Search Results (177)

As a booming application domain in sharing economy, the crowd logistics services (CLSs) have emerged in recent years to take advantage of under-utilized resources for generating economic value. However, unduly designed CLS system platforms may suffer substantial problems such as sensitive information exposure, excessive commission fees, and trust issues. To mitigate such problems, we propose an approach comprising four initiatives: (1) exploring the applicability of blockchain technology and its affiliated technology, smart contract, in CLSs to manifest blockchain-enabled benefits including service traceability, process transparency, system automation and disintermediation; (2) adopting blockchain and smart contract technologies to design a blockchain application system architecture (BASA) suitable for reengineering current CLSs; (3) demonstrating the blockchain-based crowd logistics services (BCLSs) system design, implementation, and deployment details; and (4) evaluating the functionality and benefit of BCLSs approach to confirm its feasibility and applicability. After presenting the system design and implementation outcomes, this study elaborates the benefits and implications of BCLS systems through four theoretical frameworks: e-Commerce Value Creation Theory (VCT), Innovation Diffusion Theory (IDT), Principal Agent Theory (PAT), and Transaction Cost Analysis (TCA), deriving important findings and implications. Such benefits and implications suggest that BCLSs may help CLSs (1) mitigate PAT frictions and reduce transaction costs; (2) redefine value creation and accelerate innovation diffusion; (3) eliminate platform monopolies and achieve real-time settlement; (4) implement data sovereignty and enhance privacy/security; and (5) reconfigure trust mechanisms and generate digital credit assets. The research results of this study may help the CLS industry clarify the BCLS system’s upgrade path, promote business model innovation, and enhance fair governance and social sharing. Full article

The transition to low-carbon energy systems requires large-scale expansion and spatial reconfiguration of electricity infrastructure. While power system planning models provide detailed techno-economic pathways for achieving decarbonization targets, their real-world implementation is frequently constrained by social acceptance. This study identifies a structural “Modelling Gap”—defined as the systematic divergence between how social factors are represented in optimization frameworks and how they manifest as institutional constraints in realized infrastructure deployment. Based on a systematic review of 76 research articles—comprising 43 modelling studies, 32 empirical studies, and 1 mixed contribution—this paper develops a five-pillar taxonomy to analyze how qualitative social variables are translated into formal decision-making constraints. The analysis reveals a fundamental divergence between modelling and empirical approaches. In optimization models, social acceptance is typically represented as a parametric variable—such as cost penalties, spatial exclusions, or weighted preferences—implying that social resistance can be mitigated through marginal adjustments. In contrast, empirical evidence shows that social friction often operates through institutional mechanisms, including permitting decisions, legal rulings, and administrative processes, which function as categorical constraints on infrastructure deployment. The results further demonstrate that current models systematically underrepresent key dimensions of implementation risk. In particular, temporal delays, regulatory dynamics, and project abandonment are only partially captured in existing frameworks, despite being major drivers of real-world outcomes. This mismatch leads to planning outputs that may be technically optimal but operationally infeasible. By identifying the structural limitations of current modelling approaches, this study contributes a conceptual foundation for integrating social acceptance into sustainable power system planning. The findings suggest that improving the alignment between optimization models and institutional realities is critical for developing sustainable energy system pathways that are not only cost-efficient, but also socially and legally implementable. Full article

Rock-bed thermal energy storage (RTES) systems are attracting growing interest as low-cost, robust, and scalable sensible heat storage solutions for applications ranging from low-temperature building and greenhouse heating to medium- and high-temperature solar or waste-heat recovery systems. However, their thermo-hydraulic performance is strongly influenced by the complex interactions among heat-transfer-fluid flow, irregular rock morphology, porosity, pressure drop, interphase heat transfer, and transient thermal-front development. This review provides a focused evaluation of computational fluid dynamics (CFD) modelling strategies for packed beds of rocks, with particular attention to continuum porous-medium approaches and the closure correlations required for reliable simulation. First, the distinction between pore-scale and volume-averaged continuum modelling is discussed in terms of the trade-off between physical resolution and computational feasibility. The main pressure-drop and friction-factor correlations are then reviewed and compared, including classical packed-bed models and rock-bed-specific formulations. It is shown that hydraulic-resistance predictions are highly sensitive to particle shape, surface roughness, porosity, the bed-to-particle diameter ratio, and packing arrangement. Particle-fluid heat-transfer correlations are also examined and, when possible, converted into a consistent particle Nusselt-number form to enable direct comparison. Particular attention is given to generalized correlations, dispersion-corrected models, and air–rock-bed correlations applicable to thermal storage systems. Finally, a methodological framework for modelling RTES systems using local thermal equilibrium (LTE) and local thermal non-equilibrium (LTNE) formulations is proposed. Dimensionless criteria, including the interphase thermal coupling number and particle Biot number, are introduced to support the selection between LTE and LTNE formulations. The selection of pressure-drop/friction-factor and solid–fluid heat-transfer/particle Nusselt-number correlations should be based on the similarity between the original experimental conditions and the target RTES system, and system-specific validation is recommended whenever possible. Full article

Land-based airborne wind energy systems (LB-AWESs) offer a promising approach to harvesting high-altitude wind resources while significantly reducing costs. However, overall performance is heavily constrained by energy dissipation along the power chain, spanning from aerial traction to ground electromechanical conversion. While existing research and reviews predominantly focus on aircraft configurations or control strategies, comprehensive analyses of the energy transmission efficiency remain scarce. To fill this gap, this paper provides a holistic review of four critical stages: wind energy capture, tether transmission, ground mechanics, and electromechanical coupling. Distinct from traditional reviews centered on individual components, this study adopts a holistic perspective of the transmission chain to prioritize the analysis of loss mechanisms across different stages. In particular, it highlights that internal friction losses within multi-strand braided tethers under large-scale, cyclic loading conditions constitute a significant yet long-overlooked factor affecting energy transmission efficiency. Additionally, the stability and performance factors of umbrella-ladder configurations are qualitatively evaluated. By integrating existing theoretical studies, experimental findings and engineering practices, this paper identifies the key design factors affecting transmission efficiency, comprehensively elucidates the energy dissipation mechanisms of various subsystems, and proposes core efficiency enhancement methodologies, providing a foundational reference for the optimal design of next-generation LB-AWESs. Full article

A solar air heater generates heated air using solar energy. This system has a relatively simple design, which reduces the initial cost and facilitates maintenance compared with other solar systems. However, its thermal conversion efficiency is limited by the poor thermal conductivity of air. Previous studies have improved thermal efficiency by enhancing either the heat transfer area or the heat transfer coefficient, but most have applied only one of these approaches. In this work, a novel solar air heater with longitudinal fins and blocks, designed to simultaneously enhance the heat transfer area and heat transfer coefficient, is investigated for various block shapes (rectangular, forward-chamfered, backward-chamfered, and triangular blocks) utilizing computational fluid dynamics. Compared to the smooth fin channel, heat transfer is enhanced by a maximum of 1.61 times with the backward-chamfered block, while the corresponding enhancement factors for the rectangular, forward-chamfered, and triangular blocks are 1.52, 1.46, and 1.54, respectively. The thermo-hydraulic performance parameter, which simultaneously evaluates heat transfer augmentation and frictional penalty, further indicates that the backward-chamfered block is most effective at Reynolds numbers below 6000, while the rectangular block performs best above 9000. Full article

Nonlinear Model Predictive Control (NMPC) has garnered significant attention in autonomous systems due to its ability to predict future states and manage complex vehicle dynamics. However, the adaptability of existing NMPC methods is constrained by having to manually set the weight coefficients in the NMPC cost function. This study aims to explore a novel approach that integrates NMPC with Reinforcement Learning (RL), specifically employing Proximal Policy Optimization (PPO), to dynamically adjust NMPC weight matrices. The investigation begins by establishing a physics-based model for a two wheeled differential drive vehicle. A PPO model is then trained and deployed in real time to adapt to the NMPC weight matrices, achieving a 71% reduction in tracking error compared with the NMPC baseline. Importantly, the performance gain arises from PPO’s ability to reshape the NMPC cost function in real time, amplifying both orientation and lateral penalties in curves while relaxing them on straights, thereby enabling adaptive trade-offs between accuracy and control effort that static-weight NMPC cannot achieve. To enhance safety, the controller is integrated with a Control Barrier Function (CBF) layer for real-time obstacle avoidance, while PPO’s real-time weight adaptation contributes to improved tracking performance relative to NMPC+CBF. Finally, robustness evaluations under friction uncertainty, sensor noise, and path disturbances demonstrate that the PPO+NMPC+CBF method maintains reliable tracking accuracy and safety margins. Full article

Digital technologies are increasingly promoted as enablers of decarbonization and environmental, social, and governance (ESG) compliance in shipping, yet adoption remains constrained by high upfront costs, uncertain returns, supply–demand mismatch, and the risk of symbolic ESG disclosure and greenwashing. This study develops a collaborative governance framework to explain how technology provision, enterprise adoption, and public regulation co-evolve under ESG constraints. We construct a tripartite evolutionary game involving technology providers, shipping enterprises, and the government, incorporating ESG-driven market preference, technology matching efficiency, supply- and demand-side subsidies, regulatory intensity, greenwashing detection and penalties, and system-wide ESG benefits. Replicator dynamics and equilibrium stability analysis are used to derive convergence conditions, and numerical simulations together with system dynamics are employed to examine adjustment paths and convergence speed under alternative policy scenarios. Results indicate that a high-compliance equilibrium emerges when the net benefits of supply and adoption are positive and regulatory benefits offset enforcement and subsidy costs. Matching efficiency is identified as a key friction that slows diffusion and delays convergence even under favorable ESG market signals. Subsidies reduce cost pressure on both supply and demand sides, while greenwashing penalties and effective detection strengthen compliance incentives and accelerate convergence. Overall, the findings suggest that policy packages combining targeted incentives with credible enforcement are more effective than single-instrument approaches, and that improving technology–business fit is essential for transforming ESG pressure from external compliance into sustained internal adoption. Full article

This research examines the transformation of film and TV series consumption within the contemporary media landscape, characterized by digital plenitude and Over-the-Top (OTT) dominance. The study investigates how users navigate the transition from linear broadcasting toward on-demand, platform-centric environments. Through an exploratory qualitative approach as the initial phase of a broader study, 18 semi-structured interviews were conducted with a demographically diverse group of Hungarian participants whose primary commonality is active film and TV series consumption. The findings highlight a rejection of traditional linear television, driven by an aversion to intrusive advertising and a demand for temporal autonomy. While mobile devices, particularly smartphones, are central to this shift, consumption remains predominantly stationary; users prioritize the flexibility of cross-device access within the domestic environment over mobile viewing during transit. Furthermore, the study identifies a growing friction caused by content fragmentation between different OTT platforms and rising subscription costs, while digital piracy persists as a marginal alternative. Ultimately, the study concludes that the modern audience acts as a strategic user navigating a complex ecosystem of excess. This underscores a fundamental shift where the cultural value of content is increasingly defined by the tension between individual agency and the systemic constraints of competing services. Full article

Tool wear in CNC milling increases friction and torque demand at the tool-workpiece interface, which is reflected in spindle motor current. This study develops a non-intrusive tool wear condition classification method using spindle motor current monitoring during practical CNC milling of commercial medium-carbon steel workpieces (JIS S50C/AISI SAE 1050-equivalent; as-received and non-heat-treated; nominal laboratory hardness approximately 4.3 HRC). Experiments were performed on a Tongtai MDV-508 vertical machining center at fixed cutting conditions (3000 rpm spindle speed, 2 mm axial depth of cut, 5 mm cutting width, and 300 mm/min feed rate) using eight TiAlN-coated fine-grain WC–Co solid carbide end mills (10 mm diameter, four flutes; nominal Co binder approximately 10 wt%). An oil-based HS Highstart/HS-SSHS-BH10 cutting fluid was applied through the machine external coolant nozzle in flood mode at an estimated nominal flow rate of approximately 3 L/min and near-room coolant temperature (25 ± 2 °C), and was used as supplied without dilution. A clamp-type AC current sensor was installed on one phase line supplying the spindle motor, and current was acquired using an NI-9221 module at 20 kHz. Cutting intervals were isolated by envelope-based segmentation, concatenated, and divided into 1 s windows (0.5 s overlap) for feature extraction. Three feature sets were evaluated: time-domain statistics, frequency-domain statistics, and an FFT→PCA hybrid representation. Tool states (New, Mid-life, Old) were labeled using post-process surface roughness Ra thresholds supported by microscope observation. The PCA transformation was fitted only on training data and then applied to the held-out test data. A logistic regression classifier achieved 97.44% test accuracy (152/156 windows; 95% Wilson CI: 93.59–99.00%) with the PCA-hybrid features, outperforming time-domain (89.74%) and frequency-domain (94.87%) models. The results support spindle current monitoring as a low-cost approach for quality-aligned tool condition monitoring, while the external validity remains limited to the tested machine, material, tool, coolant, and cutting-parameter combination. Full article

The development of resin-regolith composites represents a promising In Situ Resource Utilization (ISRU) strategy for future lunar missions. While unsuitable for primary habitat construction due to the payload cost of transporting polymers from Earth, these composites offer a highly efficient solution for manufacturing non-structural, everyday items (e.g., containers, tools, and plant cultivation pots) directly on the Moon via mold–casting. This approach significantly reduces the volume and mass of pre-formed plastic payloads. In this work, the influence of the particle size distribution of a lunar highland simulant (LHS-1E) on the mechanical properties of epoxy-based composites was systematically investigated for such applications. First, the regolith-to-resin ratio was optimized for castability, establishing a maximum regolith content of 60 wt.%. Then, four different size fractions of the simulant were prepared by sieving (>200 µm, 200–100 µm, 100–50 µm, and <50 µm), and composite samples were cast maintaining this optimal ratio. X-ray microtomography revealed that using larger particles (>200 µm) increased composite porosity, whereas smaller fractions promoted more compact structures. Three-point bending tests showed that intermediate particle sizes (200–100 µm and 100–50 µm) led to enhanced flexural strength, while the smallest particles (<50 µm) decreased mechanical performance, likely due to a lower basalt content in this finer fraction. Finally, ball-on-disk tribological analyses highlighted that composites made with larger particles (>200 µm) exhibited superior wear resistance, whereas particle size had negligible effects on the coefficient of friction. Overall, the results demonstrate that both particle size and mineralogical composition significantly influence the performance of regolith–epoxy composites, providing essential guidelines for the in situ manufacturing of functional, non-structural objects for lunar outposts. Full article

Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity and cost. To overcome these limitations, this study proposes a wavelet-based health monitoring structure for detecting electrical and mechanical faults using motor current signal analysis. A dynamic model of the train door actuation mechanism, including a DC motor, gearbox, and lead screw, was developed in MATLAB/Simulink to simulate conditions such as armature electrical faults, brush wear, increased friction, and lead screw misalignment. Motor current signals were analyzed using the Discrete Wavelet Transform with a Daubechies (db10) mother wavelet to extract diagnostic features based on the L1-norms of wavelet coefficients at levels W8 and W9 along with the motor starting current peak. Experimental validation using a LabVIEW-based test platform demonstrated fault detection accuracy above 96% with a response time below 0.3 s, confirming the effectiveness of the proposed approach for predictive maintenance of railway door systems. Full article

This paper presents a finite element study of friction ring springs, with emphasis on the internal stress distribution between inner and outer rings and their damping capacity. A detailed two-dimensional axisymmetric model was developed and compared against experimental measurements, showing close agreement in load–displacement response. In parallel, the classical analytical approach was validated in terms of stress and deformation values. To enable efficient parametric studies, a reduced one-element finite element model representing the periodic structure of the spring was also developed. This simplified model reproduces the response of the complete axisymmetric model while reducing the computational cost by over 80%. Beyond reproducing global mechanical behavior, the study provides detailed insight into the ring interactions as a function of the cone angle, friction coefficient, and the ratio of inner to outer cross-sectional areas. The results show that an optimal design should favor higher circumferential stresses in the inner rings, as their compressive stress state and radial confinement make them more resistant to buckling and crack initiation than the outer rings, which are subjected to tension. The findings provide useful guidelines for the modeling and design of friction ring springs and contribute to the broader understanding of friction-based energy-dissipation systems. Full article

This study investigates the thermal effects of machining aluminium matrix composites reinforced with rice husk ash (RHA) using orthogonal cutting tools. Utilizing DEFORM 3D simulation software, version V12, key thermal parameters were analysed, including final shear plane temperatures, steady-state tool temperatures, and frictional power across varying spindle speeds (200–800 rpm) and RHA contents (0–12 wt.%). The findings reveal significant thermal accumulation, with temperatures ranging from 49.6 °C to 564.8 °C, correlating positively with increased spindle speeds and RHA reinforcement levels. Higher frictional power requirements were observed, indicating increased machining resistance and higher operational costs. Heat partition coefficients, derived from multiple models, highlighted decreasing heat absorption by the cutting tool as the percentage content of RHA increased. These insights emphasise the need for optimised machining parameters, robust thermal management solutions, and appropriate tool materials to mitigate thermal loads and enhance machining performance. The study underscores the balance between the mechanical benefits of RHA reinforcement and the associated thermal challenges, advocating for a comprehensive approach to improve the machinability and sustainability of aluminium–RHA composites in industrial applications. Full article

As a solid-state welding technique, friction stir welding (FSW) has many advantages over conventional fusion welding. Its applications in the manufacturing and joining of parts in aerospace, automotive, and shipbuilding have significantly increased. Friction heat generation is the fundamental driver of the FSW process. It governs material flow, microstructural evolution, mechanical properties, and residual stresses. Understanding the effect of heat generated on the joint quality is essential for process parameter optimization, ensuring defect-free welds and high-quality joints. Thus, evaluating the thermal history of the FSW process is a key requirement for effective analysis. This comprehensive review critically discusses research studies published over the past three decades (1991–2025) that have examined different approaches to predict and measure heat generation in FSW. A total of 136 highly relevant articles were selected from the Scopus database and systematically analyzed. The effects of various welding parameters on heat generation, microstructural evolution, and joints’ mechanical properties have been reported. Different heat generation prediction and measurement techniques, such as analytical models, finite element models (FEM), and experimental methods have been discussed in terms of their feasibility, accuracy, advantages, disadvantages, and cost. The evolution, state of the art of analytical models and FEM over the last three decades are analyzed and future research directions are outlined. Finally, the correlation between process parameters, heat generated, microstructural development, and mechanical performance of the welded joints for various workpiece materials is investigated. This review provides a critical and comparative perspective that highlights the strengths and limitations of each method, offering practical guidance for researchers and industry practitioners. Full article

The results of the development and practical application of a comprehensive system for studying gear cutting processes are presented. The processes are traditional hobbing, modern power skiving, and radial-circular methods. Carrying out these processes is based on the gear teeth continuous generating method using complex kinematics. This complicates the analysis, description and modeling of the processes. The developed system provides for a logical sequence of step-by-step modeling and simulation of interrelated processes and phenomena accompanying gear processing. Reproducing volumetric chips and calculating their parameters provides the basis for determining deformation and contact processes, cutting forces, elastic deformations, machining accuracy and energy costs per operation. After establishing the operation to overcome friction and heat flows, the degree of heating and the temperature of the working surfaces are calculated to predict tool wear and its service life. Based on the parametric non-uniformity of the considered processes, the intensity of oscillations and vibrations of gear cutting machines is predicted, and their impact on the quality of gear surfaces and the accuracy of gears is determined. These approaches enable the study of such processes at the level of individual teeth and blades during cutting. They also allow gear cutting technology and cutting tools to be optimized according to the most important criteria and performance assessments. Full article