Investigation of aircraft auxiliary power unit acoustic signatures for condition monitoring

Ali, Fakhre - Associate SupervisorThe auxiliary power unit (APU) of an aircraft is a key system responsible for providing electrical and pneumatic power during ground operations and in-flight emergencies. APU failures can result in delay or cancellation of a flight and fault diagnostic practices are in place to identify the cause of failure. The existing strategies generally require human intervention to identify the fault by traversing through a troubleshooting manual and examining the data acquired from multiple intrusive sensors. The complete process is cumbersome and prone to misjudgement; fault identification in its entirety may not be possible due to limited sensor coverage. Incorporating additional sensors may not be feasible due to accessibility issues, space constraints and certification requirements. However, incorporating microphones, which have previously been used for noise source characterization and verification of noise abatement solutions, is a promising non- intrusive approach. This PhD focuses on ascertaining the potential of microphones for fault detection / identification and condition monitoring of an aircraft APU. The research has been based on the far-field and near-field acoustic data acquired from Cranfield University’s Boeing 737-400 aircraft and the aim has been to determine the degradation / faults that can be detected using microphones. While addressing this aim, a far-field noise model has been developed for sensitivity analysis, near-field data has been analysed, classification / regression models have been proposed and an acoustics-based scheme for ignition system monitoring has been conceived. The results suggest that the far-field acoustic data is not suitable for condition monitoring, and the near-field microphones are unable to monitor tonal frequencies for monitoring the gearbox and bearing. However, there is a huge potential in using microphones for monitoring the lubrication system, pneumatic system components and ignition system for faulty / degraded conditions. The proposed methodologies have online capability and require only a limited set of microphones inside the APU compartment.PhD in Transport System

Communication RSSI prediction and validation framework for advanced air mobility

This paper proposes a communication signal strength prediction and validation framework for the use of advanced air mobility. Advanced air mobility, including urban air mobility and unmanned aerial vehicles, requires a scalable, safe, and seamless communication infrastructure different from conventional aircraft. This paper proposes a hybrid regression-based prediction method that combines synthetic data generated from a ray-tracing model and real flight test data in the urban airspace and uses k-fold cross-validation to evaluate the predicted signal strength. The results show that the proposed framework provides reliable performance indices, effectively mitigating the insufficiency of flight data. This research will enable evaluating the communication infrastructure and identifying high-risk areas for advanced air mobility stakeholders.Innovate UKThis work was conducted as part of “Advanced Air Mobility: Communication Evaluation for Safe and Seamless Operations”, supported by Innovate UK (grant number 10117151) and Korea Agency for Infrastructure Technology Advancement (grant number RS-2024-00412531).2025 International Wireless Communications and Mobile Computing (IWCMC

Dataset DrivAer hp-F: Wake Total Pressure Measurements in Yaw Conditions

Dataset for the wake total pressure measurements conducted on the 35% scale DrivAer hp-F model at various yaw angles in the 8x6 Wind Tunnel at Cranfield University. The measurements are performed on the DrivAer hp-F rear wing configuration with an angle of attack of 15°. The dataset includes the total pressure coefficient results from measurements on the P1, P2, and P3 wake planes, which are located 400 mm, 700 mm, and 1000 mm downstream of the vehicle model respectively. Additionally, the horizontal and vertical measurements positions (in mm) are provided for each wake plane. A horizontal sweep on the P3 wake plane has been conducted three times for repeatability. In reference to the publication: Steven Rijns, Tom-Robin Teschner, Kim Blackburn, Anderson Ramos Proenca, James Brighton; Experimental and numerical investigation of the aerodynamic characteristics of high-performance vehicle configurations under yaw conditions. Physics of Fluids 1 April 2024; 36 (4): 045112. https://doi.org/10.1063/5.0196979 CAD files for the DrivAer hp-F rear wing configuration are available at: Rijns, Steven; Teschner, Tom-Robin; Blackburn, Kim; Ramos Proenca, Anderson; Brighton, James (2024). DrivAer hp-F: Spoiler & Rear Wing Configurations Geometry Pack. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.25715202 Note: The updated dataset retains all original data while adding calibrated data to provide (new) users with an additional reference option

Composite material defect segmentation using deep learning models and infrared thermography

For non-destructive assessment, the segmentation of infrared thermographic images of carbon fiber composites is a critical task in material characterization and quality assessment. This study focuses on applying image processing techniques, particularly adaptive thresholding, alongside neural network models such as U-Net and DeepLabv3 for infrared image segmentation tasks. An experimental analysis was conducted on these networks to compare their performance in segmenting artificial defects from infrared images of a carbon-fibre reinforced polymer sample. The performance of these models was evaluated based on the F1-Score and Intersection over Union (IoU) metrics. The findings reveal that DeepLabv3 demonstrates superior results and efficiency in segmenting patterns of infrared images, achieving an F1-Score of 0.94 and an IoU of 0.74, showcasing its potential for advanced material analysis and quality control.This study was financed in part by the Coordenacao de Aper-feicoamento de Pessoal de Nivel Superior – Brasil (CAPES) –Finance Code 001 and by the National Council for Scientificand Technological Development - Brazil (CNPq) – Finance Codes 407140/2021-2 and 312530/2023-4.Revista de Informática Teórica e Aplicad

Understanding HRM financial value from obtaining more star performers: introduction on a paper and commentary collection

Joo, Aguinis, Lee, Kremer and Villamor demonstrated in their article entitled HRM’s financial value from obtaining more star performers in the International Journal of Human Resource Management (HRM) the financial value of acquiring star performers by using utility analysis on 206 samples of individual performance encompassing 824,924 workers. The analyses showed that HRM adds greater financial value by obtaining more star performers. Four (teams of) scholars, Michael Sturman, Xueging Fan, and Hanbo Shim, Michal Biron, Carol Kulik, and Mark Huselid responded to an invitation to comment on this article. In this introduction of the first commentary collection, we provide short summaries of the Joo et al. article and the four commentaries and discuss future research

Ontology-driven knowledge graphs for personnel management within the UK Ministry of Defence: a conceptual overview

Ontology-driven knowledge graphs visualise complex relationships between entities such as people and concepts. This conceptual paper explores the potential for using ontology-driven knowledge graphs to enhance personnel management within the Ministry of Defence (MOD). It reviews existing literature on ontologies, structured frameworks to store domain knowledge based on relationships between data, and knowledge graphs and outlines the concept of an ontology-driven knowledge graph for a skill management system. The paper argues that this approach can provide a unified, standardised method for managing personnel skills, improving decision-making, and enhancing operational efficiency. A further benefit identified is the potential for the system to be expanded to exchange information with other systems, such as the NATO Defence Planning Process (NDPP), allowing external data to improve the quality of inferences made by the system.DSTLDefence and Security Doctoral Symposia 2024 (DSDS24

The Female FTSE Report 2008: A Decade of Delay

2008 marks our tenth index and report with a slight, incremental increase ofdirectorships held by women on the FTSE 100 corporate boards, bringing the totalnumber to 131. Ten years ago there were only 66 women on the FTSE 100 boards.The most meaningful increase has occurred in the 39 companies that now each havetwo or more women on their boards

AI-assisted in silico trial for the optimization of osmotherapy after ischaemic stroke

Over the past few decades, osmotherapy has commonly been employed to reduce intracranial pressure in post-stroke oedema. However, evaluating the effectiveness of osmotherapy has been challenging due to the difficulties in clinical intracranial pressure measurement. As a result, there are no established guidelines regarding the selection of administration protocol parameters. Considering that the infusion of osmotic agents can also give rise to various side effects, the effectiveness of osmotherapy has remained a subject of debate. In previous studies, we proposed the first mathematical model for the investigation of osmotherapy and validated the model with clinical intracranial pressure data. The physiological parameters vary among patients and such variations can result in the failure of osmotherapy. Here, we propose an AI-assisted in silico trial for further investigation of the optimisation of administration protocols. The proposed deep neural network predicts intracranial pressure evolution over osmotherapy episodes. The effects of the parameters and the choice of dose of osmotic agents are investigated using the model. In addition, clinical stratifications of patients are related to a brain model for the first time for the optimisation of treatment of different patient groups. This provides an alternative approach to tackle clinical challenges with in silico trials supported by both mathematical/physical laws and patient-specific biomedical information.Stephen J. Payne is supported by a Yushan Fellowship from the Ministry of Education, Taiwan (111V1004-2). David A. Clifton is supported by the Pandemic Sciences Institute at the University of Oxford; the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC); an NIHR Research Professorship; a Royal Academy of Engineering Research Chair; and the InnoHK Hong Kong Centre for Centre for Cerebro-cardiovascular Engineering (COCHE).IEEE Journal of Biomedical and Health Informatic

Autonomous path selection of unmanned aerial vehicle in dynamic environment using reinforcement learning

The Unmanned Aerial Vehicle (UAV) is an emerging area within the aviation industry. Currently, fully autonomous UAV operations in real-world scenarios are rare due to low technology readiness and a lack of trust. However, Artificial Intelligence (AI) offers powerful tools to adapt to changing conditions and handle complex perceptions. In autonomous vehicles, automotive self-driving technologies have made significant advances. To enhance the level of autonomy in aviation, it is beneficial to analyze these frameworks and extend autonomous driving principles to autonomous flying. This research introduces a novel solution for ensuring safe navigation in UAVs by adopting the concept of autonomous lane or path selection strategies used in cars. The approach employs deep reinforcement learning (DRL) for high-level decision-making in selecting the appropriate path generated by various established algorithms that consider different scenarios. Specifically, the Interfered Fluid Dynamical System (IFDS) \cite{IFDS_OG} is utilized for guidance and the PID for the flight control system. The UAV can choose between global and local paths and determine the appropriate speed for following these paths. This proposed framework lays the foundation for future research into practical and safe navigation strategies for UAVs.AIAA SCITECH 2025 Foru

Control allocation problem transformation approaches for over-actuated vectored thrust VTOLs

One main challenge of vectored thrust VTOLs is actuator thrust control saturation because it may lead to undesired behaviour and loss of control if the control channels are not prioritised. Another challenge of vectored thrust VTOLs is that the vectored thrust results in non-linear effector mapping preventing the direct use of standard linear control allocation approaches. Linear control allocation approaches have lower online computational and complexity burden, and have simplier requirements for fault tolerance and reconfigurability than nonlinear control allocation approaches. This paper proposes three real-time control allocation approaches for transforming a nonlinear control allocation problem to a linear problem so that classical linear control allocation approaches can then be used. The approaches which addresses the two main challenges of the particular VTOL configuration are then tested using three selected flight test manoeuvres on a generic over-actuated vectored thrust three degrees of freedom planar VTOL with no aerodynamics. The first approach transfers the non-linearity from the effector mapping to the computation of the actuator limits by formulating the real controls in cartesian form and then converts the physical actuator limits from polar to cartesian form. The second approach transforms the non-linear effector mapping to a linear mapping via numerical linearisation of the non-linear effector mapping in real-time. The third approach is similar to second approach except an extra step which transforms the virtual controls from cartesian to polar before performing an analytical linearisation resulting in a different and more complicated linear Effector mapping. The results demonstrate the effectiveness of the proposed control allocation schemes to allocate remaining control authority to higher priority and critical control channels in order to maintain operational safety and stability during certain flight conditions while there is limited control authority.Aerospace Science and Technolog