王兴建特别研究员/副教授/博导

办公电话:(010) 62791804

电子邮箱:xingjianwang@tsinghua.edu.cn

通讯地址:北京市清华大学能动系李兆基大楼A461

邮编:100084

教育背景

· 2010/08-2016/08,佐治亚理工学院 机械工程专业 博士

· 2006/08-2010/07,中国科学技术大学 热能与动力工程专业 学士

工作履历

· 2025/12-至今,清华大学 行健书院,院长助理

· 2023/07-至今,清华大学,能动系,特别研究员,副教授

· 2021/06-2023/06,清华大学,能动系,特别研究员,助理教授

· 2019/08-2021/05,佛罗里达理工学院机械与土木系助理教授

· 2018/08-2019/07,佐治亚理工学院航空航天学院高级研究工程师

· 2016/08-2018/07,佐治亚理工学院航空航天学院博士后

· 2016/05-2016/08,美国通用电气全球研发中心机械工程师实习生

学术兼职

· AIAA Associate Fellow

· AIAA Journal 副主编

· De Gruyter publishing ML-STEM系列丛书主编

· 航空学报,青年编委

· Propulsion and Energy,青年编委

· 美国航空航天协会高速吸气式推进技术委员会委员(AIAA High Speed Air Breathing Propulsion Technical Committee)

· 2022-2024年中国工程热物理学会燃烧学学术年会湍流燃烧分会主席

· 2024-2025年教育部重点领域教学资源建设项目重燃燃烧室知识领域专家协作组专家

· 领域内多个期刊的特邀评论员,包括AIAA J, JPP, JFM, PoF, CNF, JCP, AST等

研究领域

· 极端条件下湍流流动与燃烧复杂过程的仿真建模理论

· 低排放燃烧室设计与燃烧稳定性调控

· 机器学习与动力工程交叉

· 氢燃烧室设计与试验

· 降阶代理模型

科研概况

在研科研项目

主持项目:国家自然科学基金面上、全国重点实验室基金多项以及航天院所和企业委托多个项目等;子课题:重点研发计划、国家科技重大专项、国家基金委企业联合基金

已完成科研项目

LJ基础科学中心重点项目、LJ基础科学中心重大项目子课题、航天科技/科工集团多个委托项目、管网集团掺氢燃机项目等

获奖情况

· 国家级青年人才,2021

· Best paper award in ILASS Asia 2020

· Front Cover paper in June issue of Physics of Fluids, 2019

· Statistics in Physical Engineering Sciences (SPES) Award by the American Statistical Society, 2019

· Best presentation paper in ILASS Americas 2018

学生培养情况

· 丁思宇,2022级博士,北京市优秀毕业生/国家奖学金/2025全国计算流体力学优秀论文

· 周明烁,2022级博士,国家奖学金

· 倪晨旭,2022级硕士,国家奖学金

· 王龙飞,2023级硕士,国家奖学金/2023和2025工程热物理年会燃烧分会优秀墙报论文

· 张斓菲,2024级硕士,国家奖学金/2026年全国流体力学大会亮点报告

学术成果

专著

1. “Emulation of Complex Fluid Flows: Projection-Based Reduced-Order Modeling and Machine Learning,” 德古意特出版社, 作者: Xingjian Wang and Vigor Yang

已发表的期刊论文

1. L Wang, S Ding, M Zhu, X Wang*, “Spray combustion dynamics in a strut-cavity integrated afterburner with closely-coupled fuel injection,” Combustion and Flame 295(2027), 115354

2. R. Zuo, M. Zhou, G. Ribert, X Wang*, “Progress variable optimization using encoder-residual neural networks for supercritical and transcritical flames,” Proceedings of the Combustion Institute, 42 (2026) 106799

3. Q. Lu, L. Zhang, S. Ding, Z. Weng, W. Ding*, X. Wang*, “Local multi-fidelity surrogates for data-efficient parametric studies of combustion problems,” Proceedings of the Combustion Institute, 42 (2026) 106513

4. M. Zhou, Z. Liu, T. Wan, X Wang*, “Direct numerical simulation of supercritical turbulent combustion of CH₄/O₂ shear coaxial injector,” Proceedings of the Combustion Institute, 42 (2026) 106595

5. S. Ding, L. Wang, Z. Weng, Q. Lu, C.-Y. Wen, X Wang*, “Global linear stability analysis framework for supercritical fluid flows,” Journal of Fluid Mechanics, 1042 (2026), A3

6. I Sarani, S Shan, H Fang, X Wang*, “Data-driven modeling of thermal discharge in a gyroid-lattice latent heat storage unit,” Journal of Energy Storage, 182 (2026) 124793

7. G. Yang, Q. Xu, Ni Liu, W. Wu, M. Zhou, and X Wang*, "Accurate prediction of thermodynamic properties of R1234yf refrigerant using Gaussian process regression models," International Journal of Refrigeration 186 (2026) 106907.

8. Z. Liu, M. Zhou, C.L. Sung, Y. Zhang, X Wang*, “Efficient and accurate data-driven modeling of thermophysical properties from small data samples,” Journal of Thermophysics and Heat Transfer, 40 (2026) 876-890

9. T. Wan, P.X. Jiang, P. Zhao, X Wang*, “Enthalpy transformation for wall-bounded supercritical flows with heat transfer,” Journal of Fluid Mechanics, 1026 (2026), A36

10. Z. Yu, L. Wang, X. Liu, X. Wang*, “Surrogate model-based optimization for mitigating thermoacoustic instability in a partially premixed swirl combustor,” Applied Thermal Engineering, 279 (2025) 127969

11. Y Tong, Q Lu, S Ding, X Wang*, “A parametric reduced-order model based on tensor decomposition for unstructured mesh data,” Journal of Computational Physics, 541 (2025) 114300

12. R Zuo, L Wang, X Wang*, ”Differential diffusion effect on near-field characteristics of hydrogen-enriched oxy-methane flames,” International Journal of Hydrogen Energy 144 (2025), p. 445-457

13. L Zhang, X Chu, S Ding, M Zhou, C Ni, X Wang*, “Surrogate Modeling of Hydrogen-Enriched Combustion Using Autoencoder-Based Dimensionality Reduction,” Processes 13 (4) (2025): 1093

14. T Wan, M Zhou, P Zhao, X Wang*, “Challenges in the modeling and simulation of turbulent supercritical fluid flows and heat transfer,” Propulsion and Energy, 1 (2025):6

15. M Zhou, R Zuo, CL Sung, Y Tong, X Wang*, “Region-optimal Gaussian process surrogate model via Dirichlet process for cold-flow and combustion emulations,” Computer Methods in Applied Mechanics and Engineering 439 (2025): 117894

16. T. Wan, P. Zhao, X. Wang*, “Turbulence anisotropy in fully developed channel flow at supercritical pressure,” International Journal of Heat and Mass Transfer, 241 (2025): 126734

17. T. Wan, X. Wang*, Y. Jin, P. Zhao*, “Effects of large density variations on near-wall turbulence and heat transfer in channel flow at supercritical pressure,” Journal of Fluid Mechanics, Vol. 1007 (2025): A68

18. S. Ding, C. Ni, X. Chu, Q. Lu, X. Wang*, “Reduced-order modeling via convolutional autoencoder for combustion of hydrogen/methane fuel blends,” Combustion and Flame, 274 (2025): 113981

19. S. Ding, W. Wang, X. Wang*, “Spray characteristics of axial-vaned slinger atomizer in air crossflow,” Applied Thermal Engineering, 261 (2025): 125107  

20. J. Geng, H. Qi, J. Li, X. Wang*, “Local surrogate modeling for spatial emulation of gas-turbine combustion via similarity-based sample processing,” Journal of Engineering for Gas Turbines and Power, 146(10) (2024): 101019

21. C. Ni, S. Ding, J. Li, X. Chu, Z. Ren, X. Wang*, “Projection-based reduced order modeling of multi-species mixing and combustion,” Physics of Fluids 36, 077168 (2024)

22. S. Ding, L. Wang, Q. Lu, X. Wang*, “Data-driven surrogate modeling and optimization of supercritical jet into supersonic crossflow,” Chinese Journal of Aeronautics, 2024, 37(12): 139-155

23. S. Ding, J. Li, X. Wang*, “Dynamics of elevated dodecane jets in crossflow at supercritical pressure,” Physics of Fluids, 36 (2024), 075135

24. C.L. Sung, W. Wang, L. Ding, X Wang, “Mesh-clustered Gaussian process emulator for partial differential equation boundary value problems,” Technometrics, Vol. 66:3(2024), p. 406-421

25. L. Wang , H. Xiao , B. Yang , X. Wang*, “Steam dilution effect on laminar flame characteristics of hydrogen-enriched oxy-combustion,” International Journal of Hydrogen Energy, Vol. 71 (2024), p. 375–386

26. M. Zhou, C. Ni, and X. Wang*, “Modeling of thermophysical properties and vapor-liquid equilibrium using Gaussian process regression,” International Journal of Heat and Mass Transfer, 219 (2024) 124888

27. S. Ding, J. Li, L. Wang, and X. Wang*, “Flow Dynamics of a Dodecane Jet in Oxygen Crossflow at Supercritical Pressures,” AIAA Journal, Vol. 62 No. 5 (2024), p. 1840-1853

28. J. Geng, X. Wang, J. Liu, F. Teng, and H. Qi, “Surrogate model of combustor flow mixing process,” Journal of Tsinghua University, Vol. 63, No. 4 (2023), p. 633-641

29. M. Zhou, S. Ding, and X. Wang*, “Review of subgrid models of equation of state in the large eddy simulation of transcritical and supercritical flows andcombustion,” Journal of Tsinghua University, Vol. 63, No. 4 (2023), p. 473-486

30. M. Zhou, W. Chen, X. Su, C.-L. Sung, X. Wang*, and Z. Ren, “Data-Driven Modeling of General Fluid Density Under Subcritical and Supercritical Conditions,”, AIAA Journal, 2023, Vol. 61, No. 4 (2023), p. 1519-1531

31. C. Ni, X. Wang*, H. Liu, K. Zhang, X. Zheng, and Y. Duan, “Physics-informed deep learning for thermophysical properties of carbon dioxide,” Journal of Thermophysics and Heat Transfer, Vol. 37, No. 2 (2023), p. 382-393

32. S. Ding, C. Ni, W. Wang*, “Nearfield flow characteristics of kerosene injection at supercritical pressures,” Journal of Propulsion Technology, 2022

33. X. Wang*, T. Liu, D. Ma, and V. Yang, “Linear stability of real-fluid mixing layers at supercritical pressures,” Physics of Fluids, Vol. 34 (2022), 084106

34. L. Zhang, Y. Li, X. Wang, and V. Yang, “Effect of Recess Length on Flow Dynamics in Gas-Centered Liquid-Swirl Coaxial Injectors under Supercritical Conditions,” Aerospace Science and Technology, Vol. 128 (2022), 107757

35. P. Milan, J.-P. Hickey, X. Wang, and V. Yang, “Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields,” Journal of Computational Physics, Vol. 444 (2021), 110567

36. Y.H. Chang, X. Wang, L. Zhang, Y. Li, S. Mak, C.F.J. Wu, and V. Yang, “An efficient reduced-order model CKSPOD for emulation of spatiotemporally evolving flows,”, AIAA Journal, Vol.59, No. 9 (2021), pp. 3291–3303

37. T. Liu, X. Wang*, and V. Yang*, “Flow dynamics of shear-coaxial cryogenic nitrogen jets under supercritical conditions with and without acoustic excitations,” Physics of Fluids, Vol. 33, No. 7, (2021), pp. 076111

38. U. Unnikrishnan, H. Huo, X. Wang, and V. Yang, “Subgrid scale modeling considerations for large eddy simulation of supercritical turbulent mixing and combustion,”. Physics of Fluids, Vol. 33, No. 7, (2021), pp. 075112.

39. X. Wang, Y.H. Chang, Y. Li, V. Yang, and Y.H. Su, “Surrogate-based modeling for emulation of supercritical injector flow and combustion,” Proceedings of the Combustion Institute, Vol.38, No. 4 (2021) pp. 6393-6401

40. X. Wang, P. Lafon, D. Sundaram, and V. Yang, “Liquid vaporization under thermodynamic phase non-equilibrium condition at the gas-liquid interface,” Science China Technological Sciences, Vol. 63, No. 12 (2020) pp. 2649-2656.

41. S. Yang, X. Wang, W. Sun, and V. Yang, “Comparison of Finite Rate Chemistry and Flamelet/Progress-Variable Models: Sandia Flames and the Effect of Differential Diffusion,” Combustion Science and Technology, Vol. 192, No. 7 (2020), pp. 1137-1159.

42. S. Yang, X. Wang, H. Huo, W. Sun, and V. Yang, “An Efficient Finite-Rate Chemistry Model for a Preconditioned Compressible Flow Solver and its Comparison with the Flamelet/Progress-Variable Model,” Combustion and Flame, Vol. 210 (2019), pp. 172-182

43. Y.-H. Chang, L. Zhang, X. Wang, S.-T. Yeh, S. Mak, C.L. Sung, C.F.J. Wu, and V. Yang, “Kernel-smoothed proper orthogonal decomposition (KSPOD)-based emulation for spatiotemporally evolving flow dynamics prediction,” AIAA Journal, AIAA Journal, Vol. 57 No. 12 (2019), 5269-5280

44. X. Wang, Y. Wang, and V. Yang, “Three-dimensional flow dynamics and mixing in a gas-centered liquid-swirl coaxial injector at supercritical pressure,” Physics of Fluids, Vol. 31, (2019) 065109. (FRONT COVER)

45. Y. Wang, X. Chen, X. Wang, and V. Yang, “Vaporization of liquid droplet with large deformation and high mass transfer rate, II: variable-density, variable-property case,” Journal of Computational Physics, Vol. 394 (2019), pp. 1-17

46. X. Wang, S.-T. Yeh, Y.-H. Chang, and V. Yang, “A high-fidelity design methodology using LES-based simulation and POD-based emulation: a case study of swirl injectors,” Chinese Journal of Aeronautics, Vol. 31 No. 9 (2018), pp. 1855-1869.

47. X. Wang, L. Zhang, Y. Li, S.-T. Yeh, and V. Yang, "Supercritical combustion of gas-centered liquid-swirl coaxial injectors for staged-combustion engines," Combustion and Flame, Vol. 197 (2018), pp. 204-214.

48. L. Zhang, X. Wang, Y. Li, S.-T. Yeh, and V. Yang, "Supercritical flow dynamics in a gas-centered liquid-swirl coaxial injector," Physics of Fluid, Vol. 30 (2018) 075106 (Editor’s Pick)

49. X. Wang, H. Huo, U. Unnikrishnan, and V. Yang, “A systematic approach to high-fidelity modeling and efficient simulation of supercritical fluid mixing and combustion,” Combustion and Flame, Vol. 195 (2018), pp. 203-215.

50. S.-T. Yeh, X. Wang*, C. Sung, S. Mak, Y. Chang, V. R. Joseph, V. Yang, and C.F. Wu, "Common proper orthogonal decomposition-based spatiotemporal emulator for design exploration," AIAA Journal, Vol. 56, No. 6 (2018), pp. 2429-2442.

51. S. Mak, C. Sung, X Wang, S. Yeh, Y. Chang, R. Joseph, V. Yang, C.F. Wu, “An efficient surrogate model for emulation and physics extraction of large eddy simulations,”  Journal of the American Statistical Association, 113 No. 524 (2018), 1443-1456. (SPES Award)

52. Y. Wang, X. Wang, V. Yang, “Evolution and transition mechanisms of internal swirling flows with tangential entry,” Physics of Fluids Vol. 30, No. 1 (2018), pp. 013601 (Editor’s Pick)

53. X. Wang, Y. Li, Y. Wang, and V. Yang, "Near-field flame dynamics of liquid oxygen/kerosene bi-swirl injectors at supercritical conditions," Combustion and Flame, Vol.190 (2018), pp. 1-11.

54. X. Wang, Y. Wang, and V. Yang, "Geometric effects on liquid oxygen/kerosene bi-swirl injector flow dynamics at supercritical conditions," AIAA Journal, Vol. 55, No. 10 (2017), pp. 3467-3475.

55. X. Wang, H. Huo, Y. Wang, and V. Yang, “Comprehensive study of cryogenic fluid dynamics of swirl injectors at supercritical conditions,” AIAA Journal, Vol. 55, No. 9 (2017), pp. 3109-3119.

56. X. Wang and V. Yang, "Supercritical mixing and combustion of liquid-oxygen /kerosene bi-swirl injectors ," Journal of Propulsion and Power, 33(2) (2017), p. 316-322.

57. X. Wang, H. Huo, and V. Yang, "Counterflow diffusion flames of oxygen and n-alkane hydrocarbons (CH4-C16H34) at subcritical and supercritical conditions," Combustion Science and Technology, 187(1-2) (2015), p. 60-82.

58. H. Huo, X. Wang, and V. Yang, "A general study of counterflow diffusion flames at subcritical and supercritical conditions: Oxygen/hydrogen mixtures," Combustion and Flame, 161(12) (2014), p. 3040-3050.