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An evaluation of the performance of a scroll machine in a reversible high-temperature heat pump - organic Rankine cycle system using R1233zd(E) as the working fluid

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International Journal of Refrigeration 2025年 174卷 321-332页

作者： Ravindran, Rahul Velanparambil Cotter, Donal Wilson, Christopher Huang, Ming Jun Hewitt, Neil J Centre for Sustainable Technologies Belfast School of Architecture and the Built Environment Faculty of Computing Engineering and the Built Environment University of Ulster Belfast County AntrimBT15 1ED United Kingdom

A reversible high-temperature heat pump (HTHP) - Organic Rankine cycle (ORC) system can effectively recover industrial waste heat in the lower temperature band (sf,ev,in = 90 °C, Ncp = 2,000 RPM) and a volumetric efficiency of 92.3 % (Tsf,ev,in = 90 °C, Ncp = 2,500 RPM). In expander mode, the overall isentropic efficiencies were lower than in compressor mode, reaching a maximum value of 57.7 %. The results also demonstrated that the scroll machine could produce a gross power output of 1.01 kW (rp = 3.6, Tsf,ev,in = 90 °C) at higher pressure ratios in expander mode. © 2025 The Author(s)

关键词： Scroll compressors

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Simulating Social Pressure: Evaluating Risk Behaviors in Construction Using Augmented Virtuality

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IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 2494-2503页

作者： Pooladvand, Shiva Hasanzadeh, Sogand Takahashi, George Park, Kenneth Jongwon Marroquin, Jacob School of Sustainable Engineering and the Built Environment Arizona State University United States Lyles School of Civil and Construction Engineering Purdue University United States Rosen Center for Advanced Computing Purdue University United States

Drawing on social influence and behavioral intention theories, coworkers' risk-taking serves as an "extra motive" —an exogenous factor—for risk-taking behaviors among workers in the workplace. Social influence theories have shown that social factors, such as social pressure and coworker risk-taking, may predict risk-taking behaviors and significantly affect decision-making. While immersive technologies have been widely used to create close-to-real simulations for construction safety-related studies, there is a paucity of research considering the impact of social presence in evaluating workers' risk decision-making within immersive environments. To bridge this gap, this study developed a state-of-the-art Augmented Virtuality (AV) environment to investigate roofers' risk-taking behaviors when exposed to social stressors (working alongside a safe/unsafe peer). In this augmented virtuality environment, a virtual peer with safe and unsafe behaviors was simulated in order to impose peer pressure and increase participants' sense of social presence. Participants were asked to install asphalt shingles on a physical section of a roof (passive haptics) while the rest of the environment was projected virtually. During shingle installation, participants' cognitive and behavioral responses were captured using psychophysiological wearable technologies and self-report measures. The results demonstrated that the developed AV model could successfully enhance participants' sense of presence and social presence while serving as an appropriate platform for assessing individuals' decision-making orientations and behavioral changes in the presence of social stressors. Such information shows the value of immersive technologies to examine the naturalistic responses of individuals without exposing them to actual risks. © 1995-2012 IEEE.

关键词： Augmented reality

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Analysis of energy consumption for residential building- A case study in Libya

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Journal of Umm Al-Qura University for engineering and Architecture 2025年 1-17页

作者： Alafya, Abdusalam Yehmed Maka, Ali O. M. School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom The Libyan Centre for Research and Development of Saharian Communities Murzuq Libya

An energy-efficient building maximises the utilisation of resources by providing comfortable living conditions to occupancy inside;while consuming the lowest amount of energy required. Building materials and electrical appliances used in a structure affect how power is consumed;hence, a building's characteristics are essential for further development. Notwithstanding, throughout the year, either in cold or hot weather, buildings are designed to provide comfort for their residents. Households in Libya turn on their air conditioning throughout the day to decrease the impact of the summertime weather, so the rise of temperature. That significantly demands more energy from the grid, and during peak energy demand might cause a deficiency in power generation. In residential houses, air conditioning units consume a large volume of Libya's electricity consumption, approximately 36% of the total electricity generated. Therefore, to obtain an overview of the amount of power required for cooling in Libyan homes;an investigation was created with an emphasis on air conditioning usage, followed by modelling of energy consumption. This work aims to minimise the amount of electricity required from a design perspective. It used surveys and modelling to determine the cooling requirements of different types of architectural buildings (ABs). Besides, the investigation indicated there is a substantial excessive waste of energy in the dwelling when using air conditioning units (ACUs). The analysis deduced consumption patterns of Libyan households are high in electricity, whereas approximately 40.6% of the respondents live in a one-floor house, which needs further consideration. At the same time, it addresses the challenges of electricity demand from the architectural viewpoint. © The Author(s) 2025.

关键词： Energy consumption Libyan homes Load profile Modelling and analysis Occupancy behaviour

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A computational study of a solid oxide fuel cell - turbofan engine hybrid propulsion system fuelled by ammonia and hydrogen.

A computational study of a solid oxide fuel cell - turbofan ...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Wagner, Luca Tingas, Efstathios-Al. School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This study presents the theoretical evaluation of a hybrid aero-propulsion system that combines a Solid Oxide Fuel Cell (SOFC) with a commercial turbofan engine (JT9D), fuelled by an ammonia and hydrogen fuel blend, the latter produced by an onboard fuel reformer. As concerns over emissions from the aviation sector continue to rise along with the ongoing search for sustainable energy alternatives, this research investigates the thermodynamic performance, environmental impacts, and operational efficiencies of the proposed SOFC-turbofan hybrid system. A new hybrid model is developed on the basis of NASA’s well-established T-MATS model. Comprehensive parametric studies are carried out to evaluate the system’s performance under a range of operational parameters, such as altitude, flight conditions, equivalence ratio, and compressor pressure ratio. Emission analyses revealed that the use of the proposed fuel blend of ammonia/hydrogen resulted in low emissions, demonstrating the system’s potential for significantly reducing the environmental footprint of aviation propulsion systems. This research not only substantiates the feasibility of integrating SOFCs with existing turbofan technology but also highlights the substantial improvements in efficiency and reductions in emissions that such hybrid systems could offer. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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Navigating the Building Energy Efficiency Research Landscape: Bibliometric Analysis and Literature Review (2003–2023): Part I Publication trends, collaborative networks, research themes and emerging issues

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Johnson Matthey Technology Review 2025年第3期69卷 331-345页

作者： Bello, Ahmed Usman Rahman, Mohd Shahril Abdul Samsudin, Salfarina Nyakuma, Bemgba Bevan Jagun, Zainab Toyin Department of Real Estate Faculty of Built Environment and Survey Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Department of Chemical Sciences Faculty of Science and Computing North-Eastern University P. M. B. 0198 Gombe State Gombe Nigeria School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS1 3HE United Kingdom

Research on building energy efficiency has increased significantly over the past twenty years, creating a complex and fragmented landscape that complicates a thorough comprehension of the field’s development and present condition. This study utilises a mixed-method approach that integrates bibliometric analysis and systematic literature review to investigate the building energy efficiency research environment from 2003 to 2023. We examined 1458 papers from the Scopus® database, concentrating on publication trends, collaborative networks, research themes and emerging issues. Research on building energy efficiency has expanded significantly, exhibiting a compound annual growth rate of 15.3% in publications. Artificial intelligence, the internet of things and improved materials are crucial catalysts of contemporary advancements. Collaborations among academics, industry and policymakers have increased, promoting more applied research. This two-part paper presents the inaugural complete, longitudinal examination of the building energy efficiency research environment, elucidating its evolution, present condition and prospective trajectories. © 2025 Johnson Matthey.

关键词： Architecture

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Partial Safety Factors Determination to Model Uncertainty on Persian Historic Masonry Elements

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Iranian Journal of Science and Technology - Transactions of Civil engineering 2025年 1-24页

作者： Ghamari, Mehrdad Karimi, MohammadSaeed School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh EH10 5DT United Kingdom Faculty of Civil Engineering Semnan University Semnan Iran

This research explores the determination of partial safety factors for Persian historical masonry walls, following Eurocode-6 principles, which account for material and geometric uncertainties. The study explores the impact of lateral constraints or boundary conditions (B.C.), Height-Length Proportion (α), the thickness of wall (t), and pre-compression levels as a gravity loading (P) on the structural behavior of these walls when subjected to uncertain conditions. Nonlinear pushover analyses were carried out on 200 masonry wall specimens, considering four α, ten variations in B.C. (including the impact of vertical (lateral walls) and horizontal (upper slabs) components), and three standard t (0.20, 0.35, and 0.50 m), all subjected to three different P (0.05, 0.1, and 0.2 MPa). The research also evaluates how variations in the modulus of elasticity and t affect the results under nonlinear analysis. Findings show that the Maximum Shear Capacity (F_u), maximum drift limit (δ_u/H), and effective stiffness (K_eff) are highly responsive to these variables. Higher F_u and K_eff were observed with increased t, P, and B.C., while reducing α had the opposite effect. As B.C., t, and P increased, δ_u/H showed a reduction, while a decrease in α led to an increase. The estimated F_u ranged 114.8–1476.5 kN, δu/H varied 1.56–3.43, and Keff spanned 7.72–250.80 kN/mm. The proposed partial safety factors are 1.17–1.96 for material strength (γ_M), 1.16–1.77 for displacement capacity (γ_du), and 1.15–1.96 for effective stiffness (γ_k). The results emphasize that increasing B.C., t, and reducing α result in higher γ_M and γ_k values, while γ_du values decrease. Additionally, all safety factors rise with increased P. These findings offer important insights for enhancing the preservation of Persian historical masonry structures. © The Author(s), under exclusive

关键词： Eurocode-6 Masonry walls Nonlinear analysis Partial safety factors Persian historical structures Structural behavior

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Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios 24

Scalable Machine Learning Architectures for IPA-Driven Maint...

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7th International Conference on Information Science and Systems, ICISS 2024

作者： Huang, Zhimeng Liu, Xiaodong Romdhani, Imed Shih, Chi-Sheng School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400717567

This research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an Intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. This innovative model harnesses the synergies between Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies to transition from reactive to proactive and predictive building maintenance strategies. The study highlights the critical gap in current BMM practices—the absence of intelligent systems for anticipating and addressing maintenance issues before they escalate. Through an extensive literature review, the transformative potential of AI and IoT for enhancing building maintenance management within smart cities is explored, establishing a foundation for the IBMM model’s application. The core of this research lies in its novel application of scalable machine learning architectures to automate and optimize maintenance task allocation in large-scale building portfolios. The practicality of the IBMM model is demonstrated via a proof of concept (POC) in an industrial setting, evidencing its capacity to improve efficiency, reduce costs, and bolster sustainability in building maintenance operations. The model epitomizes a paradigm shift in BMM by integrating IPA, which combines AI and ML, facilitating automated, intelligent decision-making and task allocation. Among its advancements, the IBMM model introduces enhanced predictive maintenance through real-time data analysis, adaptive learning and optimization, automated decision-making, and human-machine collaboration, contributing to energy efficiency and alignment with smart city objectives. The paper delineates the methodology, design, and implementation of a machine learning model for engineer task assignments, culminating in a case study that validates the model’s efficacy. This research not only signifies a significant advancement in BMM by leveraging IPA technologies for autonomous p

关键词： Predictive maintenance

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Computational Investigation of Laminar Premixed Hydrogen/Ammonia flames at Sub-Atmospheric Conditions

Computational Investigation of Laminar Premixed Hydrogen/Amm...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Tule, Davud Tingas, Efstathios-Al School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom Furtwangen University Germany

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The increasing urgency to address global warming has highlighted the need to explore alternative fuels for the transport sector, a significant contributor to carbonemissions. Hydrogen and ammonia have emerged as promising candidates, each with distinct advantages and challenges. Hydrogen boasts high reactivity and beneficial cooling properties but suffers from low volumetric density and storage difficulties. Conversely, ammonia offers better infrastructure compatibility and higher volumetric density but has low flammability. This study investigates computationally hydrogen/ammonia flames under sub-atmospheric conditions pertinent to high altitude aviation, focusing on laminar flame dynamics. One-dimensional simulations of freely propagating laminar flames were conducted using the PREMIX software and analyzed with computational singular perturbation (CSP) tools to elucidate the flame structure and dynamics. Results reveal that hydrogen/ammonia blends improve flame speed and stability compared to pure ammonia, especially under lean conditions. CSP analysis identified key reactions driving flame behavior, highlighting differences in chemical dynamics between hydrogen and ammonia. The findings underscore the potential of hydrogen/ammonia blends in aviation applications, particularly for high-altitude relight scenarios, and provide insights into optimizing fuel mixtures for enhanced performance. Further research is needed to validate these findings under varied conditions and explore practical implementation strategies. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Premixed flames

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Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS's Digital Twin 5

Accurate AI-Driven Emergency Vehicle Location Tracking in He...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Al-Shareeda, Sarah Celik, Yasar Bilgili, Bilge Al-Dubai, Ahmed Canberk, Berk Istanbul Technical University Department of AI and Data Engineering Turkey BTS Group Turkey United States Edinburgh Napier University School of Computing Engineering and the Built Environment United Kingdom

ISBN: (纸本)9798331519957

Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time, and tracking their real-Time location is crucial to the medical authorities. Despite the claim of real-Time representation, a temporal misalignment persists between the physical and virtual domains, leading to discrepancies in the ambulance's location representation. This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework to anticipate the medical vehicle's next location in the virtual world. These models align virtual representations with their physical counterparts, i.e., metaphorically offsetting the synchronization delay between the two worlds. Trained meticulously on a historical geospatial dataset, SVR and DNN exhibit exceptional prediction accuracy in MATLAB and Python environments. Through various testing scenarios, we visually demonstrate the efficacy of our methodology, showcasing SVR and DNN's key role in significantly reducing the witnessed gap within the HITS's DT. This transformative approach enhances real-Time synchronization in emergency HITS by approximately 88% to 93%. © 2025 IEEE.

关键词： Deep neural networks

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An interpretable machine learning model to predict hospitalizations

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Clinical eHealth 2025年 8卷 53-65页

作者： Elbatanouny, Hagar Tawfik, Hissam Khater, Tarek Gorbenko, Anatoliy Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom Department of Biomedical Engineering Khalifa University Abu Dhabi United Arab Emirates

Hospital management plays a pivotal role in ensuring the efficient delivery of medical services, especially in the face of challenges posed by pandemics such as COVID-19. This paper explores the application of machine learning techniques in addressing the challenge of hospitalization during pandemics. Leveraging a comprehensive dataset sourced from the Mexican government, various supervised learning algorithms including Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron are trained and evaluated to discern factors contributing to hospitalizations. Feature importance analysis and dimensionality reduction techniques are employed to enhance models predictive performance. The best model was Gradient Boosting algorithm with an accuracy of 85.63% and AUC score of 0.8696. The interpretability plots showed that pneumonia had a positive impact on the hospitalization prediction of the model. Our analysis indicates that women aged over 45 with pneumonia and concurrent COVID-19 exhibit the highest likelihood of hospitalization. This study underscores the potential of interpretable machine learning in aiding hospital managers to optimize resource allocation, hospitalization cases, and make data-driven decisions during pandemics. © 2025

关键词： COVID-19 Explainable AI Hospital management Interpretable models Machine learning Pandemics

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