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Sizing and mass estimation of truss-braced wings, considering emerging propulsion systems
No full textTo advance sustainable and fuel-efficient aircraft, novel configurations such as strut- and truss-braced wings are increasingly being explored. However, conceptual design limitations persist, particularly in the methods for structural sizing and mass estimation of these wings, especially when incorporating emerging propulsion technologies such as electric, hydrogen, and distributed propulsion. This study addresses these gaps by developing a quasi-analytical method for rapid and accurate mass estimation of the wings. Analytical load analysis methods are derived and applied to the structural sizing of struts, juries, and offsets. The proposed method achieves reduced validation errors for wing box, strut, and jury mass compared to existing methods, with an error of [Formula: see text] for total wing mass. With a computation time of just 0.1 s per case, the method is ideal for early-stage multidisciplinary design optimization. Results indicate minimal weight penalties with distributed propulsion across varying engine counts, along with significant structural efficiency gains for truss-braced wing (TBW) configurations. Underwing fuel tanks on TBW designs further enhance structural and mass efficiency, particularly for dry wing scenarios. Additionally, offset effects reveal a potential reduction in total wing mass while improving aerodynamic efficiency. These findings underscore the promise of TBW designs to support net-zero emissions and drive sustainable aerospace innovation.Ministry of National Education, Republic of Türkiye.Journal of Aircraf
Decarbonisation of industrial power generation gas turbines with bio-alcohols
Get PDFThe intermittency of renewable power generation means that dispatchable power sources are required to meet global energy demands. Gas turbines firing low carbon fuels, such as bio- and e-alcohols, offer flexible dispatchable power generation.
Despite previous work highlighting the suitability of ethanol in lean-premixed gas turbines, the present study shows that in practice evaporated ethanol is less attractive than previously suggested due to higher than anticipated NOx emissions and higher evaporation temperatures if the fuel has significant water content.
This paper introduces a “dual phase” burner concept where methanol may be fired either as a liquid or evaporated as a gas. Liquid methanol could be fired in a normal dual fuel distillate-natural gas burner with little or no modification to the liquid fuel paths/nozzles. Evaporated methanol could be fired with relatively minor modifications to the fuel gas path and nozzle. The gas turbine could be started on liquid methanol until sufficient exhaust heat was available to evaporate the methanol. Switching to evaporated methanol firing would result in an estimated 5–6% reduction in fuel consumption compared to liquid firing because of the exhaust waste heat recovery.
Whilst this study demonstrates the suitability of evaporated alcohols in a particular lean-premixed gas turbine combustion system, each different combustion system must be individually evaluated.ASME Turbo Expo 2025: Turbomachinery Technical Conference and Expositio
Distribution and ecological risk of Iodinated and Gadolinium-based contrast agents in an impacted basin of central Mexico
Get PDFIodinated X-ray contrast media (ICM) and Gadolinium-based contrast agents (Gd-CAs) are extensively used in medical imaging and are excreted unchanged by patients. However, conventional wastewater treatment plants (WWTPs) are ineffective at removing these compounds, leading to their discharge into surface waters, where they persist due to their recalcitrant nature. Although the presence of both groups of contrast agents in the aquatic environment has been documented, data on their co-occurrence, distribution, and ecological risk in freshwater systems remain scarce. This study addresses this knowledge gap by providing the first comprehensive assessment of the co-occurrence, spatial distribution, and ecological risks of ICM, specifically amidotrizoic acid (DIA), iomeprol (IOM), iopamidol (IOD), and iopromide (IOP), as well as anthropogenic Gd (Gdanth, encompassing all Gd-CAs), in the surface waters of the Atoyac River basin in Mexico. Concentrations of these contaminants ranged from 6.38 ng L-1 for Gdanth to 3,700 ng L-1 for IOP, with the highest levels measured near the WWTP discharge sites and areas with a high density of medical facilities. Elevated concentrations were also observed in river sections with fewer upstream medical facilities, suggesting additional sources or transport mechanisms. In the reservoir, the concentrations of IOD and IOP were high across nine sites, ranging from 261.10 ± 61.20 ng L-1 to 412.20 ± 48.50 ng L-1. This finding highlights their persistence and resistance to biodegradation in aquatic environments. Interestingly, while ICM and Gd-CAs were released into the river from medical facilities, they did not co-occur in the river waters, indicating distinct environmental behavior and/or sources. Finally, deterministic ecological risk assessments revealed that contrast agents posed no ecological risk to aquatic species. This study is the first to document the co-contamination of ICM and Gd-CAs in river waters and to evaluate their associated ecological risks, providing critical insights into the environmental presence of these medical imaging agents.Sistema Nacional de Investigadores: 894800; 733847; 120939Environmental Researc
AI’s learning paradox: how business students’ engagement with AI amplifies moral disengagement-driven misconduct
Get PDFArtificial intelligence (AI) in higher education creates a learning paradox, enhancing productivity while enabling hard-to-detect misconduct, challenging ethical boundaries, and university policies. Drawing on moral disengagement (MD) theory, this study examines how AI engagement conditions, captured by the Motive, Means, Opportunity (MMO) framework, amplify MD’s effect on misconduct among graduate business students. Self-Regulated Learning (SRL) offers a learning process lens to locate where MD and its MMO conditions unfold within the learning cycle. Survey data from 226 UK-based students shows that MD predicts AI misconduct, with amplification from AI-related factors (usefulness, habit, obsessive passion, prompt engineering skill) and past misconduct. Policy enforcement mitigates this effect, while policy clarity is effective only when paired with enforcement. Unexpectedly, high-performing students are more likely to act on MD when scanning for misconduct opportunities. Our findings underline how AI engagement undermines ethical regulation, offering insights for institutional policy in AI-enabled learning environments.Studies in Higher Educatio
An innovative digital liquid metal manufacturing method for aerospace applications: incorporating life cycle assessment for sustainability
Get PDFThe Ultra Clean Cast (UCC) system presents an innovative approach to aerospace manufacturing by prioritizing component quality and manufacturing repeatability. It incorporates a cradle-to-gate life cycle assessment to highlight its additional environmental benefits, with a greater focus on enhancing sustainability. This novel approach improves upon traditional shape-casting by maintaining the high cleanliness of melt metal, critical for aluminum alloys, and difficult to achieve in general for aerospace parts, under varied conditions. By providing a sustainable, cost-efficient route for fabricating complex components, UCC is adaptable across aerospace platforms and evaluates the use of recycled aluminum, supporting the sector’s shift towards a circular economy. This paper outlines the UCC system’s integration of technological advancements with environmental responsibility, incorporating recycled aluminium raw material, material manufacturing, and product manufacturing stages. This system provides a new benchmark for environmentally friendly aircraft manufacturing by outlining process improvements and their implications for industry sustainability and efficiency. The findings highlight UCCs potential to affect aerospace manufacturing in the future, combining high-quality output with environmental considerations.The authors gratefully acknowledge the funding by the Ultra Clean Cast DLMM Program No 10065261.20th Global Conference on Sustainable Manufacturing (GCSM)Lecture Notes in Mechanical Engineerin
Enabling sustainability-by-design with multi-disciplinary computer aided systems
Get PDFSustainability-by-Design requires information and communication technologies (ICTs) capable of integrating design engineering, manufacturing processes, materials, and sustainability considerations. Currently, methodologies for assessing environmental sustainability, such as product life cycle assessment, are often implemented too late in the design process, reducing opportunities for early intervention. Integrating environmental sustainability modeling in computer-aided systems (CAx) allows engineers to concurrently evaluate trade-offs between technical performance and environmental impact, facilitating informed decision-making during embodiment design stages. Using a prosthetic device produced via material jetting additive manufacturing as case study, we demonstrate the transformative role of advanced CAx tools capable of analyzing trade-offs among competing objectives.(Research Council of Finland|346874)This work was supported by the project D2M (346874) Research council of Finland - Academy Research Fellow program.CIRP Annal
Analysis of China’s high-speed railway network using complex network theory and graph convolutional networks
Get PDFThis study investigated the characteristics and functionalities of China’s High-Speed Railway (HSR) network based on Complex Network Theory (CNT) and Graph Convolutional Networks (GCN). First, complex network analysis was applied to provide insights into the network’s fundamental characteristics, such as small-world properties, efficiency, and robustness. Then, this research developed three novel GCN models to identify key nodes, detect community structures, and predict new links. Findings from the complex network analysis revealed that China’s HSR network exhibits a typical small-world property, with a degree distribution that follows a log-normal pattern rather than a power law. The global efficiency indicator suggested that stations are typically connected through direct routes, while the local efficiency indicator showed that the network performs effectively within local areas. The robustness study indicated that the network can quickly lose connectivity if key nodes fail, though it showed an ability initially to self-regulate and has partially restored its structure after disruption. The GCN model for key node identification revealed that the key nodes in the network were predominantly located in economically significant and densely populated cities, positively contributing to the network’s overall efficiency and robustness. The community structures identified by the integrated GCN model highlight the economic and social connections between official urban clusters and the communities. Results from the link prediction model suggest the necessity of improving the long-distance connectivity across regions. Future work will explore the network’s socio-economic dynamics and refine and generalise the GCN models.Big Data and Cognitive Computin
Artificial intelligence-driven innovation in Ganoderma spp.: potentialities of their bioactive compounds as functional foods
No full textGanoderma spp., which are essential decomposers of lignified plant materials, can affect trees in both wild and cultivated settings. These fungi have garnered significant global interest owing to their potential to combat several chronic, complicated, and infectious diseases. As technology progresses, researchers are progressively employing artificial intelligence (AI) for studying various fungal strains. This novel approach has the potential to accelerate the knowledge and application of Ganoderma spp. in the food industry. The development of extensive Ganoderma databases has markedly expedited research on them by enhancing access to information on bioactive components of Ganoderma and promoting collaboration with the food sector. Progress in AI techniques and enhanced database quality have further advanced AI applications in Ganoderma research. Techniques such as machine learning (ML) and deep learning employing various methods, including support vector machines (SVMs), Bayesian networks, artificial neural networks (ANNs), random forests (RFs), and convolutional neural networks (CNNs), are propelling these advancements. Although AI possesses the capacity to transform Ganoderma research by tackling significant difficulties, continuous investment in research, data dissemination, and interdisciplinary collaboration are necessary. AI could facilitate the development of customized functional food products by discerning patterns and correlations in customer data, resulting in more specific and accurate solutions. Thus, the future of AI in Ganoderma research looks auspicious, presenting prospects for ongoing advancement and innovation in this domain.This research work has been funded by DST-SERB, Govt. of India under CRG project vide sanction order number CRG/2021/001815 and this article has been produced with the financial support of the European Union under the REFRESH – Research Excellence for region Sustainability and High-tech industries project number CZ.10.03.01/00/22_003/0000048 via the operational Programme Just Transition.Sustainable Food Technolog
Automated coffee roast level classification using machine learning and deep learning models
No full textThe coffee roasting process is a critical factor in determining the final quality of the beverage, influencing its flavour, aroma, and acidity. Traditionally, roast‐level classification has relied on manual inspection, which is time‐consuming, subjective, and prone to inconsistencies. However, advancements in machine learning (ML) and computer vision, particularly convolutional neural networks (CNNs), have shown great promise in automating and improving the accuracy of this process. This study evaluates multiple ML models for coffee roast level classification, including a CNN with Xception as a feature extractor, alongside AdaBoost, random forest (RF), and support vector machine (SVM). The models were trained and tested on a public dataset of 1,600 high‐quality images, balanced across four roast levels: green, light, medium, and dark, to ensure robust performance. Experimental results demonstrate that all models achieved 100 % accuracy and F‐1 scores, confirming their effectiveness in accurately distinguishing roast levels. Furthermore, the proposed approach was compared with previous studies, showing strong performance in roast classification. Image augmentation techniques were applied to improve generalizability in real‐world applications. This research presents a reliable, scalable, and fully automated solution for roast‐level classification, significantly contributing to quality control in the coffee industry.
Practical Applications
This research offers a reliable and automated way to classify coffee bean roast levels using image analysis and ML. It can help coffee producers and roasters improve quality control by providing faster, more consistent, and objective assessments of roast levels, ultimately ensuring a better product for consumers.This study was financed in part by the Coordenação de Aperfeiçoamentode Pessoal de Nível Superior—Brazil (CAPES), 00x0ma614.Journal of Food Scienc
Design and experimental tests for novel shapes of floating OWC wave energy converters with the additional purpose of breakwater
Get PDFThe oscillating water column (OWC) is a type of wave energy converter (WEC) that captures the energy of incoming waves. As waves reach the structure, their movement causes the water within an enclosed chamber to oscillate, creating airflow that powers a turbine, generating electricity. This principle can be applied to the design of breakwaters, which can protect marine structures such as floating solar farms and wind turbines. This study involved designing two types of buoyancy chambers for the OWC-WEC and two underneath baffles with adjustable spacing. These configurations were tested in a wave tank to assess wave energy capture, wave attenuation, hydrodynamics, and mooring forces. The experimental results demonstrate that a baffle spacing of 1 m, combined with a V-type buoyancy chamber, significantly enhances the wave energy capture and wave attenuation performance of the OWC. This configuration achieves up to a 57.09 % increase in the capture width ratio and a maximum reduction of 20.88 % in the wave transmission coefficient. Furthermore, mooring line forces are reduced by 21.86 %, while the baffles effectively mitigate pitch motion. Notably, greater pitch reduction improves the capture width ratio. In conclusion, this study introduces a novel wave energy converter, providing key insights for future marine energy development.L.H. acknowledges grants received from Innovate UK (No. 10048187, 10079774, 10081314), the Royal Society (IEC∖NSFC∖223253, RG∖R2∖232462) and UK Department for Transport (TRIG2023 – No. 30066).Ocean Engineerin