Prof. Gang Sun | 孙刚教授
Fudan University, China | 复旦大学
Sun Gang, professor, doctoral supervisor, the current head of the Department of Aeronautics and Astronautics of Fudan University, the head of Fudan University in the joint construction of the "State Key Laboratory of Aieliner Integration Technology and Flight Simulation", the Innovation Center for Engine Numerical Simulation with AECC Commercial Aero Engine Co., Ltd, the Joint Laboratory of Civil Aircraft/Engine Digital Flight Test with Shanghai Engineering Technology Research Center of Civil Aircraft Flight Testing, the editorial board member of some journals such as Journal of Aerodynamics, Applied Mechanics and Mathematics, Civil Aircraft Design and Research, Aeronautical Science and Technology, the senior writer and reviewer of top journals in the field of aviation and power, such as Aerospace Science and Technology, Journal of Aerospace Engineering, Chinese Journal of Aeronautics, and the member of many organizations such as the Teaching Steering Committee of the Ministry of Education, the Chinese Society of Aeronautics and Astronautics, the Academic Committee of the National Key Laboratory.
Research interests include complex flow mechanism, aerodynamic optimization and design of aircraft/engines, engine flow and overall performance simulation design, artificial intelligence and digital twin methods, aerodynamic noise calculation and noise reduction design, etc. Once participated in the development of ARJ21-700, C919, C929, CJ-1000, CJ-2000 and other major national projects. Research results have been tracked and evaluated by many well-known scholars. So far has published more than 300 papers and conference reports, and won 5 provincial and ministerial science and technology awards.
Title of Speech: Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine
Abstract: Fault diagnosis is essential for ensuring the safety and reliability of aero-engines. Current performance-based fault diagnosis methods typically establish a mapping between measurable parameters and engine states, without taking into account the inherent physical constraints of multimodal information. In this study, a novel graph structure embedded with physical constraints is proposed to effectively fuse sensor information and physical-based model (PBM) simulation information. Since the fault information is primarily manifested in the sensor data rather than the PBM data, the probability distribution between these two types of data can serve as a constraint for constructing the edges in the graph, as it reflects the physical association among the sample points. Selected sensor parameters are used as node characteristics in the graph. A self-supervised representation learning training structure based on graph convolutional network and canonical correlation analysis can effectively utilize labeled and unlabeled data. Furthermore, a robust statistics method is embedded into the training architecture to interpret the behavior of the model by identifying the impact of data samples. The verification results indicate that the proposed model can effectively perform multimodal information fusion and achieve more efficient and high-accuracy component-level fault diagnosis.
Assoc. Prof. Xuhui Li | 李旭晖副教授
Harbin Engineering University, China | 哈尔滨工程大学
Xuhui Li is an associate professor at Harbin Engineering University. He obtained his Ph.D. degree from Kyushu University(Japan) in 2016, and conducted postdoctoral research at Ecole Centrale Nantes(France) and Southern University of Science and Technology(China) from 2016 to 2020. Currently, his research primarily focuses on computational fluid dynamics in naval architecture and ocean engineering, particularly involving the theory, algorithms, and GPU parallel computing of the lattice Boltzmann method and high-order finite volume method, as well as their applications in complex free-surface flows, cavitation, noise and related fields. He has presided over more than 10 projects, including the National Natural Science Foundation of China General Program, and has published over 20 papers.
Title of Speech: A Multiple-relaxation-times Regularized Lattice Boltzmann Collision Model: From Weakly Compressible to Compressible Flow
Abstract: In the present work, a regularized lattice Boltzmann collision model is proposed. In the proposed collision model, only the moment or moment combination which are of translational invariance and rotational invariance can be distributed an independent relaxation rate. This constraint or principal realized the rigorous independent relaxation process of different transport phenomena. Recently, this regularized lattice Boltzmann collision model with multiple-relaxation-times (RLB-MRT) has been extended to multiphase/multicomponent lattice Boltzmann models, such as Shan-Chen model and Phase field model, which can be applied in the cavitation flow and free surface flow.
Dr. Zhaolin Chen | 陈肇麟博士
Nanjing University of Aeronautics and Astronautics, China | 南京航空航天大学
Dr. Zhaolin Chen obtained both his MEng (Aerospace Engineering) in 2009 and his Ph.D (Aerodynamics) in 2014 from the University of Sheffield, UK. He started his carrier working as a Post-Doctoral Research Associate at the University of Sheffield after his Pd.D under Professor Ning Qin. From 2015 to 2018, he served as a Senior Engineer in the Turbomachinery Design Department at FlaktGroup Ltd. in the United Kingdom. In 2019, He joined in the department of aircraft design of NanJing University of Aeronautics and Astronautics (NUAA). Research wise, his field of study focuses on the development of computational methods for solving governing fluid flow equations as well as optimization techniques. Specific research areas encompass steady and unsteady flow simulation solutions, mesh deformation techniques, optimization methodologies, and aerodynamic-structural coupling. Specific applications encompass aerodynamic and aeroacoustic simulation as well as optimization of wings and rotor blades. Recent efforts have concentrated on the design of aerial vehicles for Mars exploration. This includes the development of multi-fidelity codes for rotor design, aero-structural optimization of rotors, and performing experimental tests simulating the Martian environment.
Title of Speech: Experimental and Numerical Study on the Aerodynamic Performance of a Mars Counter-Rotor System
Abstract: This study explores the aerodynamic behavior of Mars rotor systems (single/coaxial configurations) through experimental and computational methods. Validation tests on standard APC propellers showed less than 5% deviation between simulations and experiments. Detailed analysis of an ultra-thin blade rotor system revealed pitch-angle-dependent flow mechanisms.: At \phi=\ 15.8°, a stable leading-edge separation bubble forms on the upper surface, which is penetrated and split by shock waves in the tip region (r/R≈0.7-0.9) with increasing rotation speed, creating shock-separation bubble interaction. For \phi=\ 19.8°, mid-span shock-vortex interactions (r/R=0.5-0.75) with different phase vortex shedding phenomena, demonstrating the intricate dynamics of compressible low-Reynolds flows. Systematic evaluation of pitch angles (15.8°-19.8°) elucidates critical performance trade-offs for Martian hovering rotors. Additionally, reduced rotor spacing ratio (H/D=0.09) amplifies nonlinear aerodynamic interactions, lowering thrust-power ratios (upper rotor: 52.7% of a single rotor) due to altered effective angles of attack. Larger spacing (H/D=0.18) mitigates interference, improving upper and lower rotor thrust-power ratio to 92.3% and 72%, respectively. Doubling spacing nearly doubles thrust (7.42 N achieved at 2103 RPM vs. 3124 RPM for H/D=0.09), reducing blade interference and shortening leading-edge separation bubbles. This enhances system efficiency, elevating the Figure of Merit to 0.602 and narrowing the efficiency gap with single rotors from 32.6% to 7.1%. Optimal spacing balances thrust performance and rotational energy consumption.