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Green hydrogen revolution in aviation: requirements and possibilities
Get PDFPilidis, Pericles - Associate SupervisorThis work investigates the case for sustainable aviation for the Asia Pacific region
and focusses on Hong Kong. Hong Kong presently generates 50% of its energy
from coal. The aim is to remove carbon from aviation fuels for Hong Kong and
replace it with green hydrogen. This is a viable choice of sustainable aviation fuel
but switching it from kerosene would require the overcoming of some challenges.
This thesis comprises of three parts: firstly, the modelling and simulation of the
Trent-XWB-97 jet engine as a baseline combusting Jet-A fuel. Its aircraft
performance is then compared to a cryogenically fuelled green hydrogen engine.
Secondly, the reference engine is then fitted with an intercooler with the gain of
aircraft performance benefits in mind as well as to utilise it as a heat exchanger
to vapourise the liquid hydrogen and thirdly, large-scale green hydrogen
production using renewables is envisaged as part of a Green Hydrogen Hub
network encompassing a green hydrogen logistical supply chain paving the way
for a future aviation hydrogen micro-economy. It was discovered that relative to
the Jet-A fuelled engine, the engine with a constant net thrust for ODP (Take-Off)
and DP (Cruise) conditions decreased the ESFC for both baseline and
intercooled engines. For the reference case at ODP this drop in ESFC was
(1.71%) and (1.3%) for the intercooled scenario. There was also an
accompanying decrease in TET; for the baseline engine this was 47K and for the
intercooled engine this was 50K. The addition of the intercooler achieved the
greater aircraft thrust requirement of 448kN and also vapourised the cryogenic
hydrogen to high enough temperatures. These were calculated to be 536K and
296K for the ODP and DP respectively. The results showed that the baseline
engine carried the greatest payload of 31866 kg with a block fuel burn of 36267
kg and a flight duration of 11.83 hours. The intercooled engine yielded a
maximum carried payload of 27184 kg and the block fuel burn was 43349 kg and
a flight time of 11.97 hours. The study also discovered that the 43.4 tonnes of
green hydrogen can be generated in Hong Kong using wind and solar power and
that its usage reduced the civil aviation carbon footprint of Hong Kong by 11.6%.MSc by Research in Aerospac
Complex network analysis of China's integrated air-high-speed rail network: topological characteristics, centrality measures, and cluster analysis
Get PDFThis paper presents a comprehensive complex network analysis of China's integrated air-High-Speed Rail (HSR) network by constructing a directed weighted network and comparing its complex characteristics with its sub-networks. The findings reveal that, beyond small-world properties, the networks exhibit broad-scale characteristics with a rapid decline in degree distribution, deviating from the traditional scale-free model due to operational constraints and market saturation. Centrality analysis highlights the rising importance of secondary hubs, such as Xi'an, Kunming, and Zhengzhou, as strategic transit points linking urban centres and peripheral regions. The integrated network achieves enhanced efficiency through hybrid modularity, combining the aviation network's centralised structure with the HSR network's corridor-focused design. While this integration fosters economic connectivity and regional development, resilience challenges emerge due to reliance on high-centrality nodes. These findings offer implications for intermodal transport planning and regional development.Journal of Transport Geograph
Damping identification sensitivity in flutter speed estimation
Get PDFData supporting this study (− method MATLAB implementation) are openly available from the Zenodo Repository at https://doi.org/10.5281/zenodo.15176140. Furthermore, this study used existing authors’ data made available under licence at https://doi.org/10.5281/zenodo.11635814 and derived from the following resource available in the public domain: [11].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.Engineering and Physical Sciences Research Council (EPSRC)The authors from Cranfield University disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) [grant number 2277626]. The third author is supported by the Centro Nazionale per la Mobilità Sostenibile (MOST–Sustainable Mobility Center), Spoke 7 (Cooperative Connected and Automated Mobility and Smart Infrastructures), Work Package 4 (Resilience of Networks, Structural Health Monitoring and Asset Management).Vibratio
Advances in numerical modelling of tyre fatigue performance: a review
Get PDFThe growing emphasis on sustainability and environmental impact has driven increased demand for eco-friendly tyres. Tyre components are usually subjected to substantial static and dynamic load and often fail due to crack initiation and crack propagation. Understanding of the deformation mechanism of tyre components under fatigue loading is essential for enhancing the safety and reliability of tyres. In recent years, advanced tools for predicting fatigue and wear have been introduced, improving the accuracy of virtual prototyping and enabling more extensive evaluation of design concepts at early stages. This paper reviews recent advancements in the use of numerical methods for predicting fatigue failure and damage in tyre design. Given the limited research on numerical modelling for fatigue and fracture, there is a need for further investigation to develop reliable simulations for predicting tyre behaviour under fatigue loads. This review summarises the current applications of numerical fatigue modelling, providing engineers with a systematic overview of the literature, highlighting key achievements, and promoting further development in the field. The paper begins by discussing tyre components, followed by an exploration of material modelling techniques. It then addresses numerical modelling strategies for full-scale tyres under real-life loading conditions. Challenges in predicting fatigue failure using finite element (FE) modelling are examined, along with the issue of potential damage accumulation. Finally, the paper outlines recommendations for future research on FE modelling techniques, offering insights into current approaches and encouraging further investigation in the field.This work was supported by Innovate UK Knowledge Transfer Partnership (grant number: 10022506) at Cranfield University, with partial funding from Dunlop Aircraft Tyres Ltd.Journal of Materials Science: Materials in Engineerin
Contextual factors shaping surgeons' power dynamics and influence in hospital inventory systems: an exploratory study
No full textAlthough many studies have addressed the impact of stakeholders' power
dynamics on supply chain processes within the healthcare sector, a
comprehensive understanding of how hospital context, specifically funding
orientation and surgeons’ contract arrangements, shapes these dynamics within
Inventory Management (IM) in the Operating Theatres (OTs) remains notably
absent. This doctoral thesis endeavours to bridge this gap by conducting an in-
depth investigation into the influence of surgeons through power dynamics in four
hospitals using multiple case study methods.
In doing so, the central inquiry focuses on how a hospital's funding orientation
(public or private) and surgeons’ contract arrangements (hospital-based or non-
hospital-based) influence the power dynamics over SCM practices, particularly in
the inventory management (IM) field. The case studies reveal a close association
between surgeons’ power and these organisational and environmental contextual
elements. Notably, a high level of surgeons' influence in the IM process is
observed in contexts characterised by private-funding orientation and non-
hospital-based physician arrangements.
Conversely, the hospital case with public-funding orientation and hospital-based
surgeons exhibited low levels of surgeons’ influence within the IM process.
Recognising these contextual distinctions is crucial for Healthcare managers,
SCM professionals and policymakers. This thesis gives them valuable insights
into improving and implementing hospital SCM practices, acknowledging key
stakeholders' power dynamics and influence.Doctor of Business Administratio
Unmanned aerial vehicles as an efficient platform to enable agriculture's digital future
No full textIgnatyev, Dmitry I. - Associate SupervisorThe potential for Unmanned Aerial Vehicles to revolutionise agriculture,
particularly small-scale farms, is enormous. Today, most agriculture UAVs are
bulky, expensive, and primarily designed for heavy-lift applications, making them
unaffordable for most small-scale farmers. Despite the availability of smaller
UAVs, they were not designed for agricultural purposes, and have limited
endurance. This project presents an innovative solution that involves integration
of cylindrical Li-ion cells into the multirotor’s airframe structure, thereby cost-
effectively enhancing endurance. Through the proposed engineering design
approach, a prototype UAV is built and tested, demonstrating a significant
endurance improvement over similarly sized vehicles powered by LiPo batteries.
This design presents a budget friendly solution that could enable small-scale
farmers to take advantage of affordable UAV technology.MSc by Research in Aerospac
AI-driven maintenance optimisation for natural gas liquid pumps in the oil and gas industry: a digital tool approach
Get PDFNatural Gas Liquid (NGL) pumps are critical assets in oil and gas operations, where unplanned failures can result in substantial production losses. Traditional maintenance approaches, often based on static schedules and expert judgement, are inadequate for optimising both availability and cost. This study proposes a novel Artificial Intelligence (AI)-based methodology and digital tool for optimising NGL pump maintenance using limited historical data and real-time sensor inputs. The approach combines dynamic reliability modelling, component condition assessment, and diagnostic logic within a unified framework. Component-specific maintenance intervals were computed using mean time between failures (MTBFs) estimation and remaining useful life (RUL) prediction based on vibration and leakage data, while fuzzy logic- and rule-based algorithms were employed for condition evaluation and failure diagnoses. The tool was implemented using Microsoft Excel Version 2406 and validated through a case study on pump G221 in a Saudi Aramco facility. The results show that the optimised maintenance routine reduced the total cost by approximately 80% compared to conventional individual scheduling, primarily by consolidating maintenance activities and reducing downtime. Additionally, a structured validation questionnaire completed by 15 industry professionals confirmed the methodology’s technical accuracy, practical usability, and relevance to industrial needs. Over 90% of the experts strongly agreed on the tool’s value in supporting AI-driven maintenance decision-making. The findings demonstrate that the proposed solution offers a practical, cost-effective, and scalable framework for the predictive maintenance of rotating equipment, especially in environments with limited sensory and operational data. It contributes both methodological innovation and validated industrial applicability to the field of maintenance optimisation.Processe
ML-based surrogate cure simulation for predicting process time and temperature overshoot in resin transfer moulding
No full textThe cure stage of thermosetting composites production is critical for the overall process duration and manufacturing costs. While process simulations are commonly used to estimate cure behaviour, real-time predictive capabilities in resin transfer moulding (RTM) remain limited, primarily due to the computational cost of finite element (FE) methods. To address this gap and accurately estimate cure process parameters in RTM, this study proposes a surrogate cure simulation approach based on two state-of-the-art machine learning (ML) voting ensemble models – XGBoost and Light Gradient Boosting Machine – designed to predict cure time and temperature overshoot. To train the model, the cure of an epoxy/carbon fibre flat plate was simulated using the FE solver Marc, providing data over a wide range of conditions. The predictions of temperature overshoot and cure time demonstrate remarkable consistency and high accuracy (R2values up to 98%) with execution times under 30 ms for both variables. Performance was validated against unseen simulation data and further verified through RTM manufacturing trials and differential scanning calorimetry (DSC), confirming cure completion and a final glass transition temperature of 191°C–194°C. Unlike existing studies that remain simulation-focused, this approach bridges process simulation and data-driven modelling, offering a practical tool for real-time optimisation in industrial RTM applications.This work has received funding from the European Union’s Horizon 2020 innovation programme under grant agreement No. 871875 (SEER).Journal of Reinforced Plastics and Composite
Big data analytics in supply chain management: uncovering emerging trends through a bibliometric network analysis and a systematic literature review
No full textPurpose
This study aims to investigate the intersection of supply chain management (SCM) and big data analytics (BDA) through a multidimensional approach that incorporates bibliometric and network analysis (BNA) and a systematic literature review (SLR).
Design/methodology/approach
BNA and SLR are academic research methods, each with distinct purposes and limitations. BNA manages large datasets, while SLR focuses on smaller ones for in-depth review. As of January 2023, we analysed 851 articles retrieved from the Web of Science (WoS) core collection via BNA. To mitigate against BNA's limitations, we performed an SLR of 194 articles in 2023–2024, unveiling new themes in the “BDA in SCM” domain that were not apparent through BNA alone.
Findings
The findings demonstrate global collaboration patterns, highlighting China's lead in publications but lower international engagement than the USA's. BDA and the emerging discipline of supply chain resilience are closely interlinked; Industry 4.0 intertwines with sustainability and circular economy (CE) themes, and both are contributions that have been underexplored in previous reviews. Future studies will explore how BDA and other digital technologies enhance Supply Chain Ambidexterity by improving information processing in uncertain environments. Healthcare 4.0 technologies – specifically BDA, AI and Blockchain – boost efficiency, innovation, and risk management. However, the full potential of an intelligent Food Supply Chain (IFSC) lies in the integration of AI-driven systems, BDA and advanced analytics – a step that is still in its early stages.
Originality/value
Big data analytics in supply chain management is a rapidly evolving research domain without a review paper since 2020 and with more than 500 papers published in 2021–2022. Our novel methodology of supplementing BNA with an SLR allows us to capture newer or niche contributions in the domain of big data analytics in supply chain management. This approach sets the study apart, ensuring insights into the field's current state and future directions. Integrating quantitative (BNA) and qualitative (SLR) approaches provides a well-rounded perspective on the field.Journal of Enterprise Information Managemen
Seed germination response of various cannabis landraces to polyethylene glycol-induced drought stress
No full textThis study examined the impact of PEG-induced water stress on seed germination of eight cannabis landraces, namely, ‘B1’, ‘B2’, ‘B3’, ‘H1’, ‘L1’, ‘L2’, ‘M1’ and ‘M2’, sourced from four distinct regions of KwaZulu-Natal, South Africa: Bergville (B), Hammersdale (H), Ladysmith (L), and Msinga (M). Drought stress was induced using PEG 8000 (0, − 0.2, − 0.4, − 0.6, − 0.8, and − 1.0 MPa) in laboratory and greenhouse conditions. Germination percentage (GP), germination rate index (GRI), germination stress tolerance index (GSTI), seedling length, and seed vigour index (SVI) were recorded. In general, there were statistically significant differences (p 0.05) in germination percentage under greenhouse conditions. The germination characteristics were reduced under PEG treatments relative to the control. Under laboratory conditions, germination declined progressively with increasing PEG concentration. Complete inhibition of GP occurred at − 1.0 MPa in landraces ‘M1’ and ‘L1’. However, at this stress level, ‘L2’ had the highest GP of 58.67% and GRI of 1.78 seeds/day, while ‘B1’ exhibited the highest GSTI at 18.41%. In corresponding greenhouse experiment at − 1.0 MPa, ‘H1’ maintained the highest GP of 92.00%, while ‘B3’ achieved the highest GRI (5.28 seeds/day), GSTI (100.99%), seedling length (167.7 mm), and SVI (1.114). This study identified ‘L2’, ‘B1’, ‘H1’, and ‘B3’ as promising landraces for cultivation in water-limited environments and for use in breeding programs.This research was funded by Moses Kotane Institute 211526424Journal of Plant Growth Regulatio