Grigorios Delipei

Rivas, A., Delipei, G. K., Davis, I., Bhongale, S., Yang, J., & Hou, J. (2024). A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation. RELIABILITY ENGINEERING & SYSTEM SAFETY, 247. https://doi.org/10.1016/j.ress.2024.110121

Rivas, A., Delipei, G. K., Davis, I., Bhongale, S., & Hou, J. (2024). A system diagnostic and prognostic framework based on deep learning for advanced reactors. PROGRESS IN NUCLEAR ENERGY, 170. https://doi.org/10.1016/j.pnucene.2024.105114

Faure, Q., Delipei, G., Petruzzi, A., Avramova, M., & Ivanov, K. (2023). Fuel performance uncertainty quantification and sensitivity analysis in the presence of epistemic and aleatoric sources of uncertainties. FRONTIERS IN ENERGY RESEARCH, 11. https://doi.org/10.3389/fenrg.2023.1112978

Delipei, G. K., Rouxelin, P., Abarca, A., Hou, J., Avramova, M., & Ivanov, K. (2022). CTF-PARCS Core Multi-Physics Computational Framework for Efficient LWR Steady-State, Depletion and Transient Uncertainty Quantification. ENERGIES, 15(14). https://doi.org/10.3390/en15145226

Rivas, A., Delipei, G. K., & Hou, J. (2022). Predictions of component Remaining Useful Lifetime Using Bayesian Neural Network. PROGRESS IN NUCLEAR ENERGY, 146. https://doi.org/10.1016/j.pnucene.2022.104143

Delipei, G. K., Hou, J., Avramova, M., Rouxelin, P., & Ivanov, K. (2021). Summary of comparative analysis and conclusions from OECD/NEA LWR-UAM benchmark Phase I. NUCLEAR ENGINEERING AND DESIGN, 384. https://doi.org/10.1016/j.nucengdes.2021.111474