Biography: Jiaao Hao is an Associate Professor in the Department of Aeronautical and Aviation Engineering at The Hong Kong Polytechnic University. His research focuses on aerodynamics, aerothermodynamics, and flow stability. He has led projects funded by the National Natural Science Foundation of China and the Hong Kong Research Grants Council, and has published over 70 papers in leading journals such as Journal of Fluid Mechanics and AIAA Journal. Prof. Hao received his BEng in Aircraft Design and Engineering in 2013 and PhD in Fluid Mechanics in 2018 from Beihang University. After completing his PhD, he joined PolyU as a postdoctoral researcher. He was appointed Assistant Professor in 2020 and promoted to Associate Professor in 2025. In 2025, he also served as a visiting professor at École Nationale Supérieure d'Arts et Métiers in Paris, France.
Speech Title: Secondary global instabilities in shock-induced separated flow
Abstract：Two-dimensional shock-induced flow separation can sustain three-dimensional (3-D) global instabilities. Under marginally unstable conditions, only a single stationary global mode is typically observed, implying that the flow undergoes a primary bifurcation to a steady 3-D state. However, direct numerical simulations (DNS) of a Mach 2.15 impinging shock-wave/boundary-layer interaction reveal that, following the primary bifurcation, secondary instabilities emerge and drive the flow into a limit-cycle state. To explain the origin of this unsteadiness, a 3-D global stability analysis is performed, which identifies an oscillatory global mode associated with strong flow distortion due to the primary instability. The effect of spanwise domain width is also discussed.
Biography: Jingbo Wei, male, born in 1986, is an associate professor and doctoral advisor. He currently serves as Chair of the Department of Aeronautical Engineering at the School of Aeronautics and Astronautics, Sun Yat-sen University, and is a recipient of the university’s “Hundred Talents Program.” His primary research interests include new-concept unmanned aerial vehicle (UAV) technology and high-performance electromagnetic actuation and control technology. He has published more than ten papers in high-impact journals such as "IEEE Robotics and Automation Letters" and "IEEE Transactions on Power Electronics", and has led more than ten national and military research projects.
Speech Title: Research on Overdriven Active and Passive Tiltrotor UAV Technology
Abstract: Tiltrotor technology transforms traditional multirotor UAVs from underdriven to overdriven by expanding thrust vectoring from a fixed direction to a controllable one, thereby enabling decoupled control of position and attitude. In terms of active tilting, the team developed a two-degree-of-freedom tiltable trirotor UAV, whose thrust vector can be directed toward any orientation in three-dimensional space, enabling omnidirectional maneuverable flight; the tilting quadcopter, meanwhile, achieved a major breakthrough in wind-resistant control under wind conditions of force 8, with each rotor tilting independently to instantly compensate for external disturbances. In terms of passive tilting, the team proposed a tiltrotor design based on passive articulation, which enables thrust vector deflection without the use of tilt servos, resulting in a simpler structure and higher reliability; by further integrating a lift-generating fuselage design, the system utilizes lift generated by the fuselage itself to effectively enhance range and efficiency. The research has established distinctive strengths in modal transition control, overdrive control allocation, and wind-resistant robustness, demonstrating clear application potential in scenarios such as emergency rescue, ship-based takeoff and landing in rough seas, and high-wind inspections.
Biography: Lifang Zeng, born in January 1991 in Shaoyang, Hunan, holds a Doctor of Philosophy in Fluid Mechanics. She is currently a Master’s Supervisor at the School of Aeronautics and Astronautics, Zhejiang University. In the past five years, Dr. Zeng has published more than 20 SCI/EI indexed journal papers as the first or corresponding author, and obtained 10 invention patents as the primary inventor. She presides over one research project supported by the National Natural Science Foundation of China (NSFC), together with three provincial and ministerial-level research projects. Dr. Zeng acts as a judge for the China University Aircraft Design Competition (CUADC), and serves on the Youth Editorial Board of Journal of Rocket Propulsion and Aerospace Technology. Her main research interests cover intelligent design of unmanned aerial vehicles, bio-inspired aerodynamics, rotorcraft aerodynamics, and the development of unmanned aerial systems.
Speech Title: Generative Concept and Aerodynamic Design of UAVs Driven by Artificial Intelligence
Abstract: Conventional aircraft design relies on expert experience, extensive numerical simulations and physical tests. Such trial-and-error design processes is time-consuming and always fails to obtain the optimal configuration. Combined with considerable number of UAV design databases and aircraft design knowledge, a theoretical framework and methodology for generative concept and aerodynamic design of UAVs are proposed. Large language models (LLMs) and generative artificial intelligence are applied in this framework. Prompt engineering and retrieval-augmented generation (RAG) techniques are integrated to realize end-to-end mapping from natural-language design requirements to UAV conceptual configurations. Furthermore, a generative innovation framework based on diffusion models is constructed to rapidly generate three-dimensional aerodynamic shapes. This report finally discusses the development trends and prospects of UAV intelligent design.
Biography: Dr. Ming Zhao currently serves as a researcher/doctoral supervisor at Tianjin University, focusing on research on "high-precision and high-efficiency multi field simulation - active and passive load control". Published over 60 SCI papers and 37 first author/corresponding papers in JCP, AIAAJ, PRF, CAF, etc. Hosted 10 national level projects, including 2 National Natural Science Foundation/Youth Project, 1 National Numerical Wind Tunnel Project, 2 National Key Research and Development Program sub projects, and 2 National Defense Basic Research Projects. Hosted 8 provincial and ministerial level projects.
Speech Title： Efficient Discontinuous Finite Element Method and Its Application in Flow Field Simulation
Abstract: This work presents two complementary enhancement strategies to address the key bottlenecks of implicit discontinuous Galerkin (DG) methods—namely, inefficient linear system iteration and degraded time-marching due to increased degrees of freedom. First, a Morton curve reordering based on bitwise operations is introduced to improve cache performance by ensuring contiguous memory storage of neighboring cells. Second, an information compressed Jacobian difference (ICJD) scheme is developed, which achieves linear time complexity and ease of implementation through graph coloring estimation adapted to cell adjacency graphs. Both algorithms are integrated into an implicit DG framework and validated via viscous and inviscid flow simulations under subsonic and transonic conditions. The Morton reordering not only enhances cache performance by up to 39.4% but also accelerates GMRES convergence in certain cases. Quantitatively, ICJD executes in approximately 0.1% of the runtime of the original finite-difference method, while Morton reordering reduces iteration counts by 88.9% in a viscous case compared to the reverse Cuthill–McKee (RCM) ordering. Performance gains are more pronounced for viscous flows, indicating strong potential for efficient turbulence simulations.
In parallel, to further accelerate DG solvers, we investigate the discontinuous Galerkin spectral element method (DGSEM) with a p-multigrid approach. Recognizing that fixed level-skipping coarsening strategies are often problem-dependent, we propose an adaptive p-coarsening multigrid method based on quasi-a priori truncation error estimation (τ-estimation), which dynamically determines the hierarchy of polynomial orders. Exploiting the tensor-product property of DGSEM, both global and locally anisotropic coarsening strategies are developed. Two-dimensional numerical experiments for inviscid and viscous flows demonstrate significant efficiency improvements, with the locally anisotropic strategy achieving over 45% CPU time acceleration in viscous cases. Notably, the non-isolated truncation error formulation consistently outperforms its isolated counterpart, and the inclusion of a correction term proves necessary when using non-converged solutions in the error estimation. The proposed method is further extended to a 3D viscous flow around a sphere, yielding 44% acceleration in multigrid cycles, and applied to a large-eddy simulation (LES) of flow past an SD7003 airfoil, confirming its feasibility and promise for complex turbulent flow simulations.
Together, these two enhancement frameworks provide robust and efficient tools for implicit DG and DGSEM solvers, with particular efficacy for viscous and turbulent flow problems.
Biography: Dr. Ping Zhou is an Assistant Researcher at Harbin Institute of Technology, Master's Supervisor (Mechanics). She has achieved a series of accomplishments in multibody dynamics numerical methods and explicit simulation, spacecraft multibody system dynamics modeling, multi-solver and multiphysics simulation. Published over 30 SCI journal papers, with more than 20 as first and corresponding author in authoritative journals such as IJMS, AST, JSV, ND, and MSD, including 2 highly cited papers. She holds 3 software copyrights and has filed 3 invention patents. Led and participated in 7 national, provincial, and enterprise-level projects. She have served as chair or invited speaker at numerous domestic and international conferences, and currently is an expert committee member for the Anhui Key Laboratory of Automotive Industry Software and the guest editor for the Multibody Dynamics Special Issue of Mathematics (SCI JCR Q1 journal).
Speech Title: Dynamic Modeling and Model Order-Reduction of On-Orbit Assembled Multi-Plate Structures
Abstract: On-orbit assembly is a pivotal enabling technology for advancing spacecraft modularity and autonomy. However, the accurate modeling and efficient simulation of on-orbit assembled structures face significant challenges, due to their evolving configurations, large degrees of freedom, and high interconnection complexity. This work proposes an efficient model order-reduction (MOR) method to enable fast and accurate simulation for space on-orbit assembled multi-plate structures. The dynamic model is developed using a reference nodal coordinate formulation, which captures the high-fidelity dynamics, albeit resulting in a high-dimensional system. A nonlinear modal reduction approach is further applied to generate a compact reduced-order model, incorporating global vibration modes and modal derivatives to preserve good accuracy. The key issue in MOR-optimal mode selection-is resolved through a novel kinetic-energy-based modal interaction method, which identifies dominant modes without prior full-order simulations, making it particularly suited for varying topologies. The proposed method is validated with octagonal and rectangular assemblies. Numerical results demonstrate that the method exhibits high accuracy, strong adaptability, and excellent efficiency. Notably, the computational cost is decreased for nearly 99%. The proposed method offers a promising technique for the dynamic analysis and control in space missions.
Biography: Yaping Ju is currently a professor and doctoral advisor at Xi’an Jiaotong University, as well as the director of the Shaanxi International Joint Research Center for Fluid Machinery. He has long been engaged in research on the aerodynamic design of compressors and the quantification of uncertainty. He has led nine national-level research projects, including grants from the National Natural Science Foundation of China and basic research topics under the national “Two Engines” Special Project. As the first or corresponding author, he has published more than 60 academic papers, and his research findings have received sustained attention and positive citations from leading international research institutions in the fields of internal combustion engines and fluid machinery, such as Pratt & Whitney (U.S.), Rolls-Royce (U.K.), the German Aerospace Center (DLR), and ABB (Switzerland).
Speech Title: A Study on the Intelligent Optimization of Centrifugal Compressor Series Based on a Zero-Dimensional Prediction Model
Abstract: The serialization and standardization of centrifugal compressor stages are critical during the preliminary design phase, in which zero-dimensional design is responsible for planning the dimensionless design parameters of each master stage. However, existing research and engineering practices primarily rely on designers’ experience or traditional geometric progression rules to plan the distribution of master stages, resulting in either insufficient coverage of the aerodynamic performance range or excessive overlap. This, in turn, leads to repeated iterative adjustments between subsequent three-dimensional aerodynamic design and zero-dimensional design, significantly increasing the design cycle and computational costs. An optimal planning model based on an improved zero-dimensional aerodynamic prediction model is proposed. For the zero-dimensional aerodynamic prediction model, the prediction accuracy and generalization capability were significantly improved by modifying the peak efficiency correlation and introducing a radial basis function neural network to correlate the dimensionless design parameters. The optimization model aims to maximize the overall performance coverage and minimize the overlap between the performance spectra of adjacent stages; by combining a multi-objective optimization algorithm with the zero-dimensional prediction model, it achieves rapid optimization of the dimensionless design parameters for the master stage. This method has been validated in the zero-dimensional design of a family of centrifugal compressors with flow coefficients ranging from 0.03 to 0.11.
Biography: Dr Yiqian Mao is an Assistant Professor in the Department of Aerospace Propulsion, School of Astronautics, Beihang University, which he joined in 2025. He received
his BEng and MSc degrees from Central South University and his PhD in Aerospace Engineering from the University of Manchester in the UK in 2024. During his doctoral studies, he received First Place in the 2023 Osborne Reynolds Doctoral Prize for advances in active flow control using deep reinforcement learning. He was selected for a national postdoctoral research programme in China in 2025. His research interests encompass active flow control, reinforcement learning, generative methods and numerical modelling, with applications to bluff-body flows, combustion dynamics and aerospace propulsion.
Speech Title: Extending Deep Reinforcement Learning from Active Flow Control towards Flashback Control: Challenges and Prospects
Abstract: Deep reinforcement learning (DRL) has shown promise in active flow control, yet its application to combustion control is constrained by high computational cost, spatiotemporal partial observability, and the limited expressiveness of unimodal policies. This talk addresses these challenges through three canonical DRL-based circular-cylinder wake-control studies and examines their implications for combustion control. First, an action-informed, episode-based neural ordinary differential equation surrogate learns controlled low-dimensional dynamics for model-based DRL, attaining about 70% of the model-free DRL return with around 10% of the interaction samples and one third of the wall-clock time. Second, a delay-encoded autoregressive proximal policy optimisation (PPO) algorithm restores temporal context under post-action delay; at a Reynolds number of 400, it reduces fluctuations in the drag and lift forces by approximately 90% while maintaining comparable mean drag reduction. Third, a progressive Gaussian-diffusion policy couples a probability-flow diffusion actor with Laplacian maximum-a-posteriori (L-MAP) state reconstruction to represent complex action distributions under spatially incomplete observations. At a Reynolds number of 100, the diffusion actor achieves 8.5% drag reduction, 78% suppression of lift fluctuations and 66% lower mean control cost than PPO. With only 25% of the probes retained and the same L-MAP reconstruction used in both cases, the probability-flow diffusion actor improves the average reward by about 32% over Gaussian-based PPO. Furthermore, DRL is coupled with three-dimensional reacting-flow simulations to investigate flashback prediction and preventive control in a bluff-body swirl combustor operating with a premixed methane-air flame, thereby exploring the potential of DRL to address combustion-safety challenges in propulsion systems.
Biography: Yong Wang is an associate professor at the School of Aeronautics and Astronautics, Sun Yat-sen University. He earned his Ph.D. from the School of Aeronautics and Astronautics at Shanghai Jiao Tong University. He was a visiting scholar at the University of Central Florida and a postdoctoral fellow at the University of Ottawa. He previously worked at the Research Department of Huawei HiSilicon. His primary research interests include multi-source perception and navigation for unmanned aerial vehicles (UAVs) and counter-UAV technologies. His work has been published in over 60 papers in renowned journals and conferences, including IEEE Transactions on Intelligent Transportation Systems, IEEE Robotics and Automation Letters, Acta Automatica Sinica, Robotics, ICME, and ICASSP. He served as Area Chair for the 2023 PRCV conference. He is a member of the Pattern Recognition and Machine Intelligence Technical Committee of the Chinese Association of Automation, a member of the Popularization Committee of the Chinese Association of Automation, a member of the Embodied Intelligence Technical Committee of the Control and Command Society, and a member of the Low-Altitude Aviation Industry Working Committee of the Control and Command Society.
Biography: Zhenbo Lu is a professor and assistant dean at the School of Aeronautics and Astronautics, Sun Yat-sen University, and deputy director of the Ministry of Education Engineering Research Center for Low-Altitude Intelligent Flight Systems. In 2025, he was elected a Fellow of the Royal Aeronautical Society (RAeS). He earned his Ph.D. in Mechanical Engineering from the Hong Kong Polytechnic University and spent several years conducting research at the Temasek Laboratory at the National University of Singapore, where he held positions as a researcher and senior researcher; he currently serves as a part-time senior researcher there. He has long been engaged in research in the fields of low-altitude aircraft technology, aerodynamic design, aerodynamic noise, bionic aircraft, and smart materials and structures, and has led more than ten projects of various types. He has published more than 80 high-impact academic papers, filed more than 30 invention patent applications, and received the Second Prize of the Guangdong Provincial Science and Technology Progress Award. He currently serves as an editorial board member or young editorial board member for the journals "Journal of Aeronautics", "Journal of Mechanics", and "Aerospace Technology"; as a member of the Aerodynamic Acoustics Technical Committee of the Chinese Society of Aerodynamics; as a member of the Shenzhen Low-Altitude Economy Standardization Technical Committee; and as a technical advisor to the Beijing Civil Aircraft Technology Research Center of COMAC.
Speech Title: Research on Aerodynamic Design and Low-Noise Control Technologies for Low-Altitude Aircraft
Abstract: This study conducts a systematic investigation into the aerodynamic design and low-noise control technologies for low-altitude aircraft, with a particular focus on electric vertical takeoff and landing (eVTOL) aircraft. In terms of aerodynamic configuration, the research team drew inspiration from biomimicry—specifically from flying creatures such as dragonflies, swifts, and owls—to develop high-efficiency, high-lift airfoils, tandem-wing configurations, and distributed tilting ducted fan systems, thereby significantly enhancing the aircraft’s aerodynamic efficiency and flight stability. To address the issue of unsteady aerodynamic disturbances during tilt transition, the team established parametric modeling and high-precision numerical simulation methods, systematically revealing the strong coupling mechanism among propulsion, aerodynamics, and noise. In terms of low-noise control, drawing on the silent flight mechanisms of owls, we innovatively designed leading-edge serrations, trailing-edge fringes, and biomimetic propeller structures; experiments demonstrate that these can achieve noise reduction of up to 18 dB under typical operating conditions. The research further integrated artificial intelligence with multi-objective optimization algorithms to construct a forward-design closed-loop system comprising “parametric modeling—numerical evaluation—intelligent optimization,” effectively balancing the trade-off between lift-to-drag ratio and acoustic performance.
Biography: Dr. Zhijie Zhao is a lecturer and master’s supervisor at national university of defense technology, and a provincial‑level young talent. His research focuses on intelligent flow control for flight vehicles. He has led 10 projects, including general program and joint fund sub‑projects of national natural science foundation of China. His honors include national aerospace doctoral competition grand prize, the first prize in technical innovation from Chinese society of aerodynamics (5/15), major scientific and technological progress award in Chinese aviation (3/15), AVIC golden idea grand prize, and three consecutive first prizes of China future aircraft design competition (ranked first each time). He has published over 20 SCI papers as first/corresponding author, including a best cover and highly cited paper in the top journal, and has authored one monograph. He holds 10 authorized invention patents. He currently serves as a youth committee member of the flow control and thermal management branch of the Chinese society of aeronautics and astronautics.
Speech Title: Dual-synthetic-jet-based Intelligent Flow Control Technology for Aircraft
Abstract: Active flow control using dual synthetic jets (DSJ) is characterized by high dimensionality, nonlinearity, and time delays in actuation and sensing, which make it difficult to establish an accurate dynamic model. Even if such a model is established, deriving a high‑performance nonlinear feedback control policy via Lyapunov stability theory and optimal control allocation remains challenging. Artificial intelligence offers a new opportunity to address these issues and is expected to overcome difficulties in modeling and controlling DSJ-based active flow control. Following a research roadmap from open‑loop parameter optimization, through closed‑loop PID control, to explicit/implicit policy optimization, authors have progressively developed a series of integrated control methods for DSJ-based active flow control, including particle swarm optimization‑based parameter control, convolutional neural network‑based flow field recognition control, PID self‑tuning control, linear genetic programming for explicit policy optimization, and deep reinforcement learning for implicit policy optimization. These methods have facilitated the emergence of new high‑efficiency flow control strategies and mechanisms. In addition, authors have built the first cloud platform for parallel intelligent flow control with hundreds of environments, which achieves the highest computational training efficiency reported in the literature. A typical application is the use of deep reinforcement learning for synergistic lift enhancement on a control surface with DSJ. The learning algorithm discovered a novel collaborative pulsed control strategy for distributed DSJ, revealing a new flow evolution mechanism in which flow separation control and the virtual Gurney flap effect produce a “1+1>2” lift augmentation. Compared with open‑loop control, the operational envelope is expanded by 16.8%, energy consumption is reduced by 52%, and the efficiency‑to‑cost ratio is increased by 146%.
© MEAE 2018-2026. All Rights Reserved | Contact Us meae_conf@163.com
