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Randomised nano-/micro- impact testing – A novel experimental test method to simulate erosive damage caused by solid particle impacts
Get PDFA novel randomised nano-/micro-scale impact test method has been developed to experimentally simulate particulate erosion where statistically distributed impacts with defined energy occur sequentially within the test area. Tests have been performed on two brittle glasses (fused silica and BK7) to easily highlight the interaction between impacts, as well as on two ceramic thermal barrier coating systems (TBCs, yttria stabilised zirconia, 7YSZ, and gadolinium zirconate, GZO) that experience erosion in service. Differences in erosion resistance were reproduced in the randomised impact tests, with GZO less impact resistant than 7YSZ, and BK7 significantly worse than fused silica. The impact data show that erosion resistance is influenced by different factors for the glasses (crack morphology, longer-length interaction of radial-lateral cracks in BK7 vs cone-cracking in fused silica) and TBCs (fracture toughness).Support from Innovate UK under Smart Award project #10020751, High temperature tools for designing sustainable erosion resistant coatings, is gratefully acknowledged.Tribology Internationa
Managing upward and downward through informal networks in Jordan: the contested terrain of performance management
Get PDFThis study explores how local managers, in practicing Human Resource management (HRM), may pursue their own interests that are out of line with the agendas of headquarters in multinational companies (MNCs). It is widely acknowledged that informal networks have an impact on HRM practices in emerging markets. While these networks are often regarded as beneficial for organizations in compensating for institutional shortfalls, they may also lead to corruption, nepotism, or other ethical transgressions. Indigenous scholarship on informal networks in emerging markets has highlighted how their impact occurs through a dynamic process; powerful placeholders deploy informal networks to entrench existing power and authority relations when managing people. Qualitative data were gathered through 43 in-depth interviews and documentary evidence from MNCs operating in Jordan. MNCs are subject to both home and host country effects; we highlight how, in practicing HRM, country of domicile managers deploy the cultural scripts of wasta informal network to secure and enhance their own relative authority. HRM practices are repurposed by actors who secure and consolidate their power through wasta. They dispense patronage to insiders and marginalize outsiders; the latter includes not only more vulnerable local employees but also expatriates. This phenomenon becomes particularly evident during the performance appraisal process, which may serve as a basis for the differential treatment and rewards of employees. Consequently, this further dilutes the capacity of MNCs to implement—as adverse to espousing—centrally decided approaches to HRM
Surviving the storm: navigating the quadruple whammy impact on Europe’s food supply chain
Get PDFThis article explores the impact of the ‘Quadruple Whammy’ consisting of Brexit, COVID-19, Conflicts (Russia-Ukraine and Israel-Palestine) and Natural disasters on the food supply chain in Europe. This research adopted a two-phase methodology comprised of the e-Delphi technique followed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach within the context of these four identified challenges. The objective of this article is to analyse the challenges faced by the European food supply chain due to these four factors. The article examines the impact of political isolationism such as Brexit on trade, cost and border controls, while also discussing the effects of COVID-19 on labour, supply chains and the rise of e-commerce. In addition, the article examines the impact of conflicts on food access and availability and the role of international aid and assistance. The effects of natural disasters, such as the Turkish and Moroccan earthquakes, floods in Spain and Portugal and the Moroccan drought, on food security are also analysed. The article offers several strategies for taming the quadruple whammy, such as investing in local food production and supply chains, diversifying supply chains and trade partnerships and strengthening food safety regulations and standards. The importance of building resilience and preparedness in the face of these challenges is emphasised and the article concludes with final thoughts and recommendations.International Journal of Food Science & Technolog
Artificial neural network for preliminary design and optimisation of civil aero-engine nacelles
Get PDFWithin the context of preliminary aerodynamic design with low order models, the methods have to meet requirements for rapid evaluations, accuracy and sometimes large design space bounds. This can be further compounded by the need to use geometric and aerodynamic degrees of freedom to build generalised models with enough flexibility across the design space. For transonic applications, this can be challenging due to the non-linearity of these flow regimes. This paper presents a nacelle design method with an artificial neural network (ANN) for preliminary aerodynamic design. The ANN uses six intuitive nacelle geometric design variables and the two key aerodynamic properties of Mach number and massflow capture ratio. The method was initially validated with an independent dataset in which the prediction error for the nacelle drag was 2.9% across the bounds of the metamodel. The ANN was also used for multi-point, multi-objective optimisation studies. Relative to computationally expensive CFD-based optimisations, it is demonstrated that the surrogate-based approach with ANN identifies similar nacelle shapes and drag changes across a design space that covers conventional and future civil aero-engine nacelles. The proposed method is an enabling and fast approach for preliminary nacelle design studies.The authors thank Rolls-Royce plc for supporting this research. Partial financial support was received from the INNOVATE UK FANFARE and the INNOVATIVE UK iFAN projects
Are you as safe at a concert or Music Festival as you think you are?
No full textPoster contribution to the Defence and Security Doctoral Symposium 2023
Collective Anomaly Perception During Multi-Robot Patrol: Constrained Interactions Can Promote Accurate Consensus
Get PDFPoster contribution to the Defence and Security Doctoral Symposium 2023 University of Bristo
Integrated power and thermal management systems for civil aircraft: review, challenges, and future opportunities
Get PDFProjects related to green aviation designed to achieve fuel savings and emission reductions are increasingly being established in response to growing concerns over climate change. Within the aviation industry, there is a growing trend towards the electrification of aircraft, with more-electric aircraft (MEA) and all-electric aircraft (AEA) being proposed. However, increasing electrification causes challenges with conventional thermal management system (TMS) and power management system (PMS) designs in aircraft. As a result, the integrated power and thermal management system (IPTMS) has been developed for energy-optimised aircraft projects. This review paper aims to review recent IPTMS progress and explore potential design solutions for civil aircraft. Firstly, the paper reviews the IPTMS in electrified propulsion aircraft (EPA), presenting the architectures and challenges of the propulsion systems, the TMS cooling strategies, and the power management optimisation. Then, several research topics in IPTMS are reviewed in detail: architecture design, power management optimisation, modelling, and analysis method development. Through the review of state-of-the-art IPTMS research, the challenges and future opportunities and requirements of IPTMS design are discussed. Based on the discussions, two potential solutions for IPTMS to address the challenges of civil EPA are proposed, including the combination of architecture design and power management optimisation and the combination of modelling and analysis methods.Applied Science
Arbitrary-order unstructured finite-volume methods for implicit large eddy simulation of turbulent flows with adaptive dissipation/dispersion adjustment (ADDA)
No full textImplicit Large Eddy simulation (iLES) has gained substantial popularity for modelling high-Reynolds-number turbulent flows, found across several engineering and scientific fields. iLES on a first read represents a simple, computationally efficient, and straightforward way to model turbulent flows. The numerical dissipation/dispersion errors of high-resolution high-order numerical schemes employed in this context, can mimic the effects of the unresolved flow scales, and therefore acting like a subgrid-scale model, most often successfully. The numerical dissipation and dispersion of high-resolution methods are intertwined and controlling them to obtain physically meaningful results in under-resolved grid settings of compressible flows is challenging due to the presence of discontinuities and smooth flow features simultaneously. A numerical method should have the right amount of dissipation/dispersion, such that it can avoid the unphysical “build-up” of energy in the high modes or excessive diffusion, since this will render the method unsuitable for iLES. Several elegant approaches to master this delicate balance have been presented in the literature, including polynomial de-aliasing, Riemann solver dissipation adjustment, and adaptive blending of central and upwind schemes. This work presents an adaptive dissipation/dispersion adjustment (ADDA) algorithm that determines a well-resolved and under-resolved region and adjusts the numerical dissipation/dispersion of a high-order CWENOZ scheme, followed by further adjusting the flux-dissipation term depending on the presence of discontinuities. Implemented within an arbitrary-order finite-volume framework for unstructured meshes in compressible flows, the ADDA algorithm is put to the test across a range of under-resolved iLES simulations encompassing subsonic, transonic, and supersonic regimes. The developed framework exhibits enhanced robustness and scale-resolving capabilities, all while achieving physically meaningful results in a computationally efficient manner. All the methods have been implemented and have been made available to the research community in the open-source UCNS3D CFD solver to accelerate the improvement of the methods.P.T. acknowledges the computing time on ARCHER2 through EPSRC UK Turbulence Consortium [EP/X035484/1] and the support provided by the EPSRC grant for “Adaptively Tuned High-Order Unstructured Finite-Volume Methods for Turbulent Flows” [EP/W037092/1].Journal of Computational Physic
Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches
Get PDFTuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the disease. Whilst many DR mutations in the Mtb genome are known, analysis of large datasets generated using whole genome sequencing (WGS) platforms can reveal new variants through the assessment of genotype-phenotype associations. Here, we apply tree-based ensemble methods to a dataset comprised of 35,777 Mtb WGS and phenotypic drug-susceptibility test data across first- and second-line drugs. We compare model performance across models trained using mutations in drug-specific regions and genome-wide variants, and find high predictive ability for both first-line (area under ROC curve (AUC); range 88.3–96.5) and second-line (AUC range 84.1–95.4) drugs. To aggregate information from low-frequency variants, we pool mutations by functional impact and observe large improvements in predictive accuracy (e.g., sensitivity: pyrazinamide + 25%; ethionamide + 10%). We further characterise loss-of-function mutations observed in resistant phenotypes, uncovering putative markers of resistance (e.g., ndh 293dupG, Rv3861 78delC). Finally, we profile the distribution of known DR-associated single nucleotide polymorphisms across discretised minimum inhibitory concentration (MIC) data generated from phenotypic testing (n = 12,066), and identify mutations associated with highly resistant phenotypes (e.g., inhA − 779G > T and 62T > C). Overall, our work demonstrates that applying machine learning to large-scale WGS data is useful for providing insights into predicting Mtb binary drug resistance and MIC phenotypes, thereby potentially assisting diagnosis and treatment decision-making for infection control.Biotechnology and Biological Sciences Research Council, Engineering and Physical Sciences Research Council, Medical Research CouncilScientific Report
Enhancing sustainability in manufacturing through cognitive digital twins powered by generative artificial intelligence
Get PDFThe rise of Industry 4.0 has brought new advancements in manufacturing, with a focus on integrating digital technologies to optimise processes and increase sustainability. Cognitive Digital Twins (CDTs) are emerging as a powerful paradigm in this area. They leverage advanced analytics, artificial intelligence (AI), and machine learning to create dynamic, real-time representations of physical manufacturing systems. This paper explores how CDTs can improve sustainability within the manufacturing sector. It proposes integrating generative artificial intelligence (GenAI) into the platforms that operate these digital twins to grant them cognitive capabilities. The work introduces a method for mapping and integrating energy consumption data to an Internet of Things (IoT) platform that includes the digital twin and a generative AI language model, such as ChatGPT. This proposed approach serves as a stepping stone towards unlocking the full potential of CDTs. It empowers manufacturers to achieve higher levels of sustainability and environmental responsibility.57th CIRP Conference on Manufacturing Systems 2024Procedia CIR