Search Results (384)

Lattice structures are lightweight architected materials particularly suitable for aerospace and automotive applications due to their ability to combine mechanical strength with reduced mass. Among various topologies, Body-Centered Cubic (BCC) lattices are widely employed for their geometric regularity and favorable strength-to-weight ratio. Advances in Additive Manufacturing (AM) have enabled the precise and customizable fabrication of such complex architectures, reducing material waste and increasing design flexibility. This study investigates the low-velocity impact behavior of two polylactic acid (PLA)-based BCC lattice panels differing in strut diameter: BCC1.5 (1.5 mm) and BCC2 (2 mm). Experimental impact tests and finite element simulations were performed to evaluate their energy absorption () capabilities. In addition to conventional global performance indices, a dimensionless parameter, is introduced to quantify the ratio between local plastic indentation and global displacement, allowing for a refined characterization of deformation modes and structural efficiency. Results show that BCC1.5 absorbs more energy than BCC2, despite the latter’s higher stiffness. This suggests that thinner struts enhance energy dissipation under dynamic loading. Despite minor discrepancies, numerical simulations provide accurate estimations of and support the robustness of the index within the examined configuration, highlighting its potential to deformation heterogeneity. Full article

Advanced energy storage solutions are required to mitigate grid destabilization caused by high-penetration renewable energy integration. Superconducting Magnetic Energy Storage (SMES) offers ultrafast response (<1 ms), high efficiency (>95%), and almost unlimited cycling life. However, its commercialization is hindered by the complex modeling of critical current with anisotropic behaviors and the computational inefficiency of high-dimensional optimization for megajoule (MJ)-class magnets. This paper proposes an integrated design framework synergizing a two-dimensional axisymmetric magnetic field model based on Conway’s current-sheet theory, a critical current anisotropy characterization model, and an adaptive genetic algorithm (AGA). A superconducting magnet optimization model incorporating co-calculation of electromagnetic parameters is established. A dual-module chromosome encoding strategy (discrete gap index + nonlinear increment) and parallel acceleration techniques were developed. This approach achieved efficient optimization of MJ-class magnets. For a single solenoid, the critical current increased by 22.6% (915 A) and energy storage capacity grew by 41.8% (1.12 MJ). A 20-unit array optimized by coordinated gap adjustment achieved a matched inductance/current of 0.15 H/827 A (20 MJ), which can enhance transient stability control capability in smart grids. The proposed method provides a computationally efficient design paradigm and user-friendly teaching software tool for high-current SMES magnets, supporting the development of large-scale High-Temperature Superconducting (HTS) magnets, promoting the deployment of large-scale HTS magnets in smart grids and high-field applications. Full article

The multi-objective optimization of an electric prototype racing vehicle is addressed in this study. The goal was to identify the optimal combination of battery type, pilot weight, and power mode to maximize operational time and distance while minimizing energy consumption. A structured $2\times 3\times 3$ factorial design was implemented, and the resulting data were analyzed through Response Surface Methodology (RSM) in combination with the Desirability Function Approach (DFA). The experimental design included two battery configurations, three weight levels, and three power settings, while data acquisition was performed through a custom Arduino-based system validated against commercial instruments. The results revealed that the configuration with the smallest battery, the lowest weight (66 kg), and the lowest power mode (N5) achieved the most efficient performance, yielding an operating time of 1.12 h, a travel distance of 24.63 km, and an energy performance index of 2.90 km/Ah. The integration of RSM with DFA provided a robust framework for identifying optimal multiparameter conditions under competition constraints. Unlike previous studies that examined these variables in isolation, this work advances the state of the art by demonstrating the feasibility of multiparameter optimization in real-world racing contexts, offering methodological and practical insights for sustainable electric mobility. Full article

Compost barn systems are relevant alternatives to discussing production efficiency, welfare, and sustainability in dairy farming. However, studies evaluating these systems in different climates are still scarce, especially in subtropical climate zones. Here, we assess whether dairy cows’ behaviour, health and hygiene in compost barn systems are influenced by different climatic conditions and calving orders in Argentina’s central and extra-Pampean basins from the perspective of Bayesian inference. We evaluated dairy cows (n = 40) in a compost barn system simultaneously at two locations in Argentina: Rafaela and Salta. The following variables were evaluated: environmental factors, animal behaviour, respiratory rate, udder and hock hygiene, and locomotion degree of milking cows. There was a total of 10 primiparous cows and 10 multiparous cows at each location, randomly selected, which were in the first third of lactation (<90 DIM). Using Bayesian inference, we observed that Rafaela had a temperature-humidity index (THI) above 70, and Salta had a milder environment, with lower average temperature and higher relative humidity. Thus, climatic interference is evident in behaviour, triggering more behavioural and physiological mechanisms for heat abatement in primiparous females in Rafaela. At the same time, the mild conditions in Salta led to better thermal energy transfer by multiparous females compared to primiparous cows. This shows that the microclimate could interfere with the social hierarchy of cows when they are under heat stress. These findings highlight the importance of considering both calving orders and climate when designing management strategies for dairy systems. Full article

Evaluating green data centers is a multi-attribute decision problem. To enhance the rigor and precision of green data center assessment, this study verifies the weighting of the national green data center evaluation index system using the Analytic Hierarchy Process (AHP) with the participation of 19 domain experts from various data center sectors. The aim is to gain an in-depth understanding of the perspectives and priorities of different types of institutions regarding evaluation indicators and to investigate the underlying reasons for these perspectives and priorities. Through an analysis of expert sample distribution, this paper reveals the preferences of financial, internet, research, and design, as well as technical consulting service institutions, regarding indicators such as energy-efficient utilization, computational resource utilization, green low-carbon development, scientific layout, and intensive construction. Specifically, financial institutions tend to place a relatively lower emphasis on energy efficiency due to their focus on transaction speed and security. In contrast, internet companies prioritize efficient utilization of computational resources. Research and design institutions consider scientific layout and intensive construction more crucial, while technical consulting service institutions emphasize green and low-carbon development. Meanwhile, we identified substantial discrepancies among experts in determining the weights of specific indicators, suggesting a lack of consensus within the industry about the correlation between these indicators and green data centers. To propel the sustainable development of green data centers, future assessments should refine evaluation dimensions, consider disparities such as data center types and embrace regional differences, actively adopt novel technologies and innovative practices, and establish mechanisms for long-term monitoring and evaluation. Full article

This study introduces the Double-Diamond Reactor (DDR), a novel planar passive microreactor designed to overcome the following conventional limitations: inefficient mass transfer, high flow resistance, and clogging. The DDR integrates splitting–turning–impinging (STI) hydrodynamic principles via CFD-guided optimization, generating chaotic advection to enhance mixing. Experimental evaluations using Villermaux–Dushman tests showed a segregation index ($X_s$) as low as 0.027 at 100 mL·min⁻¹, indicating near-perfect mixing. In BaSO₄ nanoparticle synthesis, the DDR achieved a 46% smaller average particle size (95 nm) and narrower distribution (${\sigma }_g=1.27$) compared to reference designs (AFR-1), while maintaining low pressure drops (<20 kPa at 60 mL·min⁻¹). The DDR’s superior performance stems from its hierarchical flow division and concave-induced vortices, which eliminate stagnant zones. This work demonstrates the DDR’s potential for high-throughput nanomaterial synthesis with precise control over particle characteristics, offering a scalable and energy-efficient solution for advanced chemical processes. Full article

This study examines the dynamic response of autoclaved aerated concrete (AAC) under solar radiation and ambient temperature coupling. A comparative analysis is conducted between traditional sintered bricks (brick), AAC, and autoclaved aerated concrete sandwich insulated wall panels (ATIM), using three thermal engineering models. The experimental group focuses on the south wall, with differentiated designs: Model A (brick), Model B (AAC), and Model C (ATIM). Temperature data collectors assess heat transfer and internal temperature regulation in winter. The results show that the AAC sandwich system significantly reduces thermal fluctuations, with a 26% and 14.8% attenuation in temperature amplitude compared to brick and AAC. The thermal inertia index of the AAC sandwich structure system is 51.5% and 14.58% higher than that of traditional brick walls and AAC walls, respectively. The heat consumption index of ATIM is, on average, 14% lower than that of AAC and 74.5% lower than that of the brick system. The study confirms that the AAC sandwich rock wool wall structure enhances temperature stability and energy efficiency, supporting green building and low-carbon energy-saving goals. Full article

Efficient material handling is critical for mining productivity, safety energy and cost control. This paper analyzes the energy efficiency of five alternative designs for a 3 km inclined underground conveyor system for copper ore transport, considering route geometry, belt specifications, drive configurations, and operational parameters. Two main design approaches were examined: a single long conveyor and two shorter conveyors. Variants differed in belt tensile strength, the use of intermediate drives, and system layout. Calculations results achieved by using dedicated QNK-TT software (version 4.45.21.08.10.18.11) show differences in the specific energy consumption index between variants for both average and peak capacities and highlight that high-capacity performance requires non-standard solutions: either higher belt strength or an intermediate drive system. The study shows that conveyor energy efficiency depends strongly on load level, with near-maximum throughput yielding the best performance. The authors conclude that conveyor system component selection should be based on a multi-criteria evaluation—including the capacity margin, operational safety and maintenance complexity—rather than energy efficiency alone. Full article

With the rapid development of edge computing and deep learning, the efficient deployment of deep neural networks (DNNs) on resource-constrained terminal devices faces multiple challenges (background), such as execution delay, high energy consumption, and resource allocation costs. This study proposes an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for PSO. Unlike the conventional PSO, our approach integrates a historical optimal solution detection mechanism and a dynamic temperature regulation strategy to overcome its limitations in this application scenario. First, an end–edge–cloud collaborative computing framework is constructed. Within this framework, a multi-objective optimization model is established, aiming to minimize time delay, energy consumption, and cloud configuration cost. To solve this model, an optimization method is designed that integrates a historical optimal solution detection mechanism and a dynamic temperature regulation strategy into the MOPSO algorithm. Experiments on six types of DNNs, including the Visual Geometry Group (VGG) series, have shown that this algorithm reduces execution time by an average of 58.6%, the average energy consumption by 61.8%, and optimizes cloud configuration costs by 36.1% compared to traditional offloading strategies. Its Global Search Capability Index (GSCI) reaches 92.3%, which is 42.6% higher than the standard PSO algorithm. This method provides an efficient, secure, and stable cooperative computing solution for multi-constraint task unloading in an edge computing environment. Full article

This research proposes a novel multi-objective optimization framework for virtual power plants (VPPs) operating in day-ahead electricity markets. The VPP integrates diverse distributed energy resources (DERs) such as wind turbines, solar photovoltaics (PV), fuel cells (FCs), combined heat and power (CHP) systems, and microturbines (MTs), along with demand response (DR) programs and energy storage systems (ESSs). The trading model is designed to optimize the VPP’s participation in the day-ahead market by aggregating these resources to function as a single entity, thereby improving market efficiency and resource utilization. The optimization framework simultaneously minimizes operational costs, maximizes system flexibility, and enhances reliability, addressing challenges posed by renewable energy integration and market uncertainties. A new flexibility index is introduced, incorporating both the technical and economic factors of individual units within the VPP, offering a comprehensive measure of system adaptability. The model is validated on IEEE 24-bus and 118-bus systems using evolutionary algorithms, achieving significant improvements in flexibility (20% increase), cost reduction (15%), and reliability (a 30% reduction in unsupplied energy). This study advances the development of efficient and resilient power systems amid growing renewable energy penetration. Full article

Reducing energy consumption in belt conveyor systems is critical to improving the overall energy efficiency of lignite mining operations. This study presents a theoretical and empirical analysis of energy use in overburden and coal conveyors, with a focus on balancing the relationship between belt speed and load. Building on the theory of conveyor motion resistance, the energy consumption index (WskZE)—previously introduced by the authors—is revisited as a function of two key variables: belt speed (v) and real-time material flow rate (Q_r). Empirical validation was conducted using operational data from variable-speed conveyors in the Konin lignite mine and compared to similar-length conveyors in the Bełchatów mine. Energy consumption measurements allowed for the analysis of energy consumption for two different scenarios: (i) in the Bełchatów mine the belt speed was constant and the excavator capacity was variable and (ii) in the Konin mine the excavator capacity was kept constant and the conveyor belt speed was varied. The results confirm that WskZE is linearly dependent on belt speed and inversely proportional to throughput, as predicted by theoretical models. However, findings also show that lowering belt speed—while effective in reducing energy use—results in a higher proportion of power being consumed to move the belt and heavy idlers, especially when these components are sized for peak loads. This study suggests a revised conveyor design philosophy (a new paradigm) that emphasizes maximizing the mass ratio of transported material to moving components. Additionally, it recommends integrating real-time monitoring of energy performance indicators into mine control systems to enable energy-aware operational decisions. Full article

In the context of Industry 4.0 and the proliferation of smart buildings, elevators represent critical assets whose performance is often inadequately measured by traditional indicators that overlook energy consumption. This study addresses the need for a more holistic Key Performance Indicator (KPI) by developing the Overall Equipment Effectiveness for Elevators (OEEE), an index designed to integrate operational effectiveness with energy efficiency. The methodology involves adapting the classical OEE framework through a comprehensive literature review and an analysis of elevator energy standards. This leads to a novel structure that incorporates a dedicated energy efficiency dimension alongside the traditional pillars of availability, performance, and quality. The framework further refines the performance and energy efficiency dimensions, resulting in six distinct sub-indicators that specifically measure operational uptime, speed adherence, electromechanical conversion, fault-free cycles (as a proxy for operational quality), and energy use during both movement and standby modes. The primary result is the complete mathematical formulation of the OEEE, a single, integrated KPI derived from these six metrics and designed for implementation using data from modern IoT-enabled elevators. The study concludes that the OEEE provides a more accurate and comprehensive tool for asset management, enabling data-driven decisions to enhance reliability, optimise energy consumption, and reduce operational costs in smart vertical transportation systems. Full article

This study develops a ship propeller diagnostic system to address the issue of insufficient ship maintenance capacity and enhance operational efficiency. It uses the Remaining Useful Life (RUL) prediction technology to establish a sensing platform for ship propellers to capture vibration signals during ship operations. The Diagnosis and RUL Prediction Model is designed to assess bearing aging status and the RUL of the propeller. The synchronized signal reshaping technology is employed in the Diagnosis and RUL Prediction Model to process the original vibration signals as input to the model. The vibration signals obtained are used to analyze the temporal and spectral energy of propeller bearings. Exponential functions are used to generate the health index as model outputs. Model inputs and outputs are simultaneously input into a Long Short-Term Memory (LSTM) model for training, culminating as the Diagnosis and RUL Prediction Model. Compared to Recurrent Neural Network and Support Vector Regression models used in previous studies, the Diagnosis and RUL Prediction Model developed in this study achieves a Mean Squared Error (MSE) of 0.018 and a Mean Absolute Error (MAE) of 0.039, demonstrating outstanding performance in prediction results and computational efficiency. This study integrates the Diagnosis and RUL Prediction Model, bearing aging experimental data, and real-world vibration measurements to develop the diagnosis and RUL prediction system for ship propellers. Experiments with ship propellers show that when the bearing of the propeller enters the worn stage, this diagnostic system for ship propellers can accurately determine the current status of the bearing and its remaining useful life. This study offers a practical solution to insufficient ship maintenance capacity and contributes to improving the operational efficiency of ships. Full article

Skywells are crucial for climate regulation in traditional Chinese dwelling architecture, exhibiting significant variations across climatic regions. This study focuses on humid–hot China, using Huangshan, to explore skywell parameters’ impact on thermal comfort and energy efficiency. Field research on 24 buildings in the World Heritage Site Xidi, Hong Villages, and Chinese Historical Pingshan Village, combined with Grasshopper’s Ladybug tool, established a parametric model. Using orthogonal design, performance simulation, and Python-based machine learning, six morphological parameters were analyzed: width-to-length ratio, height-to-width ratio, orientation, hall depth, wing width, and shading width. After NSGA-II multi-objective optimization, the summer Percentage of Time Comfortable (P_TC) increased by 5.3%, 38.14 h; the Universal Thermal Climate Index (UTCI) relatively improved by 2%; energy consumption decreased by 8.6%, 0.14 kWh/m²; and the useful daylight illuminance increased by 28%, 128.4 h. This confirms the climate adaptability of courtyard-style buildings in humid–hot China and identifies optimized skywell parameters within the study scope. Full article

Wood-derived ceramics represent a novel class of bio-based composite materials that integrate the hierarchical porous architecture of natural wood with high-performance ceramic phases such as silicon carbide (SiC). This review systematically summarizes recent advances in the fabrication of SiC woodceramics via two predominant sintering routes—reactive infiltration sintering and hot-press sintering—and elucidates their effects on the resulting microstructure and mechanical properties. This review leverages the intrinsic anisotropic vascular network and multiscale porosity and mechanical strength, achieving ultralightweight yet mechanically robust ceramics with tunable anisotropy and dynamic energy dissipation capabilities. Critical process–structure–property relationships are highlighted, including the role of ceramic reinforcement phases, interfacial engineering, and multiscale toughening mechanisms. The review further explores emerging applications spanning extreme protection (e.g., ballistic armor and aerospace thermal shields), multifunctional devices (such as electromagnetic shielding and tribological components), and architectural innovations including seismic-resistant composites and energy-efficient building materials. Finally, key challenges such as sintering-induced deformation, interfacial bonding limitations, and scalability are discussed alongside future prospects involving low-temperature sintering, nanoscale interface reinforcement, and additive manufacturing. This mini overview provides essential insights into the design and optimization of wood-derived ceramics, advancing their transition from sustainable biomimetic materials to next-generation high-performance structural components. This review synthesizes data from over 50 recent studies (2011–2025) indexed in Scopus and Web of Science, highlighting three key advancements: (1) bio-templated anisotropy breaking the porosity–strength trade-off, (2) reactive vs. hot-press sintering mechanisms, and (3) multifunctional applications in extreme environments. Full article