Search Results (2,122)

This paper presents a detailed numerical investigation of the effect of hub-mounted riblets on the thermal and aerodynamic performance of a radial turbine rotor. While prior studies have shown that riblets reduce wall shear stress and improve aerodynamic efficiency, their influence on heat transfer and thermal losses remains underexplored. Using numerical simulations, this study examines the heat transfer characteristics within the rotor passage, comparing ribbed and smooth hub configurations under the same operating conditions. Results reveal that although riblets reduce frictional drag, they also enhance convective heat transfer—leading to a 6% increase in the heat transfer coefficient at the hub and 2.8% at the blade surfaces. This intensification of heat transfer results in a 4.3% rise in overall thermal losses, counteracting some of the aerodynamic gains. The findings provide new insights into the thermofluidic implications of surface modifications in turbomachinery and emphasise the importance of considering surface finish not only for aerodynamic optimisation but also for thermal efficiency. These results can inform future turbine design and manufacturing practices aimed at controlling surface roughness to minimise heat loss. Full article

This study introduces a novel train-and-test approach referred to as apprenticeship learning (AL) for generating selection hyper-heuristics to solve the Quadratic Unconstrained Binary Optimisation (QUBO) problem. The primary goal is to automate the design of hyper-heuristics by learning from a state-of-the-art expert and to evaluate whether the apprentice can outperform that expert. The proposed method collects detailed search trace data from the expert and trains the apprentice based on the machine learning models to predict heuristic selection and parameter settings. Multiple data filtering and class balancing techniques are explored to enhance model performance. The empirical results on unseen QUBO instances show that indeed, “student surpasses the teacher”; the hyper-heuristic with the generated heuristic selection not only outperforms the expert but also generalises quite well by solving unseen QUBO instances larger than the ones on which the apprentice was trained. These findings highlight the potential of AL to generalise expert behaviour and improve heuristic search performance. Full article

This study presents a comprehensive numerical simulation and thermal performance analysis of a novel modular floating solar still system, featuring integrated heat-pipe vacuum tube collectors, designed for seawater desalination. This innovative system—subject of International Patent Application WO 2023/062261 A1—not only aims to enhance efficiency and scalability beyond traditional solar stills, but also addresses the significant environmental challenge of concentrated brine discharge inherent in conventional desalination methods. The study evolved from an initial theoretical model to a rigorous dynamic thermal model, validated using real hourly meteorological data from Málaga, Andalusia, Spain. This modelling approach was developed to quantify heat transfer mechanisms and accurately predict system performance. The refined hourly simulation forecasts an annual freshwater production of approximately 174 L per unit. Notably, a preliminary economic assessment estimates the Cost of Produced Water per Litre (CPL) at 0.7509 EUR/litre, establishing a valuable baseline for future optimisation. These findings underscore the critical importance of dynamic hourly simulations for realistic performance prediction and validate the technical and preliminary economic feasibility of this novel approach. The system’s projected output, modular floating design, and significant environmental advantages position it as a promising and sustainable solution for freshwater production, particularly in coastal regions and sensitive marine ecosystems. This work provides a solid foundation for future experimental validation, cost optimisation, and scalable implementation of renewable energy-driven desalination. Full article

Predicting buckling in large-scale composite structures is hindered by the need for highly detailed Finite Element (FE) models, which are computationally expensive and impractical for early-stage design iterations. This study introduces a macromodelling buckling framework that reduces those models to plate-level size without sacrificing accuracy. An equivalent bending stiffness matrix is derived from strain–energy equivalence, rigorously retaining orthotropic in-plane terms, bending–extensional coupling, and—crucially—the eccentricity of compressive loads about an unsymmetrically stiffened mid-plane, effects overlooked by conventional Parallel-Axis smearing. These stiffness terms contribute to closed-form analytical solutions for homogeneous orthotropic plates, providing millisecond-level evaluations ideal for gradient-based design optimisation. The method is benchmarked against detailed FE simulations of panels with three to ten stringers under longitudinal and transverse compression, showing less than 5% deviation in critical load prediction. Its utility is demonstrated in the sizing optimisation of the upper cover of a scaled Airbus A330 composite wing-box, where the proposed model explores the design space in minutes on a standard workstation, orders of magnitude faster than full FE analyses. By combining analytical plate theory, enhanced smearing, and rapid optimisation capability, the framework provides an accurate, ultra-fast tool for buckling analysis and the preliminary design of large-scale stiffened composite structures. Full article

The manufacturing of multiscale composite structures in aerospace engineering is governed by complex interactions among material heterogeneity, fluid rheology, and multiphysics phenomena—including thermal, chemical, electrical, and mechanical effects. These coupled processes introduce significant challenges during both processing and post-manufacturing stages, which are often difficult to resolve using traditional (experimental) trial-and-error approaches. This review explores the potential of advanced numerical methods and simulation frameworks to address these complexities. Emphasis is placed on the use of finite element and finite volume methods, along with their respective solution strategies and domain discretisation techniques, to solve the coupled governing equations involved in composite manufacturing processes. By integrating theory, computation, and physics-based understanding, these approaches enable predictive capability and design optimisation in the development of high-performance composite components for aerospace applications; many challenges though still remain in fabrication, design, and analysis. Full article

The Australian alpaca industry is continuing to develop as an alternative fibre industry to the traditional merino or angora industries. This study aimed to investigate herd behaviour in an extensive system in south eastern Australia. Healthy adult female alpacas (Huacaya n = 32, Suri n = 32) over two years old were inducted into the trial and kept together across a 10 month period. A total of 5 animals were removed during the study due to illthrift or death unrelated to the study. GoPro cameras were set up at 5 locations in the paddock for 3 days in the middle of every season (Summer, Autumn, Winter, Spring) to record alpaca behaviour without a human observer present. Visual observations were taken at 0800, 1000, 1100, 1300, 1500 for 60 min. Behaviour observations were taken every 5 min from the videos according to a prepared ethogram. A count of animals exhibiting each behaviour was recorded at each time point within each of the designated 60-minute periods. A generalised linear mixed-effects model (GLMM) was run on binary data for each behaviour. Behaviours that returned a predicted proportion of over 0.10 for all seasons were used in an ordinal logistic regression that was then utilised to determine the effect of the season, time of day, and weather conditions on the number of animals. Season significantly impacted the number of alpacas grazing, resting, and standing (p < 0.0001). Alpacas were more likely to be grazing throughout the day in cooler seasons (autumn, winter) and resting in the warmer parts of the day in summer and spring. The time of day impacted the proportion of alpacas resting and grazing (p < 0.05) but not standing (p = 0.4432). This study highlights that alpacas spend the majority of the daylight hours grazing, with some variability across different seasons, which may impact ideal management practices to optimise production in an extensive system. Full article

This study focused on developing emulsion-coated active papers with antifungal properties to extend the shelf life of tomatoes during home storage, thereby reducing food waste in households. First, a mixture of essential oils (EO_mix)—composed of 33.3% oregano and 66.7% cinnamon leaf oils—was optimised through a mixture design and emulsified with cationic starches. Based on their stability and efficacy, two different emulsions containing 10% Tween 80, 6–8% EO_mix, and HI-CAT or EVO cationic starches (82 or 84%, respectively) were selected and applied on paper. Then, the antifungal performance of the coated papers was tested in culture media against Botrytis cinerea, demonstrating strong antifungal activity in the vapour phase, effective for up to 31 days at 4 °C. In tests conducted with fresh tomatoes, the active papers improved fruit appearance and significantly reduced mould growth while maintaining overall sensory quality, indicating that these materials could extend tomato shelf life and thus offer a promising, low-cost, and biodegradable solution to reduce fruit waste at the consumer level, combining effective antifungal protection with good sensory performance in real-use conditions. Full article

This study develops a novel electric-driven metering device to address the mismatch between the seeder rotation speed and vehicle speed in traditional mechanical precision hill direct-seeding metering devices for rice, which is caused by wheel slippage. The device integrates a Global Navigation Satellite System (GNSS) speed measurement module and an optimised incremental Proportional-Integral-Derivative (PID) control algorithm, enabling precise seeding through electric drive. A multidisciplinary collaborative design approach is employed, and field experiments are conducted to evaluate the performance of the novel device under conditions of vehicle speeds ranging from 3 to 5 km/h, theoretical hill spacings of 0.15–0.25 m, and seeding rate adjustment positions of 1/3–1. The experiments use two rice varieties, “Longken 2021” from the northern rice growing region and “Jingliangyou Huazhan” from the southern rice growing region. The results demonstrate that the novel electric-driven metering device significantly outperformed the traditional mechanical device in terms of seeding precision, hill formation performance, and seeding rate accuracy. The novel device achieves a qualified rate of seeds per hill of 90.54%, with seedling omission and seed damage rates reduced to 4.98% and 0.69%, respectively. The hill diameter qualification rate increases to 95.21%, with no empty hills observed. The coefficient of variation of seeds per hill is maintained at 21.98%, meeting the agronomic requirement of 2–12 seeds per hill for conventional rice. However, for seeds with high moisture content and poor flowability (soaked seeds), the seed damage rate increases slightly by 0.47 percentage points. This study provided an efficient and reliable technical solution for the intelligent upgrading of precision rice direct-seeding equipment. Full article

This protocol paper outlines a robust and reproducible framework for a ¹H nuclear magnetic resonance (NMR) metabolomics analysis of rodent plasma, designed to facilitate preclinical biomarker discovery. The protocol details optimised steps for plasma collection in a preclinical rodent model, sample preparation, and NMR data acquisition using presaturation Carr–Purcell–Meiboom–Gill (PRESAT-CPMG) pulse sequences, ensuring high-quality spectral data and effective suppression of macromolecule signals. Comprehensive spectral processing and metabolite assignment are described, with guidance on multivariate and univariate statistical analyses to identify metabolic changes and potential biomarkers. The framework emphasises methodological rigour and reproducibility, enabling accurate quantification and interpretation of metabolites relevant to disease mechanisms or therapeutic interventions. By providing a standardised approach, this protocol supports longitudinal and translational studies, bridging findings from rodent models to clinical applications and advancing the reliability of metabolomics-based biomarker discovery in preclinical research. Full article

Intelligent reflecting surface (IRS) is a promising technique which aims to shift the paradigm of uncontrollable wireless environment to a controllable one by adding the function of reconfigurability using multiple passive reflecting elements. In this work, optimal beamforming design for maximising achievable rate with respect to variable location of the IRS is considered. In particular, a single-cell wireless system that employs an IRS to aid communication between the user and an access point (AP) equipped with multiple antennas is adopted. An optimisation problem is formulated which aims to maximise the achievable rate, subject to signal-to-interference-plus-noise ratio (SINR) constraint of each individual user as well as the total transmit power constraint at the AP. The problem is solved by jointly optimising the transmit beamforming using active aerial array at the AP and the reflection coefficients using passive phase shifting at the IRS. Since the original optimisation problem is strictly non-convex, the problem is solved optimally by solving a corresponding power minimisation problem. Rigorous simulations have been carried out and the results demonstrate that the IRS-enabled system outperforms benchmark systems and employs significantly fewer RF power amplifiers. Full article

In the context of the global response to climate change and the active promotion of energy transformation, a number of low-carbon policies coupled with the development of synergies to help power system transformation is an important initiative. However, the insufficient articulation of the green power market, tradable green certificate (TGC) market, and carbon emission trading (CET) mechanism, and the ambiguous policy boundaries affect the trading decisions made by its market participants. Therefore, this paper systematically analyses the composition of the main players in the electricity-CET-TGC markets and their relationship with each other, and designs the synergistic mechanism of the electricity-CET-TGC markets, based on which, it constructs the optimal profit model of the thermal power plant operators, renewable energy manufacturers, power grid enterprises, power users and load aggregators under the electricity-CET-TGC markets synergy, and analyses the behavioural decision-making of the main players in the electricity-CET-TGC markets as well as the electric power system to optimise the trading strategy of each player. The results of the study show that: (1) The synergistic mechanism of electricity-CET-TGC markets can increase the proportion of green power grid-connected in the new type of power system. (2) In the selection of different environmental rights and benefits products, the direct participation of green power in the market-oriented trading is the main way, followed by applying for conversion of green power into China certified emission reduction (CCER). (3) The development of independent energy storage technology can produce greater economic and environmental benefits. This study provides policy support to promote the synergistic development of the electricity-CET-TGC markets and assist the low-carbon transformation of the power industry. Full article

Fumonisins, primarily produced by Fusarium spp. and Aspergillus section nigri, are common contaminants in maize, cereal grains, and other processed and derived products, representing a significant risk to food safety and public health. This study presents the development and optimisation of a high-performance liquid chromatography method with fluorescence detection (HPLC-FLD) for the quantification of fumonisin B1 (FB1) and B2 (FB2) in various food matrices. In contrast with conventional protocols employing potassium phosphate buffers as the mobile phase, the proposed method utilises formic acid, offering enhanced compatibility with liquid chromatography systems. An automated online precolumn derivatisation with o-phthaldialdehyde (OPA) was optimised through experimental design and response surface methodology, enabling baseline separation of FB1 and FB2 derivatives in less than 20 min. The method demonstrated high sensitivity, with limits of detection of 0.006 µg mL⁻¹ for FB1 and 0.012 µg mL⁻¹ for FB2, and excellent repeatability (intraday RSD values of 0.85% and 0.83%, respectively). Several solid-phase extraction (SPE) strategies were evaluated to enhance sample clean-up using a variety of food samples, including dried figs, raisins, dates, corn, cornmeal, wheat flour, and rice. FumoniStar Inmunoaffinity columns were the only clean-up method that provided optimal recoveries (70–120%) across all tested food matrices. However, the MultiSep™ 211 column yielded good recoveries for both fumonisins in dried figs and raisins. Additionally, the C18 cartridge achieved acceptable recoveries for both fumonisins in dried figs and wheat flour. Full article

With the advance of digital transformation, the assessment of the environmental impact of digital tools and technologies grows more relevant. Considering the inflated expectations of environmental responsibility in higher education, this study analyses how websites of Polish universities conform to sustainable web design criteria. The sustainability audit employed a methodology encompassing carbon emissions measurement, technical website analysis, and SEO evaluation. The author analysed 63 websites of public universities in Poland using seven independent audit tools, including an original AI Custom GPT agent preconfigured in the ChatGPT ecosystem. The results revealed a substantial differentiation in CO₂ emissions and website optimisation, with an average EcoImpact Score of 66.41/100. Nearly every fourth website exhibited a significant carbon footprint and excessive component sizes, which indicates poor asset optimisation and energy-intensive design techniques. The measurements exposed considerable variability in emission intensities and resource intensity among the university websites, suggesting the need for standardised digital sustainability practices. Regulations on the carbon footprint of public institutions’ websites and mobile applications could become vital strategic components for digital climate neutrality. Promoting green hosting, “Green SEO” practices, and sustainability audits could help mitigate the environmental impact of digital technologies and advance sustainable design standards for the public sector. The proposed auditing methodology can effectively support the institutional transition towards sustainable management of digital infrastructure by integrating technical, sustainability, and organisational aspects. Full article

To sustainably power future urban communities, cities require advanced solar energy planning tools that overcome the limitations of traditional approaches, such as data fragmentation and siloed decision-making. SDTs present a transformative opportunity by enabling precision urban modelling, integrated simulations, and iterative decision support. However, their application in solar energy planning remains underexplored. This study introduces SDT4Solar, a novel SDT-based framework designed to integrate city-scale rooftop solar planning through 3D building semantisation, solar modelling, and a unified geospatial database. By leveraging advanced spatial modelling and Internet of Things (IoT) technologies, SDT4Solar facilitates high-resolution 3D solar potential simulations, improving the accuracy and equity of solar infrastructure deployment. We demonstrate the framework through a proof-of-concept implementation in Ballarat East, Victoria, Australia, structured in four key stages: (a) spatial representation of the urban built environment, (b) integration of multi-source datasets into a unified geospatial database, (c) rooftop solar potential modelling using 3D simulation tools, and (d) dynamic visualization and analysis in a testbed environment. Results highlight SDT4Solar’s effectiveness in enabling data-driven, spatially explicit decision-making for rooftop PV deployment. This work advances the role of SDTs in urban energy transitions, demonstrating their potential to optimise efficiency in solar infrastructure planning. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article