Search Results (195)

In this paper, a discrete element method (DEM) coupled with a finite element method (FEM) was used to elucidate the impact of packing structures and size ratios on the cold die compaction behavior of pure copper powders. HCP structure, SC structure, and three random packing structures with different particle size ratios (1:2, 1:3, and 1:4) were generated by the DEM, and then simulated by the FEM to analyze the average relative density, von Mises stress, and force chain structures of the compact. The results show that for HCP and SC structures with a regular stacking structure, the average relative densities of the compact were higher than those of random packing structures, which were 0.9823, 0.9693, 0.9456, 0.9502, and 0.9507, respectively. Compared with their initial packing density, it could be improved by up to 21.13%. For the bigger particle in HCP and SC structures, the stress concentration was located between the adjacent layers, while in the small particles, it was located between contacted particles. During the initial compaction phase, smaller particles tend to occupy the voids between larger particles. As the pressure increases, larger particles deform plastically in a notable way to create a stabilizing force chain. This action reduces the axial stress gradient and improves radial symmetry. The transition from a contact-dominated to a body-stress-dominated state is further demonstrated by stress distribution maps and contact force vector analysis, highlighting the interaction between particle rearrangement and plasticity. Full article

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

In this paper, the analysis method which coupled discrete element method (DEM) and finite element method (FEM) is used to simulate the double compaction of random packing of Cu and Al composite powders with multiple particle sizes. Cu and Al composite powders with varying particle size ratios from 1:2 to 1:5 were generated by DEM and then imported to MSC. Marc software (MSC.MARC2015 version) to construct FEM analysis. The effects of metal ratios, compaction pressure and size ratios on the relative density and von Mises stress of the compact were studied. The results show that the average relative density of the compact increases with the Al content, and the stress decreases. The stress in the Cu particle is particularly higher than that in the Al particle, mainly because the contact normal force of the Cu particle is nearly parallel at each contact surface. Therefore, the phenomenon of stress concentration is easier to occur within copper particles. When Al content is 30wt.%, the particle size difference enhances densification efficiency by up to 12.3%, as evidenced by an initial relative density increase from 0.7915 to 0.8047, primarily due to smaller Cu particles effectively filling interparticle voids. When the compaction pressure is fixed, the average relative density of the compact with the particle size ratio 1:5 is higher than the others, and the contact forces inside the particles significantly decrease. Full article

Background/Objectives: Oral anticoagulation therapy (OAC) is crucial for reducing the risk of ischemic complications in patients with atrial fibrillation (AF). However, OAC also increases the risk of major bleeding events. The optimal management of OAC in patients with AF undergoing transaortic valve implantation (TAVI) is unclear. This study aimed to compare the efficacy and safety of OAC interruption vs. continuation in patients with AF scheduled for TAVI. Methods: PubMed, EMBASE, and Cochrane were searched to include all pertinent randomized and observational studies. The primary endpoint was the occurrence of net adverse clinical events (NACE), a composite of all-cause death, major vascular complications, and major bleeding at 30-day follow-up. Secondary endpoints included all-cause death, cardiovascular death, major vascular complications, major bleeding, any bleeding, stroke, non-fatal myocardial infarction, and the need for red-packed blood transfusion. Results: A total of three studies and 2773 patients were included in the analysis (1314 were allocated to continuation of OAC therapy and 1459 to interruption of OAC therapy during TAVI). The two study groups experienced a similar rate of NACE (OR = 0.89 [95% CI 0.61 to 1.31], I² = 77%, p = 0.56) compared to the OAC-interruption group. No significant differences were observed in the rate of all-cause death (p = 0.21), cardiovascular death (p = 0.35), major vascular complications (p = 0.84), major bleeding events (p = 0.47), total bleeding events (p = 0.62), or non-fatal MI (p = 0.55). Interestingly, the OAC-continuation group experienced a lower occurrence of stroke (OR = 0.62 [95% CI 0.39 to 0.97], I² = 0%, p = 0.04) and the need for red packed blood cells (OR = 0.66 [95% CI 0.50 to 0.86], I² = 20%, p < 0.01) compared to the OAC-interruption group. Conclusions: In patients with AF undergoing TAVI, there was no significant difference between interruption and continuation of OAC in terms of NACE, composite of all-cause death, major vascular complications, or major bleeding at 30-day follow-up. Of interest, the OAC-continuation group patients experienced lower rates of stroke and the need for blood transfusion. Full article

The container loading problem (CLP) has a broad spectrum of applications in industry and has been studied for over 60 years due to its high complexity. This paper addresses a realistic single-container loading scenario with practical constraints, including orientation limitations, maximum stacking weight, static stability, overall container weight limit, and fractional loading for multiple drop-off points (multidrop). We propose an open-source decision support system (DSS) implemented on a widely used platform (MS Excel®), which employs a heuristic algorithm to find efficient loading solutions under these constraints. The DSS uses a multi-start randomized constructive algorithm based on a maximal residual space representation. The constructive phase builds the loading pattern in vertical layers (columns or walls), while respecting all practical constraints. The performance of the proposed heuristic is validated through extensive computational experiments on classical benchmark instances, comparing its results against the recent state-of-the-art methods. We also analyze the impact of multi-drop constraints on utilization metrics. The DSS features an interactive interface for creating/loading instances, visualizing step-by-step packing patterns, and displaying key statistics, thus providing a user-friendly decision tool for practitioners. Full article

With the development of intelligent connected vehicles, higher demands are being placed on the capabilities of in-vehicle bus networks. Compared to traditional in-vehicle bus networks like Local Interconnect Network (LIN) and Controller Area Network (CAN), the FlexRay bus offers advantages such as high real-time performance and high transmission rates, making it the core technology of the new generation of in-vehicle bus networks. This study focuses on the phenomenon of bandwidth resource waste in the FlexRay bus and innovatively proposes the FlexRay Static Segment Heterogeneous Scheduling Algorithm (SHSA). The SHSA algorithm optimizes the message transmission performance of the FlexRay bus through heterogeneous allocation of communication channels and message scheduling methods. This study established a simulation experimental platform using the CANoe.FlexRay bus network simulation tool and conducted simulation experiments on the proposed algorithm. Experimental results show that the average bandwidth utilization of the SHSA algorithm is 72.5%, which is 20.91%, 51.14%, and 54% higher than that of the existing Heterogeneous Makespan-minimizing DAG Scheduler (HMDS), Message Packing Scheme, and Jitter-aware Message Scheduling-Simulated Annealing and Greedy Randomized Adaptive Search Procedure (JAMS-SG), respectively. This study provides technical support for message transmission in intelligent connected vehicles and enhances the communication efficiency of the in-vehicle FlexRay bus network. Full article

Battery pack casings with a total energy of 12.432 kWh were designed using two types of materials: aluminum alloy and carbon fiber reinforced composite filament based on polyphthalamide or high-performance/high-temperature nylon (PPA-CF). The effectiveness of mechanical metamaterials (lattice and auxetic structures) in mitigating the levels of random vibrations in the battery pack casings was studied using a numerical method. Both structures demonstrate outstanding capabilities with a 97% to 99% reduction in vibration levels in the aluminum casing. However, the capabilities of these structures in mitigating vibration levels in the PPA-CF casing are very limited, in that they can only mitigate approximately 63.8% and 92.8% of the longitudinal vibrations at the top cover of the casing and center of its front and back walls, respectively. Compared to PPA-CF, aluminum alloy shows better vibration mitigation performance with or without structural modification. Full article

The heat transfer mechanism of hollow glass microsphere/epoxy resin composites (HGM/ER) is intricate, and the formation of void structures during material preparation complicates the prediction of thermal conductivity. To investigate the microscopic heat transfer mechanisms of HGM/ER materials with void structures and analyze the impact of void variables on the overall thermal performance, this study addresses the issue of low packing density and poor uniformity in traditional cellular unit structures. An improved random sequential adsorption (RSA) algorithm is proposed, increasing the upper limit of particle fill rate by 25% relative to traditional RSA algorithms. The Benveniste equivalent microsphere thermal conductivity model is selected for thermal performance simulation, demonstrating its high correlation with the three-component model (air, glass, resin), with a maximum relative error of only 1.32%. A classification method for void types in HGM/ER materials is proposed, categorizing them into interfacial and free voids. The microscopic heat transfer mechanisms of HGM/ER materials are investigated under different voids levels and void types, and it was found that the effect of interfacial voids on thermal conductivity is 60% higher than that of free voids. Based on the measured voids of the material, this study provides a reference for the convenient prediction of thermal conductivity in practical engineering applications of HGM/ER composites. Full article

The contribution of this study is in the novel application of the bin packing algorithm that is used to optimize the robotic bricklaying process with the aim of minimizing the wearing of a robotic saw used for splitting brick blocks so as to minimize brick consumption. To optimize the cutting of masonry blocks with a robotic saw, a new bin packing algorithm has been developed to enhance the design of a digital cutting plan. The algorithm is based on the principle of random search for all combinations of cutting execution with respect to the maximum number of objects (cuts) found in one container (masonry block). The new bin packing algorithm (NBPA) minimizes the number of total masonry blocks (containers) and the number of cuts made with a robotic saw, thus reducing the cutting length. The algorithm can converge to a solution rather quickly and reliably to identify optimal variants of a digital plan designed for a robotic saw to be used in different object assemblies. This article describes the optimization algorithm, including step-by-step calculations, and provides a practical example and a comparison of the results with earlier algorithms. The concept of the robotic saw is also presented in detail, including a description of a prototype. The simulation of the performance on 20 different sets of elements showed that NBPA has a similar use of space compared to the First-Fit Decreasing algorithm (FFD). Multicriteria analysis demonstrated that when the weighting criterion for saw wear was 40% of all the criteria, the use of NBPA was approximately 3.5 times more effective than FFD. The application of the new methodology to a robotic bricklaying process has the potential to reduce the wear of robotic saw, to increase the speed of the construction process and to reduce the generation of construction and demolition waste (CDW). Full article

Ensuring the overall efficiency of hydraulic fracturing treatment depends on the ability to forecast bottomhole pressure. It has a direct impact on fracture geometry, production efficiency, and cost control. Since the complications present in contemporary operations have proven insufficient to overcome inherent uncertainty, the precision of bottomhole pressure predictions is of great importance. Achieving this objective is possible by employing machine learning algorithms that enable real-time forecasting of bottomhole pressure. The primary objective of this study is to produce sophisticated machine learning algorithms that can accurately predict bottomhole pressure while injecting guar cross-linked fluids into the fracture string. Using a large body of work, including 42 vertical wells, an extensive dataset was constructed and meticulously packed using processes such as feature selection and data manipulation. Eleven machine learning models were then developed using parameters typically available during hydraulic fracturing operations as input variables, including surface pressure, slurry flow rate, surface proppant concentration, tubing inside diameter, pressure gauge depth, gel load, proppant size, and specific gravity. These models were trained using actual bottomhole pressure data (measured) from deployed memory gauges. For this study, we carefully developed machine learning algorithms such as gradient boosting, AdaBoost, random forest, support vector machines, decision trees, k-nearest neighbor, linear regression, neural networks, and stochastic gradient descent. The MSE and R² values of the best-performing machine learning predictors, primarily gradient boosting, decision trees, and neural network (L-BFGS) models, demonstrate a very low MSE value and high R² correlation coefficients when mapping the predictions of bottomhole pressure to actual downhole gauge measurements. R² values are reported as 0.931, 0.903, and 0.901, and MSE values are reported at 0.003, 0.004, and 0.004, respectively. Such low MSE values together with high R² values demonstrate the exceptionally high accuracy of the developed models. By illustrating how machine learning models for predicting pressure can act as a viable alternative to expensive downhole pressure gauges and the inaccuracy of conventional models and correlations, this work provides novel insight. Additionally, machine learning models excel over traditional models because they can accommodate a diverse set of cross-linked fracture fluid systems, proppant specifications, and tubing configurations that have previously been intractable within a single conventional correlation or model. Full article

This study explored the formation of soft colloidal particles from a diblock ionomer (DI) with the monomeric composition (acrylonitrile)_x-co-(glycidyl methacrylate)_y-b-(3-sulfopropyl methacrylate potassium)_z—abbreviated as (A_xG_y)S_z, where x >> z > y. A colloidal dispersion was generated by introducing water into the pre-prepared DMSO solutions of DI, which led to micelle formation and subsequent coagulation. The assembly of the hydrophobic (A_xG_y) blocks was influenced by water content and chain conformational flexibility (the ability to adopt various forms of conformation). The resulting microgel structure (in particle form) consists of coagulated micelles characterized by discrete internal hydrophobic gel domains and continuous external hydrophilic gel layers. Characterization methods included light scattering, zeta potential analysis, and particle size distribution measurements. In contrast, the copolymer (A_xG_y) chains form random coil aggregates in DMSO–H₂O mixtures, displaying a chain packing state distinct from the hydrophobic gel domains as aforementioned. Additionally, the amphiphilic glycidyl methacrylate (G) units within the (A_xG_y) block were found to modulate the microgel dimensions. Notably, the nanoscale hydrogel corona exhibits high accessibility to reactive species in aqueous media. The typical microgel has a spherical shape with a diameter ranging from 50 to 120 nm. It exhibits a zeta potential of −65 mV in a neutral aqueous medium; however, it may precipitate if the metastable colloidal dispersion state cannot be maintained. Its properties could be tailored through adjusting the internal chain conformation, highlighting its potential for diverse applications. Full article

This study focuses on simulating the dynamic response of a novel battery housing constructed from an innovative thermoplastic composite material using the FE method, implemented in the LS-Dyna software. The composite comprises a thermoplastic matrix (ELIUM MC and Martinal ATH) reinforced by glass fibers. The initial mechanical properties of the composite are characterized through standardized mechanical tests. The housing undergoes analysis under various loading scenarios, including sine-sweep and random vibration, mechanical shock and impact loads. Throughout these analyses, the housing’s structural integrity is thoroughly assessed for potential failures. The numerical results demonstrate that the housing remains resilient against vibration and mechanical shock. Additionally, while low-energy impact induces some damage, it does not impede the battery pack’s normal operation. However, high-energy impact causes substantial damage that compromises the integrity of the battery. Importantly, the FE model of the battery housing serves as a basis for the creation of a digital twin of the battery, offering opportunities for further design and optimization strategies. Full article

Recently, fault detection has played a crucial role in ensuring the safety and reliability of inverter operation. Switch failures are primarily classified into Open-Circuit (OC) and short-circuit faults. While OC failures have limited negative impacts, prolonged system operation under such conditions may lead to further malfunctions. This paper demonstrates the effectiveness of employing Artificial Intelligence (AI) approaches for detecting single OC faults in a Packed E-Cell (PEC) inverter. Two promising strategies are considered: Random Forest Decision Tree (RFDT) and Feed-Forward Neural Network (FFNN). A comprehensive literature review of various fault detection approaches is first conducted. The PEC inverter’s modulation scheme and the significance of OC fault detection are highlighted. Next, the proposed methodology is introduced, followed by an evaluation based on five performance metrics, including an in-depth comparative analysis. This paper focuses on improving the robustness of fault detection strategies in PEC inverters using MATLAB/Simulink software. Simulation results show that the RFDT classifier achieved the highest accuracy of 93%, the lowest log loss value of 0.56, the highest number of correctly predicted estimations among the total samples, and nearly perfect ROC and PR curves, demonstrating exceptionally high discriminative ability across all fault categories. Full article

The radiative/scattering properties of cyanobacterial aggregates are crucial for understanding microalgal cultivation. This study analyzed the scattering matrix elements and cross-sections of cyanobacterial aggregates using the discrete dipole approximation (DDA) method. A stochastic random walk approach was adopted to generate a force-biased packing model for multicellular filamentous cyanobacterial aggregates. The effects of the shape and size of multicellular cyanobacterial aggregates on their scattering properties were investigated in this work. The possibility of invariance in the scattering properties of cyanobacterial aggregates was explored. The invariance interpretation intuitively represented the radiative property characteristics of the aggregates. The presented results show that the ratios of the matrix elements of cyanobacterial aggregates are nearly shape-, size-, and wavelength-invariant. The extinction and absorption cross-sections (EACSs) per unit volume exhibited shape and approximate size invariance for cyanobacterial aggregates, respectively. The absorption cross-section of aggregates is not merely a volumetric phenomenon for aggregates that exceed a certain size. Furthermore, the absorption cross-sections per unit volume are independent of the volumetric distribution of the microalgae cells. The invariance interpretation presents crucial characteristics of the scattering properties of cyanobacterial aggregates. The existence of invariance greatly improves our understanding of the scattering properties of microalgal aggregates. The scattering properties of microalgal aggregates are the most critical aspects of light propagation in the design, optimization, and operation of photobioreactors. Full article

As the issue of energy scarcity becomes increasingly critical, the adoption of electric construction machinery emerges as a pivotal strategy to address the energy crisis. During the travel and operation of electric construction machinery, the machinery-specific battery packs are subjected to long-term mechanical shocks and random vibration loads, leading to resonance and structural damage failure. To address the multi-objective optimization design issues of machinery-specific battery packs for electric construction machinery under the action of random vibration and impact loads and to enhance the fatigue life and reduce the mass of the battery pack, this paper conducts optimization design research on a newly developed battery pack for an electric excavator. Firstly, a finite element model of the battery pack is established to conduct simulation analyses on its impact resistance characteristics and fatigue life. Secondly, through a comprehensive contribution analysis method, key components are identified, with the thickness dimensions of the battery pack parts selected as design parameters. Finally, using maximum stress under mechanical shock conditions and first-order constraint mode as constraint conditions, mass minimization and fatigue life maximization are set as optimization objectives. The Box–Behnken experimental design is employed alongside a Kriging approximation model; subsequently, the NSGA-II algorithm is utilized for multi-objective optimization. The optimization results show that, while meeting the basic static and dynamic performance requirements, the mass of the optimized battery pack outer frame is reduced by 56.8 kg, a decrease of 5.75%. Concurrently, the optimized battery pack’s fatigue life has increased by 1,234,800 cycles, which is an enhancement factor of 1.65 compared to pre-optimization levels. These findings provide significant reference points for optimizing structural performance and achieving lightweight designs in electric excavator battery packs. Full article