20505 research outputs found
Sort by
A comprehensive review on enhancing wind turbine applications with advanced SCADA data analytics and practical insights
Get PDFThe aim of this study is to explore the potential and economic benefits of utilising Supervisory Control and Data Acquisition (SCADA) data to improve wind turbine operation and maintenance activities. The review identifies a gap in the current understanding of how to effectively use SCADA data in wind turbine applications. It emphasises the need for pre-processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Additionally, it highlights the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data-driven machine learning models, and statistical regression models. The review also recognises the limitations caused by the lack of public data from wind turbine developers and the imbalance between normal operation data samples and abnormal data samples, negatively impacting model accuracy. The key findings of the review demonstrate that SCADA data-driven techniques can lead to significant improvements in wind turbine operations and maintenance. The application of data-driven technologies based on SCADA data has proven effective in reducing operation and maintenance costs and enhancing wind power generation. Moreover, the development of robust decision support systems using SCADA data minimises the need for frequent maintenance interventions in offshore wind farms. To bridge the gap and further enhance wind turbine applications using SCADA data, several recommendations are provided. These include encouraging greater openness in sharing SCADA data to improve the robustness and accuracy of AI models, adopting transfer learning techniques to overcome the scarcity of quality datasets, establishing unified standards and taxonomies, and providing specialised resources such as software applications with interactive graphical user interfaces for easier storage, annotation, and analysis of SCADA data.
The authors’ review paper identifies a gap in the current understanding of how to effectively utilise SCADA data in wind turbine applications. It emphasises the importance of pre-processing SCADA data to ensure data integrity by addressing outliers and employing interpolation techniques. Furthermore, the authors highlight the challenges associated with early fault detection methods using SCADA data, including the development of physical models, data-driven machine learning models, and statistical regression models.IET Renewable Power Generatio
A novel distributed authentication of blockchain technology integration in IoT services
Get PDFInternet of Things (IoT) is currently playing a major role in how intelligent devices are interconnected and deployed to automate services in transport and smart living sectors. However, IoT is facing challenges in terms of data protection and authentication due to the heterogeneous nature of IoT devices that do not exhibit a central authority. It is crucial to provide secure and trustworthy solutions for the increasing demands of decentralized IoT environments. To this end, this research proposes a novel integration of blockchain-technologies in IoT services to enhance security, data integrity, users privacy, system scalability and interoperability of devices. This is done by leveraging smart contracts to enforce authentication, access control and data exchange mechanisms for IoT devices. The proposed approach is verified by the construction and deployment of a smart contract over the Polygon blockchain network in a simulated real-world IoT scenario. The obtained results show that the proposed approach ensures fast and secure authentication in IoT networks by decreasing the risk of unauthorized access and data tampering.IEEE Acces
Enhancing microstructural and mechanical characteristics of laser welded NiTi SMA/304 SS lap joints with medium and high entropy alloy fillers
Get PDFThe demand for high-quality nickel-titanium (NiTi) shape memory alloy (SMA) and stainless steel (SS) welded structures has led to significant challenges in fusion welding technology, which is largely influenced by filler materials. This study explores the application of CoCrNi medium entropy alloy and CoCrNiFe high entropy alloy as filler materials for laser lap welding of NiTi SMA and 304 SS. A self-fusing joint was produced for comparison. The results demonstrate that the interface zone on the NiTi side was the weakest area regarding cracking defects in NiTi/304 SS lap joints. However, using CoCrNi and CoCrNiFe fillers effectively eliminated cracking defects by considerably suppressing the formation of brittle FeTi and Fe2Ti intermetallic compounds (IMCs). The average microhardness value of the weld zone without filler was higher than with CoCrNi and CoCrNiFe. Furthermore, the addition of CoCrNi and CoCrNiFe fillers to NiTi/304 SS lap joints resulted in a substantial increase in tensile properties, with tensile strength reaching 196 ± 39 MPa without filler, 319 ± 25 MPa with CoCrNi, and 377 ± 33 MPa with CoCrNiFe, respectively.This work was supported by Beijing Natural Science Foundation (No. 3232005), the National Natural Science Foundation of China (No. 52074017) and China-CEEC Joint Education Project for Higher Education (No. 2021113).Welding in the Worl
Resilient multi-sensor UAV navigation with a hybrid federated fusion architecture
Get PDFFuture UAV (unmanned aerial vehicle) operations in urban environments demand a PNT (position, navigation, and timing) solution that is both robust and resilient. While a GNSS (global navigation satellite system) can provide an accurate position under open-sky assumptions, the complexity of urban operations leads to NLOS (non-line-of-sight) and multipath effects, which in turn impact the accuracy of the PNT data. A key research question within the research community pertains to determining the appropriate hybrid fusion architecture that can ensure the resilience and continuity of UAV operations in urban environments, minimizing significant degradations of PNT data. In this context, we present a novel federated fusion architecture that integrates data from the GNSS, the IMU (inertial measurement unit), a monocular camera, and a barometer to cope with the GNSS multipath and positioning performance degradation. Within the federated fusion architecture, local filters are implemented using EKFs (extended Kalman filters), while a master filter is used in the form of a GRU (gated recurrent unit) block. Data collection is performed by setting up a virtual environment in AirSim for the visual odometry aid and barometer data, while Spirent GSS7000 hardware is used to collect the GNSS and IMU data. The hybrid fusion architecture is compared to a classic federated architecture (formed only by EKFs) and tested under different light and weather conditions to assess its resilience, including multipath and GNSS outages. The proposed solution demonstrates improved resilience and robustness in a range of degraded conditions while maintaining a good level of positioning performance with a 95th percentile error of 0.54 m for the square scenario and 1.72 m for the survey scenario.Sensor
Understanding practices which foster inclusion: views from the top
Get PDFPurpose
This study aims to deepen our understanding of how inclusionary practices are used within organizations and how they satisfy specific inclusion needs.
Design/methodology/approach
We adopt a qualitative research design, reporting on data from semi-structured interviews conducted with 15 diversity and inclusion (D&I) directors/leads and using a thematic approach to analysis.
Findings
Our study expands understanding of inclusion practices, showing that they are not uniformly implemented and that practices may satisfy both needs to belong and differences valued, with interviewees prioritizing belonging. Well-being and career development are seen as important inclusion practices demonstrating support and appreciation of difference, thus as inputs, not outputs, of inclusion challenging existing assumptions. Inclusionary practices are malleable, and their impact depends critically on the leaders involved and their commitment to EDI.
Originality/value
Our study shows how practices satisfy inclusion needs and that the implementation of practices varies depending on the leaders involved
Catchment-scale challenges for water resources management: assessing ‘reasonable’ peak needs for irrigated agriculture in a humid climate
Get PDFRising demands and competition for water resources within all sectors are placing increasing pressure on the environment. Almost all direct abstractions in England require a licence (permit) from the regulatory authority, the Environment Agency. Assessing and setting ‘reasonable’ peak quantities of water that can be legally abstracted in an environmentally sustainable manner is central to the whole licence determination process. To protect environmental flows and other abstractors within each catchment, the regulatory authority needs to be able to set sensible limits in the licence conditions, including total seasonal volumes and peak rates of water use, particularly for abstractions from hydrologically sensitive surface water sources. This paper describes the development of a methodology to assess the ‘reasonable’ peak rates of water use for agricultural irrigation in support of catchment water resources management and planning. A daily time step water balance model was used to simulate peak monthly and daily water requirements for irrigation using long-term historical weather records for agroclimatically contrasting sites. The model-simulated outputs were then compared against observed data from selected case study farms, and against data reported in a national water abstraction database. Guidelines were then developed for setting peak monthly, daily, hourly, and absolute abstraction rates for irrigation, taking into account the environmental sensitivity of different types of water source. The application of the procedure and its relevance in other countries where catchment water resources are under intense pressure from agriculture are described.Hydrolog
Ni-based bimetallic catalysts for hydrogen production via (sorption-enhanced) steam methane reforming
Get PDFThe catalytic performance of a monometallic Ni/Al2O3 and three bimetallic catalysts (Ni3M1/Al2O3, with M = Cu, Fe, and Ge) for the (sorption-enhanced) steam methane reforming reaction was evaluated. Ni3Cu1/Al2O3 was found to be the optimal catalyst in terms of methane conversion, hydrogen yield, and purity. Ge also has a promoting effect on the monometallic Ni catalyst, whereas the addition of Fe negatively influenced its performance. Physico-chemical characterization of the materials indicated the formation of alloys upon activation of the materials with hydrogen. The addition of Cu increased the surface area and metal dispersion, and improved the overall morphology of the catalyst. The experimental observations were also supported by a numerical study combining Density Functional Theory-based calculations and Microkinetic modelling of the SMR process. Ni3Cu1 and Ni3Ge1 were calculated to have a similar level of catalytic activity as Ni, whereas Ni3Fe1 was unsuitable for the reaction. The SMR reaction was further improved by adding calcium oxide as the CO2 sorbent, which increased methane conversion, CO selectivity, hydrogen yield, and hydrogen purity. The highest methane conversion of 97 % was achieved by Ni/Al2O3 and Ni3Cu1/Al2O3 at 700 °C.Chemical Engineering Journa
Fracture behaviour of aluminium alloys under coastal environmental conditions: a review
Get PDFAluminium alloys have been integral to numerous engineering applications due to their favourable strength, weight, and corrosion resistance combination. However, the performance of these alloys in coastal environments is a critical concern, as the interplay between fracture toughness and fatigue crack growth rate under such conditions remains relatively unexplored. This comprehensive review addresses this research gap by analysing the intricate relationship between fatigue crack propagation, fracture toughness, and challenging coastal environmental conditions. In view of the increasing utilisation of aluminium alloys in coastal infrastructure and maritime industries, understanding their behaviour under the joint influences of cyclic loading and corrosive coastal atmospheres is imperative. The primary objective of this review is to synthesise the existing knowledge on the subject, identify research gaps, and propose directions for future investigations. The methodology involves an in-depth examination of peer-reviewed literature and experimental studies. The mechanisms driving fatigue crack initiation and propagation in aluminium alloys exposed to saltwater, humidity, and temperature variations are elucidated. Additionally, this review critically evaluates the impact of coastal conditions on fracture toughness, shedding light on the vulnerability of aluminium alloys to sudden fractures in such environments. The variability of fatigue crack growth rates and fracture toughness values across different aluminium alloy compositions and environmental exposures was discussed. Corrosion–fatigue interactions emerge as a key contributor to accelerated crack propagation, underscoring the need for comprehensive mitigation strategies. This review paper highlights the pressing need to understand the behaviour of aluminium alloys under coastal conditions comprehensively. By revealing the existing research gaps and presenting an integrated overview of the intricate mechanisms at play, this study aims to guide further research and engineering efforts towards enhancing the durability and safety of aluminium alloy components in coastal environments.Metal
Dual-emission single sensing element-assembled fluorescent sensor arrays for the rapid discrimination of multiple surfactants in environments
Get PDFSurfactants are considered as typical emerging pollutants, their extensive use of in disinfectants has hugely threatened the ecosystem and human health, particularly during the pandemic of coronavirus disease-19 (COVID-19), whereas the rapid discrimination of multiple surfactants in environments is still a great challenge. Herein, we designed a fluorescent sensor array based on luminescent metal–organic frameworks (UiO-66-NH2@Au NCs) for the specific discrimination of six surfactants (AOS, SDS, SDSO, MES, SDBS, and Tween-20). Wherein, UiO-66-NH2@Au NCs were fabricated by integrating UiO-66-NH2 (2-aminoterephthalic acid-anchored-MOFs based on zirconium ions) with gold nanoclusters (Au NCs), which exhibited a dual-emission features, showing good luminescence. Interestingly, due to the interactions of surfactants and UiO-66-NH2@Au NCs, the surfactants can differentially regulate the fluorescence property of UiO-66-NH2@Au NCs, producing diverse fluorescent “fingerprints”, which were further identified by pattern recognition methods. The proposed fluorescence sensor array achieved 100% accuracy in identifying various surfactants and multicomponent mixtures, with the detection limit in the range of 0.0032 to 0.0315 mM for six pollutants, which was successfully employed in the discrimination of surfactants in real environmental waters. More importantly, our findings provided a new avenue in rapid detection of surfactants, rendering a promising technique for environmental monitoring against trace multicontaminants.Analytical Chemistr
Passenger spoofing attack for artificial Intelligence-based Mobility-as-a-Service
Get PDFMobility-as-a-Service (MaaS), a new mobility service model that integrates multiple mobility providers, relies on many data processing technologies to manage multi-modal transport. Artificial Intelligence (AI) is one of the technologies to improve the services matching to passengers based on their implicit experience and preference. However, incorporating AI into MaaS may also introduce loopholes to the system. One may use the loophole in the heterogeneity of passenger experience and preference by falsifying data to prioritize their journey, which jeopardizes the trustworthiness of MaaS. In this paper, we investigate the cyber security risks in MaaS, focusing on the spoofing attack in which malicious passengers are prioritized by falsifying data to gain an advantage in journey planning. The spoofing attack is based on reinforcement learning that learns to reduce passenger satisfaction about the MaaS and its profit by requesting travel with falsifying passenger states. We conduct experiments based on New York City dataset to evaluate the spoofing attack. The experiment results indicate that the attack can reduce about 70% of the profit. By investigating the cyber security risks in MaaS, we could enhance the knowledge and understanding of the risks for building a secure and trustworthy MaaS.This work was supported by EPSRC MACRO - Mobility as a service: Managing Cybersecurity Risks across Consumers, Organisations and Sectors (EP/V039164/1)2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC