Ms. Liyuan Liu - Renewable energies - Women Researcher Award 

Newcastle University - United Kingdom

Author Profile

• Scopus
• Research Gate

Early Academic Pursuits

Ms. Liyuan Liu embarked on his academic journey at Northwestern Polytechnical University in Xi'an, China, where he pursued a Bachelor of Aeronautical Engineering, specializing in Aircraft Design from September 2010 to June 2014. This foundational period honed his technical skills and sparked an enduring interest in applying sophisticated engineering principles to real-world problems. His performance at this stage was distinguished, culminating in his recognition as an Outstanding Student Pacesetter, and being ranked 5th among undergraduates at the university in 2014.

Professional Endeavors

After completing his bachelor’s degree, Liyuan Liu continued to expand his expertise and experience across various roles that leveraged his engineering background and burgeoning interest in data science. He joined the Ministry-of-Education Key Laboratory of Fluid Mechanics in China as a Cloud HPC Engineer, where he led the development of an automated cloud platform for scientific computing. His role evolved into that of a Data Analyst at the same institution, where he applied advanced statistical methodologies to model complex, chaotic systems. Liu then pursued a Ph.D. in Engineering and Information Technology at The University of Melbourne, where he developed machine learning models for the super-resolution reconstruction of turbulent flows, significantly improving model accuracy and computation speed.

Contributions and Research Focus On Renewable energies

Ms. Liyuan Liu's research has predominantly focused on integrating machine learning with traditional engineering disciplines. His Ph.D. research at The University of Melbourne resulted in novel applications of machine learning to improve the accuracy and efficiency of simulations in turbulent flows, pertinent to renewable energy systems. This work was critical in advancing the field of engineering by incorporating AI into the analysis and design processes. Liu continued to build on this foundation through various roles post-PhD, including positions at Newcastle University and the National Institutes of Science and Technology in Rouen, France. Here, he led initiatives in forecasting chemical reactions for hydrogen and developed scalable machine learning models on cloud platforms like AWS and Google Cloud.

Accolades and Recognition

Ms. Liyuan Liu's innovative research and contributions have earned him various awards and recognition. Notably, he was a recipient of the National Scholarship from the Ministry of Education of the People’s Republic of China in 2013. He also secured top positions in the Network Mathematical Contest in Modelling in China and received an Honorable Mention in the Mathematical Contest in Modeling in the USA. His academic excellence and leadership in machine learning applications were further recognized with the Third Prize in the Australian Fluid Mechanics Society Data Visualization Competition in 2020.

Impact and Influence

Ms. Liu's work has significantly impacted the field of data science and engineering, particularly through his contributions to machine learning applications in physical systems and simulations. His research has enhanced the accuracy and efficiency of predictive models, which are critical for optimizing the performance of renewable energy systems and other complex engineering tasks. His expertise in both traditional engineering and advanced machine learning techniques positions him as a bridge between these often separate disciplines, fostering a more integrated approach in research and development.

Legacy and Future Contributions

Looking ahead, Liyuan Liu is positioned to continue making significant contributions to the fields of machine learning and engineering. His ongoing projects and collaborations indicate a commitment to advancing the integration of AI technologies in scientific and engineering applications. His future research will likely continue to focus on enhancing model accuracies and computational efficiencies, potentially revolutionizing how complex engineering problems are solved through AI. His dedication to continuous learning and adaptability suggests that he will remain at the forefront of technological advancements, contributing to both academic and practical advancements in his field.

Notable Publication

•   Liu, L., Lav, C., & Sandberg, R. (2024). A-priori evaluation of data-driven models for large-eddy simulations in Rayleigh-Bé nard convection. International Journal of Heat and Fluid Flow.   
•   Liu, L., Lav, C., & Sandberg, R. (2024). Machine-learnt closure models with embedded geometry independence for large-eddy simulations in natural convection. International Heat and Mass Transfer.
• Liu, L., Lav, C., & Sandberg, R. (2022). A-posteriori evaluation of data-driven subgrid-scale models for natural convection in a horizontal annulus. Proceedings of the 12th Australasian Heat and Mass Transfer Conference. 
• Liu, L., Lav, C., & Sandberg, R. (2020). A-priori evaluation of data-driven models for large-eddy simulations in natural convection. Proceedings of the 22nd Australasian Fluid Mechanics Conference.