Structural Design Parameters Research Articles

Bifurcations of a Laminated Circular Cylindrical Shell

In this paper, the stability and local bifurcations of a composite laminated circular cylindrical shell with radially pre-stretched membranes are explored by using analytical and numerical methods. On the basis of the four-dimensional averaged equation in the case of 1:1 internal resonance, three types of critical points are studied in detail. They are characterized as (1) one pair of purely imaginary eigenvalues and two negative eigenvalues; (2) a simple zero and one pair of purely imaginary eigenvalues; (3) two pairs of purely imaginary eigenvalues in nonresonant case. With the aid of normal form theory and Maple software, the steady-state solutions and the stability regions of the initial equilibrium solutions are obtained. The explicit expressions for the critical bifurcation curves leading to static bifurcation and Hopf bifurcation are also presented. The presence of Hopf bifurcation indicates that the circular cylindrical shell will flutter. The results contribute to the design of reasonable structure parameters to avoid flutter. Finally, numerical simulations are also presented to demonstrate the good agreement with the analytical predictions.

High-Rise Timber Offices: Main Architectural and Structural Design Parameters

High-Rise Timber Offices: Main Architectural and Structural Design Parameters

Active-Learning Reliability Analysis of Automotive Structures Based on Multi-Software Interaction in the MATLAB Environment

The reliability design of automotive structures is characterized by numerous variables and implicit responses. The traditional design of experiments for metamodel construction often requires manual adjustment of model parameters and extensive finite element analysis, resulting in inefficiency. To address these issues, active learning-based reliability methods are effective solutions. This study proposes an active-learning reliability analysis method based on multi-software interaction. Firstly, through secondary development of different software and MATLAB (version 2023a)’s batch processing function, a multi-software interactive reliability analysis method is developed, achieving automation in structural parametric design, finite element analysis and post-processing. This provides a more efficient and convenient platform for the implementation of active learning. Secondly, the polynomial chaos–kriging (PCK) active-learning method is introduced, combining the advantages of polynomial chaos expansion (PCE) and kriging. The PCK method captures the global behavior of the computational model using regression-based PCE and local variations using interpolation-based kriging. This metamodel is constructed with fewer training samples, effectively replacing the real multi-dimensional implicit response relations, thereby improving the efficiency of modeling and reliability analysis. Finally, the specific implementation scheme is detailed. The accuracy and efficiency of the proposed method are verified by a reliability engineering example of body-in-white bending and torsional stiffness.

Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers

AbstractIn recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for assessing their suitability incur significant time and cost. The emergence of Industry 4.0 has presented opportunities to address these drawbacks by leveraging machine learning (ML) methods. ML techniques have recently been used to forecast the properties and assess the importance of process parameters for efficient structural design and their broad applications. Given their wide range of applications, this work aims to perform a comprehensive analysis of ML algorithms used for predicting the mechanical properties of FRPs. The performance evaluation of various models was discussed, and a detailed analysis of their pros and cons was provided. Finally, the limitations that currently exist in these techniques were pinpointed, and suggestions were given to improve their prediction precision suitable for evaluating the mechanical properties of FRP components.

Accelerating multilayer frequency selective surface absorber design by combining equivalent circuit models with machine learning

Designing high-performance electromagnetic functional materials and artificial structures is of great significance for electromagnetic wave modulation applications. Optimizing performance in the high-dimensional parameter space of electromagnetic functional materials has posed a challenging problem. This paper proposes the use of reinforcement and transfer learning methods to facilitate the quick and accurate design of wideband circuit analog absorbers (CAA). A trained reinforcement learning network is utilized to comprehend the relationship between reflectivity performance and changes in impedance parameter. Furthermore, the transfer learning method is applied to accelerate the training process, leading in a 20-fold reduction in the number of simulations. To validate the effectiveness of this approach, a double-layer absorber aiming for reflectivity less than −20 dB at 3–10 GHz is designed, requiring only 50 simulations. This demonstrates that the proposed method provides a flexible strategy for the interactive design of equivalent circuit and electromagnetic structural parameters. Moreover, it presents a novel and efficient solution for microwave absorber design, with potential applications across various microwave design fields.

Optimization study of a Z-type airflow cooling system of a lithium-ion battery pack

The present study aims to optimize the structural design of a Z-type flow lithium-ion battery pack with a forced air-cooling system known as BTMS (battery thermal management system). The main goal is to minimize Tmax (maximum temperature) and ΔTmax (maximum temperature difference) while ensuring an even airflow distribution within the battery module. The present study thoroughly investigates critical factors, such as the inlet air velocity, tapered inlet manifold, and the number of secondary outlets, to evaluate their impact on thermal performance and airflow uniformity within the battery module. Increasing the inlet air velocity from 3 to 4.5 m/s significantly improves the thermal cooling performance of the BTMS, resulting in a decrease of 4.57 °C (10.05%) in Tmax and 0.29 °C (9.79%) in ΔTmax compared to the original 3 m/s velocity. Further, the study assesses the significance of a tapered inlet manifold as a critical factor, revealing its substantial impact on cooling performance and temperature reductions in battery cells 3–9. It also facilitates a more uniform airflow distribution, decreasing the velocity difference between channel 9 and channel 1 from 3.32 to 2.50 m/s. Incorporating seven secondary outlets significantly improves the heat dissipation ability of the BTMS, resulting in a decrease of 0.894 °C (2.18%) in Tmax and 2.23 °C (72.84%) in ΔTmax compared to the configuration with 0 secondary outlets. By optimizing these parameters, the aim is to enhance BTMS's capabilities, improving LIB (lithium-ion battery) packs' performance and reliability. The optimized structural design parameters proposed in this study yield practical applications that extend beyond theoretical insights, impacting diverse fields reliant on lithium-ion battery technology. Through enhanced thermal management systems, applications in electric vehicles and portable electronics are poised to experience improved performance and longevity. Furthermore, these advancements inform the development of next-generation battery packs, promising reduced overheating risks and extended battery life. Such innovations are critical in energy storage systems for renewable energy applications and electric vehicle technology, facilitating faster charging times and increased driving range. Moreover, the implications extend to aerospace applications, ensuring the reliability of batteries in extreme environmental conditions.

Pre-failure operational anomalies of the Kakhovka Dam revealed by satellite data

On June 6, 2023, the Kakhovka Dam in Ukraine experienced a catastrophic breach that led to the loss of life and substantial economic values. Prior to the breach, the supporting structures downstream of the spillway had shown signs of being compromised. Here, we use multi-source satellite data, meteorological reanalysis, and dam design criteria to document the dam’s pre-failure condition. We find that anomalous operation of the Kakhovka Dam began in November 2022, following the destruction of a bridge segment, which led to persistent overtopping from late April 2023 up to the breach, contributing to the erosion of the spillway foundation. Moreover, our findings also highlight safety and risk-reduction measures pivotal in avoiding such scenarios. To help prevent future disasters, we advocate for greater transparency in the design parameters of key water structures to enable risk management, and conclude that remote sensing technology can help ensuring water infrastructure safety.

Concurrent geometrico-topological tuning of nanoengineered auxetic lattices fabricated by material extrusion for enhancing multifunctionality: Multiscale experiments, finite element modeling and data-driven prediction

This study demonstrates the multifunctional performance of innovative 2D auxetic lattices through a combination of multiscale experiments, finite element modeling and data-driven prediction. A geometric modeling approach utilizing Voronoi partitioning and a unique branch-stem-branch (BSB) structure, patterned according to 2D wallpaper symmetries, enables precise concurrent geometric and topological tuning of lattices across a continuous parameter space. Selected architectures are physically realized via material extrusion of polylactic acid (PLA) infused with carbon black (CB). Experimental characterizations, supported by Finite Element modeling, reveal the significant influence of BSB structure's design parameters on mechanical and piezoresistive performance under tensile loading, with a remarkable Poisson’s ratio of -0.74, accompanied by a 15-fold increase in elastic stiffness and a 34-fold increase in strain sensitivity. Additionally, architecturally, and topologically tailored lattice structures exhibit tunable damage sensitivity, reflecting the rate of conductive network destruction within the lattice. This offers insights into the rapidity of cell wall failure, with a steeper slope of the piezoresistance curve in the inelastic regime indicating a faster breakdown and quicker onset of mechanical failure. Integration of Gaussian Process Regression enables accurate exploration of the design space beyond realized structures, highlighting the potential of these intelligent lattice structures for applications such as sensors and in situ health monitoring, marking a significant advancement in multifunctional materials.

基于EDEM的滩涂养殖双螺旋推进器仿真分析与试验

Aiming at the problems of coastal ecological damage and low yield of mudflat aquaculture caused by the invasion of M. alterniflora, in order to improve the operational efficiency of mudflat wet and soft ground, and to promote the ecological balance and the development of coastal agriculture, a walking device with twin spiral propellers for muddy wet and soft ground was designed. Using EDEM simulation software to simulate and analyze, the discrete element model of muddy soil particles is established to analyze the interaction mechanism with the spiral propeller and the operation propulsion effect, and it is concluded that the spiral propeller will not produce congestion phenomenon during the operation; data are collected through several simulation tests, and the optimal parameter design of the spiral propeller structure is derived from the response surface analysis, and the spiral propeller is designed to operate at a speed of 2.416 mph in the simulation with the optimal parameter of structural design. The field test shows that the optimal height of the spiral blades is 50 mm, the total length of the drum is 2,970 mm, the helix angle of lift is 30 °, the pitch is 453 mm, and the propelling speed is 2.36 m/s. The data collected through several simulation tests are used to find the optimal parameter design of the spiral propeller structure, and the simulation speed of the spiral propeller in the optimal structural design parameter is 2.416 m/s.

An efficient analysis method for system reliability of semi-rigid base asphalt pavement structure based on direct probability integral method

The structural design parameters of asphalt pavement inherently possess randomness. Reliability theory aids in considering the stochastic nature of these parameters in asphalt pavement design. However, as the number of considered random factors increases, the complexity of reliability calculation models and the consumption of computational resources also significantly escalate. To analyze the structural reliability of semi-rigid base asphalt pavements more efficiently and accurately, a method combining Weighted Kernel Density Estimation (Weighted-KDE) with Direct Probability Integration Method (DPIM) using sampling strategies was employed. Furthermore, integrating the equivalent extreme value principle, an asphalt pavement structural system reliability analysis method was proposed, considering multiple failure mode correlations. The numerical solution steps of this method are also outlined. Based on the typical semi-rigid asphalt pavement structure in China, this study utilized the elastic layer system method (the program Apbi) and a computational program developed in MATLAB to compare the efficiency and accuracy of calculating reliability between DPIM and the Monte Carlo Simulations (MCS). The computational outcomes indicate that, under the premise of selecting 18 random parameters, DPIM can achieve the required accuracy in assessing the reliability of asphalt pavement structure and system reliability. Analyzing how the number of samples affects the computational results reveals that using weighted-KDE and DPIM only needs a few hundred sample points to limit the relative error within 4 %, compared to the results obtained from 105 samples in MCS. The research findings demonstrate that DPIM incorporating the principle of equivalent extreme value events can serve as an efficient analytical approach for assessing the system reliability of asphalt pavement structures characterized by more complex failure modes.

SLA-3d printed building and characteristics of GelMA/HAP biomaterials with gradient porous structure

Developing a gradient porous scaffold similar to bone structure is gaining increasing attention in bone tissue engineering. The GelMA/HAP hydrogel has demonstrated potential in bone repair. Although 3D printing can build GelMA/HAP with porous structure, fabricating porous GelMA/HAP with gradient porosity and pore size in one step remains challenging. In this paper, a gradient porous structure with controllable pore size, based on gelatin methacryloyl (GelMA) and hydxroxyapatite (HAP), was engineered and printed using stereolithography. Firstly, the GelMA and HAP were mixed to prepare a hydrogel with a solid content ranging from 10 wt% to 50 wt% for stereolithography. Taking advantage of the sol-gel characteristics of GelMA/HAP hydrogel, GelMA/HAP was fed on the workbench through a combination of extrusion and paving to form a thin layer. During the curing of each layer, the hydrogel exposed to the curing of a single UV beam immediately solidified, forming a highly interconnected porous structure. Additionally, the hydrogel outside the scanning range could be further polymerized to form a relatively dense structure due to the residual laser energy. Finally, without gradient structural design or changing printing parameters, the gradient porous structure of bone-like could be printed in a single-step process. By adjusting the curing parameters of the single UV beam and the concentration and size of ceramic in the hydrogel, the printed pore diameter of the spongy structure could be controlled within the range of 50–260 μm, while the thickness of the compact area could be adjusted within 130–670 μm.

Advancing blast fragmentation simulation of RC slabs: A graph neural network approach

Accurate prediction of blast-induced fragmentation in reinforced concrete (RC) structures is pivotal for structural debris hazard assessment in an explosion event. This assessment is essential for designing effective measures for protection of people and structure in the vicinity. While conventional computational techniques are proficient, they are computationally very demanding and may not be readily adaptable in practical applications. This study proposes a novel approach, the Fragment Graph Network (FGN), a specialized variant of graph neural networks (GNNs), to simulate complex interactions between macro-level particles of RC structures subjected to close-in blast loads. The RC structure is discretised into particles, with the model predicting structural responses and fragmentation based on states of these particles. Utilizing an autoregressive prediction mechanism, our model can accurately simulate spatiotemporal responses of RC slabs against blast loads. The efficacy of the model was tested on a dataset consisting of 149 simulated cases, encompassing around 180,000 states each, covering a wide range of blasting scenarios and structural design parameters including TNT charge weight, standoff distance, slab dimension, concrete strength, and reinforcement ratio and layout. Notably, the proposed model delivered accurate predictions, achieving relative errors under 10% and R2 values exceeding 0.93 for both standard and extrapolated test scenarios as compared to the SPH simulations. Remarkably, our model dramatically outpaced traditional numerical approaches, reducing simulation times from hours to minutes on CPUs and seconds on GPUs. This pioneering effort paves the way for efficient and accurate simulations of RC blast fragmentations, offering a rapid alternative to standard numerical approaches.

Inverse design of twisted bilayer graphene metasurface for terahertz absorption broadening based on artificial neural network

With the development of terahertz (THz) technology, achieving ultra-wideband absorption of THz waves has become a significant challenge that researchers are striving to address. Different from the complex stacking of multiple graphene layers in the traditional design, inspired by the twisted magic angle of graphene structures, a twisted bilayer graphene metasurface absorber (TBGMA) is innovatively investigated for THz wave absorption broadening inverse design based on artificial neural network (ANN). Compared with the traditional manual parameter tuning, the ANN-based inverse design method can quickly accomplish the selection of the structural parameters and twist angles of the TBGMA, realizing the ultra-wideband absorption of THz waves (6.046 THz). The relationship between twisted SGLs and broadened effective absorption bandwidths is thoroughly analyzed through a combination of the electric field distribution and effective medium theory (EMT). Additionally, the tunability and absorption spectra of TBGMA under different incidence conditions have been discussed. The study of the twisted angle of SGLs can provide a unique idea for expanding the absorption bandwidth, and the inverse design of structural and angle parameters in conjunction with ANN is expected to be extended to the design of other precision structures in nanophotonics.

Poisson's ratio testing method and multi-factor influence quantification model of asphalt pavement materials

The current standard in China does not specify the standard test method for determining Poisson's ratio, and the Poisson's ratio used in pavement design is often roughly fixed, which can result in large errors in the calculations and analysis of road structure. In this paper, the research on different test methods for Poisson's ratio is carried out and the non-linear characteristics of tensile and compressive Poisson's ratio on pavement materials under different influencing factors are analyzed. The results indicate that the absolute value of the ratio between transverse rebound strain and longitudinal rebound strain under 50% of the maximum destructive load can objectively characterize the mechanical properties of Poisson's ratio, and it is the optimal measurement method for Poisson's ratio in asphalt pavement materials. The Poisson's ratio of two pavement materials both decreases with the increase of loading rate and gradually tends to a constant value. Cement dosage and temperature are the most significant factors affecting the tensile and compressive Poisson's ratio of cement stabilized crushed stone and asphalt mixture, respectively. Specifically, when cement dosage increases by 1%, the Poisson's ratio of cement stabilized crushed stone decreases by approximately 10%, and the temperature has a significant influence on the difference in tension and compression for Poisson's ratio of asphalt mixture (up to 40%). The asphalt content corresponding to the minimum Poisson's ratio is basically consistent with the optimal asphalt content obtained by the Marshall volume method. Based on these, a quantification value-taking model is established of the tensile and compressive Poisson's ratios of typical asphalt pavement materials varying with multiple factors. The research results can serve as a reference for the scientific value-taking of pavement structural design parameters considering the different mechanical characteristics of materials in tension and compression.

Structural Design and Mechanical Behavior Investigation of Steel–Concrete Composite Decks of Narrow-Width Steel Box Composite Bridge

Steel–concrete composite decks are commonly employed in narrow-width steel box composite girder bridges to augment their lateral spanning capabilities, while the concurrent omission of longitudinal stiffeners leads to a substantial reduction in the number of components, thereby yielding a structurally optimized bridge configuration. This paper delineates the structural design parameters of a narrow-profile steel box composite girder bridge and assess the mechanical behavior of its incorporated steel–concrete composite deck under static and fatigue loading conditions. To this end, two full-scale segment specimens from the composite bridge decks were subjected to equal amplitude cyclic fatigue tests. The investigation specifically concentrated on the impacts of two types of shear connectors—namely, perforated steel plates combined with shear studs and perfobond rib shear connectors (PBL connectors)—on the static and fatigue performance, including fatigue stiffness, of the steel–concrete composite bridge decks. The results indicate that, under the static bending condition, the composite deck specimen equipped with stud connectors demonstrates superior overall flexural stiffness in comparison to the specimen featuring PBL connectors. Furthermore, the flexural stiffness of the steel–concrete composite specimens experiences a negligible alteration across two million fatigue loading cycles. Upon the completion of two million fatigue loading cycles, the composite deck specimens incorporating the shear connectors composed of perforated steel plates and shear studs exhibit relatively wider crack widths under the static peak load. Both configurations of the steel–concrete composite bridge deck specimens manifest evident interfacial detachment, signifying insufficient tensile pull-out stiffness of the shear connectors. It is recommended to increase the quantity of the shear connectors or select the pertinent types in order to enhance the interface shear resistance.

Study on the Energy Absorption Characteristics of Different Composite Honeycomb Sandwich Structures under Impact Energy

A honeycomb structure is a sandwich structure widely used in fuselage, among which the hexagonal honeycomb core is the most widely used. The energy absorption characteristics and impedance ability of the structure are the main reasons that directly affect the energy absorption characteristics of the honeycomb sandwich structure. Therefore, it is necessary to study the out-of-plane mechanical properties of the composite honeycomb sandwich structure. Based on the numerical simulation results, the energy absorption characteristics of several composite honeycomb sandwich structures are verified by drop hammer impact experiments. The research shows that the transient energy absorption characteristics of the composite honeycomb sandwich structure are mainly related to the cell size of the honeycomb structure. The smaller the size of the front cell, the stronger the overall impact resistance; the strength of the composite honeycomb sandwich structure exceeds that of 7075 aluminum alloy-NOMEX and carbon fiber-NOMEX honeycomb sandwich structures. In this paper, the energy absorption characteristics of composite honeycomb sandwich structures under different impact energy are compared and studied. The displacement, force and energy curves of energy absorption characteristics related to time variables are analyzed. The difference in protective performance between the composite honeycomb sandwich structure and existing airframe structure is compared and studied. The optimal structural design parameters of composite honeycomb sandwich under low-speed impact of drop hammer are obtained. The maximum energy absorption per unit volume of the designed honeycomb sandwich structure is 171.7% and 229.8% higher than that of the NOMEX-AL and NOMEX-C structures. The 6.4 mm and 3 mm cell sizes show good characteristics in high-speed buffering and crashworthiness. The composite honeycomb sandwich airframe structure can improve the anti-damage performance of the UAV airframe structure, ensure the same thickness and lightweight conditions as the existing honeycomb sandwich airframe structure, and improve the single-core bearing mode of the existing airframe structure.

Breakdown performance of guard ring designs for pixel detectors in 150 nm CMOS technology

Silicon pixel sensors manufactured using commercial CMOS processes are promising instruments for high-energy particle physics experiments due to their high yield and proven radiation hardness. As one of the essential factors for the operation of detectors, the breakdown performance of pixel sensors constitutes the upper limit of the operating voltage. Six types of passive CMOS test structures were fabricated on high-resistivity wafers. Each of them features a combination of different inter-pixel designs and sets of floating guard rings, which differ from each other in the geometrical layout, implantation type, and overhang structure. A comparative study based on leakage current measurements in the sensor substrate of unirradiated samples was carried out to identify correlations between guard ring designs and breakdown voltages. TCAD simulations using the design parameters of the test structures were performed to discuss the observations and, together with the measurements, ultimately provide design features targeting higher breakdown voltages.

Design and Testing of Friction-Type Nail-Tooth-Chain-Plate Residual-Film-Picking System

Compared with the conventional horizontal conveyor-chain-type plastic-film-picking device, the longitudinal nail-tooth-chain-plate-type plastic-film-picking device developed by our team in the early stage has little tearing effect on the mulching film, and the separation effect of the plastic film and the impurity is better. With a view to further enhancing the performance of the plastic-film-picking device, this study optimized it and designed a friction-type nail-tooth-chain-plate plastic-film-picking chain that does not overload or slip and also facilitates the installation of functional components. The kinematic analysis of the picking nail teeth on the film-picking chain was carried out, and the motion equation and trajectory of the nail teeth during the operation were determined, as well as the requirements for the nail teeth to complete the mulching-film pickup. The key parameters of the plastic-film-picking system were determined by analyzing the no-leakage condition of the plastic film and the force. Moreover, the structural design and key parameters of the shovel-type film-lifting device were determined. According to the design results, a prototype was developed, and a multi-factor test of the operating parameters was carried out. The operating speed, spacing of the pickup nail teeth, depth of the film shovel into the soil, and distance between the tip of the film shovel and center of the picking drum were used as the experimental factors. The plastic-film pickup rate, impurity rate of the recovered mulching film, and traction resistance were used as the test indicators. A four-factor, five-level quadratic regression orthogonal combination experiment was conducted using the Central Composite Design (CCD). The effect of each test factor on the test index of the plastic-film-picking system was studied, the regression models were established, and the optimal parameter combination was acquired by using the multi-objective optimization method. When the working speed was 6 km/h, the pickup-nail-tooth spacing was 228.6 mm, the depth of the film-lifting shovel into the soil was 37 mm, and the distance between the tip of the film shovel and the center of the pickup drum was 130 mm, the field experiment shows that the plastic-film pickup rate was 90.12%, the impurity rate of the recovered mulching film was 8.96%, and the traction resistance was 19.905 kN. The relative errors between the test results and the predicted values of the regression models were less than 5%, indicating that the parameter optimization regression models were reliable, and the designed friction-type nail-tooth-chain-plate plastic-film-picking system met the technical requirements of agricultural-plastic-film recycling. The research results can provide a technical reference for the development of mulching-film collection machines.

Artificial intelligence-based surrogate model for computation of the electric field of high voltage transmission line ceramic insulator with corona ring

Purpose The ionization of the air surrounding the phase conductor in high-voltage transmission lines results in a phenomenon known as the Corona effect. To avoid this, Corona rings are used to dampen the electric field imposed on the insulator. The purpose of this study is to present a fast and intelligent surrogate model for determination of the electric field imposed on the surface of a 120 kV composite insulator, in presence of the Corona ring. Design/methodology/approach Usually, the structural design parameters of the Corona ring are selected through an optimization procedure combined with some numerical simulations such as finite element method (FEM). These methods are slow and computationally expensive and thus, extremely reducing the speed of optimization problems. In this paper, a novel surrogate model was proposed that could calculate the maximum electric field imposed on a ceramic insulator in a 120 kV line. The surrogate model was created based on the different scenarios of height, radius and inner radius of the Corona ring, as the inputs of the model, while the maximum electric field on the body of the insulator was considered as the output. Findings The proposed model was based on artificial intelligence techniques that have high accuracy and low computational time. Three methods were used here to develop the AI-based surrogate model, namely, Cascade forward neural network (CFNN), support vector regression and K-nearest neighbors regression. The results indicated that the CFNN has the highest accuracy among these methods with 99.81% R-squared and only 0.045468 root mean squared error while the testing time is less than 10 ms. Originality/value To the best of the authors’ knowledge, for the first time, a surrogate method is proposed for the prediction of the maximum electric field imposed on the high voltage insulators in the presence Corona ring which is faster than any conventional finite element method.

Research on seedling sowing method based on high voltage electrostatic characteristics

In response to the difficult problem of high leakage rate and low seed pick-up rate of traditional air-suction seed dischargers. This study proposes a new high-voltage electrostatic vegetable seedling sowing method and system based on phase sucking of opposite charges and repulsive seeding of same charges. This method does away with small orifices and long conductance suction holes. It can solve the technical bottleneck of the traditional air-suction seed rower at the source, which causes a decrease in seed rowing accuracy due to clogging of the suction holes when facing irregular small vegetable seeds that are not pelleted. Combining high voltage electrostatic polarisation for seed vigour with precision seeding technology. Improving seed vigour and crop yield while precision sowing. Modelling the mechanics of adsorption seeding and repulsive seeding systems. Identify the main factors affecting electrostatic adsorption and phase repulsion forces and their interrelationships; Analysis of the decay mechanism of the electrostatic adsorption force upon repeated action. Proposing measures for the rapid restoration of the original equilibrium state of the dielectric insulation by reverse electric field polarization; Analysis of the migration pattern of electric particles during polarisation. Solving the equations for seed dynamics during sowing; At positive input voltages of 16 kV and negative input voltages of 0, 4, 8, 12, 16 and 20 kV. Separate dielectric insulation layer adsorption seeding tests. The test results show that: The reverse electric field polarisation of the dielectric insulating layer is effective in containing the electrostatic adsorption decay. The best containment was achieved at a positive input voltage of 16 kV and a negative input voltage of 16 kV; Seeding performance verification tests at a positive input voltage of 16 kV and a negative input voltage of 16 kV. The test results show that: The sowing conformity index was 90.25 %, the reseeding index 3.40 % and the missed sowing index 6.35 %. In the evaluation of applied research, the multi-objective optimization design with the objective function of maximum seed discharge qualification index, minimum leakage index and replanting index, the optimal parameter combinations of the test factors can be obtained as follows: the size of the type hole is 5.977 mm, and the output voltage of the positive high-voltage electrostatic generator is 16.423 kV, and at this time, the seeding machine's seed discharge qualification index is 90.78 %, the replanting index is 3.05 %, and the leakage index is 6.17 %. To verify the reliability of the optimal design of structural parameters, the seeding performance verification test is carried out, which can be obtained: the qualified index of row seeding is 90.54 %, the index of reseeding is 2.86 %, and the index of leakage is 6.60 %, which meets the technical requirements of precision seeding. The research not only provides a reference for the development of precision sowing machinery and equipment for irregular small-grain vegetable seeds. It also provides an academic reference for the research of quantitative adsorption and rapid separation methods and theories for irregular small particle materials.