morris, B Khuwaileh SIAM/ASA Journal on Uncertainty Quantification 7 (1), 117-142 , 2019 2019 Citations: 29
Application of the sliding innovation filter to unmanned aerial systems M AlShabi, SA Gadsden, MEH Assad, B Khuwaileh, S Wilkerson Unmanned Systems Technology XXIII 11758, 241-248 , 2021 2021 Citations: 25
On the performance of nanofluids in APR 1400 PLUS7 assembly: Neutronics BA Khuwaileh, FI Al-Hamadi, D Hartanto, Z Said, M Ali Annals of Nuclear Energy 144, 107508 , 2020 2020 Citations: 24
Artificial Neural Network based Particle Swarm Optimization solution approach for the inverse depletion of used nuclear fuel BA Khuwaileh, M Al-Shabi, MEH Assad Annals of Nuclear Energy 157, 108256 , 2021 2021 Citations: 22
Application of the sliding innovation filter for fault detection and diagnosis of an electromechanical system MA AlShabi, SA Gadsden, MEH Assad, B Khuwaileh Signal Processing, Sensor/Information Fusion, and Target Recognition XXX … , 2021 2021 Citations: 21
User guidelines and best practices for casl vuq analysis using dakota BM Adams, K Coleman, LN Gilkey, N Gordon, R Hooper, BA Khuwaileh, ... Los Alamos National Laboratory (LANL), Los Alamos, NM (United States) , 2017 2017 Citations: 21
Surrogate based model calibration for pressurized water reactor physics calculations BA Khuwaileh, PJ Turinsky Nuclear Engineering and Technology 49 (6), 1219-1225 , 2017 2017 Citations: 21
A comparison of sigma-point Kalman filters on an aerospace actuator MA AlShabi, SA Gadsden, MEH Assad, B Khuwaileh Sensors and Systems for Space Applications XIV 11755, 150-159 , 2021 2021 Citations: 20
Technology options and cost estimates of nuclear powered desalination in the United Arab Emirates BA Khuwaileh, FE Alzaabi, B Almomani, M Ali Journal of Nuclear Science and Technology 60 (3), 223-237 , 2023 2023 Citations: 17
A multiple model-based sliding innovation filter MA AlShabi, SA Gadsden, MEH Assad, B Khuwaileh Signal Processing, Sensor/Information Fusion, and Target Recognition XXX … , 2021 2021 Citations: 17
Scalable methods for uncertainty quantification, data assimilation and target accuracy assessment for multi-physics advanced simulation of light water reactors B Khuwaileh North Carolina State University , 2015 2015 Citations: 17
The two-pass sliding innovation smoother MA AlShabi, SA Gadsden, MEH Assad, B Khuwaileh Signal Processing, Sensor/Information Fusion, and Target Recognition XXX … , 2021 2021 Citations: 16
Prediction of neutron energy spectrum in a typical MTR type research reactor using Monte Carlo simulations SR Malkawi, B Khuwaileh, M Al-Momani Annals of Nuclear Energy 56, 17-22 , 2013 2013 Citations: 13
A novel integrated direct absorption self-storage solar collector MA Al-Nimr, B Khuwaileh, M Alata International journal of green energy 8 (6), 618-630 , 2011 2011 Citations: 12
Verification of reduced order modeling based uncertainty/sensitivity estimator (ROMUSE) B Khuwaileh, B Williams, P Turinsky, D Hartanto Nuclear Engineering and Technology 51 (4), 968-976 , 2019 2019 Citations: 11
Energy and cost analysis of processing flat plate solar collectors MEH Assad, A Khosravi, M AlShabi, B Khuwaileh, AK Hamid Energy Engineering: Journal of the Association of Energy Engineering 118 (3 … , 2021 2021 Citations: 10
Subspace-based inverse uncertainty quantification for nuclear data assessment BA Khuwaileh, HS Abdel-Khalik Nuclear Data Sheets 123, 57-61 , 2015 2015 Citations: 9
Subspace methods for multi-physics reduced order modeling in nuclear engineering applications BA Khuwaileh, JM Hite, HS Abdel-Khalik Proceedings of the international conference on physics of reactors … , 2015 2015 Citations: 9
Nuclear design of an integrated small modular reactor based on the APR-1400 for RO desalination purposes: RR Alnuaimi et al. RR Alnuaimi, B Khuwaileh, M Zubair, D Hartanto Nuclear Science and Techniques 33 (8), 95 , 2022 2022 Citations: 8
