姓名：章涛

系属：能源与动力工程

学位：博士

职称：特任教授

专业：动力工程及工程热物理

导师类别：博士生导师

电子邮箱：20240014@upc.edu.cn

通讯地址：山东省青岛市黄岛区长江西路66号

个人主页：

※ 个人概况

安徽安庆人，2024年入职kaiyun体育登录网页入口（华东）从事高校教师工作。章博士综合考虑达西尺度，孔隙尺度及分子量级的多尺度耦合效应，开展地下储层中多相多组分流的多尺度耦合算法研究，并把这些方法用于石油（气）的三次开采，非常规油气开采、氢能等新能源开发预测和二氧化碳的地下封存等研究。章博士正探索将人工智能和深度学习技术引入到能源领域的工程计算中，并结合智能岩心技术实现囊括了储层多孔介质反演、建模、重构、流动计算和注入、驱替、集输等完整流程生产模拟的系统化工作，力图构建完整的储层分析、评价、监控和预测数学模型，开发稳健、高效、保真、实用的一整套算法，最终形成具有自主知识产权的多孔介质中多相流动的数字孪生体。目前共发表SCI/ESCI论文50余篇，包括CMAME,   POF, JCP, Applied Energy, Fuel, JPSE等相关领域国际知名学术期刊。截至2024年3月，总引用达1061次，H指数为20。参与科研项目总经费超过300万美元，与导师合著专著1部（Elsevier出版社出版，目前已被全球200余家图书馆收藏），并获批美国发明专利一项。2023年入选斯坦福大学评选的全球2%顶尖科学家名单。担任SCI期刊Frontiers in Physics的编委会成员，Gas Science and Engineering等四本SCI期刊的客座编辑/主编。

※ 研究方向

1)  地下多孔介质中的流动与输运现象，包括油藏模拟和二氧化碳封存等

2)  基于真实热力学状态方程和相场模型的孔观多组分多相流动模拟

3)  油藏流体计算热力学

4)  油藏模拟中机器学习算法的应用，包括加速闪蒸计算以及地质特征识别等

5)  新能源研究，包括氢能和空间能源等

6)  先进的油气输运体系研究

※ 教育经历

2016.08-2021.06，沙特阿卜杜拉国王科技大学，地球科学与工程专业，博士

2013.09-2016.06，kaiyun体育登录网页入口（北京），油气储运工程专业，硕士

2009.09-2013.06，kaiyun体育登录网页入口（北京），油气储运工程专业，学士

※ 工作经历

2024.03-今，kaiyun体育登录网页入口（华东）能源与动力工程系，特任教授

2021.07-2024.03，沙特阿卜杜拉国王科技大学，博士后

※ 学术兼职

1．InterPore沙特分会秘书长

2．SCI期刊Frontiers in Physics的编委会成员，Gas Science and Engineering等四本SCI期刊的客座编辑/主编

3.组委会秘书长，The 9th Asian Symposium on Computational Heat Transfer and Fluid Flow (ASCHT2023),Thuwal, Saudi Arabia,Dec.6th–8th, 2023

4.研讨会联席主席，分会9“The Future of Technologies in Energy Transition: Modeling,Simulation and Machine   Learning”, in the ICCES 2023 Conference (第29届国际计算和实验工程科学大会)，May. 2nd – May 5th, 2023,   https://www.iccesconf.org/symposia/

5.研讨会主席,分会39“Engineering Computations in Energy Industry”in the ICNAAM 2022 Conference (20th International Conference of Numerical Analysis and Applied Mathematics，第20届国际数值分析与应用数学大会), Sep. 19th  – 25th, 2022,   https://icnaam.org/sessions_minisymposia.htm

6.研讨会联席主席，专题研讨会“Porous Media for Energy Applications”in ICAE2022 Conference (International Conference on Applied Energy), 2022年国际应用能源大会，Aug. 9th – 11th, 2022

7.研讨会主席，分会“MISCELLANEOUS 3”in the 16th International Conference on Heat Transfer, Fluid Mechanics and  Thermodynamics(HEFAT),第16届国际传热流体力学与热力学会议Aug.8th–10th,2022,   https://whova.com/portal/webapp/hefat_202208/Agenda/2562814

8.组委会秘书长, KAUST Workshop on Porous Media, KAUST, Thuwal, Saudi Arabia, Nov. 8th – 10th, 2021

9.组委会秘书长, 第一届地球能源大数据学术会议, Wuhan, China, July 16th – 17th, 2021

※ 主要获奖情况及荣誉称号

The Best Paper Award , the 9th Asian Symposium on Computational Heat Transfer and Fluid Flow (ASCHT), 2023

最佳论文奖，第十七届全国渗流力学学术会议, 2023年，北京，中国

AGER第二届优秀国际青年编委

The Best Paper Award , the 8th Asian Symposium on Computational Heat Transfer and Fluid Flow (ASCHT), 2021

最佳论文奖，第十六届全国渗流力学学术会议, 2021年，葫芦岛，中国

The Best Poster Award, the 1st InterPore Saudi Chapter Annual Meeting, 2021

中国国家奖学金，2015年

※ 代表论文

1.Zhang, T.,Zhang, Y., Katterbauer, K.,Al Shehri, A., Sun, S.,& Hoteit, I.(2022). Phase equilibrium in the hydrogen energy chain. Fuel, 328, 125324. (SCI, IF= 8.035，中科院1区top)

2.Zhang,T., Li, Y., Chen, Y., Feng, X., Zhu, X., Chen, Z.,... & Sun, S.(2021). Review on space energy. Applied Energy, 292, 116896. (SCI, IF=11.446，中科院1区top)

3.Zhang, T., Li, Y., Li, Y., Sun, S., & Gao, X. (2020). A self-adaptive deep learning   algorithm for accelerating multi-component flash calculation. Computer Methods in Applied Mechanics and Engineering, 369, 113207. (SCI, IF=6.756，中科院1区top)

4.Zhang, T.,& Sun,S.(2019).A coupled Lattice Boltzmann approach to simulate gas flow and transport in shale reservoirs with dynamic sorption. Fuel, 246, 196-203. (SCI, IF= 8.035，中科院1区top)

5.Zhang, T.,& Sun, S.(2021). An exploratory multi-scale framework to reservoir digital twin. Advances in Geo-Energy Research, 5(3). (ESCI，中科院1区)

6.Zhang, T.,Kou, J., & Sun, S. (2017). Review on dynamic Van der Waals theory in two-phase flow. Advances in Geo-Energy Research, 1(2), 124-134. (ESCI，中科院1区)

7.Zhang, T., Liu, J., & Sun, S. (2023). Technology transition from traditional oil and  gas reservoir simulation to the next generation energy development. Advances in Geo-Energy Research, 7(1), 69-70.（ESCI，中科院1区）

8.Zhang, T., Tan, G., Bai, W., Sun, Y., Wang, Y., Luo, X., ...& Sun, S.(2023). A Disturbance Frequency Index in Earthquake Forecast Using Radio Occultation Data. Remote Sensing, 15(12), 3089. (SCI, IF= 5.349，中科院2区top)

9.Zhang, T.,Zhang, Y., Katterbauer, K.,Al Shehri, A., Sun, S., & Hoteit, I. (2023). Deep learning–assisted phase equilibrium analysis for producing natural hydrogen.International Journal of Hydrogen Energy, accepted. (SCI, IF= 7.2，中科院2区top)

10.Zhang,T.,Li,Y.,Sun, S., & Bai,H.(2020). Accelerating flash calculations in unconventional reservoirs considering capillary pressure using an optimized deep learning algorithm. Journal of Petroleum Science and Engineering, 195, 107886. (SCI, IF=5.168，中科院2区top)

11.Chen, H.,Kou, J., Sun, S., & Zhang, T. (2019). Fully mass-conservative IMPES schemes for incompressible two-phase flow in porous media. Computer Methods in Applied Mechanics and Engineering, 350, 641-663. (SCI, IF=6.756，中科院1区top，因合作者数学学科习惯，按姓氏首字母排序)

12.Li, Y.,Qiao, Z., Sun, S., & Zhang, T. (2020). Thermodynamic modeling of CO2 solubility in saline water using NVT flash with the cubic-Plus-association equation of state. Fluid Phase Equilibria, 112657. (SCI, IF=2.745，中科院2区，因合作者数学学科习惯，按姓氏首字母排序)

13.Chen, H.,Sun, S., & Zhang, T. (2018). Energy stability analysis of some fully discrete numerical schemes for incompressible Navier–Stokes equations on staggered grids. Journal of Scientific Computing, 75(1), 427-456. (SCI,   IF=2.843，中科院2区，因合作者数学学科习惯，按姓氏首字母排序)

14.章涛,白桦,黄天相,刘杰,孙树瑜.天然氢开采和集输中的相平衡分析.深圳大学学报理工版,2024,41(2):163-172.（中文双核心）

15.Zhang, T.,Gong, L.,Sun, S.,&Zhu, C. Y. (2023). Phase Equilibrium Studies in the Geothermal Energy Development: The Effect of Hydrogen Bond on the Multi-Component Fluid. ACS omega. (SCI, IF= 4.1，中科院3区)

16.Zhang, T,Sun, S, Bai,H (2022). Thermodynamically-consistent flash calculation in energy industry: From iterative schemes to a unified thermodynamics-informed neural network. International Journal of Energy Research, 2022, 1- 15. (SCI, IF=4.672，中科院3区)

17.Zhang, T., Bai, H., & Sun, S. (2022). Intelligent Natural Gas and Hydrogen Pipeline Dispatching Using the Coupled Thermodynamics-Informed Neural Network and Compressor Boolean Neural Network. Processes, 10(2), 428. (SCI, IF=3.352，中科院3区)

18.Zhang, T., Li, C., & Sun, S. (2022). Effect of Temperature on Oil–Water Separations Using Membranes in Horizontal Separators. Membranes, 12(2), 232. (SCI,   IF=4.562，中科院3区)

19.Zhang, T., Bai, H., & Sun, S. Intelligent control on urban natural gas supply using a deep-learning-assisted pipeline dispatch technique (2022). Frontiers in   Energy Research, 968. (SCI, IF=3.858，中科院4区)

20.Zhang, T., & Sun, S. (2021). Thermodynamics-Informed Neural Network (TINN) for Phase Equilibrium Calculations Considering Capillary Pressure. Energies, 14(22), 7724. (SCI, IF=3.004，中科院4区)

21.Zhang, T.,Bai, H., & Sun, S. (2021). A self-adaptive deep learning algorithm for   intelligent natural gas pipeline control. Energy Reports, 7, 3488-3496 (SCI,   IF=4.937，中科院3区)

22.Zhang T.,Bai Hua, Sun Shuyu. Fast and accurate phase equilibrium calculations for   condensate shale gas reservoirs. Chinese Journal of Theoretical and Applied Mechanics（力学学报）, 2021, 53(8): 1-12 doi: 10.6052/0459-1879-21-229 (EI,中文核心 )

23.Zhang, T.,Li, Y., Li, C., & Sun, S.(2020). Effect of salinity on oil production: review on low salinity waterflooding mechanisms and exploratory study on   pipeline scaling. Oil & Gas Science and Technology–Revue d’IFP Energies nouvelles, 75, 50. (SCI, IF=1.708，中科院3区)

24.Zhang, T., Li, Y., Cai, J., Meng, Q., Sun, S., & Li, C. (2020). A Digital Twin for Unconventional Reservoirs: A Multiscale Modeling and Algorithm to Investigate Complex Mechanisms. Geofluids, 2020. (SCI, IF=2.176，中科院4区)

25.Zhang, T., Sun, S., & Song, H. (2019). Flow mechanism and simulation approaches for shale gas reservoirs: A review. Transport in Porous Media, 126(3), 655-681. (SCI, IF=3.61，中科院3区)

26.Zhang,T.,Li, Y., & Sun, S. (2019). Phase equilibrium calculations in shale gas reservoirs. Capillarity, 2(1), 8-16.

27.Qiao,Z., Sun, S., Zhang, T., & Zhang, Y. (2019). A New Multi-Component Diffuse Interface Model with Peng-Robinson Equation of State and Its Scalar Auxiliary   Variable (SAV) Approach. Communications in Computational Physics, 26(5). (SCI, IF=3.791，中科院3区，因合作者数学学科习惯，按姓氏首字母排序)

28.Zhang, T.,Salama, A.,Sun, S., & El-Amin, M. F. (2015). Pore network modeling of drainage process in patterned porous media: a quasi-static study. Journal of Computational Science, 9, 64-69. (SCI, IF=3.817，中科院3区)

29.Liu, J.,Zhang, T*., & Sun, S.* (2024). Molecular mechanisms of hydrogen leakage through caprock in moisture and residual gas conditions: A molecular dynamics–Monte Carlo study. Physics of Fluids, 36(2).（SCI, IF=4.6, 中科院1区）

30.Zhang, Y., Yang, X., Zhang, L., Li, Y., Zhang, T*., & Sun, S.* (2023). Energy landscape analysis for two-phase multi-component NVT flash systems by using ETD type high-index saddle dynamics. Journal of Computational Physics,   111916. (SCI, IF= 4.645，中科院1区)

31.刘杰, 陈银, 章涛*, & 孙树瑜*. (2023). 页岩纳米有机质孔隙中的润湿性研究. 力学学报, 55, 1-9. (EI, 中文双核心)

32.Liu, J.,Zhang, T*., & Sun, S*. (2023). Molecular Dynamics Simulations of Ion Transport through Protein Nanochannels in Peritoneal Dialysis. International Journal of Molecular Sciences, 24(12), 10074. (SCI, IF= 6.208，中科院2区top)

33.Liu, J.,Tang, Q., Kou, J., Xu, D., Zhang, T*., & Sun, S*. (2022). A quantitative study on the approximation error and speed-up of the multi-scale MCMC (Monte   Carlo Markov chain) method for molecular dynamics. Journal of Computational   Physics, 111491. (SCI, IF= 4.645，中科院1区)

34.Liu, J., Zhang, T*., & Sun, S*. (2022).Mechanism Analysis of Shale Gas Adsorption under Carbon Dioxide–Moisture   Conditions: A Molecular Dynamic Study. Energy & Fuels. （SCI, IF= 4.654，中科院3区）

35.Liu, J., Zhang, T*., & Sun, S*. (2022). Study of the Imbibition Phenomenon in Porous Media by the Smoothed Particle Hydrodynamic (SPH) Method. Entropy, 24(9), 1212. (SCI, IF= 2.738，中科院3区)

36.Liu, J.,Zhang, T*., & Sun, S*. (2022). Stability analysis of the water bridge in organic shale nanopores: A molecular dynamic study. Capillarity, 5(4), 75-82.

37.Shi,J.,Gong, L., Zhang, T*.,& Sun, S*.(2022). Study of the Seawater Desalination Performance by   Electrodialysis. Membranes, 12(8), 767. (SCI, IF=4.562，中科院3区)

38.Liu, J.,Zhang, T*., & Sun, S*.(2023). Review of deep learning algorithms in molecular simulations and perspective applications on petroleum engineering. Geoscience Frontiers, 101735. (SCI, IF=8.9, 中科院1区top)

39.Yuqi Wu,Senyou An, Pejman Tahmasebi, Keyu Liu, Chengyan Lin, Serveh Kamrava, Chang Liu, Chenyang Yu, Tao Zhang, Shuyu Sun, Samuel Krevor, Vahid Niasar. (2023). An End-to-End Approach to Predict Physical Properties of Heterogeneous   Digital Porous Media: Coupling Deep Learning and Physics-Based Features. Fuel. (SCI, IF= 8.035，中科院1区top)

40.Zhang, Y.,Katterbauer, K., Zhang, T., AlShehri, A. A., & Hoteit, I. (2023). Deep learning-aided image-oriented history matching of geophysical data. Computational Geosciences, 1-14. (SCI, IF=2.5，中科院3区)

41.Chen,K.,Xu, H., Zhang, Z., Meng, Q., Zhang, T. Modeling of counter-current spontaneous imbibition in independent capillaries with unequal diameters. Capillarity, 2022, 5(6): 115-122.

42.Chen, S., Li, Y., Zhang, T., Zhu, X., Sun, S., & Gao, X. (2021). Lunar features detection for energy discovery via deep learning. Applied Energy, 296,   117085. (SCI, IF=11.446，中科院1区top)

43.Salama, A., Alyan, A., El Amin, M., Sun, S., Zhang, T., & Zoubeik, M. (2021). The  Effect of the Oleophobicity Deterioration of a Membrane Surface on Its  Rejection Capacity: A Computational Fluid Dynamics Study. Membranes, 11(4),   253. (SCI, IF=4.562，中科院3区)

44.Salama, A., Sun, S., & Zhang, T. (2021). A Unified, One Fluid Model for the Drag of Fluid and Solid Dispersals by Permeate Flux towards a Membrane Surface. Membranes, 11(2), 154. (SCI, IF=4.562，中科院3区)

45.Sun, S., & Zhang, T. (2020). A 6M digital twin for modeling and simulation in subsurface reservoirs. Advances in Geo-Energy Research, 4(4), 349-351. (ESCI，中科院4区)

46.Li, Y.,Zhang, T., Sun, S., & Gao, X. (2019). Accelerating flash calculation through deep learning methods. Journal of Computational Physics, 394, 153-165. (SCI, IF= 4.645，中科院1区)

47.Li, J.,Zhang, T., Sun, S., & Yu, B. (2019). Numerical investigation of the POD reduced-order model for fast predictions of two-phase flows in porous media. International Journal of Numerical Methods for Heat & Fluid Flow. (SCI,   IF=5.181，中科院3区)

48.Li, Y.,Zhang, T., & Sun, S. (2019). Acceleration of the NVT Flash Calculation  for Multicomponent Mixtures Using Deep Neural Network Models.Industrial & Engineering Chemistry Research, 58(27), 12312-12322. (SCI, IF=4.326，中科院3区)

49.Liu,P.,Zhang, T.,& Sun, S. (2019). A tutorial review of reactive transport modeling and risk assessment for geologic CO2 sequestration.Computers &   Geosciences, 127, 1-11.  (SCI, IF=5.168，中科院2区)

50.Wang, M.,Yu, G., Zhang, X., Zhang, T., Yu, B., & Sun, D. (2017). Numerical investigation of melting of waxy crude oil in an oil tank. Applied Thermal   Engineering, 115, 81-90. (SCI, IF=6.465，中科院1区top)

51.Zhang, Y., Katterbauer, K.,Zhang, T., & Hoteit, I.(2022, June). Time-Lapse Electromagnetic History Matching in a Fractured Carbonate Reservoir.In 83rd EAGE Annual Conference & Exhibition (Vol. 2022, No.1, pp.1-5). European   Association of Geoscientists & Engineers.

52.Zhang, T.,Bai, H., & Sun, S. (2020). EnerarXiv.http://www.enerarxiv.org/page/thesis.html?id=Automated control on natural gas pipelines using deep learning algorithms. 2299

53.Zhang, T., Li, Y., & Sun, S. (2019, June). Accelerated Phase Equilibrium Predictions for Subsurface Reservoirs Using Deep Learning Methods. In International Conference on Computational Science (pp. 623-632). Springer, Cham.

54.Zhang, T.,Li, Y., Cai, J., & Sun, S.(2019, March). Recent Progress on Phase Equilibrium Calculation in Subsurface Reservoirs Using Diffuse Interface Models.In International Conference on Computational & Experimental   Engineering and Sciences (pp. 969-982). Springer, Cham.

55.Zhang,T.,& Sun, S. (2018, June). A compact and efficient lattice Boltzmann scheme to simulate complex thermal fluid flows. In International Conference on Computational Science (pp. 149-162). Springer, Cham.

56.Zhang, T., Sun, S., & Yu, B. (2017). A fast algorithm to simulate droplet motions in oil/water two phase flow.

57.Zhang, T.,Salama, A.,Sun, S.,& Zhong, H.(2015). A compact numerical implementation for solving Stokes equations using matrix-vector operations. Procedia Computer Science, 51, 1208-1218.

※ 专利

1.Sun, S.,Zhang, T., Li, Y., et al. PORE-SCALE, MULTI-COMPONENT, MULTI-PHASE FLUID MODEL AND METHOD: U.S.Patent 17/298,969 [P]. 2021-6-02.

2. 宇波，张健，王欣然，章涛，张欣雨。原油管道预热投产热力过程仿真方法。CN104123425B，2017.12.08。

3. 宇波，张欣雨，章涛，谢静，王岩。空管段充液过程的模拟方法。CN104102780B，2017.05.24。

  ※ 国内外学术会议邀请报告

1. 2023年12月6日，the 9th Asian Symposium on Computational Heat Transfer and Fluid Flow, ASCHT2023,KAEC,Saudi Arabia, invited talk, title:“Technology transitions from the conventional petroleum energy resources to the next-generation New Energy”.

2. 2023年11月, the 2nd InterPore Saudi Chapter Annual Meeting, Invited Talk, title: “Deep-learning assisted energy   transition”.

3. 2023年5月 20日，全国高校工程热物理第二十九届全国学术会议，青岛，山东，特邀报告。题目：“天然气管道输氢工艺中基于机器学习技术的热力学分析”

4. 2022年11月18日，KAUST Conference on Scientific Computing and Machine Learning   (SCML2022), invited talk, title: “AI in energy transition”.