刘炜，美国Purdue University博士（2017），天津大学环境科学与工程学院英才教授，国家自然科学基金优秀青年科学基金海外项目获得者（2022）。建筑环境领域国际顶级期刊Energy and Buildings客座编辑。2017.10-2019.1任职浙江大学，助理教授；2019.2-2023.12任职瑞典皇家理工学院（KTH）, 助理教授，终身副教授。美国采暖与制冷空调工程师协会（ASHRAE）会员，国际室内空气品质学会(ISIAQ)会员。

  2024年1月起任职于天津大学环境学院。主要研究方向是建造环境的快速预测、优化逆向设计以及实时控制。2019年获得全球大学生数模竞赛最高奖项特等奖和冠名INFORMS奖, 2019年获得天津市优秀博士论文。完成国家自然科学基金青年项目1项，主持C3.ai数字化转型研究所项目1项（该研究所是由微软、美国国家超算中心、麻省理工学院、普林斯顿大学等高校和公司共同设立，从事数字化转型的研究，该项目也是和这些高校和公司的研究者竞争），瑞典科研与教育国际合作基金1项，瑞典Digital Futures Research Pair项目1项，参与瑞典能源部项目1项。正在承担国家十四五重点研发计划“国家质量基础设施体系”课题一“居住建筑及社区内病原微生物气溶胶传播机理及指标体系研究”。发表国际SCI期刊论文68篇(其中SCI第一、通讯作者论文32篇), 总他引2172次，SCI论文最高单篇他引169次，H因子29。

• 计算流体力学与机器学习
• 快速计算流体力学
• 建造环境的逆向设计、优化及控制

• 第16届“人工环境工程学科奖学金”一等奖, 2008
• Grant-in-Aid Award for Graduate Students (US$10,000), ASHRAE, 2011
• 美国暖通空调工程师协会RP-1493 CFD竞赛第一名 (US$20,000), ASHRAE, 2012
• 博士研究生国家奖学金 (RMB 30,000), 天津大学, 2012
• 普渡大学Bilsland论文奖学金 (US$ 19,000 plus tuition), Purdue University, 2016
• 校级优秀博士学位论文, 天津大学, 2017
• ROOMVENT国际会议最佳论文奖, 2018
• 天津市优秀博士论文, 2019
• 全球大学生数模竞赛最高奖项特等奖和冠名INFORMS奖, 2019
• 国家自然科学基金优秀青年科学基金项目（海外）2022

• 1. Liu, W., Liu, J., and Ma, Q. Theoretical and experimental study of cleanliness in a clean room with a directional ventilation. Building Science, China (in Chinese), 2010, 26: 76-83.
• 2. Liu, W., Mazumdar, S., Zhang, Z., Poussou, S., Liu, J., Lin, C.-H., and Chen, Q. State-of-the-art methods for studying air distributions in commercial airliner cabins. Building and Environment, 2012, 47: 5-12.
• 3. Liu, W., Wen, J., Chao, J., Yin, W., Shen, C., Lai, D., Lin, C.-H., Liu, J., Sun, H. and Chen, Q. Accurate and high-resolution boundary conditions and flow fields in the first-class cabin of an MD-82 commercial airliner. Atmospheric Environment, 2012, 56: 33-44.
• 4. Liu, W., Long, Z., and Chen,Q. A procedure for predicting pressure loss coefficients of duct fittings using CFD(RP-1493). HVAC&R Research, 2012, 18(6): 1168-1181.
• 5. Chen,C., Liu,W., Li, F., Lin, C.-H., Liu, J., Pei, J., and Chen, Q. A hybrid model for investigating transient particle transport in enclosed environments. Building and Environment, 2013, 62: 45-54.
• 6. Liu, W. and Chen, Q. Current studies on air distributions in commercial airliner cabins. Theoretical & Applied Mechanics Letters, 2013, 3(6): 1-5.
• 7. Liu, W., Wen, J., Lin, C.-H., Liu, J., Long, Z., and Chen, Q. Evaluation of various categories of turbulence models for predicting air distribution in an airliner cabin. Building and Environment, 2013, 65: 118-131.
• 8. Shen, C., Liu, J., Liu, W., and Wen, J. Establishment and certification of an airliner experimental platform with the stable thermal boundary condition in a full-size aircraft cabin. Journal of HV&AC, China (in Chinese), 2014, 44(7): 75-80.
• 9. Jin, M., Liu, W., and Chen, Q. Accelerating fast fluid dynamics with a coarse-grid projection scheme, HVAC&R Research, 2014, 20(8), 932-943.
• 10. Jin, M., Liu, W. and Chen, Q. Simulating buoyancy-driven airflow in buildings by coarse-grid fast fluid dynamics. Building and Environment, 2015, 85, 144-152.
• 11. Duan, R., Liu,W., Xu, L., Huang, Y., Li, B., Shen, X., Lin, C., Liu, J., and Chen, Q. Mesh type and number for the CFD simulations of air distribution in an aircraft cabin. Numerical Heat Transfer, Part B: Fundamentals, 2015, 67(6), 489-506.
• 12. Chen, C., Liu, W., Lin,C.-H.,and Chen,Q. Accelerating the Lagrangian method for modeling transient particle transport in indoor environments. Aerosol Science & Technology, 2015, 49(5), 351-361.
• 13. Chen, C., Liu, W., Lin, C.-H., and Chen, Q. A Markov chain model for predicting transient particle transport in enclosed environments. Building and Environment, 2015, 90, 30-36.
• 14. Dai, S., Sun, H., Liu, W., Guo, Y., Jiang, N., and Liu, J. Experimental Study on Characteristics of the Jet Flow from an Aircraft Gasper. Building and Environment, 2015, 93, 278-284.
• 15. Chen, C., Liu, W., Lin, C.-H., and Chen, Q. Comparing the Markov chain model with the eulerian and lagrangian models for indoor transient particle transport simulations. Aerosol Science & Technology, 2015, 49, 857-871.
• 16. Liu, W. and Chen, Q. Optimal air distribution design in enclosed spaces using an adjoint method. Inverse Problems in Science & Engineering, 2015, 23(5), 760-779.
• 17. Liu, W., Zhang, T., Xue, Y., Zhai, Z.J., Wang, J., Wei, Y., and Chen, Q. State-of-the-art methods for inverse design of an enclosed environment. Building and Environment, 2015, 91, 91-100.
• 18. Liu, W., Duan, R., Chen, C., Lin, C.-H., and Chen, Q. Inverse design of the thermal environment in an airliner cabin by use of the CFD-based adjoint method. Energy and Buildings, 2015, 104, 147-155.
• 19. Cao, Q., Liu, Y., Liu, W., Lin, C.-H., Wei, D., Baughcum, S., Norris, S., Shen, X., Long, Z., and Chen, Q. Experimental study of particle deposition in the environmental control systems of commercial airliners. Building and Environment, 2016, 96, 62-71.
• 20. Zhang, J., Long, Z., Liu, W., and Chen, Q. Strategy for studying ventilation performance in factories. Aerosol and Air Quality Research, 2016, 16: 442-452.
• 21. You, R., Chen, J., Shi, Z., Liu, W., Lin, C.-H., Wei, D., and Chen, Q. Experimental and numerical study of airflow distribution in an aircraft cabin mockup with a gasper on. Building Performance Simulation, 2016, 9(5): 555-566.
• 22. You, R., Liu, W., Chen, J., Lin, C.-H., Wei, D., and Chen, Q. Predicting airflow distribution and contaminant transport in aircraft cabins with a simplified gasper model. Building Performance Simulation, 2016, 9(6): 699-708.
• 23. Xue, Y., Liu, W. and Zhai, Z.J. New semi-Lagrangian-based PISO method for fast and accurate indoor environment modeling. Building and Environment, 2016, 105: 236-244.
• 24. Liu, W., Jin, M., Chen, C. and Chen, Q. Optimization of air supply location, size, and parameters in enclosed environments through using a CFD-based adjoint method. Journal of Building Performance Simulation, 2016, 9(2): 149-161.
• 25. Liu, W., Jin, M., Chen, C., You, R., and Chen, Q. ­Implementation of a fast fluid dynamics model in OpenFOAM for simulating indoor airflow. Numerical Heat Transfer, Part A: Application, 2016, 69(7): 748-762.
• 26. Liu, Y., Cao, Q., Liu, W., Lin, C.-H., Wei, D., Baughcum, S., Norris, S., Long, Z., Shen, X., and Chen, Q. Numerical modeling of particle deposition in the environmental control systems of commercial airliners on ground. Building Simulation, 2017, 10(2): 265-275.
• 27. Cao, Q., Xu, Q., Liu, W., Lin, C-H., Wei, D., Baughcumb, S., Norrisb, S., and Chen, Q. In-flight monitoring of particle deposition in the environmental control systems of commercial airliners in China. Atmospheric Environment, 2017, 154: 118-128.
• 28. Zhao, X., Liu, W., Liu, S., Zou, Y., and Chen, Q. Inverse design of an indoor environment by using a CFD-based adjoint method with adaptive step size for adjusting design parameters. Numerical Heat Transfer, Part A: Applications, 2017, 71(7): 707-720.
• 29. Liu, W., You, R., Zhang, Jie., and Chen, Q. Development of a fast fluid dynamics-based adjoint method for the inverse design of indoor environments. Journal of Building Performance Simulation, 2017, 10(3): 326-343.
• 30. Liu, W. and Chen, Q. Development of coarse grid generation methods for fast fluid dynamics in simulating indoor airflow. Journal of Building Performance Simulation, 2018, 11(4): 470-484.
• 31. Chen, C., Zhao, B., Lai, D., and Liu, W. A simple method for differentiating direct and indirect exposure to exhaled contaminants in mechanically ventilated rooms. Building Simulation, 2018, 11(5): 1039-1051.
• 32. Liu, W., Zhao, X., and Chen, Q. A novel method for measuring air infiltration rate in buildings. Energy and Buildings, 2018, 168: 309-318.
• 33. Lai, D., Chen, C., Liu, W., Shi, Y., and Chen, C. An ordered probability model for predicting outdoor thermal comfort. Energy and Buildings, 2018, 168: 261-271.
• 34. Zhao, X., Liu, W.*, Lai, D., and Chen, Q. Optimal design of an indoor environment by the CFD-based adjoint method with area-constrained topology and cluster analysis. Building and Environment, 2018, 138: 171-180.
• 35. Wei, Y., Liu, W., Xue, Y., Zhai, Z., Chen, Q., and Zhang, T. Inverse design of aircraft cabin ventilation by integrating three methods. Building and Environment, 2019, 150: 33-43.
• 36. Liu, W., You, R., and Chen, C. Modeling transient particle transport by fast fluid dynamics with the Markov chain method. Building Simulation, 2019, 12: 881-889.
• 37. Xue, Y., Liu, W.*, Wang, Q., and Bu, F. Development of an integrated approach for the inverse design of built environment by a fast fluid dynamics-based generic algorithm. Building and Environment, 160, 2019, 106205, doi: 10.1016/j.buildenv.2019.106205.
• 38. Lei, L., Chen, W., Xue, Y., and Liu, W.* A comprehensive evaluation method for indoor air quality of buildings based on rough sets and a wavelet neural network‍. Building and Environment, 162, 2019, 160296, doi: 10.1016/j.buildenv.2019.106296.
• 39. Liu, K., Nie, T., Liu, W., Liu, Y., and Lai, D. A machine learning approach to predict outdoor thermal comfort using local skin temperatures. Sustainable Cities and Society, 2020, doi: 10.1016/j.scs.2020.102216.
• 40. Lai, D., Lian, Z., Liu, W., Guo, C., Liu, W., Liu, K., and Chen, Q. A comprehensive review of thermal comfort studies in urban open spaces. Science of the Total Environment, 2020, doi: 10.1016/j.scitotenv.2020.140092.
• 41. Chen, S., Zhu, Y., Chen, Y., and Liu, W.* Usage strategy of phase change materials in plastic greenhouses, in hot summer and cold winter climate.‍ Applied Energy, 227, 2020, 115416.
• 42. Sun, J., Liu, W., Long, Z., Zhang, H., and Pan, W. Transmission and exposure of kitchen particles: a case study in an apartment. Indoor and Built Environment, 2020, doi: 10.1177/1420326X20942579.
• 43. Liu, W., van Hooff, T., An, Y., Hu, S., and Chen, C. Modeling transient particle transport in transient indoor airflow by fast fluid dynamics with the Markov chain method. Building and Environment, 186, 2020, 107323.
• 44. Xue, J., You, R., Liu, W., Chen, C., and Lai, D. Applications of local climate zone (LCZ) classification scheme to improve urban sustainability. Sustainability, 2020, 12(19), 8083.
• 45. Lei, L., Chen, W., Wu, B., Chen, C., and Liu, W.* A building energy consumption prediction model based on rough set theory and deep learning algorithms. Energy and Buildings, Volume 240, 2021, 110886.
• 46. Xue, J., Liu, W., and Liu, K. Influence of thermal environment on attendance and adaptive behaviors in outdoor spaces: a study in a cold-climate university campus. International Journal of Environmental Research and Public Health, 2021, 18(11), 6139; https://doi.org/10.3390/ijerph18116139.
• 47. Li, S., Yang, J., and Liu, W.* Estimation of aerator air demand by an embedded multi-gene genetic programming. Journal of Hydroinformatics, 2021, 23 (5): 1000-1013. https://doi.org/10.2166/hydro.2021.037.
• 48. Calzolari, G. and Liu, W.* Deep learning to replace, improve, or aid CFD analysis in built environment applications: a review. Building and Environment, 2021, 206, 108315. https://doi.org/10.1016/j.buildenv.2021.108315.
• 49. Liu, W.*, Sun, H., Lai, D., Xue, Y., Kabanshi, A., and Hu, S. Performance of fast fluid dynamics with a semi-Lagrangian scheme and an implicit upwind scheme in simulating indoor/outdoor airflow. Building and Environment, 2022, 207, 108477. https://doi.org/10.1016/j.buildenv.2021.108477.
• 50. Hu, M., Liu, W., Xue, K., Liu, L., Liu, H., and Liu, M. Comparing calculation methods of state transfer matrix in Markov chain models for indoor contaminant transport. Building and Environment, 2022, 297,108515.
• 51. Zheng, S., Zhai, Z., Wang, Y., Xue, Y., Duanmu, L., and Liu, W. Evaluation and comparison of various fast fluid dynamics modeling methods for predicting airflow around buildings. Building Simulation, 2022, 15, 1083-1095.
• 52. Dai, T., Liu, S., Liu, J., Jiang, N., Liu, W., and Chen, Q. Evaluation of fast fluid dynamics with different turbulence models for predicting outdoor airflow and pollutant dispersion, Sustainable Cities and Society, 2022, 77, 103583.
• 53. Ma, S., Liu, W., Dong, J., Liu, J., and Wang, Z. Indoor thermal environment in a rural dwelling heated by air-source heat pump air-conditioner. Sustainable Energy Technologies and Assessments, 2022, 51, 101948.
• 54. Lei, L. and Liu, W.* Predictive control of multi-zone variable air volume air-conditioning system based on radial basis function neural network. Energy and Buildings, 2022, 261, 111944.
• 55. Liu, Y., Long, Z., and Liu, W. A semi-empirical mesh strategy for CFD simulation of indoor airflow. Indoor and Built Environment, 2022, https://doi.org/10.1177/1420326X221089825.
• 56. Lei, L., Zheng, H., Xue, Y., and Liu, W.* Inverse modeling of thermal boundary conditions in commercial aircrafts based on Green's function and regularization method. Building and Environment, 2022, 217, 109062.
• 57. Lei, L., Wu, B., Fang, X., Chen, L., Wu, H., and Liu, W.* A dynamic anomaly detection method of building energy consumption based on data mining technology. Energy, 2022, doi.org/10.1016/j.energy.2022.125575.
• 58. Liu, W., Zhang, T., and Lai, D. Inverse design of a thermally comfortable indoor environment with a coupled CFD and multi-segment human thermoregulation model. Building and Environment, 2022, 227: 109769.
• 59. Sun, R., Liu, J., Lai, D.*, and Liu, W.* Building form and outdoor thermal comfort: inverse design the microclimate of outdoor space for a kindergarten. Energy and Buildings, 2023, 112824.
• 60. Ma, S., Liu, W., Meng, C., Dong, J. and Zhang, S., 2023. Temperature-dependent particle mass emission rate during heating of edible oils and their regression models. Environmental Pollution, 2023, 323,121221.
• 61. Chen, Z., O’Neill, Z., Wen, J., Pradhan, O., Yang, T., Lu, X., Lin, G., Miyata, S., Lee, S., Shen, C., Chiosa, R., Piscitelli, M.S., Capozzoli, A., Hengel, F., Kührer, A., Pritoni, M., Liu, W., Clauß, J., Chen, Y., and Herr, T., A review of data-driven fault detection and diagnostics for building HVAC systems. Applied Energy, 2023, 339, 121030.
• 62. Wang, Y., Guo, Y., Hao, W., Liu, W., and Long, Z., Simulation study of the purification system for indoor oil mist control in machining factories. Building Simulation, 2023, 16, 1361-1374.
• 63. Lei, L. and Liu, W.*, Inverse identification model for release rates of multiple gaseous pollution sources in an aircraft cabin. Indoor and Built Environment, 2023, 1420326X231170787.
• 64. Li, P., Liu, W., and Zhang, T. CFD modeling of dynamic airflow and particle transmission in an aircraft lavatory. Building Simulation, 2023, 16, 1375-1390.
• 65. Kabanshi, A., Choonya, G., Ameen, A., Liu, W. and Mulenga, E., Windows of opportunities: orientation, sizing and PV-shading of the glazed area to reduce cooling energy demand in sub-Sahara Africa. Energies, 2023, 16(9), p.3834.
• 66. Ma, S., Liu, W., Dong, J., Meng, C., and Liu, J., Comparison of the time-dependent characteristics between particle mass and particle number emissions during oil heating and emission mitigation strategies. Building and Environment, 2023, 242, 110511.
• 67. Liu, W., Lian, S., Fang, X., Shang, Z., Wu, H., Zhu, H., and Hu, S., An open-source and experimentally guided CFD strategy for predicting air distribution in data centers with air-cooling. Building and Environment, 2023, 110542.
• 68. Liu, F., Chen, H., Yuan, H., Zhang, T., and Liu Wei.*, Shape optimization of the exhaust hood in machining workshops by a discrete adjoint method. Building and Environment, 2023, 110764.
• 69. Calzolari, G. and Liu, W.*, Deep learning to develop zero-equation based turbulence model for CFD simulations of the built environment. Building Simulation, 2023, 10.1007/s12273-023-1083-4.
• 70. Wang, Y., Li, J., Liu, W., Zhang, S., Dong, J., and Liu, J., Prediction of urban airflow fields around isolated high-rise buildings using data-driven non-linear correction models. Building and Environment, 2023, 110894.
• 71. Liu, F., Liu, W.*, Long, Z., and Zhang, T. Evolution of particle size distribution and water content for oily particles in machining workshops. Journal of Building Engineering, 2024, 10.1016/j.jobe.2024.108542.