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Peculiarities of thermal and energy state variation in phase change regimes of water droplets in radiating biofuel flue gas flow
No full textData availability: Data will be made available on request.A mathematical model is provided for the heat and mass transfer processes of a semi-transparent droplet. It evaluates the variation of liquid physical properties, when the droplet warms in a high temperature gas flow. A numerical investigation methodology, based on an iterative algorithm for instantaneous droplet surface temperature, is developed to determine the dynamics of the thermal and energy states of water droplets in phase change regimes. The condensation, transitional and equilibrium evaporation regimes have been modelled for droplets at a temperature of 40ºC, which is common in biofuel combustion technologies. Flue gases were considered as a hot and humid air flow in the 150ºC-1000ºC temperature range and a humidity of 0.4 according to the water vapour volumetric fraction, where droplets are slipping with an initial velocity of up to 60 m/s and their diameter is in the range of 50-1000 μm. In phase change regimes a detailed assessment is provided for heat and mass transfer parameters for droplets slipping in a radiating high temperature humid gas flow. It was confirmed that the thermal and energy state variations of water droplets in a flue gas flow are defined by the change of heat transfer regime, which is caused by the weakening of droplet slipping in the gas flow and radiation absorption weakening during their evaporation.The authors gratefully acknowledge the partial support from project “Establishment of Nordic-Baltic PhD and researcher mobility network in the field of the bioenergy” supported by Nordic Energy Research under the grant number 10539
Project complexity management – A design-based approach to managing information technology projects
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonProject Management (PM) in addition to Software Engineering (SE), Information Technology (IT) and User Experience Design (UXD), are crucial fields in today’s age, acknowledged and well-known, with well-defined tools, methods, benchmarks, qualifications, and certified bodies. Whilst present day information technology engineering projects, establishments, practices, know-how and businesses are becoming further complex, complex projects still face poor understanding and encounter substantial hurdles and issues.
The IT engineering world becomes further complex on a daily basis. IT products are more intricate and large-scaled, projects are becoming more demanding, challenging to regulate and achieve. Complexity associates with critical risk, great failure rates, and substandard performance; Ergo investigating project complexity and how to design for it becomes pertinent for efficient IT project management.
In the same frame of reference, information technology plays a big part in today’s economy. As complexity is omnipresent in present-day engineering, in addition to project management, it performs, and provides innovation, creativeness and performance.
The intent of this project was aimed to comprehend project complexity within IT and further its theoretical groundwork and practice. It provided a rounded perspective and delivers understanding into the nature of its effects. Furthermore, it puts forward a structured framework, by incorporating methods from both project management and user experience to generate a framework comprised of a structured process: organize, establish, investigate, design responses, track and regulate. These are delineated and explained as inputs and outputs, alongside a record of accessible tools and systems. Grounded in this framework, novel practical tools using user experience methods are suggested in order to: determine complexity, evaluate its causes and impacts; and examine and regulate complexity improvement plans.
The research is anchored in practice, in addition to the literature review on project management, systems thinking, user experience design, systems engineering, vulnerability and risk management, systems and complexity theory, systems engineering and IS/IT. This was based on a qualitative exploratory approach, rooted in design science. Various sequences of design and validation were executed alongside semi-structured interviews with field experts, established on a study of complex project cases in Information Technology. A qualitative evaluation comprised of the application and reiterated valuation of the suggested tools, in various live projects.
Ultimately the thesis aims to assist project managers with tools for attaining project success and decreasing failure risk within complex project settings in Information Technology. Managing complexity allows for the accomplishment of high-risk projects within Information Technology, assists further in project cognizance, as well as permits for better organization and scheduling of tools and resources
Cost structures and socioecological conditions impact the fitness outcomes of human alloparental care in agent-based model simulations
Get PDFSupplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S1090513824000898?via%3Dihub#s0095 .Alloparental care—care provided to children who are not one's offspring—is ubiquitous across human populations. Empirical research reveals socioecological variation in who cares for children, but less attention has been paid to the type of care provided. To better understand the fitness outcomes of different forms of alloparental care, or allocare, we categorize such care into two broad forms based on economic cost structures: additive cost and declining marginal cost allocare. Additive cost allocare requires alloparents to pay equal costs for each child to whom care is provided, while declining marginal cost allocare entails reduced costs for additional children beyond the first. Given this general typology, we investigate how fitness is impacted by the type of allocare provided in socioecological conditions of scarcity or abundance. Results of an agent-based model indicate that allocare has fitness benefits in nearly all circumstances, but the impact of cost structures depends on resource availability. In contexts of abundance, the cost structure of allocare does not matter as individuals' reproductive success is instead constrained by fertility and mortality more than the availability of resources or time. In conditions of scarcity, however, the greatest increases in reproductive success are achieved when allocare has a declining marginal cost structure. This is due to an economy of scale permitting alloparents to scale up their care at discounted rates. Consequently, we expect allocare practices to exhibit these patterns cross-culturally: in contexts of scarcity allocare is anticipated to be focused on practices with declining marginal cost structures and to be much less variable than in contexts of abundance. We discuss several ethnographic examples that are consistent with the overall findings of our simulations, and we conclude with recommendations for future modeling and empirical work on allocare.Funding for this work was provided by the John Templeton Foundation, Templeton Religion Trust, and James Barnett Endowment
A framework for data regression of heat transfer data using machine learning
Get PDFData availability: The data that has been used is confidential.Machine Learning (ML) algorithms are emerging in various industries as a powerful complement/alternative to traditional data regression methods. A major reason is that, unlike deterministic models, they can be used even in the absence of detailed phenomenological knowledge. Not surprisingly, the use of ML algorithms is being explored also in heat transfer applications. It is of particular interest in systems dealing with complex geometries and underlying phenomena (e.g. fluid phase change, multi-phase flow, heavy fouling build-up). However, heat transfer systems present specific challenges that need addressing, such as the scarcity of high-quality data, the inconsistencies across published data sources, the complex (and often correlated) influence of inputs, the split of data between training and testing sets, and the limited extrapolation capabilities to unseen conditions. In an attempt to help overcome some of these challenges and, more importantly, to provide a systematic approach, this article reviews and analyses past efforts in the application of ML algorithms to heat transfer applications, and proposes a regression framework for their deployment to estimate key quantities (e.g. heat transfer coefficient), to be used for improved design and operation of heat exchangers. The framework consists of six steps: i) data pre-treatment, ii) feature selection, iii) data splitting philosophy, iv) training and testing, v) tuning of hyperparameters, and vi) performance assessment with specific indicators, to support the choice of accurate and robust models. A relevant case study involving the estimation of the condensation heat transfer coefficient in microfin tubes is used to illustrate the proposed framework. Two data-driven algorithms, Deep Neural Networks and Random Forest, are tested and compared in terms of their estimation and extrapolation capabilities. The results show that ML algorithms are generally more accurate in predicting the heat transfer coefficient than a well-known semi-empirical correlation proposed in past studies, where the mean absolute error of the most suitable ML model is 535 [Wm2K-1], compared to the error using the correlation of 1061 [Wm2K-1]. In terms of extrapolation, the selected ML model has a mean absolute error of 1819 [Wm2K-1], while for the correlation is 1111 [Wm2K-1], indicating a disadvantage of the use of semi-empirical models, although the comparison was not entirely suitable, given that the correlation was used as is and no training was done. In addition, feature selection enables simpler models that depend only on features that are potentially most related to the target variable. Special attention is needed however, as overfitting and limited extrapolation capabilities are common difficulties that are encountered when deploying these models.Hexxcell Ltd
PFFN: A Parallel Feature Fusion Network for Remaining Useful Life Early Prediction of Lithium-ion Battery
Get PDFRemaining useful life (RUL) early prediction of lithium-ion battery is crucial to develop advanced battery health management and complete security assessment. However, most of existing methods still suffer from two limitations, i.e., the inadaptability to the different data distribution and the inability to capture the relationship between input series and RUL, which always make the RUL early prediction difficult and challengeable. To address these issues, this paper proposes a parallel feature fusion network (PFFN) for RUL early prediction of lithium-ion battery. Firstly, a feature selection strategy is designed to filter the optimal feature sets (containing cycle statistical features and domain knowledge-based features) that are most related to RUL of lithium-ion battery. Secondly, two specific Transformer encoders connected in parallel configuration are developed to integrate the cycle statistical features and domain knowledge-based features, respectively, achieving original RUL early prediction results. Furthermore, the Bayesian optimization is applied for global iterative optimization, aiming to enhance the prediction accuracy and generalization capability. A series of experiments are conducted with different data distributions. Experimental results demonstrate that the proposed PFFN outperforms the state-of-the-art (SOTA) methods, achieving 6.00%~27.61%, 0.58%~6.49%, and 5.95%~7.03% reduction in Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Score respectively.National Major Scientific Research Instrument Development Project of China (Grant Number: 62227802);
Ministry of Science and Technology - Yangtze River Delta Science and Technology Innovation Program (Grant Number: YDZX20233100004028);
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62206062);
National Postdoctoral Researcher Support Program (Grant Number: GZB20230356)
A Curated Solidity Smart Contracts Repository of Metrics and Vulnerability
Get PDFData Availability Statement: Our dataset is available on Zenodo (https://zenodo.org/records/11075555), offering a repository encompassing valuable resources such as smart contracts source codes, and associated software metrics and vulnerability reports, encouraging researchers and developers to enhance the current literature in SC security and analysis.Smart contracts (SCs) significance and popularity increased exponentially with the escalation of decentralised applications (dApps), which revolutionised programming paradigms where network controls rest within a central authority. Since SCs constitute the core of such applications, developing and deploying contracts without vulnerability issues become key to improve dApps robustness to external attacks. This paper introduces a dataset that combines smart contract metrics with vulnerability data identified using Slither, a leading static analysis tool proficient in detecting a wide spectrum of vulnerabilities. Our primary goal is to provide a resource for the community that supports exploratory analysis, such as investigating the relationship between contract metrics and vulnerability occurrences. Further, we discuss the potential of this dataset for the development and validation of predictive models aimed at identifying vulnerabilities, thereby contributing to the enhancement of smart contract security. Through this dataset, we invite researchers and practitioners to study the dynamics of smart contract vulnerabilities, fostering advancements in detection methods and ultimately, fortifying the resilience of smart contracts
Hydrated Calcium Silicate Erosion in Sulfate Environments a Molecular Dynamics Simulation Study
Get PDFData Availability Statement:
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.To investigate the micro-mechanism of the erosion of hydrated calcium silicate (C-S-H gel) in a sulfate environment, a solid–liquid molecular dynamics model of C-S-H gel/sodium sulfate was developed. This model employs molecular dynamics methods to simulate the transport processes between C-S-H gel and corrosive ions at concentrations of 5%, 8%, and 10% sodium sulfate (Na₂SO₄), aiming to elucidate the interaction mechanism between sulfate and C-S-H gel. The micro-morphology of the eroded samples was also investigated using scanning electron microscopy (SEM). The findings indicate that the adsorption capacity of C-S-H for ions significantly increases with higher concentrations of Na₂SO₄ solution. Notably, the presence of sulfate ions facilitates the decalcification reaction of C-S-H, leading to the formation of swollen gypsum and AFt (ettringite). This process results not only in the hydrolysis of the C-S-H gel but also in an increase in the diffusion coefficients of Na+ and Ca^{2+}, thereby exacerbating the erosion. Additionally, the pore surfaces of the C-S-H structure exhibited strong adsorption of Na^{+}, and as the concentration of Na2SO₄ solution increased, Na^{+} was more stably adsorbed onto the C-S-H pore surfaces via Na-Os bonds. The root-mean-square displacement curves of water molecules were significantly higher than those of SO₄²-, Na^{+} and Ca^{2+}, which indicated that SO₄²- could co-penetrate and migrate with water molecules faster compared with other ions in the solution containing SO₄²- , resulting in stronger corrosion and hydrolysis effects on the C-S-H structureThis work was supported by International Science and Technology Cooperation Program of Henan Province, grant number 241111521200. This work was supported by Natural Science Foundation of Henan Province, grant number 242300420063
Unlocking enterprise blockchain adoption: A R3 Corda case study
Get PDFThis study seeks to offer a comprehensive and practical comprehension of the lessons that companies can derive from financial institutions to promote the adoption of blockchain technology for future growth. Utilising R3 Corda, the largest blockchain platform featuring prominent financial institutions, as a case study, this research collects a wealth of company-recorded videos, podcasts, webinars, and textual data that offer invaluable insights for this case analysis. This investigation strives to contribute to formulating a blockchain adoption framework, drawing inspiration from successful use cases in the financial sector. This research outlines the current institutional pressures, criteria for platform selection, a blockchain adoption checklist, and novel institutional arrangements, providing a structured approach for enterprises considering embracing blockchain solutions. This framework serves as a strategic guide, facilitating organisations in navigating the complexities of blockchain implementation and ensuring a seamless integration process that aligns with their unique goals and operational requirements. Hence, discerning how to judiciously select and participate in the right blockchain consortia/platform is paramount for fostering and managing new technological innovations as businesses undergo digital transformation.The author(s) received no financial support for the research, authorship, and/or publication of this article
New control variates for pricing basket and Asian options
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn this thesis, we investigate new control variates for simulation-based pricing of options
where the option price is a function of the sum of (or integral of) lognormal random variables.
We use two different approaches: one is the use of Hermite polynomial approximation of
the relevant function and another is the use of upper and lower bounds on the option
prices obtained using the properties of Brownian motion. We provide detailed numerical
experiments to illustrate the use of these approaches for accurate and low variance pricing
basket and Asian options. First order Hermite polynomial approximation also gives a
reasonable direct approximation to the basket or Asian option price for at the money and
in-the-money options
YOLOv8-LiDAR Fusion: Increasing Range Resolution Based on Image Guided-Sparse Depth Fusion in Self-Driving Vehicles
Get PDFConference paper presented at the 25th TAROS(Towards Autonomous Robotic Systems) Conference 2024, Brunel University London, Uxbridge, UK, 21-23 August 2024.Self-driving vehicles are significant in industrial and commercial applications, primarily driven by the development of environmental awareness systems. The need for real-time object recognition, segmentation, perception, projection, and position has significantly increased in object and line tracking, obstacle avoidance, and route planning. The primary sensors used are high-resolution cameras, Light Detection and Ranging (LiDAR), and high-precision GPS/IMU inertial navigation systems. However, out of all these sensors, LiDARs and cameras have a vital function in perception and comprehensive situations. Although LiDAR is capable of providing precise depth information, its resolution is constrained. On the other hand, cameras provide abundant semantic information but do not offer precise assessments of the distance to objects. This work presents the incorporation of YOLOv8, an advanced object identification method, into the fusion process. We specifically investigate the notion of Camera-LiDAR Projection and provide a thorough explanation of the process of projecting LiDAR point clouds onto an image coordinate frame. This is achieved by utilizing transformation matrices that establish the relationship between the LiDAR and the camera. This project aims to improve the range resolution and perception capabilities of autonomous driving systems by combining YOLOv8-based object recognition with LiDAR point cloud data by using the KITTI object detection benchmark.This work is supported in part by the Horizon Europe COVER project under grant number 101086228, via UKRI grant EP/Y028031/1, and Ahmet Serhat Yildiz’s PhD is sponsored by the Ministry of National Education of Türkiye
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