| A self-adaptive deep learning algorithm for accelerating multi-component flash calculation T Zhang, Y Li, Y Li, S Sun, X Gao Computer Methods in Applied Mechanics and Engineering 369, 113207, 2020 | 95 | 2020 |
| A coupled Lattice Boltzmann approach to simulate gas flow and transport in shale reservoirs with dynamic sorption T Zhang, S Sun Fuel 246, 196-203, 2019 | 79 | 2019 |
| Accelerating flash calculation through deep learning methods Y Li, T Zhang, S Sun, X Gao Journal of Computational Physics 394, 153-165, 2019 | 63 | 2019 |
| Flow mechanism and simulation approaches for shale gas reservoirs: A review T Zhang, S Sun, H Song Transport in Porous Media 126, 655-681, 2019 | 62 | 2019 |
| Accelerating flash calculations in unconventional reservoirs considering capillary pressure using an optimized deep learning algorithm T Zhang, Y Li, S Sun, H Bai Journal of Petroleum Science and Engineering 195, 107886, 2020 | 60 | 2020 |
| Fully mass-conservative IMPES schemes for incompressible two-phase flow in porous media H Chen, J Kou, S Sun, T Zhang Computer Methods in Applied Mechanics and Engineering 350, 641-663, 2019 | 50 | 2019 |
| Phase equilibrium in the hydrogen energy chain T Zhang, Y Zhang, K Katterbauer, A Al Shehri, S Sun, I Hoteit Fuel 328, 125324, 2022 | 48 | 2022 |
| Review on space energy T Zhang, Y Li, Y Chen, X Feng, X Zhu, Z Chen, J Yao, Y Zheng, J Cai, ... Applied Energy 292, 116896, 2021 | 48 | 2021 |
| Reservoir simulations: Machine learning and modeling S Sun, T Zhang Gulf Professional Publishing, 2020 | 43 | 2020 |
| Acceleration of the NVT Flash Calculation for Multicomponent Mixtures Using Deep Neural Network Models Y Li, T Zhang, S Sun Industrial & Engineering Chemistry Research 58 (27), 12312-12322, 2019 | 43 | 2019 |
| Thermodynamics-informed neural network (TINN) for phase equilibrium calculations considering capillary pressure T Zhang, S Sun Energies 14 (22), 7724, 2021 | 35 | 2021 |
| A tutorial review of reactive transport modeling and risk assessment for geologic CO2 sequestration P Liu, T Zhang, S Sun Computers & geosciences 127, 1-11, 2019 | 35 | 2019 |
| Effect of salinity on oil production: review on low salinity waterflooding mechanisms and exploratory study on pipeline scaling T Zhang, Y Li, C Li, S Sun Oil & Gas Science and Technology–Revue d’IFP Energies nouvelles 75, 50, 2020 | 34 | 2020 |
| Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage H Zhang, HV Thanh, M Rahimi, WJ Al-Mudhafar, S Tangparitkul, T Zhang, ... Science of The Total Environment 877, 162944, 2023 | 31 | 2023 |
| A self-adaptive deep learning algorithm for intelligent natural gas pipeline control T Zhang, H Bai, S Sun Energy Reports 7, 3488-3496, 2021 | 31 | 2021 |
| Numerical investigation of melting of waxy crude oil in an oil tank M Wang, G Yu, X Zhang, T Zhang, B Yu, D Sun Applied Thermal Engineering 115, 81-90, 2017 | 31 | 2017 |
| Thermodynamic modeling of CO2 solubility in saline water using NVT flash with the cubic-Plus-association equation of state Y Li, Z Qiao, S Sun, T Zhang Fluid Phase Equilibria 520, 112657, 2020 | 30 | 2020 |
| A new multi-component diffuse interface model with Peng-Robinson equation of state and its scalar auxiliary variable (SAV) approach Z Qiao, S Sun, T Zhang, Y Zhang Global Science Press, 2019 | 28 | 2019 |
| Deep learning–assisted phase equilibrium analysis for producing natural hydrogen T Zhang, Y Zhang, K Katterbauer, A Al Shehri, S Sun, I Hoteit International Journal of Hydrogen Energy 50, 473-486, 2024 | 25 | 2024 |
| Numerical investigation of the POD reduced-order model for fast predictions of two-phase flows in porous media J Li, T Zhang, S Sun, B Yu International Journal of Numerical Methods for Heat & Fluid Flow 29 (11 …, 2019 | 24 | 2019 |
Shuyu SunProfessor, King Abdullah University of Science and Technology (KAUST)Verified email at kaust.edu.sa