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Ceramic matrix composites for aero engine applications - a review
Get PDFCeramic matrix materials have attracted great attention from researchers and industry due to their material properties. When used in engineering systems, and especially in aero-engine applications, they can result in reduced weight, higher temperature capability, and/or reduced cooling needs, each of which increases efficiency. This is where high-temperature ceramics have made considerable progress, and ceramic matrix composites (CMCs) are in the foreground. CMCs are classified into non-oxide and oxide-based ones. Both families have material types that have a high potential for use in high-temperature propulsion applications. The oxide materials discussed will focus on alumina and aluminosilicate/mullite base material families, whereas for non-oxides, carbon, silicon carbide, titanium carbide, and tungsten carbide CMC material families will be discussed and analyzed. Typical oxide-based ones are composed of an oxide fiber and oxide matrix (Ox-Ox). Some of the most common oxide subcategories are alumina, beryllia, ceria, and zirconia ceramics. On the other hand, the largest number of non-oxides are technical ceramics that are classified as inorganic, non-metallic materials. The most well-known non-oxide subcategories are carbides, borides, nitrides, and silicides. These matrix composites are used, for example, in combustion liners of gas turbine engines and exhaust nozzles. Until now, a thorough study on the available oxide and non-oxide-based CMCs for such applications has not been presented. This paper will focus on assessing a literature survey of the available oxide and non-oxide ceramic matrix composite materials in terms of mechanical and thermal properties, as well as the classification and fabrication methods of those CMCs. The available manufacturing and fabrication processes are reviewed and compared. Finally, the paper presents a research and development roadmap for increasing the maturity of these materials allowing for the wider adoption of aero-engine applications.Applied Science
Two-dimensional perovskite functionalized fiber-type heterostructured scintillators
Get PDFA fiber-type heterostructured scintillator based on bismuth germanate (Bi4Ge3O12) functionalized with the 2D-perovskite butylammonium lead bromide ((BA)2PbBr4) has been fabricated, and its scintillation performance analyzed toward its use for fast timing applications such as time-of-flight Positron Emission Tomography. The pixel shows energy sharing between the matrix and filler component, confirming that the two components are in synergy.Applied Physics Letter
Role of digitalized sustainable manufacturing in SME’S: a bibliometric analysis
Get PDFJournal title discontinued as of 2025The evolution of digital manufacturing is inherently linked with Computer Integrated Manufacturing (CIM) since 1980. CIM triggers the partial or fully automation in the industrial sector. Due to the global competitiveness and mandatory requirement of triple bottom line sustainability approach, the industries are on the verge of adapting digitalized sustainable manufacturing. The Digital manufacturing encompasses the whole integration throughout product life cycle and process, real-time monitoring of entire system, adaption of new innovative technologies in the context of sustainability. Nevertheless, DM is not restricted to a specific area, it includes all the tools and technologies for the quality improvement, customization, and efficient production strategies. The I4.0 technologies are vital elements of the Digital manufacturing paving the way to the sustainability. DM is the culmination of all information and communication technologies with high-speed computation capability, real-time data analysis by AI technology and finally cluster of all innovative technologies. Mostly all large-scale industrial sectors are adapting the digital technologies for existence survival in the international market. But the scenario of SME’S is contradictory, as they are perplexed in financial return, and ambiguous about the impact of these technologies on the accomplishment of overall sustainability. Especially, the adaption of innovative technologies in context of overall sustainability will cause a huge economic burden on the SME’S due to lack of funds and resources, and They must be reinforced by the government legislations and full back support from all corners. However, the past academic research was focused on the economic benefits of the digitalization and seldom efforts on providing insights about the integration of digitalization with triple bottom line sustainability. In addition, they envisioned the profitable aspects of the digital technologies in large scale manufacturing sector. These research gaps had been explored in the current study. The paper aims to conduct a bibliometric analysis on the past research developments in the digitalized sustainable manufacturing enhanced by the visualization software VOS in SME’S for providing a clear insight into the strategies, impediments, and ongoing trend in the small-scale sector.Materials Today: Proceeding
PEO-based polymer blend electrolyte for composite structural battery
Get PDFHigh mechanical strength and ionic conductivity of solid-state electrolyte (SSE) are currently conflicting targets that are very difficult to achieve. Polyethylene oxide (PEO) is one of the most common polymers adopted for SSE because of its very high ionic conductivity, but its mechanical strength is very low. This work discusses the manufacturing and testing of PEO blends using polysulfone (PSf) and Lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) to assess the mechanical response under microtensile testing and the ionic conductivity with electrochemical impedance spectroscopy (EIS). Mechanical tests demonstrated a beneficial effect of LiTFSI with significant increase in maximum stress and ductility measured as strain at failure. The blending of PEO and PSf showed promising conductivity values at room temperature with the 90–10 PEO-PSf composition achieving the highest value (1.06 × 10−6 S/cm) and for the 70–30 composition achieving the highest maximum stress (3.5 MPa) with a conductivity of 3.12 × 10−7 S/cm.Polymer-Plastics Technology and Engineerin
Multi-spectral fusion using generative adversarial networks for UAV detection of wild fires
Get PDFWild fires are now increasingly responsible for immense ecological damage. Unmanned aerials vehicles (UAVs) are being used for monitoring and early-detection of wild fires. Recently, significant research has been conducted for using Deep Learning (DL) vision models for fire and smoke segmentation. Such models predominantly use images from the visible spectrum, which are operationally prone to large false-positive rates and sub-optimal performance across environmental conditions. In comparison, fire detection using infrared (IR) images has shown to be robust to lighting and environmental variations, but long range IR sensors remain expensive. There is an increasing interest in the fusion of visible and IR images since a fused representation would combine the visual as well as thermal information of the image. This yields significant benefits especially towards reducing false positive scenarios and increasing robustness of the model. However, the impact of fusion of the two spectrum on the performance of fire segmentation has not been extensively investigated. In this paper, we assess multiple image fusion techniques and evaluate the performance of a U-Net based segmentation model on each of the three image representations - visible, IR and fused. We also identify subsets of fire classes that are observed to have better results using the fused representation.European Union funding: 7783052023 5th International Conference on Artificial Intelligence in Information and Communication (ICAIIC
Federated learning of wireless network experience anomalies using consumer sentiment
Get PDFIn wireless networks, consumer experience is important for both short monitoring of the Quality of Experience (QoE) as well as long term customer retainment. Current 4G and 5G networks are not equipped to measure QoE in an automated way, and experience is still reported through traditional customer care and drive-testing. In recent years, large-scale social media analytics has enabled researchers to gather statistically significant data on consumer experience and correlate them to major events such as social celebrations or significant network outages. However, the translational pathway from languages to topic-specific emotions (e.g., sentiment) to detecting anomalies in QoE is challenging. This challenge lies in two issues: (1) the social experience data remains sparsely distributed across space, and (2) anomalies in experience jump across sub-topic spaces (e.g., from data rate to signal strength). Here, we solved these two challenges by examining the spectral space of experience across topics using federated learning (FL) to identify anomalies. This can inform telecom operators to pay attention to potential network demand or supply issues in real time using relatively sparse and distributed data. We use real social media data curated for our telecommunication projects across London and the United Kingdom to demonstrate our results. FL was able to achieve 74-92% QoE anomaly detection accuracy, with the benefit of 30-45% reduce data transfer and preserving privacy better than raw data transfer.European Union funding: 7783055th International Conference on Artificial Intelligence in Information and Communication, 2023 (ICAIIC
A physics-based modelling and control of greenhouse system air temperature aided by IoT technology
Get PDFThe need to reduce energy consumption in greenhouse production has grown. Thermal heating demand alone accounts for 80% of conventional greenhouse energy consumption; this significantly reduces production profit. Since microclimate affects crop metabolic processes and output, it is essential to monitor and control it to achieve both quantity and quality production with minimum energy consumption for maximum profit. The Internet of Things (IoT) is an evolving technology for monitoring and controlling environments that have recently been adopted to boost greenhouse efficiency in many applications by integrating hardware and software solutions; therefore, its adoption is thus critical in enabling greenhouse energy consumption minimisation. The first objective of this study is to improve and validate a greenhouse dynamic air temperature model required to simulate or predict indoor temperature. To achieve the first objective, therefore, an existing model was enhanced and a closed loop test experimental data from the IoT cloud-based control system platform deployed in the prototype greenhouse built in Cranfield University was used to validate the model using an optimisation-based model fitting approach. The second goal is to control the greenhouse air temperature in simulation using relatively simple PI and on-off control strategies to maintain the grower’s desired setpoint irrespective of the inevitable disturbances and to verify the potential of the controllers in minimising the total energy input to the greenhouse. For the second objective, the simulation results showed that the two controllers maintained the desired setpoint; however, the on-off strategy retained a sustainable oscillation, and the tuned PI effectively maintained the desired temperature, although the average energy used by the controllers is the same.Energie
Assessing supply chain innovations for building resilient food supply chains: an emerging economy perspective
Get PDFFood waste reduction and security are the main concerns of agri-food supply chains, as more than thirty-three percent of global food production is wasted or lost due to mismanagement. The ongoing challenges, including resource scarcity, climate change, waste generation, etc., need immediate actions from stakeholders to develop resilient food supply chains. Previous studies explored food supply chains and their challenges, barriers, enablers, etc. Still, there needs to be more literature on the innovations in supply chains that can build resilient food chains to last long and compete in the post-pandemic scenario. Thus, studies are also required to explore supply chain innovations for the food sector. The current research employed a stepwise weight assessment ratio analysis (SWARA) to assess the supply chain innovations that can develop resilient food supply chains. This study is a pioneer in using the SWARA application to evaluate supply chain innovation and identify the most preferred alternatives. The results from the SWARA show that ‘Business strategy innovations’ are the most significant innovations that can bring resiliency to the food supply chains, followed by ‘Technological innovations.’ The study provides insights for decision makers to understand the significant supply chain innovations to attain resilience in food chains and help the industry to survive and sustain in the long run
Can wastewater monitoring protect public health in schools?
Get PDFThe Lancet Regional Health. America
Overall equipment effectiveness as a metric for assessing operational losses in wind farms: a critical review of literature
Get PDFTo become more competitive, less dependent on financial support and more attractive for investors, wind energy needs to reduce its final cost of energy. According to Levelized Cost of Energy, there are two ways to achieve this goal, by reducing costs or increasing production. Overall Equipment Effectiveness (OEE) is a widely used metric in manufacturing systems, supporting operators to enhance productivity by reducing operational losses. Therefore, this study aims to perform a qualitative literature review of the main operational losses following the OEE metric, namely availability, performance and quality, adjusting it to wind energy systems. Introduction of this metric can be a valuable tool towards an integrated indicator linking production and losses, allowing comparison between assets deployed in different settings.International Journal of Sustainable Energ