DrRunze Li
Research Associate in Wing Design
Department of Aeronautics - Faculty of Engineering
- Research Associate in Wing DesignDepartment of Aeronautics - Faculty of Engineering
- 420, City and Guilds Building, South Kensington Campus, United Kingdom
RESEARCH
Publications
1. Li R, Zhang Y, Chen H*. Knowledge Discovery with Computational Fluid Dynamics: Supercritical Airfoil Database and Drag Divergence Prediction. Physics of Fluids, 2023, 35(1): 016113.2. Li R, Zhang Y, Chen H*. Study of transfer learning from two-dimensional supercritical airfoils to three dimensional transonic swept wings. Chinese Journal of Aeronautics, 2023.3. Li R, Zhang Y, Chen H*. Physically Interpretable Feature Learning of Supercritical Airfoils Based on Variational Autoencoders. AIAA Journal, 2022, 60(12):1-15.4. Li R, Zhang Y, Chen H*. Pressure distribution feature-oriented sampling for statistical analysis of supercritical airfoil aerodynamics. Chinese Journal of Aeronautics, 2022, 35(4): 134-147. 5. Li R, Zhang Y, Chen H. Learning the aerodynamic design of supercritical airfoils through deep reinforcement learning. AIAA Journal, 2021, 59(10): 3988-4001. 6. Yang Y, Li R, Zhang Y, et al. Flow field prediction of airfoil off-design conditions based on a modified variational autoencoder. AIAA Journal, 60(10). 2022. 7. Wang J, Li R, Chen H, et al. An inverse design method for supercritical airfoil based on conditional generative models. Chinese Journal of Aeronautics, 2022, 35(3): 62-74. 8. Wang J, Chen H, Li R, et al. Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework. Physics of Fluids, 2021, 33(8):086108. 9. Zhang Y, Yang P, Li R, et al. Unsteady Simulation of Transonic Buffet of a Supercritical Airfoil with Shock Control Bump. Aerospace, 2021, 8(8): 203.10. Zhang S, Li R, Zhang Y, et al. Aerodynamic Optimization and Noise Reduction of a Two-Stage Series Compact Fan. Journal of Aerospace Engineering, 2021, 34(5):04021057.11. Li R, Zhang Y, Chen H. Strategies and methods for multi-objective aerodynamic optimization design for supercritical wings. Acta Aeronautica et Astronautica Sinica, 2020, 41(5): 23409.12. Li R, Zhang Y, Chen H*. Design of experiment method in objective space for machine learning of flow structures. 8th European Conference for Aeronautics and Space Sciences, Madrid, Spain, 2019: 403. 13. Chen H, Deng K, Li R. Utilization of machine learning technology in aerodynamic optimization. Acta Aeronautica et Astronautica Sinica, 2019, 40(1): 22480. 14. Li R, Zhang Y, Chen H*, et al. Multi-point aerodynamic optimization design on a dual-aisle airplane wing. 31st Congress of International Council of the Aeronautical Sciences, Belo Horizonte, Brazil, 2018. 15. Li R, Zhang Y, Chen H. Pressure distribution guided supercritical wing optimization[J]. Chinese Journal of Aeronautics, 2018, 31(9): 1842-1854.
1. Li R, Zhang Y, Chen H*. Knowledge Discovery with Computational Fluid Dynamics: Supercritical Airfoil Database and Drag Divergence Prediction. Physics of Fluids, 2023, 35(1): 016113.2. Li R, Zhang Y, Chen H*. Study of transfer learning from two-dimensional supercritical airfoils to three dimensional transonic swept wings. Chinese Journal of Aeronautics, 2023.3. Li R, Zhang Y, Chen H*. Physically Interpretable Feature Learning of Supercritical Airfoils Based on Variational Autoencoders. AIAA Journal, 2022, 60(12):1-15.4. Li R, Zhang Y, Chen H*. Pressure distribution feature-oriented sampling for statistical analysis of supercritical airfoil aerodynamics. Chinese Journal of Aeronautics, 2022, 35(4): 134-147. 5. Li R, Zhang Y, Chen H. Learning the aerodynamic design of supercritical airfoils through deep reinforcement learning. AIAA Journal, 2021, 59(10): 3988-4001. 6. Yang Y, Li R, Zhang Y, et al. Flow field prediction of airfoil off-design conditions based on a modified variational autoencoder. AIAA Journal, 60(10). 2022. 7. Wang J, Li R, Chen H, et al. An inverse design method for supercritical airfoil based on conditional generative models. Chinese Journal of Aeronautics, 2022, 35(3): 62-74. 8. Wang J, Chen H, Li R, et al. Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework. Physics of Fluids, 2021, 33(8):086108. 9. Zhang Y, Yang P, Li R, et al. Unsteady Simulation of Transonic Buffet of a Supercritical Airfoil with Shock Control Bump. Aerospace, 2021, 8(8): 203.10. Zhang S, Li R, Zhang Y, et al. Aerodynamic Optimization and Noise Reduction of a Two-Stage Series Compact Fan. Journal of Aerospace Engineering, 2021, 34(5):04021057.11. Li R, Zhang Y, Chen H. Strategies and methods for multi-objective aerodynamic optimization design for supercritical wings. Acta Aeronautica et Astronautica Sinica, 2020, 41(5): 23409.12. Li R, Zhang Y, Chen H*. Design of experiment method in objective space for machine learning of flow structures. 8th European Conference for Aeronautics and Space Sciences, Madrid, Spain, 2019: 403. 13. Chen H, Deng K, Li R. Utilization of machine learning technology in aerodynamic optimization. Acta Aeronautica et Astronautica Sinica, 2019, 40(1): 22480. 14. Li R, Zhang Y, Chen H*, et al. Multi-point aerodynamic optimization design on a dual-aisle airplane wing. 31st Congress of International Council of the Aeronautical Sciences, Belo Horizonte, Brazil, 2018. 15. Li R, Zhang Y, Chen H. Pressure distribution guided supercritical wing optimization[J]. Chinese Journal of Aeronautics, 2018, 31(9): 1842-1854.