Search Results (309)

This study presents a comprehensive thermodynamic simulation and parametric analysis of a single-effect lithium bromide–water (LiBr–H₂O) vapour absorption refrigeration system (VARS) to assess the influence of key operating parameters on its performance, which is primarily measured by the coefficient of performance. The thermodynamic properties of the LiBr–H₂O solution are assessed using P-T-x diagrams to establish the operational limits of the cycle for given constraints, such as the absorber and the generator temperatures and the cycle’s operating pressures. The analysis includes the effects of generator temperature ($T_{gen}$), evaporator pressure ($P_{evap}$), and solution heat effectiveness ($\eta$) on the cycle performance. Additionally, exergy analyses of the cycle’s major components are performed. Simulation results demonstrate that $T_{gen}$ is the most dominant parameter that increases the COP non-linearly from 0.35 at 85 °C to 0.73 at 110 °C (for $\eta$ = 0.5, $T_{cond}$ = 40 °C), while the circulation ratio decreases sharply. Moreover, higher evaporator pressure positively influences the COP; for instance, increasing the evaporator pressure from 0.8 kPa to 1.2 kPa raised the COP from 0.71 to 0.76. This is directly correlated with the increased concentration difference between the strong and weak solutions. The heat recovery effectiveness proved vital for energy optimisation: increasing the recovery effectiveness from 0.5 to 0.9 improved the COP from approximately 0.72 to 0.82 at a fixed $T_{gen}$ of 100 °C. Absorber temperatures limit the minimum operating temperatures of the generator for the vapour production of the refrigerant (water). Moreover, the higher condenser/absorber temperatures significantly deteriorate the performance of the cycle; for instance, raising the operating temperature of the condenser/absorber from 40 °C to 45 °C results in the COP value dropping by up to 35% at a generator operating temperature of nearly 100 °C. Among all cycle components, the generator exhibits the highest exergy loss, especially at lower generator temperatures. These findings provide essential optimisation strategies for designing and operating solar or waste heat-driven LiBr–H₂O VARS units efficiently. Full article

Multi-energy microgrids are emerging technologies to facilitate the integration of distributed energy resources and decarbonisation of various energy consumptions. To assist in the low-carbon and efficient operation of multi-energy microgrids, this paper proposes a multi-energy coordination method for an electricity-heat-gas microgrid which integrates technologies of carbon-gas coupling (CGC) and demand side response (DSR). The carbon capture system–power-to-gas unit and water electrolyser (WE) are jointly employed to capture carbon emissions from combined heat-and-power units for methane synthesis, enabling the CGC and reducing carbon emissions and reliance on external gas supply. Then, incentive-based DSR schemes are implemented for both electricity and heat loads, leveraging the demand-side flexibility to further enhance the use of renewable generation. The operation of CGC and DSR units is co-optimised to minimise the penalties related to renewable generation curtailments and carbon emissions subject to a set of constraints including demand-side comfort coefficients. Compared to a traditional microgrid with neither CGC nor DSR, the joint implementation of CGC and DSR is estimated to reduce the total operational cost and carbon emissions of microgrid by over 20% and 40%, respectively, and increase the use of renewable generation by about 19%, illustrating the effectiveness of the proposed coordination method together with CGC and DSR technologies in reducing microgrid operating costs and carbon emissions while improving the share of renewables. Full article

To improve preparedness and minimise losses, this paper presents the development of artificial intelligence (AI)-based forecasting models using a large-scale climate index for Victoria (Australia), which is known to be one of the most fire-prone areas in the country. Using an Artificial Neural Network (ANN) approach, this study investigates the nonlinear relationships between SOI and the Forest Fire Danger Index (FFDI) to develop a robust predictive model. Levenberg–Marquardt optimisation through the backpropagation method was employed to train the ANN models. Based on local climate data, FFDI values were calculated for eight locations within southeast Australia, and SOI values of earlier months were correlated with the FFDI values of the later months. A total of 55 years (1965–2019) of monthly SOI and FFDI values were used to train, validate, and test the developed ANN models. The findings show that the developed models can predict future FFDI values, having correlation coefficients ranging 0.71~0.96, 0.70~0.95, and 0.75~0.93 for 1-month, 2-month, and 3-month lagged periods, respectively. As is obvious, one-month-ahead predictions were more accurate than two/three-month-ahead predictions. In general, the stations located in the eastern parts are attributed to higher prediction accuracy than stations located in the western regions, possibly due to their closer proximity to the location from where SOI originates (i.e., southern Pacific). These variations between the stations located in the eastern and western parts may partly exhibit the applicability of FFDI to different vegetation types. However, the outcomes hold potential for informing stakeholders, improving resource allocation for fire preparedness, and mitigating the devastating impacts of bushfires on communities and ecosystems. Full article

High-speed railway turnouts play important roles in the efficient operation of trains. However, the complex mechanical structure of turnouts and insufficient displacement of switch rails under dynamic conditions create a point of vulnerability for high-speed railways. The insufficient displacement of switch rails in high-speed railway No. 18 turnouts critically impacts operational safety. This study establishes a coupled finite element model of the switch rail and sliding track bed plate to analyse the effects of the friction coefficient and wheel–rail force. The results show that without considering the force of the iron block, the maximum insufficient displacement of a switch rail occurs at sleeper No. 27, and the maximum insufficient displacement increases linearly with the friction coefficient, with a regression coefficient of 1.02. When considering the wheel–rail force of the train, the maximum insufficient displacement of the switch rail occurs at sleeper No. 25, with the regression coefficient reduced to 0.67. Through dynamic and static tests and a case analysis, the influence of wheel–rail force on the insufficient displacement of a switch rail is verified. The results show that the application of a lateral wheel–rail force in the model significantly reduces the insufficient displacement of the switch rail, with an improvement of more than 90%. This study can significantly improve the optimisation of turnout design and the operational efficiency of a railway network. Full article

Background/Objectives: The Kocher–Langenbeck approach is widely used for surgical fixation of posterior acetabular wall fractures. While previous studies have focused on mechanical outcomes and the risk of post-traumatic osteoarthritis, the effects on peripheral circulation and neuromuscular recovery remain underexplored. This study aimed to evaluate dynamic changes in neuromuscular function and microcirculation following open reduction and internal fixation (ORIF) using this approach. Methods: A retrospective analysis was conducted on 34 patients (aged 23–75) treated for posterior acetabular wall fractures between 2014 and 2022. All patients underwent ORIF via the Kocher–Langenbeck approach. Assessments at 8 and 12 months postoperatively included electromyography (EMG), chronaximetry, and rheovasography (RVG). Asymmetry coefficients were calculated to quantify blood flow and functional differences. Results: At 12 months postoperatively, significant microcirculatory asymmetry persisted in the operated limb, with arterial and venous coefficients exceeding 25% (27.5% and 26.8%, respectively). EMG revealed sustained reductions in gluteus maximus and rectus femoris activity (asymmetry ~39%). Chronaximetry showed delayed nerve conduction recovery, particularly in the common peroneal nerve (AC = 44%). The femoral segment demonstrated the most severe impairment in both arterial inflow and venous outflow. Conclusions: ORIF via the Kocher–Langenbeck approach is associated with long-term disturbances in neuromuscular function and regional circulation. Further research should explore alternative surgical approaches (e.g., ilioinguinal, Stoppa) in prospective studies, assess vascular integrity using advanced imaging (e.g., contrast-enhanced ultrasound), and incorporate long-term functional outcomes. Studies on neurovascular-sparing techniques and optimised rehabilitation protocols may help reduce postoperative morbidity and improve recovery. Full article

This study focuses on enhancing the comprehensive utilisation of rice straw by proposing a vibration-assisted compression technology, with the aim of resolving inherent issues in traditional baling, such as uneven compression and low density. This study designed a multi-point vibration-assisted compression test rig and established a vibration-enhanced compression mechanical model based on the physical properties of rice straw. By integrating discrete element method (DEM) simulations with bench testing, the optimal length-to-width ratio of 1:1 was identified for achieving superior compaction quality. A systematic analysis was conducted to evaluate the effects of vibration point configuration, frequency, and amplitude control on straw bale integrity. The results of the DEM simulations demonstrated that vibration-assisted compression significantly enhanced the compaction uniformity and stability of rice straw. The dimensional stability coefficient and pressure transmission rates of the straw bales reached 88.25% and 58.04%, respectively, validating the efficacy of the vibration-assisted compression technique. This study provides innovative concepts and theoretical foundations for optimising the design of straw baling and in-field collection equipment. It holds critical significance for advancing the resource-efficient utilisation of agricultural residues and promoting sustainable agricultural practices. Full article

Accurate assessment of a crop’s water requirement is essential for optimising irrigation scheduling and increasing the sustainability of water use. The crop coefficient (K_c) is a dimensionless factor that converts reference evapotranspiration (ET₀) into actual crop evapotranspiration (ET_c) and is widely used for irrigation scheduling. The K_c reflects canopy cover, phenology, and crop type/variety, but is difficult to measure directly in heterogeneous perennial systems, such as vineyards. Remote sensing (RS) products, especially open-source satellite imagery, offer a cost-effective solution at moderate spatial and temporal scales, although their application in vineyards has been relatively limited due to the large pixel size (~100 m²) relative to vine canopy size (~2 m²). This study aimed to improve grapevine K_c predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing, with ground data obtained from canopy light interception measurements in three winegrape cultivars (Shiraz, Cabernet Sauvignon, and Chardonnay) in the Barossa and Eden Valleys, South Australia. A linear spectral mixture analysis approach was taken, which required estimation of vine canopy cover through beta regression models to improve the accuracy of vegetation indices that were used to build the K_c prediction models. Unmixing improved the prediction of seasonal K_c values in Shiraz (R² of 0.625, RMSE = 0.078, MAE = 0.063), Cabernet Sauvignon (R² = 0.686, RMSE = 0.072, MAE = 0.055) and Chardonnay (R² = 0.814, RMSE = 0.075, MAE = 0.059) compared to unmixed pixels. Furthermore, unmixing improved predictions during the early and late canopy growth stages when pixel variability was greater. Our findings demonstrate that integrating open-source satellite data with machine learning models and spectral unmixing can accurately reproduce the temporal dynamics of K_c values in vineyards. This approach was also shown to be transferable across cultivars and regions, providing a practical tool for crop monitoring and irrigation management in support of sustainable viticulture. Full article

Excessive exhaust backpressure (EBP) in modern diesel engines disrupts gas exchange, increases residual gas fraction (RGF), and reduces combustion efficiency. Traditional experimental approaches, including simulations and bench testing, are often time-consuming and costly, which has driven growing interest in artificial neural networks (ANNs) for accurately modelling complex engine behavior. This research introduces an ANN model designed to predict the impact of EBP on the performance and emissions of a diesel engine across varying compression ratio (CR) of 12, 14, 16, and 18 and engine load (25%, 50%, 75%, and 100%) conditions. The ANN model was developed and optimised using genetic algorithms (GA) and particle swarm optimisation (PSO). It was then trained using data from an experimentally validated one-dimensional computational fluid dynamics (1D-CFD) model developed through GT-Power GT-ISE v2024, simulating engine responses under variation CR, load, and EBP conditions. The optimised ANN architecture, featuring an optimal (3-14-10) configuration, was trained using the Levenberg–Marquardt back propagation algorithm. The performance of the model was assessed using statistical criteria, including the coefficient of determination (R²), root mean square error (RMSE), and k-fold cross-validation, by comparing its predictions with both experimental and simulated data. Results indicate that the optimised ANN model outperformed the baseline ANN and other machine learning (ML) models, attaining an R² of 0.991 and an RMSE of 0.011. It reliably predicts engine performance and emissions under varying EBP conditions while offering insights for engine control, optimisation, diagnostics, and thermodynamic mechanisms. The overall prediction error ranged from 1.911% to 2.972%, confirming the model’s robustness in capturing performance and emission outcomes. Full article

Background/Objective: Multimodal image registration plays a critical role in modern medical imaging, enabling the integration of complementary modalities such as positron emission tomography (PET) and computed tomography (CT). This study compares the performance of three widely used image registration techniques—Demons Image Registration with Modality Transformation, Free-Form Deformation using the Medical Image Registration Toolbox (MIRT), and MATLAB Intensity-Based Registration—in terms of improving PET/CT image alignment. Methods: A total of 100 matched PET/CT image slices from a clinical scanner were analysed. Preprocessing techniques, including histogram equalisation and contrast enhancement (via imadjust and adapthisteq), were applied to minimise intensity discrepancies. Each registration method was evaluated under varying parameter conditions with regard to sigma fluid (range 4–8), histogram bins (100 to 256), and interpolation methods (linear and cubic). Performance was assessed using quantitative metrics: root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), the Pearson correlation coefficient (PCC), and standard deviation (STD). Results: Demons registration achieved optimal performance at a sigma fluid value of 6, with an RMSE of 0.1529, and demonstrated superior computational efficiency. The MIRT showed better adaptability to complex anatomical deformations, with an RMSE of 0.1725. MATLAB Intensity-Based Registration, when combined with contrast enhancement, yielded the highest accuracy (RMSE = 0.1317 at alpha = 6). Preprocessing improved registration accuracy, reducing the RMSE by up to 16%. Conclusions: Each registration technique has distinct advantages: the Demons algorithm is ideal for time-sensitive tasks, the MIRT is suited to precision-driven applications, and MATLAB-based methods offer flexible processing for large datasets. This study provides a foundational framework for optimising PET/CT image registration in both research and clinical environments. Full article

With the growing deployment of electric buses (e-buses), accurate energy use modelling has become essential for fleet optimisation and operational planning. Using the SORT methodology, this study analyses instantaneous energy consumption and recuperation (IECR). Three vehicle configurations were tested (one battery with pantograph, four batteries, and eight batteries), each with ten repeatable runs. Four approaches were compared: a baseline linear regression, an extended linear model (ELM) due to the state, a feed-forward neural network, and a recurrent neural network (RNN). The extended linear model achieved a determination coefficient of R² = 0.9124 (residual standard deviation 4.26) compared with R² = 0.7859 for the baseline, while the determination coefficient for the RNN is 0.9343, and the RNN provided the highest accuracy on the test set (the correlation coefficient between real and predicted values is 0.9666). The results confirm the dominant influence of speed and acceleration on IECR and show that battery configuration mainly affects consumption during acceleration. Literature-consistent findings indicate that regenerative systems can recover 25–51% of braking energy, with advanced control methods further improving recovery. Despite non-normality and temporal dependence of residuals, the state-aware linear model remains interpretable and competitive, whereas recurrent networks offer superior fidelity. These results support real-time energy management, charging optimisation, and reliable range prediction for electric buses in urban public transport. Full article

Mycelium-based biocomposites (MBBs) represent a sustainable alternative to synthetic composites, as they are produced from lignocellulosic substrates bonded by fungal mycelium. Their mechanical performance depends on multiple interacting factors, including the substrate composition, fungal species, and processing conditions, which makes property optimisation challenging. In this study, an artificial neural network (ANN) model was developed to predict two mechanical properties of MBBs, namely internal bonding (IB) and compressive strength (CS). An ANN model was trained on experimental data, using the substrate composition, fungal species, and physical properties of MBBs. The ANN predictions were compared with measured values, and the model accuracy was evaluated. The results showed that the ANN achieved a high predictive accuracy, with coefficients of determination of 0.992 for IB and 0.979 for CS. IB values were predicted more precisely than CS, likely due to microstructural heterogeneities. The heterogeneities were visualised using scanning electron microscopy. Composites produced with Ganoderma sessile and Trametes versicolor exhibited the highest IB. Interestingly, Trametes versicolor achieved the highest CS on virgin wood particles but the lowest values on recycled wood, underlining the strong influence of the substrate quality. The study demonstrates that ANNs can effectively predict the mechanical properties, reducing the number of experimental tests needed for material characterisation. Full article

We propose a new strategy for constructing Runge–Kutta (RK) methods using evolutionary computation techniques, with the goal of directly minimising global error rather than relying on traditional local properties. This approach is general and applicable to a wide range of differential equations. To highlight its effectiveness, we apply it to two benchmark problems with oscillatory behaviour: the (2+1)-dimensional nonlinear Schrödinger equation and the N-Body problem (the latter over a long interval), which are central in quantum physics and astronomy, respectively. The method optimises four free coefficients of a sixth-order, eight-stage parametric RK scheme using a novel objective function that compares global error against a benchmark method over a range of step lengths. It overcomes challenges such as local minima in the free coefficient search space and the absence of derivative information of the objective function. Notably, the optimisation relaxes standard RK node bounds ($c_i\in [0,1]$), leading to improved local stability, lower truncation error, and superior global accuracy. The results also reveal structural patterns in coefficient values when targeting high eccentricity and non-sinusoidal problems, offering insight for future RK method design. Full article

Erosive wear from suspended sediments significantly threatens the structural integrity and efficiency of composite tidal turbine blades. This study develops a novel framework for predicting erosion in FR4 glass fibre-reinforced polymers (GFRPs)—materials increasingly adopted for marine renewable energy components. While erosion models exist for metals, their applicability to heterogeneous composites with unique failure mechanisms remains unvalidated. We calibrated the Oka erosion model specifically for FR4 using a complementary experimental–computational approach. High-velocity slurry jet tests (12.5 m/s) were conducted at a 90° impact angle, and erosion was quantified using both gravimetric mass loss and surface profilometry. It revealed a distinctive W-shaped erosion profile with 3–6 mm of peak material removal from the impingement centre. Concurrently, CFD simulations employing Lagrangian particle tracking were used to extract local impact velocities and angles. These datasets were combined in a constrained nonlinear optimisation scheme (SLSQP) to determine material-specific Oka model coefficients. The calibrated coefficients were further validated on an independent 45° impingement case (same particle size and flow conditions), yielding 0.0143 g/h predicted versus 0.0124 g/h measured (15.5% error). This additional case confirms the accuracy and feasibility of the predictive model under input conditions different from those used for calibration. The calibrated model achieved strong agreement with measured erosion rates (R² = 0.844), successfully capturing the progressive matrix fragmentation and fibre debonding, the W-shaped erosion morphology, and highlighting key composite-specific damage mechanisms, such as fibre detachment and matrix fragmentation. By enabling the quantitative prediction of erosion severity and location, the calibrated model supports the optimisation of blade profiles, protective coatings, and maintenance intervals, ultimately contributing to the extended durability and performance of tidal turbine systems. This study presents a procedure and the output of calibration for the Oka erosion model, specifically for a composite material, providing a transferable methodology for erosion prediction in GFRPs subjected to abrasive marine flows. Full article

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

The operational roles of artificial intelligence in energy security remain inconsistently defined across the scientific literature. To address this gap, the present review examines 165 peer-reviewed abstracts published between 2021 and 2025 using a triangulated methodology that combines trigram frequency analysis, manual qualitative coding, and semantic clustering with sentence embeddings. Eight core roles were identified: forecasting and prediction, optimisation of energy systems, renewable energy integration, monitoring and anomaly detection, grid management and stability, energy market operations/trading, cybersecurity, and infrastructure and resource planning. According to the results, the most frequently identified roles, based on the average distribution across all three methods, are forecasting and prediction, optimisation of energy systems, and energy market operations/trading. Roles such as cybersecurity and infrastructure and resource planning appear less frequently and are primarily detected through manual interpretation and semantic clustering. Trigram analysis alone failed to capture these functions due to terminological ambiguity or diffuse expression. However, correlation coefficients indicate high concordance between manual and semantic methods (Spearman’s ρ = 0.91), confirming the robustness of the classification. A structured typology of AI roles supports the development of more coherent analytical frameworks in energy research. Future research incorporating full texts, policy taxonomies, and real-world use cases may help integrate AI more effectively into energy security planning and decision support environments. Full article