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Coupled hydro-aero-turbo dynamics of liquid-tank system for wave energy harvesting: numerical modellings and scaled prototype tests
Get PDFThe wave-energy-harvesting (WEH) liquid tank with an air-turbine system has distinct advantages in survivability and durability. Its air-turbine effects have long been simplified using orifices, perforated plates, or empirical formulae. This study proposes an integrated numerical model to couple with actual turbine motions. A series of experiments are conducted on a scaled prototype of the WEH liquid tank with an impulse air turbine system. Benchmark experimental data are obtained for validation of the numerical model. The proposed integrated numerical model accurately reproduces the experimental observations. The effects of turbine parameters on the coupled hydro-aero-turbo behavior are systematically investigated. The optimal power take-off damping for the WEH liquid tank is identified. A multi-layered impulse air turbine system (MLATS) is creatively introduced into the liquid-tank system to explore its capability in improving efficiency and reliability. Compared to the single-rotor case, the MLATS with three rotors can increase the averaged power output of the WEH liquid tank by up to 40%. Through a series of failure tests, a three-rotor turbine shows greater reliability than a conventional single-rotor turbine.This study was funded by National Natural Science Foundation of China (Grant No. 52471271, and U22A20242). The research was partly sponsored by the Shanghai Engineering Research Center of Offshore Wind Energy Development and Utilization Open Project of Shanghai Investigation, Design & Research Institute Co. Ltd (No. FNZX2023KP03-1).Energ
CRISPR/Cas-enabled paper microfluidic device to detect SARS- CoV-2 and its variants for wastewater-based epidemiology
No full textCullen, David C. - Associate SupervisorWastewater-based epidemiology (WBE) has been demonstrated to be a
powerful tool for monitoring public health through the detection of biomarkers,
such as drugs and pathogens, in wastewater. During the COVID-19 pandemic,
it evolved into a significant supplementary approach to clinical diagnostics,
offering capabilities as an early warning system, enabling population-level
infection monitoring, and tracking down the virus including the variants of
concern. However, current methods for wastewater analysis are heavily
dependent on the centralized laboratory which requires expensive equipment,
specialized personnel, and result in slow turnround. This thesis addresses
these critical challenges by developing innovative approaches for the detection
of SARS-CoV-2 and its variants, combining molecular diagnostics with field-
deployable paper microfluidic devices. A comparative analysis of polyethylene
glycol (PEG) precipitation and ultrafiltration for concentrating SARS-CoV-2
RNA from wastewater led to the development of an optimized protocol suitable
for routine analysis. This protocol was applied for long-term monitoring of viral
loads (N-gene) in a local wastewater treatment plant, providing valuable
insights into infection trends. Additionally, a CRISPR/Cas12a-based
fluorescent assay was developed to detect SARS-CoV-2 and its variants with
high specificity and sensitivity, able to detect as low as 5 copies µL¯¹ . To enable
on-site diagnostics, a portable paper microfluidic device was designed,
integrating the optimized concentration methods and CRISPR/Cas12a assays.
The device demonstrated a rapid detection of wastewater within 90 minutes for
SARS-CoV-2 even without laboratory settings. This research findings advances
WBE by bridging the gap between laboratory-based techniques and in-field
testing. The technology provides a scalable, cost-effective platform for rapid
and onsite monitoring SARS-CoV-2 and other pathogens, contributing
significantly to global health management, particularly in resource-limited
settings.PhD in Wate
Realities of using self-administered smartphone surveys to solve sustainability challenges
Get PDFTo fill data gaps in human-environment systems, especially in difficult-to-access locations, novel tools are needed to collect (near) real-time data from diverse populations across the globe. Here we discuss the practicalities, constraints, and lessons learnt from six field studies using high spatial and temporal smartphone surveys in six different countries. We suggest that high spatiotemporal, self-administered smartphone surveys will produce novel insights into human behaviour, attitudes, and socio-economic characteristics that, when matched with high spatiotemporal resolution environmental data (e.g., from remote sensing), can be used to address sustainability challenges for global communities. Furthermore, we highlight the need for continuous refinement and improvement in future developments to enhance the efficacy of this methodology. By sharing the practical implications and constraints associated with smartphone surveys, this article contributes to the evolving landscape of data collection methods.‘Scaling-up Off-grid Sanitation’ project (SOS; ES/T007877/1), funded with support from the United Kingdom’s Global Challenge Research Fund, via the ESRC.Cereal Systems Initiative for South Asia (CSISA) of the Consultative Group on International Agricultural Research (CGIAR), with funding provided by the United States Agency for International Development (USAID)Bill and Melinda Gates Foundation. ‘Using mobile-phone technology to capture ecosystem service information’ project (MobilES; ES/R009279/1), funded with support from the United Kingdom’s Global Challenge Research Fund, via the ESRCHumanities and Social Sciences Communication
Inclusive professional education for sustainability: the Cranfield MSc Sustainability
Get PDFThere is increasing pressure on business to contribute to solving global social and environmental challenges. Diversity can help companies better represent their stakeholders and find creative solutions to systemic challenges in partnership with others. Business schools play a key role in equipping leaders with key competencies for sustainability including systems thinking, collaboration and integrated problem solving. To do this effectively, business schools themselves need to be inclusive, not only to reduce inequalities, but to facilitate learning environments conducive to developing these competencies, and to bring together diverse talents with the shared purpose of creating value for society. We reflect on how to deliver interdisciplinary and inclusive professional education for sustainability, informed by our experience of designing and running Cranfield’s part-time MSc Sustainability. Iterating between our reflections, which draw on evidence from learners, faculty, staff and employers, and the prior literature, we develop a multi-level framework setting out how inclusive learning and teaching can be embedded at individual, course, institutional and societal level. Focusing on the needs of professionals helps attract learners who are representative of society, and who can create immediate positive impact through their organisations. Institutional enablers are critical to enhancing diversity through interdisciplinarity. Considering the societal level prompts educators to think about the societal context for, and impact of, their programmes. We highlight key lessons and best practice for future development of inclusive professional learning experiences at Cranfield and beyond.Handbook of Inclusive Learning and Teaching in Business and Managemen
EXPRESS: Complexity as a domain between order and chaos: implications for organizational scholarship
No full textOrganizations are grappling with increasingly complex challenges, including those stemming from technological disruptions, geopolitical uncertainty, and climate change. Despite the increasing acknowledgement of the complexity inherent in many organizational problems, complexity theory has had limited impact on mainstream management scholarship. Synthesizing contemporary complexity literature, we conceptualize complexity as a systemic property of a certain – and, we argue, rather broad – domain of organizational problems, and complexity theory as a theory of change in such organizational contexts. This view of complexity implies that complexity theory has important implications to organizational scholarship at large, indicates limitations of using conventional scientific methods, and suggests that the credibility and replication crises in many branches of organizational research may not be treatable simply by better statistical designs. Instead, methodological choices in complex organizational domains should take account of the properties of non-linearity and emergence, and organizational scholars should embrace complexity theory not only as an explanatory framework but also to inform research design.Strategic Organizatio
Machine Learning Technology in Biomedical Engineering
Get PDFThe Special Issue on "Machine Learning Technology in Biomedical Engineering" aims to provide a platform for researchers to showcase their latest research and findings on the application of machine learning technology in the field of biomedical engineering. The use of machine learning technology in healthcare has been growing rapidly in recent years and has the potential to revolutionize many aspects of healthcare, including disease diagnosis, treatment, and personalized medicine.
The Special Issue will cover a wide range of topics related to the application of machine learning in biomedical engineering, including predictive modeling, image and signal processing, deep learning, drug discovery, biomarker discovery, and medical decision-making. Contributions from interdisciplinary teams combining expertise in machine learning and biomedical engineering are encouraged."Machine Learning Technology in Biomedical Engineering" aims to provide a platform for researchers to showcase their latest research and findings on the application of machine learning technology in the field of biomedical engineering. The use of machine learning technology in healthcare has been growing rapidly in recent years and has the potential to revolutionize multiple aspects of healthcare, including disease diagnosis, treatment, and personalized medicine. This Special Issue covers a wide range of topics related to the application of machine learning in biomedical engineering, including predictive modelling, image and signal processing, deep learning, drug discovery, biomarker discovery, and medical decision making. By applying machine learning algorithms to large datasets of biomedical information, researchers and healthcare professionals can gain new insights into disease mechanisms, identify new biomarkers for disease, and develop more effective treatments. Machine learning algorithms can also be used to improve medical imaging analysis, automate medical diagnosis and decision making, and optimize drug-discovery processes. This Special Issue is significant because it encourages interdisciplinary collaboration between machine learning and biomedical-engineering researchersMachine Learning Technology in Biomedical EngineeringBioengineerin
Impact of interpass temperature on the microstructure and mechanical properties of super duplex stainless steel in CW-GMA additive manufacturing
Get PDFThis study investigates the influence of interpass temperature (IPT) on microstructure evolution and mechanical properties of super duplex stainless steel (SDSS) manufactured by cold wire gas metal arc (CW-GMA) additive manufacturing. Thermal cycle analysis showed that cooling rates were not significantly affected by IPT under constant process parameters. However, higher IPTs resulted in higher thermal accumulation and extended exposure to elevated temperatures. Microstructural characterisation revealed the transformation of δ-ferrite grains into various austenitic phases and secondary chromium nitrides during cooling. Fine, needle-like secondary austenite formation was more pronounced at higher IPTs, driven by chromium nitride precipitation near layer transitions. Mechanical testing demonstrated consistent ultimate tensile strength around 810 MPa across IPTs, with ductility variations attributed to porosity. Hardness profiles were uniform, averaging approximately 300 Hv. These findings suggest that while IPT influences thermal accumulation and microstructural details, its effect on ferrite-to-austenite ratio and mechanical properties is minimal. Optimising IPT remains essential for increasing the productivity of SDSS in CW-GMA additive manufacturing.The author expresses gratitude to Christof Group SBN for initiating the project and providing financial support.Journal of Manufacturing Processe
A framework for business cloud services (BCSS) in SMEs
Get PDFMcLaughlin, Patrick - Associate SupervisorThe Saudi economy is changing rapidly to move from depending on energy to one with
a wide range of indigenous businesses. Successful Small and Medium Enterprises (SMEs)
are important in this economic transformation. Many Saudi Arabia SMEs are adopting
Cloud Based Enterprise IT Services as the option to grow and sustain due to its cost and
agility. However, they are often confused by the large international Cloud Services
suppliers and not all SME reap the right business value from their Cloud Services
investments.
This research aims to create an approach to assist Saudi SMEs based on a strong research
understanding. A novel framework is created that takes into account of the different
functions of different Business Cloud Services, the different business requirements of
each SME and their existing capabilities and readiness. Rather than treating all Cloud
Services as the same, the researcher differentiates into three types: Digitalisation,
Packaged and Enterprise. The research relates the Technological Resources, Organisation
Capabilities, Organisational Requirements and Business Benefits for each of the Cloud
Service types and then integrate them into one cohesive framework.
This research adopted a pragmatic paradigm. The conceptual framework is developed
through literature, contextualised, and improved qualitatively through interviews with 17
experts and 54 practitioners. The model was quantitatively examined with 395
questionnaires. The framework was developed into a readiness assessment tool and
validated with three Saudi SMEs, who value the approach as beneficial business advice.
The framework contributes to the improved structural knowledge of Cloud Services
adoption. The constructs add contextualised factors to understand the business
considerations relevant to Saudi SMEs.PhD in Manufacturin
Calibration accuracy of RCS measurements in free space
No full textWhen investigating Radar Cross Section (RCS) of targets, it is essential to establish confidence in the experimental calibration data. In this paper, experiments are presented which were carried out to determine the calibration accuracy achieved in performing free space target RCS measurements in the radar lab at Cranfield University. Calibration data were collected in the X-band (between 8.5 GHz and 12 GHz) using four off-the-shelf non-calibrated metallic spheres of different diameter: 30 mm, 38 mm, 80 mm and 100 mm. The results indicate an accuracy of less than ±2 dBm2 for the spheres measured on a styrofoam stand and with a Signal to Noise Ratio (SNR) of at least 40 dB for all spheres.2025 IEEE International Workshop on Metrology for AeroSpac
BiDGCNLLM: A graph–language model for drone state forecasting and separation in urban air mobility using digital twin-augmented remote ID data
Get PDFThis article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV ApplicationAccurate prediction of drone motion within structured urban air corridors is essential for ensuring safe and efficient operations in Urban Air Mobility (UAM) systems. Although real-world Remote Identification (Remote ID) regulations require drones to broadcast critical flight information such as velocity, access to large-scale, high-quality broadcast data remains limited. To address this, this study leverages a Digital Twin (DT) framework to augment Remote ID spatio-temporal broadcasts, emulating the sensing environment of dense urban airspace. Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. The model forecasts near-future speed variations in surrounding drones, supporting proactive conflict avoidance in constrained air corridors. Results from the AirSUMO co-simulation platform and a DT replica of the Cranfield University campus show that BiDGCNLLM outperforms state-of-the-art time series models in short-term velocity prediction. Compared to Transformer-LSTM, BiDGCNLLM marginally improves the R2 by 11.59%. This study introduces the integration of LLMs into dynamic graph-based drone prediction. It shows the potential of Remote ID broadcasts to enable scalable, real-time airspace safety solutions in UAM.Drone