Outdoor Insulation and Gas Insulated Switchgears

This book focuses on theoretical and practical developments in the performance of high-voltage transmission line against atmospheric pollution and icing. Modifications using suitable fillers are also pinpointed to improve silicone rubber insulation materials. Very fast transient overvoltage (VFTO) mitigation techniques, along with some suggestions for reliable partial discharge measurements under DC voltage stresses inside gas-insulated switchgears, are addressed. The application of an inductor-based filter for the protective performance of surge arresters against indirect lightning strikes is also discussed

Learning-Based Data-Driven and Vision Methodology for Optimized Printed Electronics

Inkjet printing is an active domain of additive manufacturing and printed electronics due to its promising features, starting from low-cost, scalability, non-contact printing, and microscale on-demand pattern customization. Up until now, mainstream research has been making headway in the development of ink material and printing process optimization through traditional methods, with almost no work concentrated on machine learning and vision-based drop behavior prediction, pattern generation, and enhancement. In this work, we first carry out a systematic piezoelectric drop on demand inkjet drop generation and characterization study to structure our dataset, which is later used to develop a drop formulation prediction module for diverse materials. Machine learning enables us to predict the drop speed and radius for particular material and printer electrical signal configuration. We verify our prediction results with untested graphene oxide ink. Thereafter, we study automated pattern generation and evaluation algorithms for inkjet printing via computer vision schema for several shapes, scales and finalize the best sequencing method in terms of comparative pattern quality, along with the underlying causes. In a nutshell, we develop and validate an automated vision methodology to optimize any given two-dimensional patterns. We show that traditional raster printing is inferior to other promising methods such as contour printing, segmented matrix printing, depending on the shape and dimension of the designed pattern. Our proposed vision-based printing algorithm eliminates manual printing configuration workload and is intelligent enough to decide on which segment of the pattern should be printed in which order and sequence. Besides, process defect monitoring and tracking has shown promising results equivalent to manual short circuit, open circuit, and sheet resistance testing for deciding over pattern acceptance or rejection with reduced device testing time. Drop behavior forecast, automatic pattern optimization, and defect quantization compared with the designed image allow dynamic adaptation of any materials properties with regards to any substrate and sophisticated design as established here with varying material properties; complex design features such as corners, edges, and miniature scale can be achieved

Learning-Based Data-Driven and Vision Methodology for Optimized Printed Electronics

Inkjet printing is an active domain of additive manufacturing and printed electronics due to its promising features, starting from low-cost, scalability, non-contact printing, and microscale on-demand pattern customization. Up until now, mainstream research has been making headway in the development of ink material and printing process optimization through traditional methods, with almost no work concentrated on machine learning and vision-based drop behavior prediction, pattern generation, and enhancement. In this work, we first carry out a systematic piezoelectric drop on demand inkjet drop generation and characterization study to structure our dataset, which is later used to develop a drop formulation prediction module for diverse materials. Machine learning enables us to predict the drop speed and radius for particular material and printer electrical signal configuration. We verify our prediction results with untested graphene oxide ink. Thereafter, we study automated pattern generation and evaluation algorithms for inkjet printing via computer vision schema for several shapes, scales and finalize the best sequencing method in terms of comparative pattern quality, along with the underlying causes. In a nutshell, we develop and validate an automated vision methodology to optimize any given two-dimensional patterns. We show that traditional raster printing is inferior to other promising methods such as contour printing, segmented matrix printing, depending on the shape and dimension of the designed pattern. Our proposed vision-based printing algorithm eliminates manual printing configuration workload and is intelligent enough to decide on which segment of the pattern should be printed in which order and sequence. Besides, process defect monitoring and tracking has shown promising results equivalent to manual short circuit, open circuit, and sheet resistance testing for deciding over pattern acceptance or rejection with reduced device testing time. Drop behavior forecast, automatic pattern optimization, and defect quantization compared with the designed image allow dynamic adaptation of any materials properties with regards to any substrate and sophisticated design as established here with varying material properties; complex design features such as corners, edges, and miniature scale can be achieved

Proceedings of the Cardiff University Engineering Research Conference 2023

The conference was established for the first time in 2023 as part of a programme to sustain the research culture, environment, and dissemination activities of the School of Engineering at Cardiff University in the United Kingdom. The conference served as a platform to celebrate advancements in various engineering domains researched at our School, explore and discuss further advancements in the diverse fields that define contemporary engineering

The research on mechanical properties and compressive behavior of graphene foam with multi-scale model?

Computational simulation is an effective method to study the deformation mechanism and mechanical behaviour of graphene-based porous materials. However, due to limitations in computational methods and costs, existing research model deviate significantly from the real material in terms of the scale of structure. Therefore, building a highly accurate computational model and maintaining an appropriate cost is both necessary and challenging. This paper proposed a multi-scale modelling approach for finite element (FE) analysis based on the concept of structural hierarchy. The stochastic feature of the microstructure of porous materials are also considered. The simulation results of the regular structure model and the Voronoi tessellation model are compared to investigate the effect of regularity on the material properties. Despite some shortcomings, other microstructural features of porous graphene materials can be gradually introduced to improve the material model step by step. Thus the developed multiscale model has great potential to simulate the properties of materials with mesoscopic size structure such as graphene foam (GF)

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

Digital Microfluidics for Isothermal Nucleic Acid Amplification: Exploring Sensing Methodologies

Digital Microfluidics (DMF) has recently emerged as a promising candidate for nucleic acid amplification for molecular diagnostics, by virtue of its precise control over unit droplets without the need of any propulsion devices, ease of integration with chemical/biological reac-tions and multiplex assay capabilities. Nevertheless, current scientific research is still far from accomplishing the full potential of the technique, so new, innovative nanotechnology/biotech-nology hybrid approaches are necessary. As such, the purpose of this work is to contribute for the paradigm shift of nucleic acid amplification from central laboratories to point-of-care (POC) by designing and fabricating DMF devices compatible with isothermal nucleic acid amplifica-tion (loop-mediated isothermal amplification - LAMP). For biological validation of the devices, detection of cancer biomarker c-Myc is performed, and further real-time amplification moni-toring is attempted through several methodologies, namely fluorescence, impedance and elec-trochemical measurements. The DMF devices produced herein enable optimal temperature control, crucial for LAMP reactions, and further allow for a novel methodology of reagent mix-ing, based on dual actuation with back-and-forth motion and actuation frequency tuning. Such innovations lead to successful amplification of 0.5 ng/μL or 90 pg of c-Myc in one hour, in line with the range reported in the literature, and further monitoring of the LAMP reaction profile by microscopy-based fluorescence measurements. Impedimetric and electrochemical method-ologies did not meet the tight criteria required for biomarker detection, yet the developments achieved herein open the path for other applications. Lastly, the dielectric layer (key element of a DMF device) was optimized to assure long reactions (up to two hours) without device degradation.A microfluídica digital (MFD) surgiu como uma tecnologia promissora para amplificação de ácidos nucleicos em diagnóstico molecular, permitindo controlo sobre gotas unitárias sem necessidade de dispositivos de propulsão, facilidade de integração com reações químicas/bi-ológicas e capacidade de realização de ensaios simultâneos. Contudo, a investigação científica atual ainda está longe de atingir o máximo potencial da técnica, pelo que são necessárias abordagens novas, inovadoras e híbridas de nanotecnologia e biotecnologia. Como tal, o pro-pósito deste trabalho é contribuir para a mudança de paradigma da amplificação de ácidos nucleicos de laboratórios centralizados para ponto-de-atendimento (PDA) através do desenho e fabricação de dispositivos de MFD compatíveis com amplificação isotérmica de ácidos nu-cleicos (loop-mediated istothermal amplification - LAMP). Para validação biológica dos dispo-sitivos, será detetado o biomarcador de cancro c-Myc, e testada a monitorização da amplifica-ção em tempo real através de várias metodologias, nomeadamente medidas de fluorescência, impedância ou medidas eletroquímicas. Os dispositivos MFD produzidos permitem um con-trolo ótimo da temperatura, crucial para reações LAMP, e introduzem uma metodologia para mistura de reagentes, com movimentos em vaivém e ajuste da frequência de atuação. Tais inovações conduziram à amplificação de 0.5 ng/μL ou 90 pg de c-Myc em uma hora, em linha com o intervalo relatado na literatura, permitindo ainda monitorização do perfil da reação LAMP através de medidas de fluorescência mediadas por microscopia. As metodologias impe-dimétricas e eletroquímicas não cumpriram os exigentes critérios requeridos para deteção de biomarcadores, no entanto, os desenvolvimentos alcançados abrem caminho para outras apli-cações. Por último, a camada dielétrica (elemento-chave de um dispositivo MFD) foi otimizada para assegurar reações mais longas (até duas horas) sem degradação do dispositivo