本人博士毕业于新加坡国立大学机械学院，并联培于剑桥大学化工学院，研究方向主要侧重于理论分析和数值计算，具体包括但不限于以下几个方面：

1，多相流，颗粒群平衡建模

2，计算流体力学，高性能计算

3，数字孪生

4，人工智能算法：卷积/图神经网络，贝叶斯推理，梯度优化算法

本人的研究手段以理论分析和数值计算为主，熟练掌握Fortran、C/C++、MatLab、Python等编程语言，开发了多款开源流体力学代码：PBM-MPM, KIVA-CHEMKIN-MPM等。此外，本人协同剑桥大学CoMo Group开发了全新的发动机模拟软件Kinetics & SRM Suite及数据处理软件MoDS。该软件已被多国科研机构和公司使用，包括剑桥大学、伯明翰大学、南洋理工大学、卡特彼勒公司、福特汽车公司等。在过去5年间本人共发表论文30余篇，覆盖了计算机、流体力学、应用能源等多个著名国际期刊。此外本人还担任了两家国际期刊的编委：International Journal on Energy and Power Engineering, American Journal of Mechanics and Applications。

发表论文(部分)：

[1] S. Wu, E. K. Y. Yapp, J. Akroyd, S. Mosbach, R. Xu, W. Yang, M. Kraft, A moment projection method for population balance dynamics with a shrinkage term. J. Comput. Phys. 330 (2017) 960-980.

[2] S. Wu, E. K. Y. Yapp, J. Akroyd, S. Mosbach, R. Xu, W. Yang, M. Kraft, Extension of moment projection method to the fragmentation process. J. Comput. Phys. 335 (2017) 516-534.

[3] S. Wu, C. Lindberg, J. Akroyd, W. Yang, M. Kraft, Bivariate extension of the moment projection method for the particle population balance dynamics. Comput. Chem. Eng. 124 (2019) 206-227.

[4] X. Han, M. Jia, Y. Chang, Y. Li, S. Wu. Directed message passing neural network (D-MPNN) with graph edge attention (GEA) for property prediction of biofuel-relevant species. Energ. AI, 10 (2022) 100201.

[5] S. Wu, S. Yang, K. L. Tay, W. Yang, M. Jia. A hybrid sectional moment projection method for discrete population balance dynamics involving inception, growth, coagulation and fragmentation. Chem. Eng. Sci. 249 (2022) 117333.

[6] S. Yang, Y. Wei, J. Hu, H. Wang, S. Wu*. Three-dimensional MP-PIC simulation of the steam gasification of biomass in the spouted bed gasifier. Energy Convers. Manag. 210 (2020) 112689.

[7] S. Wu, W. Yang, H. Xu, Y. Jiang, Investigation of soot aggregate formation and oxidation in compression ignition engines with a pseudo bi-variate soot model. Appl. Energy. 253 (2019) 113609.

[8] S. Wu, D. Zhou, W. Yang, Implementation of an efficient method of moments for treatment of soot formation and oxidation processes in three-dimensional engine simulations. Appl. Energy. 254 (2019) 113661.

[9] S. Wu, W. Yang, Comparisons of methods for reconstructing particle size distribution from its moments. Fuel. 252 (2019) 325-338.

[10] S. Wu, C. Song, F. Bin, G. Lv, J. Song, C. Gong, La1− xCexMn1− yCoyO3 perovskite oxides: Preparation, physico-chemical properties and catalytic activity for the reduction of diesel soot. Mater. Chem. Phys. 148 (2014) 181-189.

[11] S. Wu, J. Akroyd, S. Mosbach, W. Yang, M. Kraft, A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines. Appl. Energy 258 (2020) 114083.

[12] S Wu, J Akroyd, S Mosbach, G Brownbridge, O Parry, V Page, W Yang, M. Kraft*. Efficient simulation and auto-calibration of soot particle processes in Diesel engines. Appl. Energy. 262 (2020) 114484.

[13] S. Wu, K. L. Tay, W. Yu, Q. Lin, H. Li, F. Zhao*, W. Yang*. Development of a highly compact and robust chemical reaction mechanism for the oxidation of tetrahydrofurans under engine relevant conditions. Fuel 276 (2020) 118034.

[14] K. L. Tay, W. Yang*, F. Zhao, Q. Lin, S. Wu, Development of a highly compact and robust chemical reaction mechanism for unsaturated furans oxidation in internal combustion engines via multi-objective genetic algorithm and generalized polynomial chaos. Energy Fuels 34 (2020) 936-948.

[15] F. Bin, C. Song*, G. Lv, X. Li, X. Cao, J. Song, S. Wu, Characterization of the NO-soot combustion process over La0.8Ce0.2Mn0.7Bi0.3O3 catalyst. Proc. Combust. Inst. 35 (2015) 2241-2248.

[16] F. Bin, C. Song*, G. Lv, J. Song, S. Wu, X. Li. Selective catalytic reduction of nitric oxide with ammonia over zirconium-doped copper/ZSM-5 catalysts. Appl. Catal. B Environ. 150 (2014) 532-543.

[17] J. Li, Y. Liang, W. Yang, S. Wu*. A comparative study on the combustion process and emissions formation of a CI engine fueled with isomers n-octanol and di-n-butylether. Fuel 332 (2023) 126161.

[18] Z. Li, H. Xu, W. Yang, S. Wu. Numerical study on the effective utilization of high sulfur petroleum coke for syngas production via chemical looping gasification. Energy 235 (2021) 121395

[19] Z. Zhang, C. Zhang, H. Liu, F. Bin, X. Wei, R. Kang, S. Wu*, W. Yang, H. Xu, Self-sustained catalytic combustion of CO enhanced by micro fluidized bed: stability operation, fluidization state and CFD simulation. Front Environ. Sci. Eng. 17 (2023) 109.

[20] J. Li, Y. Liang, S. Wang, S. Wu, W. Yang, R. Liu, Blending n-octanol with biodiesel for more efficient and cleaner combustion in diesel engines: A modeling study. J. Clean, 403 (2023) 136877.

[21] X. Dong, H. Duan, M. Jia, S. Wu*, Y. Chang, Development of a practical soot model for diesel surrogate fuels and oxygenated fuels with specific soot precursor tracking and uniform model structure. Fuel 340 (2023) 127531.

[22] Z. Zhang, X. Han, M. Wang, Z. Wu, X. Sun, S. Wu*, A hybrid sectional moment projection method for modeling soot particle dynamics in laminar premixed flames. Fuel 331 (2023) 125731.

[23] Z. Zhang, M. Wang, Z. Wu, X. Chen, H. Li, S. Wu*, A highly robust numerical approach for handling the bi-variate soot population balance models in internal combustion engines. Int. J. Green Energy (2022) 1-15.

[24] S. Wu, KL Tay, J Li, W Yang, S Yang, Development of a compact and robust kinetic mechanism for furan group biofuels combustion in internal combustion engines. Fuel 298 (2021) 120824.

[25] H. Li, J. Lei, M. Jia, H. Xu, S. Wu*, High dimensional model representation approach for prediction and optimization of the supercritical water gasification system coupled with photothermal energy storage. Processes 11 (2023) 2313.

[26] J. Huang, H. Liu, C. Zhang, F. Bin, X. Wei, R. Kang, S. Wu*, Study on the structural evolution and heat transfer performance of Cu supported on regular morphology CeO2 in CO catalytic combustion and chemical looping combustion. J. Clean, 417 (2023) 138038.