Search Results (699)

To reduce the carbon footprint of the concrete industry and promote a circular economy, this study explores the reuse of waste materials such as glass powder (GP) and nitrile rubber (NR) fibres in concrete. However, the inclusion of these waste materials results in lower compressive strength compared to conventional concrete, limiting their application to non-structural elements. To overcome this limitation, this study adopts the concept of confined concrete by developing concrete-filled steel tube (CFST) stub columns. In total, twelve concrete mix variations were developed, with and without steel tube confinement. GP was utilised at replacement levels of 10–30% by weight of cement, while NR fibres were introduced at 0.5% and 1% by volume of concrete. The findings demonstrate that the incorporation of GP and NR fibres leads to a reduction in compressive strength, with a compounded effect observed when both materials are combined. Steel confinement within CFST columns effectively mitigated the strength reductions, restoring up to 17% of the lost capacity and significantly improving ductility and energy absorption capacity. All CFST columns exhibited consistent local outward buckling failure mode, irrespective of the concrete mix variations. A comparison with predictions from existing design codes and empirical models revealed discrepancies, underscoring the need for refined design approaches for CFST columns incorporating sustainable concrete infill. This study contributes valuable insights into the development of eco-friendly, high-performance structural systems, highlighting the potential of CFST technology in facilitating the adoption of waste materials in the construction sector. Full article

This paper addresses the challenge of maximizing power extraction in offshore wind energy systems through the development of an enhanced maximum power point tracking (MPPT) control strategy. Offshore wind energy is inherently intermittent, leading to discrepancies between power generation and electricity demand. To address this issue, we propose three advanced control algorithms to perform a comparative analysis: sliding mode control (SMC), the Integral Backstepping-Based Real-Twisting Algorithm (IBRTA), and Feed-Back Linearization (FBL). These algorithms are designed to handle the nonlinear dynamics and aerodynamic uncertainties associated with offshore wind turbines. Given the practical limitations in acquiring accurate nonlinear terms and aerodynamic forces, our approach focuses on ensuring the adaptability and robustness of the control algorithms under varying operational conditions. The proposed strategies are rigorously evaluated through MATLAB/Simulink 2024 A simulations across multiple wind speed scenarios. Our comparative analysis demonstrates the superior performance of the proposed methods in optimizing power extraction under diverse conditions, contributing to the advancement of MPPT techniques for offshore wind energy systems. Full article

This study conducts a numerical simulation of the working performance of a single horizontal shaft forced mixer using the Discrete Element Method (DEM). It systematically investigates the effects of blade installation angle, feeding method, mixing speed, and coarse aggregate particle size on the mixing uniformity. A 1:2 scale model was developed, incorporating Newton’s laws of motion and a soft-sphere contact model to simulate the particle trajectories and interactions during mixing. The results indicate that top–bottom feeding enhances mixing efficiency significantly by forming vertical convective circulation, achieving a mixing uniformity above 0.9. A moderate rotation speed of 30 rpm provides the best balance between energy consumption and mixing performance. As the coarse aggregate size increases (from 9 mm to 15 mm), the enhanced particle inertia leads to a decrease in mixing uniformity (from 0.9 to 0.6). Additionally, the discrepancy between simulation and experimental results is less than 0.1, validating the reliability of the model. This research offers theoretical guidance for the structural optimization and parameter selection of single-shaft mixers, contributing to improved mixing efficiency and concrete quality in engineering applications. Full article

Implementing energy-efficient solutions and developing energy complexes to decentralise power supply are key objectives for enhancing national security in Ukraine and Eastern Europe. This study compares the design, numerical, and experimental parameters of a channel-type jet-reaction turbine. A steam turbogenerator unit and a pilot industrial experimental test bench were developed to conduct full-scale testing of the unit. The article presents experimental data on the operation of a steam turbogenerator unit with a capacity of up to 475 kW, based on a channel-type steam jet-reaction turbine (JRT), and includes the validation of a computational fluid dynamics (CFD) model against the obtained results. For testing, a pilot-scale experimental facility and a turbogenerator were developed. The turbogenerator consists of two parallel-mounted JRTs operating on a single electric generator. During experimental testing, the system achieved an electrical output power of 404 kW at a turbine rotor speed of 25,000 rpm. Numerical modelling of the steam flow in the flow path of the jet-reaction turbine was performed using ANSYS CFX 25 R1 software. The geometry and mesh setup were described, boundary conditions were defined, and computational calculations were performed. The experimental results were compared with those obtained from numerical simulations. In particular, the discrepancy in the determination of the power and torque on the shaft of the jet-reaction turbine between the numerical and full-scale experimental results was 1.6%, and the discrepancy in determining the mass flow rate of steam at the turbine inlet was 1.34%. JRTs show strong potential for the development of energy-efficient, low-power turbogenerators. The research results confirm the feasibility of using such units for decentralised energy supply and recovering secondary energy resources. This contributes to improved energy security, reduces environmental impact, and supports sustainable development goals. Full article

Despite a growing body of research on supercritical carbon dioxide (SCO₂) heat transfer in microchannels, comprehensive reviews remain scarce. Existing studies predominantly focus on isolated experiments or simulations, yielding inconsistent findings and lacking a unified theory or optimization framework. This review systematically consolidates recent SCO₂ microchannel heat transfer advancements, emphasizing key performance factors, degradation mechanisms, and optimization strategies. We critically analyze over 260 studies (1962–2024), evaluating the experimental and numerical methodologies, heat transfer deterioration (HTD) phenomena, and efficiency enhancement techniques. Key challenges include the complexity of heat transfer mechanisms, discrepancies in experimental outcomes, and the absence of standardized evaluation criteria. Future research directions involve refining predictive models, developing mitigation strategies for HTD, and optimizing microchannel geometries to enhance thermal performance. This work not only integrates the current knowledge but also provides actionable insights for advancing SCO₂-based technologies in energy systems. Full article

Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications. Full article

To enhance the accuracy of SOC prediction in EVs, which often suffers from significant discrepancies between displayed and actual driving ranges, this study proposes a data-driven model guided by an energy consumption framework. The approach addresses the problem of inaccurate remaining range prediction, improving drivers’ travel planning and vehicle efficiency. A PCA-GA-K-Means-based driving cycle clustering method is introduced, followed by driving style feature extraction using a GMM to capture behavioral differences. A coupled library of twelve typical driving cycle style combinations is constructed to handle complex correlations among driving style, operating conditions, and range. To mitigate multicollinearity and nonlinear feature redundancies, a Pearson-DII-based feature extraction method is proposed. A stacking ensemble model, integrating Random Forest, CatBoost, XGBoost, and SVR as base models with ElasticNet as the meta model, is developed for robust prediction. Validated with real-world vehicle data across −21 °C to 39 °C and four driving cycles, the model significantly improves SOC prediction accuracy, offering a reliable solution for EV range estimation and enhancing user trust in EV technology. Full article

Considering the climate crisis, global environmental awareness, and the pursuit of sustainable architecture, various methodologies and global standards have been developed to assess and reduce the environmental impact of construction projects. Green Building Codes (GBCs) and rating systems have been implemented worldwide to support green building practices based on the use of simulation models to evaluate energy consumption, such as the ENERGYui and others to rate buildings based on their simulated energy performance. Israel has also established green building standards, such as SI 5281, which provide practical tools for architects to promote the use of green building methods. However, several studies have cast doubt on the actual measured performance of certified buildings. This study evaluates the effectiveness of the Israeli green building certification process (SI 5281/SI 5282) through a comparison between simulation-based ratings with measured post-occupancy electricity consumption. Through four case studies, the research identifies discrepancies, explores their causes, and proposes refinements to certification assumptions and evaluation methods. The research is intended to enhance the effectiveness of assessment tools in architectural design and contribute to more precise and sustainable green building practices. This study identifies significant gaps between simulated and actual energy consumption in Israeli green buildings, highlighting that, within this framework, educational buildings tend to exceed predicted usage, while residential buildings often consume less, thereby exposing limitations in current simulation assumptions and standard evaluation criteria. Full article

Low-temperature radiant heating systems utilizing heating cables face challenges including low heat dissipation efficiency and high energy consumption, hindering widespread application. Graphene coatings, characterized by high thermal conductivity and far-infrared radiation properties, offer a novel approach to enhance cable heat dissipation efficiency. This study systematically investigates the effects of coating position, thickness, and ambient temperature on cable heat dissipation using numerical simulations and experiments. A three-dimensional heat transfer model of the heating cable was established using Fluent software (2022R1). The radiation heat transfer equation was solved using the Discrete Ordinates (DO) model, and the coating position and thickness parameters were optimized. The reliability of the simulation results was validated using a temperature-rise experimental platform. The results indicate that graphene coatings significantly improve the heat dissipation performance of cables. Under optimal parameters (coating thickness: 100 μm, coating position: aluminum fin surface, initial temperature: 5 °C), the heat flux increased by approximately 26%, aluminum fin surface temperature decreased to 41.5 °C, and experimental temperature-rise efficiency improved by nearly 50%. The discrepancy between simulated and experimental results was within 8.5%. However, when coating thickness exceeded 100 μm, interfacial thermal resistance increased, reducing heat dissipation efficiency. Additionally, higher ambient temperatures suppressed heat dissipation. These findings provide a theoretical basis for optimizing the energy efficiency of low-temperature radiant heating systems. Full article

To address the challenges of high computational cost and energy consumption posed by deep neural networks in embedded systems, this paper presents CaDCR, a lightweight dynamic collaborative reasoning framework. By integrating a feature discrepancy-guided skipping mechanism with a depth-sensitive early exit mechanism, the framework establishes hierarchical decision logic: dynamically selects execution paths of network blocks based on the complexity of input samples and enables early exit for simple samples through shallow confidence assessment, thereby forming an adaptive computational resource allocation strategy. CaDCR can both constantly suppress unnecessary computational cost for simple samples and satisfy hard resource constraints by forcibly terminating the inference process for all samples. Based on this framework, we design a cascaded inference system tailored for embedded system deployment to tackle practical deployment challenges. Experiments on the CIFAR-10/100, SpeechCommands datasets demonstrate that CaDCR maintains accuracy comparable to or higher than baseline models while significantly reducing computational cost by approximately 40–70% within a controllable accuracy loss margin. In deployment tests on the STM32 embedded platform, the framework’s performance matches theoretical expectations, further verifying its effectiveness in reducing energy consumption and accelerating inference speed. Full article

This study proposes an advanced thermodynamic energy equation to accurately simulate the stress–dilatancy relationship in granular soils for both uncrushed and crushed sands. Traditional energy formulations primarily consider dissipation energy and often neglect the role of free energy. Recent developments have introduced free energy components to account for plastic energy contributions from dilation and particle crushing. However, significant discrepancies between theoretical predictions and experimental observations remain, largely due to the omission of complex mechanisms such as contact network rearrangement, force-chain buckling, grain rolling, rotation without slip, and particle crushing. To address these gaps, the proposed model incorporates dual exponential decay functions into the free energy framework. Rather than explicitly modeling each mechanism, this formulation aims to phenomenologically capture the interplay between fundamentally opposing thermodynamic forces arising from complex mechanisms during granular microstructure evolution. The model’s applicability is validated using the experimental results from both uncrushed silica sand and crushed calcareous sand. Through extensive comparison with over 100 drained triaxial tests on various sands, the proposed model shows substantial improvement in reproducing stress–dilatancy behavior. The average discrepancy between predicted and measured $\eta –D$ relationships is reduced to below 15%, compared to over 60% using conventional models. This enhanced energy equation provides a robust and practical tool for predicting granular soil behavior, supporting a wide range of geotechnical engineering applications. Full article

Predicted natural ventilation (NV) often diverges from actual performance in dwellings. This discrepancy arises in part because most design tools do not account for how occupants actually operate windows. This study aims to determine how window geometry and orientation should be adjusted when occupant behavior is considered. Survey data from 150 Melbourne residents were converted into two window-operation schedules: Same Behavior (SB), representing average patterns, and Probable Behavior (PB), capturing stochastic responses to comfort, privacy, and climate. Both schedules were embedded in EnergyPlus and applied to over 200 annual simulations across five window-design stories that varied orientations, placements, and window-to-wall ratios (WWRs). Each story was tested across two living room wall dimensions (7 m and 4.5 m) and evaluated for air-change rate per hour (ACH) and solar gains. PB increased annual ACH by 5–12% over SB, with the greatest uplift in north-facing cross-ventilated layouts on the wider wall. Integrating probabilistic occupant behavior into window design remarkably improves NV effectiveness, with peak summer ACH reaching 4.8, indicating high ventilation rates that support thermal comfort and improved IAQ without mechanical assistance. These results highlight the potential of occupant-responsive window configurations to reduce reliance on mechanical cooling and enhance indoor air quality (IAQ). This study contributes a replicable occupant-centered workflow and ready-to-apply design rules for Australian temperate climates, adapted to different climate zones. Future research will extend the method to different climates, housing types, and user profiles and will integrate smart-sensor feedback, adaptive glazing, and hybrid ventilation strategies through multi-objective optimization. Full article

(1) Background: Ionizing radiation in the Earth’s atmosphere drives key chemical transformations affecting atmospheric composition. Despite their environmental relevance, experimental data on proton collisions with hydrofluorocarbons remain limited, and theoretical models for total cross-sections and stopping power are still underdeveloped. (2) Methods: This study applies Rudd’s semiempirical model to calculate proton impact ionization cross-sections for the CF₃CH₂F molecule, considering contributions from both outer and inner electron shells. The model enables the estimation of differential cross-sections and the average energy of secondary electrons. In addition, we calculate the photoionization cross-sections using a discretized representation of the continuum—the so-called pseudo-spectrum—obtained through TDDFT with PBE0 as an exchange–correlation functional and compare it with the cross-section obtained for proton impact in the high-energy limit. (3) Results: The Rudd model proves highly adaptable and suitable for numerical applications. However, its validation is hindered by the scarcity of experimental data. Existing models, such as SRIM and Bethe–Bloch, show significant discrepancies due to their limited applicability at intermediate energies and lack of molecular structure consideration. (4) Conclusions: A comparison between the Rudd and BEB models reveals strong agreement in the analyzed energy range. This consistency stems from both models accounting for the molecular structure of the target, as well as from the fact that protons and electrons possess charges of the same magnitude, supporting a coherent description of ionization processes at these energies. Full article

This study aimed to investigate the distribution of thermal neutron fluence generated during proton-beam therapy (PBT) scanning, focusing on neutrons produced within the body using Monte Carlo simulations (MCSs). MCSs used the Particle and Heavy Ion Treatment Code System to define a 35 × 35 × 35 cm³ water phantom, and proton-beam energies ranging from 70.2 to 228.7 MeV were investigated. The MCS results were compared with neutron fluence measurements obtained from gold activation analysis, showing good agreement with a difference of 3.54%. The internal thermal neutron distribution generated by PBT was isotropic around the proton-beam axis, with the Bragg peak depth varying between 3.45 and 31.9 cm, while the thermal neutron peak depth ranged from 5.41 to 15.9 cm. Thermal neutron generation depended on proton-beam energy, irradiated particle count, and depth. Particularly, the peak of the thermal neutron fluence did not occur within the treatment target volume but in a location outside the target, closer to the source. This discrepancy between the Bragg peak and the thermal neutron fluence peak is a key finding of this study. These data are crucial for optimizing beam angles to maximize dose enhancement within the target during clinical applications of neutron capture-enhanced particle therapy. Full article

As immersive audio is gaining popularity, the perceptual aspects of spatial sound reproduction become relevant. The authors investigate a measure related to spatial resolution, the Minimum Audible Angle (MAA), which is understudied in the context of Ambisonics. This study examines MAA thresholds in the horizontal plane in three ambisonic decoders—the Sample Ambisonic Decoder (SAD), Energy-Preserving Ambisonic Decoder (EPAD), and All-Round Ambisonic Decoder (AllRAD). The results demonstrate that the decoder type influences spatial resolution, with the EPAD exhibiting superior performance in MAA thresholds (${1.24}^{\circ }$ at $0^{\circ }$ azimuth) compared to the SAD and AllRAD. These differences reflect the discrepancies in the decoders’ energy vector distribution and angular error. The MAA values remain consistent between decoders up to ${30}^{\circ }$ azimuth but diverge significantly beyond this range, especially in the ${60}^{\circ }$–${135}^{\circ }$ region corresponding to the cone of confusion. The findings of this study provide valuable insights for spatial audio applications based on ambisonic technology. Full article