Publications

Refereed Journal Publications

  1. Furlong, A., Alsafadi, F., Palmtag, S., Godfrey, A., Hayes, S., and Wu, X. (2024). Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks. (under review)
  2. Moloko, L., Bokov, P., Wu, X., and Ivanov, K. (2024). Clustering and uncertainty analysis to improve the machine learning-based predictions of SAFARI-1 control follower assembly axial neutron flux profiles. Annals of Nuclear Energy, 206:110630
    https://doi.org/10.1016/j.anucene.2024.110630
  3. Brady, C., Murray,W., Moss, L., Zino, J., Saito, E., and Wu, X. (2024). Design considerations and Monte Carlo criticality analysis of spiral plate heat exchangers for Molten Salt Reactors. Progress in Nuclear Energy, 173:105266
    https://doi.org/10.1016/j.pnucene.2024.105266
  4. Akins, A., Furlong, A., Kohler, L., Clifford, J., Brady, C., Alsafadi, F., and Wu, X. (2024). ARTISANS – Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology. Nuclear Engineering and Design, 423:113170
    https://doi.org/10.1016/j.nucengdes.2024.113170
  5. Baccou, J., Glantz, T., Ghione, A., Sargentini, L., Fillion, P., Damblin, G., Sueur, R., Iooss, B., Fang, J., Liu, J., Yang, C., Zheng, Y., Ui, A., Saito, M., Mendizábal Sanz, R., Bersano, A., Mascari, F., Skorek, T., Tiborcz, L., Hirose, Y., Takeda, T., Nakamura, H., Choi, C., Heo, J., Petruzzi, A., Zeng, K., Xie, Z., Wu, X., Eguchi, H., Pangukir, F., Breijder, P., Franssen, S., Perret, G., Clifford, I., Coscai, T. M., Di Maio, F., Zio, E., Pedroni, N., Zhang, J., Freixa, J., Ciurluini, C., Giannetti, F., and Adorni, M. (2024). A systematic approach for the adequacy analysis of a set of experimental databases: Application in the framework of the ATRIUM activity. Nuclear Engineering and Design, 421:113035
    https://doi.org/10.1016/j.nucengdes.2024.113035
  6. Xie, Z., Yaseen, M., and Wu, X. (2024). Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data. Computer Methods in Applied Mechanics and Engineering, 420:116721
    https://doi.org/10.1016/j.cma.2023.116721
  7. Yaseen, M., Yushu, D., German, P., and Wu, X. (2023). Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning. The International Journal of Advanced Manufacturing Technology, 129:3123–3139
    https://link.springer.com/article/10.1007/s00170-023-12471-1
  8. Wang, C., Wu, X., Xie, Z., and Kozlowski, T. (2023). Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference. Energies, 16(22):7664
    https://www.mdpi.com/1996-1073/16/22/7664
  9. Alsafadi, F. and Wu, X. (2023). Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets. Nuclear Engineering and Design, 415:112712
    https://www.sciencedirect.com/science/article/pii/S0029549323005617
  10. Moloko, L., Bokov, P., Wu, X., and Ivanov, K. (2023). Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks. Annals of Nuclear Energy, 188:109813
    https://www.sciencedirect.com/science/article/pii/S0306454923001329
  11. Yaseen, M. and Wu, X. (2023). Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models. Nuclear Science and Engineering, 197:947–966
    https://www.tandfonline.com/doi/abs/10.1080/00295639.2022.2123203
  12. Xie, Z., Jiang,W., Wang, C., and Wu, X. (2022). Bayesian inverse uncertainty quantification of a MOOSE-based melt pool model for additive manufacturing using experimental data. Annals of Nuclear Energy, 165:108782
    https://www.sciencedirect.com/science/article/pii/S0306454921006599
  13. Wu, X., Xie, Z., Alsafadi, F., and Kozlowski, T. (2021). A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes. Nuclear Engineering and Design, 384:111460
    https://www.sciencedirect.com/science/article/pii/S002954932100412X 
  14. Xie, Z., Alsafadi, F., and Wu, X. (2021). Towards Improving the Predictive Capability of Computer Simulations by Integrating Inverse Uncertainty Quantification and Quantitative Validation with Bayesian Hypothesis Testing. Nuclear Engineering and Design, 383:111423
    https://www.sciencedirect.com/science/article/pii/S0029549321003757
  15. Che, Y., Wu, X., Pastore, G., Li, W., and Shirvan, K. (2021). Application of Kriging and Variational Bayesian Monte Carlo method for improved prediction of doped UO2 fission gas release. Annals of Nuclear Energy, 153:108046
    https://www.sciencedirect.com/science/article/pii/S0306454920307428
  16. Lu, C., Wu, Z., and Wu, X. (2021). Enhancing the one-dimensional sfr thermal stratification model via advanced inverse uncertainty quantification methods. Nuclear Technology, 207(5):692–710
    https://www.tandfonline.com/doi/abs/10.1080/00295450.2020.1805259
  17. Jin, Y., Wu, X., and Shirvan, K. (2020). System code evaluation of near-term accident tolerant claddings during pressurized water reactor station blackout accidents. Nuclear Engineering and Design, 368:110814
    https://www.sciencedirect.com/science/article/pii/S0029549320303083
  18. Wu, X. and Shirvan, K. (2020). System code evaluation of near-term accident tolerant claddings during boiling water reactor short-term and long-term station blackout accidents. Nuclear Engineering and Design, 356:110362
    https://www.sciencedirect.com/science/article/pii/S0029549319303942
  19. Wu, X., Shirvan, K., and Kozlowski, T. (2019). Demonstration of the Relationship between Sensitivity and Identifiability for Inverse Uncertainty Quantification. Journal of Computational Physics, 396:12–30
    https://www.sciencedirect.com/science/article/pii/S0021999119304401
  20. Wang, C., Wu, X., and Kozlowski, T. (2019). Gaussian process–based inverse uncertainty quantification for trace physical model parameters using steady-state PSBT benchmark. Nuclear Science and Engineering, 193(1-2):100-114
    https://www.tandfonline.com/doi/abs/10.1080/00295639.2018.1499279
  21. Wu, X., Kozlowski, T., Meidani, H., and Shirvan, K. (2018). Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE. Nuclear Engineering and Design, 335:417–431
    https://www.sciencedirect.com/science/article/pii/S0029549318306411
  22. Wu, X., Kozlowski, T., Meidani, H., and Shirvan, K. (2018). Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, part 1: theory. Nuclear Engineering and Design, 335:339–355
    https://www.sciencedirect.com/science/article/pii/S0029549318306423
  23. Wu, X., Kozlowski, T., and Meidani, H. (2018). Kriging-based Inverse Uncertainty Quantification of Nuclear Fuel Performance Code BISON Fission Gas Release Model using Time Series Measurement Data. Reliability Engineering & System Safety, 169:422–436
    https://www.sciencedirect.com/science/article/pii/S095183201730532X
  24. Wu, X., Mui, T., Hu, G., Meidani, H., and Kozlowski, T. (2017). Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model. Nuclear Engineering and Design, 319:185–200
    https://www.sciencedirect.com/science/article/pii/S0029549317302406
  25. Wu, X. and Kozlowski, T. (2017). Inverse uncertainty quantification of reactor simulations under the Bayesian framework using surrogate models constructed by polynomial chaos expansion. Nuclear Engineering and Design, 313:29–52
    https://www.sciencedirect.com/science/article/pii/S0029549316304824
  26. Wu, X., Kozlowski, T., and Hales, J. D. (2015). Neutronics and fuel performance evaluation of accident tolerant FeCrAl cladding under normal operation conditions. Annals of Nuclear Energy, 85:763–775
    https://www.sciencedirect.com/science/article/pii/S0306454915003461
  27. Wu, X. and Kozlowski, T. (2015). Coupling of system thermal–hydraulics and Monte-Carlo code: Convergence criteria and quantification of correlation between statistical uncertainty and coupled error. Annals of Nuclear Energy, 75:377–387
    https://www.sciencedirect.com/science/article/pii/S0306454914004010

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