Staff publications (AIRS)

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Open Access
A comparative analysis of hybrid sensor fusion schemes for visual–inertial navigation
(Institute of Electrical and Electronics Engineers (IEEE), 2025-12-31) Tabassum, Tarafder Elmi; Petrunin, Ivan; Rana, Zeeshan A.
Visual Inertial Odometry (VIO) has been extensively studied for navigation in GNSS-denied environments, but its performance can be heavily impacted by the complexity of the navigation environments such as weather conditions, illumination variation, flight dynamics, and environmental structure. Hybrid fusion approaches integrating Neural Networks (NN), especially Gated Recurrent units (GRU) with the Kalman filters (KF), such as Error-State Kalman Filter (ESKF) have shown promising results mitigating system nonlinearities due to challenging environmental conditions data issues, there is a lack of systematic studies quantitively analysing and comparing performance differences unhand. To address this gap and enable robust navigation in complex conditions, this study proposes and systematically analyses the performance of three hybrid fusion schemes for VIO-based navigation of Unmanned Aerial Vehicles (UAV). These three hybrid VIO schemes include Visual Odometry (VO) error compensation using NN, KF error compensation using NN, and prediction of Kalman gain using NN. The comparative analysis is performed using data generated in MATLAB incorporating the Unreal Engine involving diverse challenging environmental conditions: fog, rain, illumination level variability and variability in the number of features available for extraction during the UAV flight in the urban environment. The results demonstrate the performance improvement achieved by hybrid VIO fusion schemes compared to ESKF-based traditional fusion methods in the presence of multiple visual failure modes. Comparative analysis reveals notable improvement achieved by method 1 with enhancements of 93% in sunny, 91% in foggy and 90% in rainy conditions than the other two hybrid VIO architectures.
Open Access
A comprehensive review and future challenges of energy-aware path planning for small unmanned aerial vehicles with hydrogen-powered hybrid propulsion
(Cambridge University Press (CUP), 2025) Çinar, Hasan; Ignatyev, Dmitry; Zolotas, Argyrios
Unmanned aerial vehicles (UAVs) with fully electric propulsion systems mainly use lithium-based batteries. However, using fuel cells, hybrid propulsion systems are created to improve the flight time and payload capacity of the UAVs. Energy management and energy-aware path planning are important aspects to be explored in hybrid-propulsion powered UAV configurations. These facilitate optimal power distribution among energy sources and motion planning considering energy consumption, respectively. In the literature, although there are many studies on the energy management of hybrid-powered UAVs and path planning of only battery-powered UAVs, there are research gaps in energy-aware path planning of hybrid-powered UAVs. Additionally, the energy management of hybrid-powered UAVs is usually considered independent of path planning in the literature. This paper thoroughly reviews recent energy-aware path planning for small UAVs to address the listed critical challenges above, providing a new perspective and recommendations for further research. Firstly, this study evaluates the recent status of path planning, hydrogen-based UAVs, and energy management algorithms and identifies some challenges. Later, the applications of hydrogen-powered UAVs are summarised. In addition, hydrogen-based hybrid power system topologies are defined for small UAVs. Then, the path-planning algorithms are classified, and existing studies are discussed. Finally, this paper provides a comprehensive and critical assessment of the status of energy-aware path planning of UAVs, as well as detailed future work recommendations for researchers.
Open Access
A review of diffuse interface-capturing methods for compressible multiphase flows
(MDPI, 2025-04-03) Adebayo, Ebenezer Mayowa; Tsoutsanis, Panagiotis; Jenkins, Karl W.
This paper discusses in detail the classification, historical development, and application of diffuse interface-capturing models (DIMs) for compressible multiphase flows. The work begins with an overview of the development of DIMs, highlighting important contributions and key moments from classical studies to contemporary advances. The theoretical foundations and computational methods of the diffuse interface method are outlined for the full models and the reduced models or sub-models. Some of the difficulties encountered when using DIMs for multiphase flow modelling are also discussed.
Open Access
An advanced performance-based method for soft and abrupt fault diagnosis of industrial gas turbines
(Elsevier, 2025-04-15) Chen, Yu-Zhi; Zhang, Wei-Gang; Tsoutsanis, Elias; Zhao, Junjie; Tam, Ivan C. K.; Gou, Lin-Feng
Integrating gas turbines with intermittent renewable energy must operate for prolonged periods under transient conditions. Existing research on fault diagnosis in such systems has concentrated on the primary rotating components in steady-state conditions. There is a gap in investigating the interplay between shaft bearing failure and performance metrics, as well as fault identification under transient conditions. This study aims to identify faults not only in the main rotating components but also in the shaft bearings under transient conditions. Firstly, the performance model and fault propagation model of gas turbines are established, and the influence of bearing fault on the whole engine performance is analysed. Then, the fault diagnosis method is determined and the dynamic effects are compensated in fault identification at each time interval. Finally, the steady-state and transient fault diagnosis are carried out considering the constant and sudden faults for the main rotating components and bearings. The average run time and maximum error during the engine life cycle are 0.1064 s and 0.0086 %. For the proposed dynamic effects compensation method, the average computation time and peak error at every moment are 0.1152 s and 0.0143 %, clearly superior to the benchmark method. These results provide evidence that the proposed method can correctly diagnose the fault of the main rotating components and shaft bearings under transient conditions. Therefore, the findings mark an advancement in real-time fault diagnostic techniques, ultimately enhancing engine availability while upholding secure and affordable energy production.
Open Access
Analysis of China’s high-speed railway network using complex network theory and graph convolutional networks
(MDPI, 2025-04-16) Xu, Zhenguo; Li, Jun; Moulitsas, Irene; Niu, Fangqu
This study investigated the characteristics and functionalities of China’s High-Speed Railway (HSR) network based on Complex Network Theory (CNT) and Graph Convolutional Networks (GCN). First, complex network analysis was applied to provide insights into the network’s fundamental characteristics, such as small-world properties, efficiency, and robustness. Then, this research developed three novel GCN models to identify key nodes, detect community structures, and predict new links. Findings from the complex network analysis revealed that China’s HSR network exhibits a typical small-world property, with a degree distribution that follows a log-normal pattern rather than a power law. The global efficiency indicator suggested that stations are typically connected through direct routes, while the local efficiency indicator showed that the network performs effectively within local areas. The robustness study indicated that the network can quickly lose connectivity if key nodes fail, though it showed an ability initially to self-regulate and has partially restored its structure after disruption. The GCN model for key node identification revealed that the key nodes in the network were predominantly located in economically significant and densely populated cities, positively contributing to the network’s overall efficiency and robustness. The community structures identified by the integrated GCN model highlight the economic and social connections between official urban clusters and the communities. Results from the link prediction model suggest the necessity of improving the long-distance connectivity across regions. Future work will explore the network’s socio-economic dynamics and refine and generalise the GCN models.
Open Access
Blockchain and distributed digital watermarking effort on federated learning: innovating intellectual property protection
(IEEE, 2024-12-02) Chao, Kailin; Li, JunJie; Jiang, Yirui; Xiao, Jianmao; Cao, Yuanlong
Federated Learning with Digital Watermarks (FLDW) have been recognized as a promising solution for property protection. However, the existing FLDW-related technologies neglect the requirements of decentralized settings, leading to recurrent issues such as discrepancies in distributed client data. This paper introduces a Blockchain Federated Learning Intellectual Property Protection Framework (BFLIPR), to address the data security and model validation challenges in decentralized federated learning environments. BFLIPR merges blockchain, digital watermarking, and federated learning technologies. By harnessing the blockchain’s tamper-proof properties, digital watermarking’s concealment capabilities, and federated learning’s distributed feature, the framework offers a solution that aligns with intellectual property protection mechanism, to bolster data security and property safeguarding. Experimental findings demonstrate its high feasibility and robust for data privacy and model security in the federated learning.
Open Access
Cascade network stability of synchronized traffic load balancing with heterogeneous energy efficiency policies
(IEEE, 2024-12-08) Zou, Mengbang; Guo, Weisi
Cascade stability of load balancing is critical for ensuring high efficiency service delivery and preventing undesirable handovers. In energy efficient networks that employ diverse sleep mode operations, handing over traffic to neighbouring cells' expanded coverage must be done with minimal side effects. Current research is largely concerned with designing distributed and centralized efficient load balancing policies that are locally stable. There is a major research gap in identifying largescale cascade stability for networks with heterogeneous load balancing policies arising from diverse plug-and-play sleep mode policies in ORAN, which will cause heterogeneity in the network stability behaviour. Here, we investigate whether cells arbitrarily connected for load balancing and having an arbitrary number undergoing sleep mode can: (i) synchronize to a desirable load-balancing state, and (ii) maintain stability. For the first time, we establish the criterion for stability and prove its validity for any general load dynamics and random network topology. Whilst its general form allows all load balancing and sleep mode dynamics to be incorporated, we propose an ORAN architecture where the network service management and orchestration (SMO) must monitor new load balancing policies to ensure overall network cascade stability.
Open Access
Damping identification sensitivity in flutter speed estimation
(MDPI , 2025-06-01) Dessena, Gabriele; Pontillo, Alessandro; Civera, Marco; Ignatyev, Dmitry I.; Whidborne, James F.; Zanotti Fragonara, Luca
Predicting flutter remains a key challenge in aeroelastic research, with certain models relying on modal parameters, such as natural frequencies and damping ratios. These models are particularly useful in early design stages or for the development of small Unmanned Aerial Vehicles (maximum take-off mass below 7 kg). This study evaluates two frequency-domain system identification methods, Fast Relaxed Vector Fitting (FRVF) and the Loewner Framework (LF), for predicting the flutter onset speed of a flexible wing model. Both methods are applied to extract modal parameters from Ground Vibration Testing data, which are subsequently used to develop a reduced-order model with two degrees of freedom. The results indicate that FRVF- and LF-informed models provide reliable flutter speed, with predictions deviating by no more than 3% (FRVF) and 5% (LF) from the N4SID-informed benchmark. The findings highlight the sensitivity of flutter speed predictions to damping ratio identification accuracy and demonstrate the potential of these methods as computationally efficient alternatives for preliminary aeroelastic assessments.
Open Access
Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization
(IEEE, 2024-12-08) Li, Jiawei; Wang, Dawei; Zhang, Jiankang; Alfarraj, Osama; He, Yixin; Al-Rubaye, Saba; Yu, Keping; Mumtaz, Shahid
This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.
Open Access
Designing and testing of HDPE–N2O hybrid rocket engine
(MDPI, 2025-03-13) Arora, Triyan Pal; Buttrey, Noah; Kirman, Peter; Khadtare, Sanmukh; Kamath, Eeshaan; del Gatto, Dario; Isoldi, Adriano
Hybrid Rocket Engines (HREs) combine the advantages of solid and liquid propellants, offering thrust control, simplicity, safety, and cost efficiency. Part of the research on this rocket architecture focuses on optimising combustion chamber design to enhance performance, a process traditionally reliant on time-consuming experimental adjustments to chamber lengths. In this study, two configurations of HREs were designed and tested. The tests aimed to study the impact of post-chamber lengths on rocket engine performance by experimental firings on a laid-back test engine. This study focused on designing, manufacturing, and testing a laid-back hybrid engine with two chamber configurations. The engine features a small combustion chamber, an L-shaped mount, a spark ignition, and nitrogen purging. Data acquisition includes thermocouples, pressure transducers, and a load cell for thrust measurement. Our experimental findings provide insights into thrust, temperature gradients, pressure, and plume characteristics. A non-linear regression model derived from the experimental data established an empirical relationship between performance and chamber lengths, offering a foundation for further combustion flow studies. The post-chamber length positively impacted the engine thrust performance by 2.7%. Conversely, the pre-chamber length negatively impacted the performance by 1.3%. Further data collection could assist in refining the empirical relation and identifying key threshold values.
Open Access
Distributed Spaceborne SAR: a review of systems, applications, and the road ahead
(IEEE, 2025-12-31) Hu, Cheng; Li, Yuanhao; Chen, Zhiyang; Liu, Feifeng; Zhang, Qingjun; Monti-Guarnieri, Andrea V.; Hobbs, Stephen E.; Anghel, Andrei; Datcu, Mihai
As a crucial sensor for wide-area Earth observation, spaceborne synthetic aperture radar (SAR) plays a pivotal role in large-scale terrain mapping, ocean observation, disaster monitoring, and so forth. Driven by the increasing demands for diverse applications, enhanced performance, and the continuous advancement of satellite and radar technologies, the distributed configuration has emerged as a key developmental trend for spaceborne SAR. This review comprehensively summarizes the systems and typical applications of distributed spaceborne SAR. The system configurations encompass homogenous distributed SAR, formed by multiple identical or similar platforms, and heterogeneous distributed SAR, characterized by significant differences between the transmitting and receiving platforms. Typical applications of distributed SAR include intelligent target recognition, terrain mapping, deformation retrieval, atmosphere measurement, and ocean observation, among others. Finally, the review offers a prospective outlook on the future development of distributed spaceborne SAR.
Open Access
Dual-Chamber microbial fuel cell for Azo-Dye degradation and electricity generation in Textile wastewater treatment
(Elsevier, 2025-09) Ndive, Julius Nnamdi; Eze, Simeon Okechukwu; Nnabuife, Somtochukwu Godfrey; Kuang, Boyu; Rana, Zeeshan A.
Textile wastewater, particularly azo dyes, poses significant environmental challenges due to its poor biodegradability and toxicity. This study explores a dual-chamber microbial fuel cell (MFC) for simultaneous wastewater treatment and electricity generation. The MFC consists of an anaerobic anode chamber and an aerobic cathode chamber, separated by a proton exchange membrane (PEM). Electroactive microorganisms in the anode chamber metabolize organic substrates, including azo dye contaminants, breaking them down into simpler by-products. Electrons released during this process flow through an external circuit to generate current, while protons migrate across the PEM to the cathode chamber for oxygen reduction. Electrochemically active microbes were isolated from azo-dye-contaminated soil, and their degradation abilities validated through assays. Optimized carbon-based electrodes and a Nafion 117 PEM were used to enhance conductivity and microbial activity. UV–Vis spectroscopy tracked dye degradation, with the absorbance peak of reactive yellow dye at 410 nm decreasing from 2.9 to 0.4, indicating effective azo-bond cleavage. The MFC achieved peak voltage and current outputs of 0.20 mV and 0.16 mA, respectively, demonstrating its dual functionality. Adding NaCl as a supporting electrolyte further improved ionic conductivity and performance. This study demonstrates MFC technology as a sustainable solution for industrial wastewater challenges, integrating microbial degradation with bioelectricity generation. Future work should address scalability, operational stability, and advanced electrode designs to enhance its practical applications.
Open Access
Enhancing performance and interpretability of multivariate time-series model through sparse saliency
(IEEE, 2024-09-24) Kong, Xiangqi; Xing, Yang; Liu, Zeyu; Tsourdos, Antonios; Wikander, Andreas
Explainable time-series modelling is an essential task for modern intelligent transportation systems (ITS). How-ever, balancing accuracy and interpretability in multivariate time series forecasting presents significant challenges. These challenges arise from the necessity to understand the significance of features and their temporal variations. Factors such as autocorrelation in time series and data processing techniques like sliding windows expand feature sets, thereby complicating pattern recognition using traditional post-hoc explanation methods and making the issue even more complex. To overcome these challenges, in this study, we propose a flexible post-process approach which generates sparse and normalized saliency values based on existing saliency generation methods such as GradientSHAP. Additionally, an optional window aggregation and alignment strategy is introduced to align with the original time series dataset, enhancing the intuitive understanding of feature importance. Furthermore, the potential use of sparse saliency for data augmentation to improve the model is explored. Lastly, we utilize naturalistic data from San Francisco airport to demonstrate our approach for ITS time-series prediction and explanation. The evaluation results indicate that integrating sparse saliency from high-performing models not only boosts the performance of XGBoost models by 10.92% but also simplifies model complexity, facilitating easier interpretation.
Open Access
Explainable reinforcement and causal learning for improving trust to 6G stakeholders
(IEEE, 2025-06-01) Arana-Catania, Miguel; Sonee, Amir; Khan, Abdul-Manan; Fatehi, Kavan; Tang, Yun; Jin, Bailu; Soligo, Anna; Boyle, David; Calinescu, Radu; Yadav, Poonam; Ahmadi, Hamed; Tsourdos, Antonios; Guo, Weisi; Russo, Alessandra
Future telecommunications will increasingly integrate AI capabilities into network infrastructures to deliver seamless and harmonized services closer to end-users. However, this progress also raises significant trust and safety concerns. The machine learning systems orchestrating these advanced services will widely rely on deep reinforcement learning (DRL) to process multi-modal requirements datasets and make semantically modulated decisions, introducing three major challenges: (1) First, we acknowledge that most explainable AI research is stakeholder-agnostic while, in reality, the explanations must cater for diverse telecommunications stakeholders, including network service providers, legal authorities, and end users, each with unique goals and operational practices; (2) Second, DRL lacks prior models or established frameworks to guide the creation of meaningful long-term explanations of the agent's behaviour in a goal-oriented RL task, and we introduce state-of-the-art approaches such as reward machine and sub-goal automata that can be universally represented and easily manipulated by logic programs and verifiably learned by inductive logic programming of answer set programs; (3) Third, most explainability approaches focus on correlation rather than causation, and we emphasise that understanding causal learning can further enhance 6G network optimisation. Together, in our judgement they form crucial enabling technologies for trustworthy services in 6G. This review offers a timely resource for academic researchers and industry practitioners by highlighting the methodological advancements needed for explainable DRL (X-DRL) in 6G. It identifies key stakeholder groups, maps their needs to X-DRL solutions, and presents case studies showcasing practical applications. By identifying and analysing these challenges in the context of 6G case studies, this work aims to inform future research, transform industry practices, and highlight unresolved gaps in this rapidly evolving field.
Open Access
How to find opinion leader on the online social network?
(Springer, 2025-05-01) Jin, Bailu; Zou, Mengbang; Wei, Zhuangkun; Guo, Weisi
Online social networks (OSNs) provide a platform for individuals to share information, exchange ideas, and build social connections beyond in-person interactions. For a specific topic or community, opinion leaders are individuals who have a significant influence on others’ opinions. Detecting opinion leaders and modeling influence dynamics is crucial as they play a vital role in shaping public opinion and driving conversations. Existing research have extensively explored various graph-based and psychology-based methods for detecting opinion leaders, but there is a lack of cross-disciplinary consensus between definitions and methods. For example, node centrality in graph theory does not necessarily align with the opinion leader concepts in social psychology. This review paper aims to address this multi-disciplinary research area by introducing and connecting the diverse methodologies for identifying influential nodes. The key novelty is to review connections and cross-compare different multi-disciplinary approaches that have origins in: social theory, graph theory, compressed sensing theory, and control theory. Our first contribution is to develop cross-disciplinary discussion on how they tell a different tale of networked influence. Our second contribution is to propose trans-disciplinary research method on embedding socio-physical influence models into graph signal analysis. We showcase inter- and trans-disciplinary methods through a Twitter case study to compare their performance and elucidate the research progression with relation to psychology theory. We hope the comparative analysis can inspire further research in this cross-disciplinary area.
Open Access
Interaction-aware and driving style-aware trajectory prediction for heterogeneous vehicles in mixed traffic environment
(IEEE, 2025) Zhang, Qixiang; Xing, Yang; Wang, Jinxiang; Fang, Zhenwu; Liu, Yahui; Yin, Guodong
Trajectory prediction (TP) of surrounding vehicles (SVs) is crucial for autonomous vehicles (AVs) to understand traffic situations and achieve safe-efficient decision-making and motion planning. However, different drivers’ personalized driving preferences will bring uncertainties for long-term TP in the mixed traffic environment. To this end, this paper proposes a TP model with interaction awareness and driving style awareness for long-term TP of heterogeneous SVs. Firstly, the driving conditions in the highD dataset are distinguished, and three different driving styles of the vehicle in the car-following condition are obtained based on an unsupervised clustering algorithm. Then, an encoder-decoder architecture based on novel lane attention and multi-head attention mechanisms is proposed, where the encoder analyzes historical trajectory patterns and the decoder generates future trajectory sequences. The lane attention mechanism enhances the spatial perception capability of vehicles towards the target lane, and the multi-head attention mechanism extracts high-dimensional global interaction information about the heterogeneous vehicle group (HVG) surrounding the target vehicle (TV). Experimental results show that the proposed model outperforms state-of-the-art models in root-mean-square-error (RMSE) for long-term TP and exhibits excellent adaptability to diverse driving tasks. Moreover, this paper verifies that the driving style topology within the HVG has multiple impacts on the TP accuracy of the TV.
Open Access
Mitigating no fault found phenomena through ensemble learning: a mixture of experts approach
(IEEE, 2024-09-24) Liu, Zeyu; Kong, Xiangqi; Chen, Yang; Wang, Ziyue; Jia, Huamin; Al-Rubaye, Saba
In the aviation industry, the reliance on precise fault diagnostic decision-making is critical for equipment maintenance. A significant challenge encountered is the erroneous categorization of components under 'No Fault Found' (NFF), which subjects these components to unwarranted repairs or further testing. Such misclassifications not only trap on airlines through costly cycles of unnecessary maintenance but also exacerbate degeneration and potential safety hazards. Consequently, there is a heightened demand for the development of effective fault diagnosis models that are adapting to the aircraft complex systems and adeptly addressing issues related to the NFF phenomenon. In this study, we draw inspiration from ensemble learning and propose a multiple Naive Bayes experts (MNBMoEs) approach based on a mixture of experts (MoEs) model. This method leverages the predictive advantages of each sub-model on specific features, allowing the hybrid expert decision to outperform any single expert. It also includes a quantitative analysis method for the NFF issue, derived from the confusion matrix according to the industrial definition of NFF. Experiments evaluated on public datasets results show that the ensemble learning approach, based on Mixture of Multiple Naive-Bayes expert models, can effectively utilize the strengths of different models, improving fault diagnosis accuracy to 96.96%, with a maximum reduction in NFF occurrence rates of up to 94.17% and 84.2% model performance improvement.
Open Access
Mixed-precision federated learning via multi-precision over-the-air aggregation
(IEEE, 2025-03-24) Yuan, Jinsheng; Wei, Zhuangkun; Guo, Weisi
Over-the-Air Federated Learning (OTA-FL) is a privacy-preserving distributed learning mechanism, by aggregating updates in the electromagnetic channel rather than at the server. A critical research gap in existing OTA - FL research is the assumption of homogeneous client computational bit precision. While in real world application, clients with varying hardware resources may exploit approximate computing (AxC) to operate at different bit precisions optimized for energy and computational efficiency. Model updates with varying precisions among clients present a significant challenge for OTA - FL, as they are incompatible with the wireless modulation superposition process. Here, we propose an mixed-precision OTA-FL framework of clients with multiple bit precisions, demonstrating the following innovations: (i) the superior trade-off for both server and clients within the constraints of varying edge computing capabilities, energy efficiency, and learning accuracy requirements compared to homogeneous client bit precision, and (ii) a multi-precision gradient modulation scheme to ensure compatibility with OTA aggregation and eliminate the overheads of precision conversion. Through case study with real world data, we validate our modulation scheme that enables AxC based mixed-precision OTA-FL. In comparison to homogeneous standard precision of 32-bit and 16-bit, our framework presents more than 10% in 4-bit ultra low precision client performance and over 65% and 13% of energy savings respectively. This demonstrates the great potential of our mixed-precision OTA-FL approach in heterogeneous edge computing environments.
Open Access
On the application of trapped vortices in motorsport application for improved aerodynamic performance using passive and active flow controls
(SAE International, 2025-04-15) Ng, Ming Kin; Teschner, Tom-Robin
New regulations introduced by the Fédération Internationale de l’Automobile (FIA) for the 2026 Formula 1 season mark the first instance of active flow control methods being endorsed in Formula 1 competition. While active methods have demonstrated significant success in airfoil development, their broader application to grounded vehicle aerodynamics remains unexplored. This research investigates the effectiveness of trapped vortex cavity (TVC) technology in both active and passive flow controls, applied to a NACA0012 airfoil and an inverted three-element airfoil from a Formula 1 model. The investigation is conducted using numerical methods to evaluate the aerodynamic performance and potential of TVC in this paper. In the single-airfoil case, a circular cavity is placed along the trailing edge (TE) on the suction surface; for the three-element airfoils, the cavity is positioned on each airfoil to determine the optimum location. The results show that the presence of a cavity, particularly with active flow control, significantly improves the lift-to-drag ratio (CL/CD) for both the single airfoil and the three-element airfoils. A maximum enhancement of 1160% was recorded for the single airfoil, while the three-element airfoils saw an improvement of 313% compared to their original configurations. However, when the TVC was placed in positions other than the TE of the mid-airfoil, a performance reduction was observed, even with active blowing applied. The passive flow control approach, which requires no additional energy input, yielded a modest improvement of 3.52% for the NACA0012 airfoil. However, passive control underperformed due to unstable vortex interactions with each airfoil element for the inverted three-element airfoil case. Even with optimal placement and geometrical modifications, the maximum CL/CD ratio for passive control was only 96% of the original CL/CD of the unmodified three-element airfoils, suggesting that passive flow control is less effective here compared to active flow control.
Open Access
Personalizing driver agent using large language models for driving safety and smarter human–machine interactions
(IEEE, 2025-12-31) Xu, Zixuan; Chen, Tiantian; Huang, Zilin; Xing, Yang; Chen, Sikai
Driver assistance systems have been shown to reduce crashes by providing real-time warnings or assistance, with their effectiveness depending on communication with driver. Due to their unique characteristics, human drivers possess varying hazard perception skills and interaction preferences, making personalized assistance crucial to improving the user experience and system acceptance. However, how to leverage multimodal interfaces that dynamically adapt to warning contents and driver characteristics remains an open question. At the same time, large language models (LLMs) have demonstrated advanced capabilities in knowledge acquisition, planning, and human–machine collaboration, offering potential solutions for existing warning systems. Thus, we develop an LLM-based personalized driver agent (PDA), which provides personalized warnings through multimodal interactions (visual, voice, and tactile). The agent’s architecture mimics human cognitive processes via four core modules: memory, perception, control, and action. Results from our experiments indicate that the LLM-PDA effectively customizes warning contents for different drivers in various situations, providing enhanced safety and driver support. This article pioneers the integration of LLMs into automotive human–vehicle interaction and offers novel insights into personalized human–machine interaction in intelligent vehicles.

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