**Book chapter**

- Liu, Y., and Dinh, N. (2018). “Flow Boiling in Tubes”. In Kulacki F., editors, Handbook of Thermal Science and Engineering. Springer, Cham. DOI: 10.1007/978-3-319-32003-8_47-1

**Journal Publications**

- Liu, Y., Alsafadi, F., Mui, T., O’Grandy, D., & Hu, R. (Under review). Development of whole system digital twins for advanced reactors: leveraging graph neural networks and SAM simulations.
- Liu, Y., Mui, T., Xie, Z., & Hu, R. (2023). Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification. Annals of Nuclear Energy, 192, 110010. DOI: 10.1016/j.anucene.2023.110010
- Liu, Y., Dinh, N., Sun, X., & Hu, R. (2023). Uncertainty quantification for multiphase-CFD closure relations with a physics-informed Bayesian approach. Nuclear Technology, 1-14. DOI: 10.1080/00295450.2022.2162792
- Liu, Q., Liu, Y., Burak, A., Kelly, J., Bajorek, S., & Sun, X. (2023). Tree-based ensemble learning models for wall temperature predictions in post-CHF flow regimes. ASME Journal of Heat and Mass Transfer, 145, 1-34. DOI: 10.1115/1.4055469
- Liu, Y., Hu, R., Zou, L., & Nunez, D. (2022). SAM-ML: Integrating data-driven closure with nuclear system code SAM for improved modeling capability. Nuclear Engineering and Design, 400, 112059. DOI: 10.1016/j.nucengdes.2022.112059
- Liu, Y., Hu, R., Balaprakash, P., Kraus, A., & Obabko, A. (2022). Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks. Nuclear Engineering and Design, 390, 111716. DOI: 10.1016/j.nucengdes.2022.111716
- Liu, Y., Wang, D., Sun, X., Liu, Y., Dinh, N., & Hu, R. (2021). Uncertainty quantification for multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments. Reliability Engineering and System Safety, 212, 107636. DOI: 10.1016/j.ress.2021.107636
- Liu, Q., Sun, H., Liu, Y., Sun, X., & Kelly, J. (2021). Experimental study of post-CHF heat transfer in a vertical tubular test section. International Journal of Heat and Mass Transfer, 166, 120697. DOI: 10.1016/j.ijheatmasstransfer.2020.120697
- Wang, C., Liu, Y., Sun, X., & Sabharwall, P. (2021). A hybrid porous model for full reactor scale CFD investigation of a prismatic HTGR. Annals of Nuclear Energy, 151, 107916. DOI: 10.1016/j.anucene.2020.107916
- Liu, Y., Wang, C., Qian, Y., Sun, X., & Liu, Y. (2020). Uncertainty analysis of PIV measurements in bubbly flows considering sampling and bubble effects with ray optics modeling. Nuclear Engineering and Design, 364, 110677. DOI: 10.1016/j.nucengdes.2020.110677
- Wu, X., Liu, Y., Kearfott, K., & Sun, X. (2020). Evaluation of public dose from FHR tritium release with consideration of meteorological uncertainties. Science of the Total Environment, 709, 136085. DOI: 10.1016/j.scitotenv.2019.136085
- Liu, Y., Sun, X., & Dinh, N. (2019). Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments. Nuclear Engineering and Design, 354, 110200. DOI: 10.1016/j.nucengdes.2019.110200
- Liu, Y., Dinh, N., Smith, R.C., & Sun, X. (2019). Uncertainty quantification of two-phase flow and boiling heat transfer simulations through a data-driven modular Bayesian approach. International Journal of Heat and Mass Transfer, 138, 1096-1116. DOI: 10.1016/j.ijheatmasstransfer.2019.04.071ke
- Liu, Y. & Dinh, N. (2019). Validation and uncertainty quantification for wall boiling closure relations in multiphase-CFD solver. Nuclear Science and Engineering, 193, 81-99. DOI: 10.1080/00295639.2018.1512790
- Liu, Y., Dinh, N., Sato, Y., & Niceno, B. (2018). Data-driven modeling for boiling heat transfer: Using deep neural networks and high-fidelity simulation results. Applied Thermal Engineering, 144, 305-320. DOI: 10.1016/j.applthermaleng.2018.08.070
- Liu, Y. & Dinh, N. (2016). Analysis of heat transfer under high heat flux nucleate boiling conditions. Kerntechnik, 81, 308-314. DOI: 10.3139/124.110750

**Conference Proceedings**

- Liu, Y., Alsafadi, F., Mui, T., O’Grandy, D., & Hu, R. (2023). Digital twin development for advanced reactor system based on graph neural networks using SAM code simulation. In Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20), Washington DC, August 2023.
- Liu, Q., Liu, Y., Sun, X., Wang, D., Liu, Y., Buchanan, J. R., & Worosz, T. (2023). Experimental study and CFD simulation of air-water bubbly flow in a rectangular channel. In Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20), Washington DC, August 2023.
- Wang, D., Fu, Y., Sun, H., Liu, Y., Liu, Q., Liu, Y., Sun, X., Worosz, T., & Buchanan, J. (2023). A comprehensive measurement of bubbly flow in a 30 mm x 10 mm rectangular channel. In Proceedings of the 20th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-20), Washington DC, August 2023.
- Liu, Y., Alsafadi, F., Mui, T., O’Grandy, D., & Hu, R. (2023). Digital twin development as a holistic representation for advanced reactor systems using graph neural networks. In Proceedings of the 13th Nuclear Plant Instrumentation, Control & Human-Machine Interface Technologies (NPIC&HMIT 2023), Knoxville, TN, July 2023.
- Liu, Y., Dinh, N., Sun, X., & Hu, R. (2022). Quantifying model form uncertainty in MCFD simulations of bubbly flows with physics-informed machine learning. In 12th Japan-U.S. Seminar on Two-Phase Flow Dynamics, Virtual, May 2022.
- Liu, Y., Hu, R., Zou, L., Hu, G., & Nunez, D. (2022). SAM-ML: Machine learning-enhanced system analysis module for thermal stratification analysis. In Proceedings of the 19th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-19), Virtual, March 2022.
- Liu, Y., Hu, G., & Hu, R. (2022). Benchmark simulation of the FFTF LOFWOS Test #13 using SAM. In Proceedings of the 19th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-19), Virtual, March 2022.
- Vegendla, P., Liu, Y., Zou, L., & Hu, R. (2022). Development of wall heat transfer correlation for laminar flow in a cylindrical tube. In Proceedings of the 19th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-19), Virtual, March 2022.
- Wang, C., Sun, X., Liu, Y., & Shabharwall, P. (2022). CFD study of the effects of bypass gaps on MHTGR thermal flow. In Proceedings of the 19th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-18), Virtual, March 2022.
- Liu, Y., Hu, R., Balaprakash, P., Brunett, A., & Obabko, A. (2021). Application of deep neural networks to support coarse mesh turbulence modeling of reactor transients. In Proceedings of the International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C-2021), Raleigh, North Carolina, USA, October 2021.
- Liu, Y., Hu, R., & Balaprakash, P. (2021). Uncertainty quantification of deep neural network-based turbulence model for reactor transient analysis. In Proceedings of the ASME-Verification and Validation Symposium 2021, Virtual, May 2021.
- Liu, Y., Hu, R., Balaprakash, P., Brunett, A., & Obabko, A. (2020). Coarse mesh CFD turbulence prediction for reactor transient using densely connected convolutional networks. In Transactions of the ANS Winter Meeting, Virtual, November 2020.
- Liu, Y., Sun, X., Liu, Y., & Dinh, N. (2019). Uncertainty quantification and reduction for multiphase-CFD solvers: a data-driven Bayesian approach supported by high-resolution local measurements. In Proceedings of the 18th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-18), Portland, Oregon, USA, August 2019.
- Liu, Y., Qian, Y., Wang, C., & Sun, X. (2019). Uncertainty analysis of PIV measurements for liquid velocity in two-phase bubbly flows. In Proceedings of the 18th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-18), Portland, Oregon, USA, August 2019.
- Liu, Y., Shi, S., Qian, Y., Sun, X., & Dinh, N. (2018). Inverse uncertainty quantification of turbulence modeling in multiphase-CFD solver using high-resolution data from particle image velocimetry. In Proceedings of the Advances in Thermal Hydraulics (ATH-2018), Orlando, FL, USA, November 2018.
- Zhang, X., Liu, Y., Sun, X., & Dinh, N. (2018). Design of validation experiments for model improvement of dispersed flow film boiling in COBRA-TF. In Transactions of the American Nuclear Society, Orlando, FL, USA, November 2018.
- Liu, Y., Dinh, N., Sato, Y., & Bojan, N. (2018). Validation and uncertainty quantification of DNB closures in MCFD solver using inverse Bayesian inference method. In Proceedings of the ANS Best Estimate Plus Uncertainty International Conference (BEPU-2018), Lucca, Italy, May 2018.
- Liu, Y., & Dinh, N. (2017). Development of a VUQ framework for wall boiling model in MCFD solver. In Proceedings of the 17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-17), Xi’an, China, September 2017.
- Pointer, W.D., & Liu, Y. (2017). Eulerian two-fluid RANS-based CFD simulations of a helical coil steam generator boiling tube. In Proceedings of the 17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-17), Xi’an, China, September 2017.
- Liu, Y., Rollins, C., Dinh, N., & Luo, H. (2017). Sensitivity analysis of interfacial momentum closure terms in two phase flow and boiling simulations using MCFD solver. In Proceedings of the ASME 2017 Heat Transfer Summer Conference (HT-2017), Bellevue, Washington, USA, July 2017.
- Liu, Y., & Dinh, N. (2015). Analysis of heat transfer under high heat flux nucleate boiling conditions. In Proceedings of the Second International Seminar on Subchannel Analysis (ISSCA-2), Shenzhen, China, December 2015.
- Liu, Y., & Dinh, N. (2015). Treatment of nucleation and bubble dynamics in high heat flux boiling. In Proceedings of the 16th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-16), Chicago, IL, USA, September 2015.