Staff publications (AA)

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Open Access
Adaptive UAV control with sensor and actuator faults recovery
(MDPI, 2025-03-01) Bekhiti, Abdellah; Souanef, Toufik; Toubakh, Houari; Horri, Nadjim; Kafi, Mohamed Redouane; Bouzid, Zakaria
This paper presents an adaptive fault-tolerant control strategy tailored for fixed-wing unmanned aerial vehicles (UAV) operating under adverse conditions such as icing. Using radial basis function neural networks and nonlinear dynamic inversion, the proposed framework effectively handles simultaneous actuator and sensor faults with arbitrary nonlinear dynamics caused by environmental effects, model uncertainties and external disturbances. A nonlinear disturbance observer is incorporated for accurate sensor fault detection and estimation, thereby enhancing the robustness of the control system. The integration of the radial basis function neural network enables an adaptive estimation of the faults, ensuring accurate fault compensation and system stability under challenging conditions. The observer is optimised to minimise the deviation of the closed-loop dynamics eigenvalues from the assigned eigenvalues and to approach unity observer steady-state gain. The stability of the control architecture is mathematically proven using Lyapunov analysis, and the performance of the approach is validated through numerical simulations on a six Degrees of Freedom fixed-wing unmanned aerial vehicles model. The results show superior performance and robustness to challenging fault scenarios. This research provides a comprehensive fault management solution that enhances the safety and reliability of unmanned aircraft operations in extreme environments.
Open Access
Advanced diagnostics of aircraft structures using automated non-invasive imaging techniques: a comprehensive review
(MDPI, 2025-04-01) Bardis, Kostas; Avdelidis, Nicolas P.; Ibarra-Castanedo, Clemente; Maldague, Xavier P. V.; Fernandes, Henrique
The aviation industry currently faces several challenges in inspecting and diagnosing aircraft structures. Current aircraft inspection methods still need to be fully automated, making early detection and precise sizing of defects difficult. Researchers have expressed concerns about current aircraft inspections, citing safety, maintenance costs, and reliability issues. The next generation of aircraft inspection leverages semi-autonomous and fully autonomous systems integrating robotic technologies with advanced Non-Destructive Testing (NDT) methods. Active Thermography (AT) is an example of an NDT method widely used for non-invasive aircraft inspection to detect surface and near-surface defects, such as delamination, debonding, corrosion, impact damage, and cracks. It is suitable for both metallic and non-metallic materials and does not require a coupling agent or direct contact with the test piece, minimising contamination. Visual inspection using an RGB camera is another well-known non-contact NDT method capable of detecting surface defects. A newer option for NDT in aircraft maintenance is 3D scanning, which uses laser or LiDAR (Light Detection and Ranging) technologies. This method offers several advantages, including non-contact operation, high accuracy, and rapid data collection. It is effective across various materials and shapes, enabling the creation of detailed 3D models. An alternative approach to laser and LiDAR technologies is photogrammetry. Photogrammetry is cost-effective in comparison with laser and LiDAR technologies. It can acquire high-resolution texture and colour information, which is especially important in the field of maintenance inspection. In this proposed approach, an automated vision-based damage evaluation system will be developed capable of detecting and characterising defects in metallic and composite aircraft specimens by analysing 3D data acquired using an RGB camera and a IRT camera through photogrammetry. Such a combined approach is expected to improve defect detection accuracy, reduce aircraft downtime and operational costs, improve reliability and safety and minimise human error.
Open Access
Advanced thermal imaging processing and deep learning integration for enhanced defect detection in carbon fiber-reinforced polymer laminates
(MDPI, 2025-03-25) Garcia Rosa, Renan; Pereira Barella, Bruno; Garcia Vargas, Iago; Tarpani, José Ricardo; Herrmann, Hans-Georg; Fernandes, Henrique
Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and first- and second-order derivatives were applied to refine thermographic signals, enhancing defect visibility and SNR. The U-Net architecture was used to assess segmentation performance on datasets with and without preprocessing. The results demonstrated that preprocessing significantly improved defect detection, achieving an Intersection over Union (IoU) of 95% and an F1-Score of 99%, outperforming approaches without preprocessing. These findings emphasize the importance of preprocessing in enhancing segmentation accuracy and reliability, highlighting its potential for advancing non-destructive testing techniques across various industries.
Open Access
Assessing advanced propulsion systems using the impact monitor framework
(MDPI, 2025-03-28) Gupta, Utkarsh; Riaz, Atif; Brenner, Felix; Lefebvre, Thierry; Ratei, Patrick; Alder, Marko; Prakasha, Prajwal Shiva; Weber, Lukas; Pons-Prats, Jordi; Markatos, Dionysios
Presented in this paper is the Impact Monitor framework and interactive Dashboard Application (DA) validated through a use case, focusing on investigating the viability and competitiveness of future propulsion architectures for next-generation aircraft concepts. This paper presents a novel collaborative framework for integrated aircraft-level assessments, focusing on secure, remote workflows that protect intellectual property (IP) while enabling comprehensive and automated analyses. The research addresses a key gap in the aerospace domain: the seamless matching and sizing of aircraft engines within an automated workflow that integrates multiple tools and facilitates real-time data exchanges. Specifically, thrust requirements are iteratively shared between aircraft and engine modeling environments for synchronized sizing. Subsequently, the fully defined aircraft data are transferred to other tools for trajectory analysis and emissions and other assessments. The Impact Monitor framework and Dashboard Application demonstrate improved efficiency and data security, promoting effective collaboration across institutions and industry partners.
Open Access
Assessing uninstalled hydrogen-fuelled retrofitted turbofan engine performance
(MDPI, 2025-03-12) Farabi, Jarief; Mourouzidis, Christos; Pilidis, Pericles
Hydrogen as fuel in civil aviation gas turbines is promising due to its no-carbon content and higher net specific energy. For an entry-level market and cost-saving strategy, it is advisable to consider reusing existing engine components whenever possible and retrofitting existing engines with hydrogen. Feasible strategies of retrofitting state-of-the-art Jet A-1 fuelled turbofan engines with hydrogen while applying minimum changes to hardware are considered in the present study. The findings demonstrate that hydrogen retrofitted engines can deliver advantages in terms of core temperature levels and efficiency. However, the engine operability assessment showed that retrofitting with minimum changes leads to a ~5% increase in the HP spool rotational speed for the same thrust at take-off, which poses an issue in terms of certification for the HP spool rotational speed overspeed margin.
Open Access
Fault diagnosis across aircraft systems using image recognition and transfer learning
(MDPI, 2025-03-02) Jia, Lilin; Ezhilarasu, Cordelia Mattuvarkuzhali; Jennions, Ian K.
With advances in machine learning, the fault diagnosis of aircraft systems is becoming more efficient and accurate, which makes condition-based maintenance possible. However, current fault diagnosis algorithms require abundant and balanced data to be trained, which is difficult and expensive to obtain for aircraft systems. One solution is to transfer the diagnostic knowledge from one system to another. To achieve this goal, transfer learning was explored, and two approaches were attempted. The first approach uses relational similarity between the source and target domain features to enable the transfer between two different systems. The results show it only works when transferring from the fuel system to ECS but not to APU. The second approach uses image recognition as the intermediate domain linking the distant source and target domains. Using a deep network pre-trained with fuel system images or the ImageNet dataset finetuned with a small amount of target system data, an improvement in accuracy is found for both target systems, with an average of 6.90% in the ECS scenario and 5.04% in the APU scenario. This study outlines a pioneering approach that transfers knowledge between completely different systems, which is a rare transfer learning application in fault diagnosis.
Open Access
Impact monitor framework: development and implementation of a collaborative framework for aviation impact assessment
(MDPI, 2025-03-18) Alder, Marko; Ratei, Patrick; Riaz, Atif; Gupta, Utkarsh; Lefebvre, Thierry; Prakasha, Prajwal Shiva
The development and implementation of a collaborative framework for aviation impact assessment studies is presented. The focus is on which technologies can be used to enable the collaborative aspect, including the use of a common data model, the secure transfer of data between domain experts in different locations, and the automated execution of impact assessment workflows. It is demonstrated how the selected technologies can be extended to meet the requirements of air transport systems assessment and how they can be integrated into a common framework. The results of the developments are discussed in terms of their technical capabilities and the lessons learned from their practical use. The proposed framework shows that collaborative impact assessment studies can be conducted efficiently and securely. This forms the basis for three application studies in the same research project.
Open Access
Installation effects of supersonic inlets on next-generation SST turbofan engines
(MDPI, 2025-03-14) Adamidis, Stylianos; Del Gatto, Dario; Mourouzidis, Christos; Brown, Stephen; Pachidis, Vassilios
This study explores inlet-related installation effects on next-generation SST aircraft, focusing on supersonic business jets. Using a comprehensive framework with consistent thrust/drag bookkeeping and realistic modeling of inlet losses, including operational limits for “buzz” and distortions, the inlet drag accounts for 8.8% to 14.2% of the installed net thrust during the supersonic segment of the mission. Variable airflow control technology is assessed, with a scheduling methodology developed to optimize the inlet operation by minimizing the installed SFC. The results show that this technology improves the installed SFC by 0.80% during supersonic cruise, enhancing the overall propulsion system performance.
Open Access
Integrated power and thermal management system in a parallel hybrid-electric aircraft: an exploration of passive and active cooling and temperature control
(MDPI, 2025-03-13) Ouyang, Zeyu; Nikolaidis, Theoklis; Jafari, Soheil; Pontika, Evangelia
Hybrid-electric aircraft (HEAs) represent a promising solution for reducing fuel consumption and emissions. However, the additional heat loads generated by the electrical propulsion systems in HEAs can diminish these benefits. To address this, an integrated power and thermal management system (IPTMS) is essential to mitigate these challenges by optimizing the interaction between thermal management and power management. This paper presents a preliminary IPTMS design for a parallel HEA operating under International Standard Atmosphere (ISA) conditions. The design includes an evaluation of active cooling, passive cooling, and active temperature control strategies. The IPTMS accounts for heat loads from the engine system, including the generators, shaft bearings, and power gearboxes, as well as from the electrical propulsion system, such as motors, batteries, converters, and the electric bus. This study investigates the impact of battery power (BP) contribution to cooling power on required coolant pump power and induced ram air drag. A comparison of IPTMS performance under 0% and 100% BP conditions revealed that the magnitude of battery power contribution to cooling power does not significantly impact the thermal management system (TMS) performance due to the large disparity between the total battery power (maximum 950 kW) and the required cooling power (maximum 443 W). Additionally, it was determined that the motor-inverter loop accounts for 95% of the pump power and 97% of the ram air drag. These findings suggest that IPTMS optimization should prioritize the thermal domain, particularly the motor-inverter loop. This study provides new insights into IPTMS design for HEAs, paving the way for further exploration of IPTMS performance under various operating conditions and refinement of cooling strategies.
Open Access
Multidisciplinary design optimization of the NASA metallic and composite common research model wingbox: addressing static strength, stiffness, aeroelastic, and manufacturing constraints
(MDPI, 2025-06-01) Dababneh, Odeh; Kipouros, Timoleon; Whidborne, James F.
This study explores the multidisciplinary design optimization (MDO) of the NASA Common Research Model (CRM) wingbox, utilizing both metallic and composite materials while addressing various constraints, including static strength, stiffness, aeroelasticity, and manufacturing considerations. The primary load-bearing wing structure is designed with high structural fidelity, resulting in a higher number of structural elements representing the wingbox model. This increased complexity expands the design space due to a greater number of design variables, thereby enhancing the potential for identifying optimal design alternatives and improving mass estimation accuracy. Finite element analysis (FEA) combined with gradient-based design optimization techniques was employed to assess the mass of the metallic and composite wingbox configurations. The results demonstrate that the incorporation of composite materials into the CRM wingbox design achieves a structural mass reduction of approximately 17.4% compared to the metallic wingbox when flutter constraints are considered and a 23.4% reduction when flutter constraints are excluded. When considering flutter constraints, the composite wingbox exhibits a 5.6% reduction in structural mass and a 5.3% decrease in critical flutter speed. Despite the reduction in flutter speed, the design remains free from flutter instabilities within the operational flight envelope. Flutter analysis, conducted using the p-k method, confirmed that both the optimized metallic and composite wingboxes are free from flutter instabilities, with flutter speeds exceeding the critical threshold of 256 m/s. Additionally, free vibration and aeroelastic stability analyses reveal that the composite wingbox demonstrates higher natural frequencies compared to the metallic version, indicating that composite materials enhance dynamic response and reduce susceptibility to aeroelastic phenomena. Fuel mass was also found to significantly influence both natural frequencies and flutter characteristics, with the presence of fuel leading to a reduction in structural frequencies associated with wing bending.
Open Access
Normalised diagnostic contribution index (NDCI) integration to multi objective sensor optimisation framework (MOSOF)—An environmental control system case
(MDPI, 2025-05-01) Suslu, Burak; Ali, Fakhre; Jennions, Ian K.
In modern aerospace systems, effective sensor optimisation is essential for ensuring reliable diagnostics, efficient resource allocation, and proactive maintenance. This paper presents Normalised Diagnostic Contribution Index (NDCI) integration into the Multi-Objective Sensor Optimisation Framework (MOSOF) to address application-specific performance nuances. Building on previous work, the proposed approach leverages a multi-objective genetic algorithm to optimise key criteria, including performance, cost, reliability management, and compatibility. NDCI is derived from simulation data obtained via the Boeing 737-800 Environmental Control System (ECS) using the SESAC platform, where degradation level readings across four fault modes are analysed. The framework evaluates sensor performance from the perspectives of Original Equipment Manufacturers (OEM), Airlines, and Maintenance Repair Overhaul (MRO) organisations. Validation against the Minimum Redundancy Maximum Relevance (mRMR) method highlights the distinct advantage of NDCI by identifying an optimal set of three sensors compared to mRMR’s six-sensor solution, and MOSOF’s multi-objective insertion enhances sensor deployment for different stakeholders. This integration not only expands the feasible solution space for sensor-pair configurations but also emphasises diagnostic value over redundancy. Overall, the enhanced NDCI-MOSOF offers a scalable, multi-stakeholder approach for next-generation sensor optimisation and predictive maintenance in complex aerospace systems. The results demonstrate significant improvements in diagnostics efficiency for stakeholders.
Open Access
Use of Bayesian Networks to understand sustainability requirements
(MDPI, 2025-03-13) Spinelli, Andrea; Kipouros, Timoleon
Sustainability is a key requirement in contemporary engineering design, but it is difficult to quantify due to its multidimensionality. We propose the application of Bayesian Networks for modeling the cause and effect of engineering systems and their environment. Emphasis is placed on capturing the impact on sustainability indicators of design decisions. These include the performance of the system, its economic viability in terms of cost, and its environmental and societal impacts. The method leverages data from simulation models, enabling the designer to perform assumption-free inferences on the variables at play.

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