Search Results (34)

This study proposes a method for separating asphalt and aggregates in recycled asphalt pavement (RAP) materials using surfactants as solvents. This method utilizes surfactants to soften the asphalt by reducing its surface tension, separating the RAP clusters, and washing away the asphalt from the RAP. The wastewater is recycled during the emulsification–separation process without discharge. Factors affecting the separation effect of RAP, including the type of anionic surfactants, the surfactant concentration, the emulsion-to-RAP ratio, temperature, the rotation rate and time, and the RAP’s particle size, were investigated in depth, and the separation effect and its influence on the aggregate properties were evaluated. The experimental results indicate that when using the optimal process to mix and treat 13.2 mm and 9.5 mm RAP clusters, it is possible to achieve 100% separation of the coarse RAP above 4.75 mm, with a 64.58% reduction in the asphalt content. The angularity of the aggregate remained unchanged after separation. It was observed from scanning electron microscopy (SEM) images that the asphalt on the surface of the coarse aggregate had been eluted, and the morphology of the aggregate surface was completely exposed. This environmentally friendly separation method provides new possibilities for high-content RAP recycling in pavement engineering. Full article

Concrete-like composites are widely used in the building of civil engineering applications such as houses, dams, and roads. Mesoscale modeling is a powerful tool for the physical and mechanical analysis of concrete-like composites. A novel discrete element method (DEM)-enhanced external force-free method for multi-phase concrete-like composite modeling with an interface transition zone (ITZ) is presented in this paper. Firstly, randomly distributed particles with arbitrary shapes are generated based on a grading curve. Then, a Minkowski sum operation for particles is implemented to control the minimum gap between adjacent particles. Secondly, a transition from particles to clumps is realized using the overlapping discrete element cluster (ODEC) method and is randomly placed into a specific space. Thirdly, the DEM simulation with a simple linear contact model is employed to separate the overlapped clumps. Meanwhile, the initial position, displacement, and rotation of clumps are recorded. Finally, the mesoscale model is reconstructed based on the displacement and rotation information. The results show that this method can efficiently generate multi-phase composites with arbitrary particle shapes, high volume fractions, an overlapped ITZ, and a periodic structure. This study proposes a novel, efficient tool for analyzing and designing composite materials in resilient civil infrastructure. Full article

Microswimmers are key for unveiling new physical phenomena underlying their propulsion, especially when driven inside complex fluids. Liquid crystals are anisotropic complex fluids that feature long-range orientational order. The propulsion of non-charged dielectric particles can be accomplished in these systems by breaking the particles’ fore-aft symmetry thanks to anisotropies in the conductivity and dielectric permittivity parameters of the liquid crystal. Under the application of an AC electric field, asymmetric osmotic flows are generated to propel non-spherical particles, whose direction of motion depends on the orientational order of the liquid crystal molecules around the inclusions. This means that, by controlling the LC orientation, one will be able to steer driven colloidal inclusions. In this experimental work, we show that a homogeneous magnetic field that is able to control the orientation of the liquid crystal molecules also allows us to determine the direction of motion of driven particles without significant changes in the propulsion mechanism. Additionally, we show that a radial configuration of the magnetic field lines can be used to generate topological defects in the liquid crystal orientational field that attract colloidal particles, leading to their clustering as rotating mills. The generated clusters were tested to study the collective motion of particles, suggesting the presence of particle–particle interactions. Full article

This work presents an innovative, energy-efficient IoT routing protocol that combines advanced data fusion grouping and routing strategies to effectively tackle the challenges of data management in smart cities. Our protocol employs hierarchical Data Fusion Head (DFH), relay DFHs, and marine predators algorithm, the latter of which is a reliable metaheuristic algorithm which incorporates a fitness function that optimizes parameters such as how closely the Sensor Nodes (SNs) of a data fusion group (DFG) are gathered together, the distance to the sink node, proximity to SNs within the data fusion group, the remaining energy (RE), the Average Scale of Building Occlusions (ASBO), and Primary DFH (PDFH) rotation frequency. A key innovation in our approach is the introduction of data fusion techniques to minimize redundant data transmissions and enhance data quality within DFG. By consolidating data from multiple SNs using fusion algorithms, our protocol reduces the volume of transmitted information, leading to significant energy savings. Our protocol supports both direct routing, where fused data flow straight to the sink node, and multi-hop routing, where a PDF relay is chosen based on an influential relay cost function that considers parameters such as RE, distance to the sink node, and ASBO. Given that the proposed protocol incorporates efficient failure recovery strategies, data redundancy management, and data fusion techniques, it enhances overall system resilience, thereby ensuring high protocol performance even in unforeseen circumstances. Thorough simulations and comparative analysis reveal the protocol’s superior performance across key performance metrics, namely, network lifespan, energy consumption, throughput, and average delay. When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. Specifically, our protocol extends network lifespan by 299% over PSO-EEC, 264% over LDIWPSO, 306% over OFCA, and 249% over NPSOP. It also reduces energy consumption by 254% relative to PSO-EEC, 264% compared to LDIWPSO, 247% against OFCA, and 253% over NPSOP. The throughput improvements reach 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By fusing data and optimizing routing strategies, our protocol sets a new benchmark for energy-efficient IoT DFG, offering a robust solution for diverse IoT deployments. Full article

Numerical modeling of the direct magnetoelectric (ME) effect in a PVDF–cobalt ferrite (CFO) composite film has been performed. The problem is solved within the framework of the mesoscopic RVE approach, where each elementary cell contains three particles with varying mutual positions. Both modes of mechanical stress generation are taken into account: magnetostrictive and magnetorotational, i.e., changes in the shape and rotation of the particle as a whole. Depending on the sign of the magnetostriction constants, these sources of piezopolarization can either enhance or reduce the overall ME effect. A significant dependence of the ME effect on the mutual arrangement of CFO particles in the cell has been discovered; for instance, the effect is minimal when the particles are closest to each other. In other words, clustering is a negative factor. In a system where the magnetic moments of the magnetically hard CFO particles are ordered, the maximum ME effect is attained when the poling direction is at an angle of about ${40}^{\circ }$ to the film plane. As it turns out, a fairly good estimate of this angle can be obtained from the solution of a single-particle problem; the main contribution here comes from the ‘diagonal’ components of the piezotensor: $d_{31}$ and $d_{33}$. The ‘tangential’ component $d_{15}$ plays a special role: changing its sign can reverse the polarity of the charge generated on the film. Full article

The prediction of the health status of critical components is an important influence in making accurate maintenance decisions for rotating equipment. Since vibration signals contain a large amount of fault information, they can more accurately describe the health status of critical components. Therefore, it is widely used in the field of rotating equipment health state prediction. However, there are two major problems in predicting the health status of key components based on vibration signals: (1) The working environment of rotating equipment is harsh, and if only one feature in the time or frequency domain is selected for fault analysis, it will be susceptible to harsh operating environments and cannot completely reflect the fault information. (2) The vibration signals are unlabeled time series data, which are difficult to accurately convert into the health status of key components. In order to solve the above problems, this paper proposes a combined prediction model combining a bidirectional long- and short-term memory network (BiLSTM), a self-organizing neural network (SOM) and particle swarm optimization (PSO). Firstly, the SOM is utilized to fuse the fault characteristics of multiple vibration signals of key components to obtain an indicator (HI) that can reflect the health status of rotating equipment and to also compensate for the vulnerability of single signal characteristics in the time or frequency domain to environmental influences. Secondly, the K-means clustering method is employed to cluster the health indicators and determine the health state, which solves the problem of determining the health of a component from unsupervised vibration signal data which is quite difficult. Finally, the particle swarm optimized BiLSTM model is used to predict the health state of key components and the bearing dataset from the IEEE PHM 2012 Data Challenge verifies the method’s effectiveness and validity. Full article

Quantum private comparison (QPC) enables two users to securely conduct private comparisons in a network characterized by mutual distrust while guaranteeing the confidentiality of their private inputs. Most previous QPC protocols were primarily used to determine the equality of private information between two users, which constrained their scalability. In this paper, we propose a QPC protocol that leverages the entanglement correlation between particles in a four-particle cluster state. This protocol can compare the information of two groups of users within one protocol execution, with each group consisting of two users. A semi-honest third party (TP), who will not deviate from the protocol execution or conspire with any participant, is involved in assisting users to achieve private comparisons. Users encode their inputs into specific angles of rotational operations performed on the received quantum sequence, which is then sent back to TP. Security analysis shows that both external attacks and insider threats are ineffective at stealing private data. Finally, we compare our protocol with some previously proposed QPC protocols. Full article

In coal-fired power plants, coal piles serve as the fundamental management units. Acquiring point clouds of coal piles facilitates the convenient measurement of daily coal consumption and combustion efficiency. When using servo motors to drive Light Detection and Ranging (LiDAR) scanning of large-scale coal piles, the motors are subject to rotational errors due to gravitational effects. As a result, the acquired point clouds often contain significant noise. To address this issue, we proposes a Rapid Point Cloud Stitching–Constrained Particle Filter (RPCS-CPF) method. By introducing random noise to simulate servo motor rotational errors, both local and global point clouds are sequentially subjected to RPCS-CPF operations, resulting in smooth and continuous coal pile point clouds. Moreover, this paper presents a coal pile boundary detection method based on gradient region growing clustering. Experimental results demonstrate that our proposed RPCS-CPF method can generate smooth and continuous coal pile point clouds, even in the presence of servo motor rotational errors. Full article

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (airborne particles with an aerodynamic diameter of less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley: one in the west/northwest and the other in the southeast. Wavelet analysis revealed the annual cycle to be the most persistent temporal mode of PM10 variability in both subregions, with intermittent signals also observed at time scales of around 120, 30~90, and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere. Full article

In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency and hastened system aging. This paper presents a novel differentiated power distribution strategy comprising three control variables: the rotation status, and the operating boundaries for both depth of discharge (DOD) and C-rates (C) within a control period. The proposed strategy integrates an aging cost prediction model developed to express the mapping relationship between these control variables and aging costs. Additionally, it incorporates the multi-colony particle swarm optimization (Mc-PSO) algorithm into the optimization model to minimize aging costs. The aging cost prediction model consists of three functions: predicting health features (HFs) based on the cumulative charge/discharge throughput quantity and operating boundaries, characterizing HFs as comprehensive scores, and calculating aging costs using both comprehensive scores and residual equipment value. Further, we elaborated on the engineering application process for the proposed control strategy. In the simulation scenarios, this strategy prolonged the service life by 14.62%, reduced the overall aging cost by 6.61%, and improved module consistency by 21.98%, compared with the traditional equalized distribution strategy. In summary, the proposed strategy proves effective in elongating service life, reducing overall aging costs, and increasing the benefit of energy storage systems in particular application scenarios. Full article

Additive manufacturing (AM) techniques are widely investigated for the cost-effective use of titanium (Ti) alloys in various aerospace applications. One of the AM techniques developed for such applications is plasma transferred arc solid free-form fabrication (PTA-SFFF). Materials manufactured through AM techniques often exhibit anisotropies in mechanical properties due to the layer-by-layer material build. In this regard, the present study investigates the isothermal directional fatigue of a Ti-TiB metal matrix composite (MMC) manufactured by PTA-SFFF. This investigation includes a rotating beam fatigue test in the fully reversed condition (stress ratio, R = −1), electron microscopy, and calculations for fatigue life predictions using Paris’ and modified Paris’ equations. The fatigue experiments were performed at 350 °C using specimen with the test axis oriented diagonally (45°) and parallel (90°) to the AM builds directions. The fatigue values from the current experiments along with literature data find that the Ti MMC manufactured via PTA-SFFF exhibit fatigue anisotropy reporting highest strength in 90° and lowest in perpendicular (0°) AM build directions. Furthermore, calculations were performed to evaluate the optimum values of the stress intensity modification factor (λ) for fatigue life prediction in 0°, 45°, and 90° AM build directions. It was found that for the specimens with 45°, and 90° AM build directions, the computed intensity modification factors were very similar. This suggests that the initial fatigue crack characteristics such as location, shape, and size were similar in both 45°, and 90° AM build directions. However, in 0° AM build direction, the computed stress intensity modification factor was different from that of the 45°, and 90° AM build directions. This indicates that the fatigue crack initiation at 0° AM build direction is different compared to the other two directions considered in this study. Moreover, the quality of fatigue life prediction was assessed by calculating R² values for both Paris and modified Paris predictions. Using the R² values, it was found that the fatigue life predictions made by the modified Paris equation resulted in improved prediction accuracy for all three builds, and the percentage improvement ranged from 30% to 60%. Additionally, electron microscopy investigations of 0°, 45°, and 90° AM build specimens revealed extensive damage to the TiB particle compared to the Ti matrix as well as frequent TiB clusters in all three AM build directions. These observations suggest that the spread of these TiB clusters plays a role in the fatigue anisotropy of Ti-TiB MMCs. Full article

The gelation kinetics of agar aqueous solutions were studied by means of the viscosity flow curves using a coaxial Couette cylinder viscometer. The viscosity curves show an unusual sigmoidal trend or an exponential decay to a viscous steady state. An original theory of gelation kinetics was developed considering the coarsening of increasingly larger and more stable clusters due to Ostwald ripening and the breakup of clusters that were too large due to the instability of rotating large particles induced by the shear rate. The developed Bounded Ripening Growth model takes into account the trend of the viscosity curves by means of an autocatalytic process with negative feedback on aggregation according to the logistic kinetic equation, in which the constants $k_1(\gamma )$ and $k_{-}(\nu )$ are governed by the surface tension and shear rate, respectively. A dimensionless equation based on the difference between the Weber number and the ratio of the inverse kinetic constant to forward constant, accounts for the behavior of the dispersed phase in equilibrium conditions or far from the hydrostatic equilibrium. Full article

In this study, friction stir welding (FSW) was employed to join AA3003-H18 sheets by incorporating in situ Al-Cu intermetallic compounds within the stir zone. The FSW process was carried out under three distinct conditions: (I) without applying powder, (II) by introducing Cu powder, and (III) by incorporating Cu-Al mixed powder (50 vol.% Cu, 50 vol.% Al). The powder was embedded into the gap between two sheets. Subsequently, two-pass FSW, involving both forward and backward movements, was conducted with a rotational speed of 1200 rpm and traverse speed of 100 mm/min across all three experimental conditions. In the second and third conditions, the formation of in situ intermetallic compounds occurred through a solid-state reaction between Cu particles and Al within the stir zone. Examination of the stir zone through optical and electron microscopic studies revealed that the utilization of Cu-Al mixed powder resulted in finer and more uniformly distributed Cu clusters and Al-Cu intermetallics than samples welded with Cu powder alone. Notably, the stir zone of samples incorporating Cu-Al mixed powder exhibited finely dispersed, completely gray Al-Cu intermetallic particles, whereas those with only Cu powder displayed predominantly coarse core-shell particles in the microstructure. The introduction of Cu-Al mixed powder during FSW resulted in a stir zone with an average hardness of 74 HB, showing a 14% increase compared to the cases where Cu powder alone was added (65 HB). Tensile tests, conducted in both transverse and longitudinal directions on the FSWed samples, did not exhibit a consistent trend across the three mentioned conditions. Transverse tensile strength consistently ranged between 107 and 110 MPa, with joint efficiency varying from 52% to 54%. However, the longitudinal tensile strength of the joint with added Cu-Al mixed powder (158 MPa) surpassed those welded with Cu powder alone (134 MPa). Full article

In order to increase industrial production quality and efficiency, it is essential to understand how the aeration and no-aeration condition affects liquid and solid material mixing in the stirred tank. Due to complicated shear flows, the related mass-transfer mechanism confronts numerous difficulties. This paper put forward an improved computational fluid dynamics and discrete element method (CFD–DEM) modeling approach to explore the effect mechanism of aeration conditions on liquid–solid material mixing. Firstly, a mass-transfer dynamic model is set up with a volume of fluid and piecewise linear interface construction (VOF–PLIC) coupling strategy to explore flow modes and vorticity evolution trends under aeration control. Then, a self-developed interphase coupling interface is utilized to modify the coupling force and porosity of the porous media model in the DEM module, and random dispersion properties of the particle phase under non-aeration and aeration are obtained. Results show that the aeration and flow-blocking components transform fluid tangential speeds into axial and radial speeds, which can improve the material mixing quality and efficiency. The mixed flow field can reach a greater turbulent process under the impeller rotation, making the particles have an intensive disorder and complex flow patterns. The enhanced motion efficiency of the vortex clusters encourages their nesting courses and improves cross-scale mixed transport. It can serve as some reference for the three-phase flow mixing mechanism, vorticity distribution law, and particle motion solution and has a general significance for battery homogeneous mixing, biopharmaceutical processes, and chemical process extraction. Full article

Single-cell analysis is an emerging discipline that has shown a transformative impact in cell biology in the last decade. Progress in this field requires systems capable of accurately moving the cells and particles in a controlled manner. Here, we present a microfluidic platform equipped with C-shaped magnetic thin films to precisely transport magnetic particles in a tri-axial rotating magnetic field. This innovative system, compared to the other rivals, offers numerous advantages. The magnetic particles repel each other to prevent undesired cluster formation. Many particles move synced with the external rotating magnetic field, which results in highly parallel controlled particle transport. We show that the particle transport in this system is analogous to electron transport and Ohm’s law in electrical circuits. The proposed magnetic transport pattern is carefully studied using both simulations and experiments for various parameters, including the magnetic field characteristics, particle size, and gap size in the design. We demonstrate the appropriate transport of both magnetic beads and magnetized living cells. We also show a pilot mRNA-capturing experiment with barcode-carrying magnetic beads. The introduced chip offers fundamental potential applications in the fields of single-cell biology and bioengineering. Full article