🎓 Early Academic Pursuits
Shaohua Wu began his academic journey at Tianjin University, where he earned a B.S. in Thermal Energy and Dynamic Engineering (2007-2011) and later completed an M.A. in Power Machinery and Engineering (2011-2013). His early academic foundation laid the groundwork for his deep engagement with energy systems and thermodynamics. His education was marked by honors, such as receiving the Best Master Thesis Award in 2014.
Wu further pursued a Ph.D. at the National University of Singapore (NUS) (2015-2018), under the supervision of Prof. Wenming Yang, with a joint doctoral training at the University of Cambridge (2016-2018) under Prof. Markus Kraft and Dr. Jethro Akroyd. This dual-institution experience provided him with a solid interdisciplinary foundation in mechanical and chemical engineering.
💼 Professional Endeavors
Shaohua Wu has held various academic positions, starting as a Research Associate at Cambridge CARES (2018-2019) before becoming a Research Fellow at NUS (2019-2020). In 2021, he took on the role of Associate Professor at Dalian University of Technology, where he currently leads the Multiphase Flow Group as a principal investigator. His leadership in this role has allowed him to mentor over 10 researchers, contributing to high-fidelity numerical algorithms and AI-driven multiscale simulations for propulsion and power systems.
🔬 Contributions and Research Focus
Wu’s research spans first-principles-based modeling and simulation of reactive flows, including combustion, multiphase flows, and their multiscale interactions. He specializes in Computational Fluid Dynamics (CFD) and population balance modeling (PBM), and has integrated machine learning algorithms into these fields for predictive and computational efficiency. Some of his key research topics include:
Multiphase systems in propulsion and power generation (reciprocating engines, gas turbines).
AI-driven multivariate PBM for predicting particle behavior.
Chemical kinetics simulation using AI for mechanism construction and reduction.
🌍 Impact and Influence
Shaohua Wu’s work has had a significant impact on the fields of combustion, particle dynamics, and energy system optimization. His development of next-generation simulation software, such as the Kinetics & SRM Engine Suite, has been used for optimizing internal combustion engines and reactors. He has also contributed to soot particle modeling and reduction technologies that help lower emissions in various energy sectors. With numerous patents, his innovations extend to real-world applications, such as vehicle exhaust purification devices and ABS braking systems for motorcycles.
📖 Academic Citations
Shaohua Wu’s work has been widely recognized and cited in leading journals, including Journal of Aerosol Science, Energy and AI, Applied Energy, and Chemical Engineering Science. His contributions to reactive flow simulations, particle dynamics, and CFD have earned him a notable presence in the academic community, particularly through high-impact publications and as a reviewer for prestigious journals.
💻 Technical Skills
Wu is highly proficient in a range of programming languages (Fortran, C/C++, Python, MATLAB) and commercial/open-source software (ANSYS Fluent, OpenFOAM, CHEMKIN, KIVA). His expertise extends to mathematics and statistical algorithms for CFD, machine learning, and optimization, with experience in:
Deep learning algorithms (CNN, RNN, GAN, GNN, PINN)
CFD-related algorithms (Finite Volume Method, Multigrid method, SIMPLE algorithm)
Optimization algorithms (Genetic Algorithm, Particle Swarm Optimization)
🎓 Teaching Experience
With over six years of teaching, Shaohua Wu has lectured extensively at the Dalian University of Technology, focusing on courses such as Engineering Thermodynamics, Computational Fluid Dynamics (CFD), and Big Data and Machine Learning in Energy. His dedication to teaching has earned him excellent student ratings. He also taught and assisted courses at NUS, such as Heat and Mass Transfer and Numerical Algorithms for Scientific Computing.
🏆 Legacy and Future Contributions
Shaohua Wu has made substantial strides in particle dynamics, population balance modeling, and the AI-driven simulation of reactive flows, contributing to both academic and industrial advancements. His future work aims to integrate AI with CFD to develop smarter, more efficient energy and propulsion systems, focusing on energy sustainability. Wu’s efforts in deep learning-based chemical kinetics could revolutionize fuel modeling and lead to cleaner combustion technologies.
With his current projects, including research on soot particle dynamics and deep learning-driven solvers, Shaohua Wu is poised to make lasting contributions to energy research, helping drive the development of more sustainable energy technologies.
📝Notable Publications
Selective catalytic reduction of nitric oxide with ammonia over zirconium-doped copper/ZSM-5 catalysts
Authors: F Bin, C Song, G Lv, J Song, S Wu, X Li
Journal: Applied Catalysis B: Environmental
Volume: 150
Pages: 532-543
Year: 2014
Extension of moment projection method to the fragmentation process
Authors: S Wu, EKY Yapp, J Akroyd, S Mosbach, R Xu, W Yang, M Kraft
Journal: Journal of Computational Physics
Volume: 335
Pages: 516-534
Year: 2017
Three-dimensional MP-PIC simulation of the steam gasification of biomass in a spouted bed gasifier
Authors: S Yang, F Fan, Y Wei, J Hu, H Wang, S Wu
Journal: Energy Conversion and Management
Volume: 210
Pages: 112689
Year: 2020
A moment projection method for population balance dynamics with a shrinkage term
Authors: S Wu, EKY Yapp, J Akroyd, S Mosbach, R Xu, W Yang, M Kraft
Journal: Journal of Computational Physics
Volume: 330
Pages: 960-980
Year: 2017
Numerical study on the effective utilization of high sulfur petroleum coke for syngas production via chemical looping gasification
Authors: Z Li, H Xu, W Yang, S Wu
Journal: Energy
Volume: 235
Pages: 121395
Year: 2021