Basic Structural Parameters Research Articles

Fast Optimization Method of Flexible Support Structure Based on Mathematical Model

In order to realize the rapid analysis and optimization of the flexible support structure, the parametric design method is used to establish the three-dimensional model. Basic dimension parameters of the flexible support structure are linked with external data sources, and the functional relationship between dimensions is established to ensure the rationality of the model. It only needs to change the data in the data source to realize the automatic update of the 3D model when the model needs to be reconstructed. Compared with the finite element method, the optimization model improves the efficiency of simulation analysis and optimization design. The model proposed in this paper can obtain the optimal solution by weighing the mass and deformation displacement of the structure.

Optimized design of armored vehicle door structure with negative Poisson’s ratio core layer under blast impacts

Abstract The door is the main part of the side wall of the vehicle body and the key to the side explosion protection of the vehicle. First of all, this paper takes a certain type of armored vehicle as the research object to analyze the side blast protection performance of the equivalent composite door based on the sandwich structure with a negative Poisson’s ratio core layer. Then, the effect of the cell length, cell height, cell angle, cell thickness, and thickness gradient of the core layer on the explosion protection performance of the sandwich structure is studied. Finally, the door structure is optimally designed based on the basic structural parameters of the cells. The results show that the side blast protection of the vehicle is improved compared with the original door, and the shoulder and head injuries of the occupants under side blast impact are reduced to a great extent.

Analysis and Optimization Design of U-Shaped Ironless Permanent Magnet Linear Synchronous Motor

The working speed of the U-shaped ironless permanent magnet linear synchronous motor is determined by the frequency and pole pitch. When the motor’s working speed is required to be constant, different combinations of frequency and pole pitch can be chosen. This article mainly discusses the influence of the pole pitch on the motor performance when other parameters are kept constant. First, the magnetic field of the motor permanent magnet is analyzed using the equivalent magnetizing current method to discuss the relationship between the pole pitch and the motor air gap magnetic field. Then, the finite element method is used to analyze the influence of the pole pitch on the motor air gap magnetic field, transverse edge effect, and electromagnetic thrust considering the saturation effect of the magnetic yoke. The research shows that under the conditions of constant motor basic structural parameters and speed, and the same amount of permanent magnet material, both the air gap magnetic density and the motor thrust line density have maximum values as the pole pitch changes. The research results have certain guiding significance for the design of this type of motor.

Multivariate Gaussian process surrogates for predicting basic structural parameters of refractory non-dilute random alloys

Refractory non-dilute random alloys consist of two or more principal refractory metals with complex interactions that modify their basic structural properties such as lattice parameters and elastic constants. Atomistic simulations (ASs) are an effective method to compute such basic structural parameters. However, accurate predictions from ASs are computationally expensive due to the size and number of atomistic structures required. To reduce the computational burden, multivariate Gaussian process regression (MVGPR) is proposed as a surrogate model that only requires computing a small number of configurations for training. The elemental atom percentage in the hyper-spherical coordinates is demonstrated to be an effective feature for surrogate modeling. An additive approximation of the full MVGPR model is also proposed to further reduce computations. To improve surrogate accuracy, active learning is used to select a small number of alloys to simulate. Numerical studies based on AS data show the accuracy of the surrogate methodology and the additive approximation, as well as the effectiveness and robustness of the active learning for selecting new alloy designs to simulate.

Theoretical investigation on graphene induced self-alignment of donor-acceptor structures

Non-covalent functionalization of graphene (Gr) with conducting polymers (CPs) improves the electrical and mechanical properties of both materials owing to the noncovalent π-π stacking interaction between the surface and the aromatic rings in CPs. However, a comprehensive understanding of the self-organization and chain orientation of CPs on the Gr surface remains lacking, which may limit the engineering of CP-Gr complexes with optimized morphology for optoelectronic applications. This study focuses on the Density Functional Theory (DFT) calculations and homemade Python programming code to investigate the self-organization of donor (D)-acceptor (A) type molecules on the Gr surface, aiming to elucidate factors governing the direction of self-alignment. Specifically, basic structural parameters characterizing the aromatic D-A molecules, such as the intramolecular aromatic ring distances, are explored to determine the rules for the preferred alignment direction of the CP monomers on the Gr surface, either in armchair or zigzag paths. Additionally, the influence of Gr on the morphological properties of aromatic molecules, variations in interaction energies between Gr and D-A-D systems with changing A units, and the impact of Gr size on molecular fitting characteristics are examined. The accurate determination of chain alignment direction, which can be applied for all conjugated small molecules and polymers at 2D material interfaces is crucial for guiding experimental studies aimed at tailoring structures and enhanced properties, such as promoting the crystalline domains with high electrical conductivity.

Operating Properties of Deep Hole Boring Tools with Modified Design.

This paper presents the results of research work on the revised design of a deep hole boring tool. The study was divided into three stages: theoretical, experimental and operational. In the theoretical part, a 3D model of the actual boring bar was created, which was subjected to strength tests using the Finite Element Method (FEM), and then prototypes of new deep hole boring tools were made with structural modifications to the shank part of the tool. For the polymer concrete core of a shank, there was a 14.59% lower displacement, and for the rubber-doped polymer concrete (SBR-styrene butadiene rubber) core of a shank there was a 4.84% lower displacement in comparison to the original boring bar. In the experimental part of the study, the original boring bar and the prototypes were subjected to experimental modal analysis and static analysis tests to compare dynamic and static properties. In the operational part of the study, boring tests were carried out for various workpiece materials, during which the basic parameters of the surface geometric structure (SGS), such as roughness Ra and Rz, were studied. Despite the promising preliminary results of the theoretical and experimental studies, using the described modifications to the boring bar is not recommended.

Effects of Chemical Short-Range Order and Temperature on Basic Structure Parameters and Stacking Fault Energies in Multi-Principal Element Alloys

In the realm of advanced material science, multi-principal element alloys (MPEAs) have emerged as a focal point due to their exceptional mechanical properties and adaptability for high-performance applications. This study embarks on an extensive investigation of four MPEAs—CoCrNi, MoNbTa, HfNbTaTiZr, and HfMoNbTaTi—alongside key pure metals (Mo, Nb, Ta, Ni) to unveil their structural and mechanical characteristics. Utilizing a blend of molecular statics and hybrid molecular dynamics/Monte Carlo simulations, the research delves into the impact of chemical short-range order (CSRO) and thermal effects on the fundamental structural parameters and stacking fault energies in these alloys. The study systematically analyzes quantities such as lattice parameters, elastic constants (C11, C12, and C44), and generalized stacking fault energies (GSFEs) across two distinct structures: random and CSRO. These properties are then evaluated at diverse temperatures (0, 300, 600, 900, 1200 K), offering a comprehensive understanding of temperature’s influence on material behavior. For CSRO, CoCrNi was annealed at 350 K and MoNbTa at 300 K, while both HfMoNbTaTi and HfNbTaTiZr were annealed at 300 K, 600 K, and 900 K, respectively. The results indicate that the lattice parameter increases with temperature, reflecting typical thermal expansion behavior. In contrast, both elastic constants and GSFE decrease with rising temperature, suggesting a reduction in resistance to stability and dislocation motion as thermal agitation intensifies. Notably, MPEAs with CSRO structures exhibit higher stiffness and GSFEs compared to their randomly structured counterparts, demonstrating the significant role of atomic ordering in enhancing material strength.

Study on the evolution characteristics of molecular surface active sites of low-rank coal in low-temperature oxidation stage

Compared with other coal types of coal, low - metamorphic coal contains numerous active sites, making it susceptible to spontaneous combustion and posing significant risks during production, transportation, and storage. To further reveal the evolution characteristics and laws of low-rank coal's active sites during the process of low-temperature oxidation of coal (LTOC), X-ray photoelectron spectroscopy (XPS), 13C nuclear magnetic resonance (13C NMR), and Fourier transform infrared (FTIR) experiments were used to determine the basic structural parameters such as the valence state of non-carbon features, types of carbon elements in coal macromolecules, and structure and quantity of functional groups. Combined with molecular modelling software (GaussView 6.0) and macromolecular calculation software (Grimme-XTB), macromolecular models of coal at different temperature points were constructed, and the surface energy field distribution was calculated. The number and energy change information of chemical and physical adsorption sites of low-rank coal during heating were obtained. The results show that during the LTOC, the chemical adsorption sites such as aliphatic hydrocarbons (-CH3/-CH2) have higher reactivity and can be converted into oxygen-containing functional groups (-CHO, –OH, –COOH); the number of physical adsorption sites increased first and then decreased, and reached the maximum at 50 °C. Coal exhibited a higher affinity for O2 at this temperature. The peroxide (-C-O-O-H) undergoes thermal decomposition, leading to H2O and weight loss of coal. The –CO and –COO structures undergo decarboxylation to generate CO, CO2, and other gases. This study comprehensively revealed the evolution of active sites through two key coal oxygen processes, physical and chemical adsorption, based on the gradual temperature elevation of low-rank coal. This holds immense significance for improving and enriching the understanding of the mechanism of coal spontaneous combustion(CSC), preparing more targeted CSC inhibition, and formulating efficient fire prevention and extinguishing measures.

A real-time automatic rail extraction algorithm for low-density mobile laser scanning data

Automatic detection of railroad infrastructure using Mobile Laser Scanning systems is a key technology for both advanced rail driver assistance and intelligent track maintenance. The recent research into railway facility extraction or condition monitoring usually relies on high-density point cloud dataset with known sensor parameters but ignores the processing performance in actual deployment and has high requirements for the acquisition device. To address these limitations, a novel rail extraction algorithm is proposed for processing Mobile Laser Scanning data in real time during acquisition, which could extract rail features by a hierarchical coarse-to-fine method with basic structural parameters. Using the geometric and global statistical characteristics of rail in raw data, a new rail descriptor is defined based on the quantitative statistics of points satisfying height difference in generalized local neighborhood. The approach is evaluated experimentally by a simulated real-time acquisition data and compared with a reference method. The experimental results show that the proposed algorithm has a finer extraction effect and good real-time performance.

Knitted Structures Made of Antibacterial Fibers Intended for Protective Gloves

At a time of growing epidemic hazards caused by a very rapid evolution of dangerous pathogens, there is a pressing demand for bioactive textiles. Therefore, the development of high-quality knitted structures that could be used as bioactive protective materials has become a priority. This publication describes the fabrication of functional knitted structures from previously prepared antibacterial cellulose fibers containing nanosilica with immobilized silver nanoparticles. The structural and physical parameters of knitted fabrics made from them were studied with a view to their potential application in bioactive protective gloves. Tests of the basic structural and physical parameters of the knitted fabrics did not show that the nanomodifier applied in fibers significantly impacts the physical properties of the resulting fabrics. Moreover, water vapor permeability, cut resistance, and pH test results relevant to the functional and protective properties of interest and to user comfort showed that the obtained fabrics can be used in the production of bioactive protective gloves.

A Comparative Analysis of the Magnetization Methods Used in the Magnetic Nondestructive Testing of Reinforced Concrete Structures

This work presents how significantly the proper selection of the magnetization method can improve almost all parameters of the magnetic method and affect the effectiveness of the evaluation of reinforced concrete (RC) structures. Three magnetization methods are considered in this paper: opposite pole magnetization (typical solution), same pole magnetization, and (as a reference point) no magnetization. The experiments are carried out in a three-dimensional (XYZ) space. Measurements along each of the axes are discussed in a separate section. The results show that the appropriate selection of the magnetization method can affect noise reduction, signal strength, and the separation of measurements carried out on different samples. This paper also discusses the situations when the magnetization may change the shape, cause deformations of waveforms, affect the area testing, and be used to significantly increase the efficiency of simultaneous evaluation of three basic parameters of RC structure. Experiments and simulations have proven that properly applied magnetization may strongly affect the evaluation’s effectiveness, making the magnetic method one of the most promising techniques in testing RC constructions.

Revolutionizing Connectivity: The Breakthrough of Batteryless Mouse Using Magnetic Resonant Coupling for Communication Devices

With the rapid development of electronic technology and information technology, personal computer has gradually become the public's office or entertainment tools, which also makes the computer mouse a widely used tool. However, most computer mouse on the market today come with batteries. In this context, it is necessary to design a batteryless mouse that without using power supply such as battery. In view of such demand, on the basic of theoretical analysis, this paper summarizes and designs a classical wireless transmission structure – magnetic resonant coupling and antenna that used to transmit and received power for communication devices. In order to understand the operation of wireless power transmission technology, the paper analysed antenna construction and wireless power transmission technology. The basic design parameters of the axial mode helical antenna structure are also explained. Finally, by using CST STUDIO software, axial mode helical antenna with gain of 6.45 dBi was obtained. The system's output voltage is successfully simulated at 1.516 V that can powered up the computer mouse.

Comparing interatomic potentials in calculating basic structural parameters and Peierls stress in tungsten-based random binary alloys

The field of machine learning-based interatomic potentials (ML-IAPs) has seen increasing development in recent years. In this work, we compare three widely used ML-IAPs–the moment tensor potential (MTP), the spectral neighbor analysis potential (SNAP), and the tabulated Gaussian approximation potential (tabGAP)with a conventional non-ML-IAP, the embedded atom method (EAM) potential. We evaluated these potentials on the basis of their accuracy and efficiency in determining basic structural parameters and Peierls stress under equivalent conditions. Three tungsten (W)-based alloys (Mo-W, Nb-W, and Ta-W) are considered, and their lattice parameter, formation energy, elastic tensor, and Peierls stress of edge dislocation are calculated. Compared with DFT results, MTP demonstrates the highest accuracy in predicting the lattice parameter and the best computational efficiency among the three ML-IAPs, while tabGAP accurately predicts two independent elastic constants, C 11 and C 12. Despite being the slowest, SNAP shows the highest accuracy in predicting the third independent elastic constant C 44 and its Peierls stress value is comparable to that based on MTP.

Flow characteristics of cooling nozzle for high strength after hot rolling

To further improve the cooling strength and uniformity of hot rolled strip steel after rolling and optimize the structure and process parameters of high-strength cooling equipment, this paper combined the basic structural characteristics and characteristic parameters of high-strength cooling nozzles of hot rolled strip steel, using finite element analysis software. The effect of various pressure and structural arguments on the internal flow characteristics of high-strength cooling nozzles was studied. The results show that with the increasing of the system pressure, the flow rate at the manifold outlet also increases gradually, and the fluctuation of the jet velocity increases gradually, but the flow line trend of the internal velocity is unchanged. The change in the position of the main shunt hole can affect the change in flow rate at the nozzle outlet. However, when the pressure is greater than 0.3 MPa, the fluctuation range of the flow rate at the slot outlet tends to be larger. The relevant results provide some reference for the design of high-strength cooling equipment in hot rolling.

Effect of non-uniform inflow on the internal flow and hydrodynamic characteristics of a small modular reactor coolant pump

Small reactors have broad application prospects due to their advantages of safety, economy, and portability. In the first circuit of a small reactor nuclear power plant, the steam generator is directly connected to a reactor coolant pump, and there is a non-uniform flow situation, which has not been studied. Taking the small modular reactor coolant pump as the research object, the influence of non-uniform inflow on the internal flow and hydrodynamic characteristics of the small modular reactor coolant pump was investigated using CFD numerical simulation methods, and was verified by experiment. Firstly, the basic structural parameters of key hydraulic components are determined based on the design input parameters, and the initial model of the impeller and diffuser of the small modular reactor coolant pump is modeled. The channel-head, impeller, diffuser, and casing are modeled using three-dimensional software; Secondly, the hydrodynamic performance of the small modular reactor coolant pump was experimented, and the experimental results verified the reliability of the numerical simulation; After comparing the external characteristics of the straight-pipe small modular reactor coolant pump coupling model and channel-head small modular reactor coolant pump coupling model, it was found that the head and efficiency of the straight-pipe model were better than channel-head model, indicating that non-uniform inflow had a certain impact on the performance of the small modular reactor coolant pump. Finally, the internal flow states of the straight-pipe model and the channel-head model were compared and analyzed from the perspectives of pump internal flow field, pump internal velocity and pressure distribution. The results showed that the sudden contraction of the direct connection between the channel-head and the small modular reactor coolant pump and the short length of the direct connection caused the inlet flow of the pump to be non-uniform, which adversely affected the hydrodynamic and internal flow characteristics of the pump. This study provides a certain idea for the research on the suppression of non-uniform inflow from small modular reactor coolant pumps and the improvement of non-uniform inflow from small modular reactor coolant pumps.

Designing three-dimensional changed spacer weft-knitted fabrics for efficient absorption and quick-drying partitioned garments

Enhancing the thermal-wet comfort of garments is a key factor in sportswear research. In this study, we designed a new three-dimensional moisture conduction structure model, developed double-sided fabrics of a changed spacer weft-knitted structure, and compared them with single-sided plain stitch and cellular structure fabrics, focusing on the morphology, basic structural parameters and absorption, and quick-drying performance. The findings indicated that the changed spacer weft-knitted fabric consisting of 18 tex Tencel and 33.3 tex (96 f) recycled polyester filament has a smaller surface density and thickness, the water absorption of 436%, the water evaporation rate of 0.354 g/h, and the water vapor transmission of 7476.75 g/(m2 · d). Compared with other fabrics, it has excellent absorption and quick-drying performance. Partitioned T-shirts were designed by using the human body sweating law, and four fabrics were applied to one to four areas according to the sweating degree. Through infrared image monitoring and subjective evaluation, we concluded that the partitioned T-shirt has efficient absorption and quick-drying ability, and has potential to be applied to the development of thermal-wet comfort sportswear.

Application of CAE for mold flow analysis in plastic molding mold design

Abstract In this paper, injection molding is used as a method of plastic molding, which is a method in which plastic in the molten state is injected into a closed mold cavity by compression, and the molten material fills the cavity and is then cured by cooling to maintain the shape in the mold. The basic structural form and parameters of the mold’s parting surface, winding injection system, and cooling system are determined by mold flow analysis. CAE analysis is used to move the mold filling analysis to the computer, and the mold casting and cooling system’s design scheme can be evaluated and optimized according to the analysis results. After Moldflow software analysis of warpage deformation, the total warpage deformation of the molded part was obtained, the maximum deformation reached 0.931mm, the maximum deformation from uneven cooling was 0.192mm, the maximum deformation from uneven shrinkage was up to 0.942mm, the deformation from shrinkage was even larger than the integrated deformation, which had a greater impact on the final deformation of the molded part. It has a relatively large impact on the final deformation of the plastic part. Therefore, using CAE can improve the efficiency of mold design, optimize the structural design of the mold, effectively improve the quality of the mold and reduce the mold manufacturing cost. The promotion of its application is in line with the development of the times and promotes the rapid development of the injection mold field.

A novel geometric feature-based wood-leaf separation method for large and crown-heavy tropical trees using handheld laser scanning point cloud

ABSTRACT As the prerequisite of non-destructively measuring structural parameters and leaf distribution, wood-leaf separation plays an essential role in forest inventories. However, fewer studies focus on the separation and measurement of large tropical trees with heavy crowns and complex branch structures. This study proposed a novel method to automatically separate the leaf and wood points of large tropical trees based on geometric features. Instead of identifying all wood points using the same rules, we used different methods to separate small and large wood components, respectively. The identification of small wood components was implemented mainly by the differences in point density and linear distribution pattern between leaf and wood points, while the identification of large wood components was implemented through the comprehensive analysis of verticality, linearity, anisotropy, as well as point density. To improve the separation accuracy and implementation effectiveness, the segment-wise and point-wise methods were combined in this study. The robustness and generalization of the proposed method were tested using two datasets, i.e. twenty large tropical trees with heavy crowns and twenty-four general tropical trees without heavy crowns. The separation results verified that the proposed method could achieve good separation of wood and leaf points of large crown-heavy tropical trees with the accuracy of up to 91.5%. The highest separation accuracy of general tropical trees was about 95.03%. The examination of the tropical trees without heavy crowns demonstrated that the proposed method has promising robustness and generalization ability. In addition, to fill the gap in the large tropical tree point clouds, an open-source dataset library was built for the wood-leaf separation research, including manually labelled 20 large crown-heavy tropical trees with different types of branch structures and basic structural parameters of each tree (tree height, crown spread, and diameter at the breast height (DBH)).

Chloroform-infiltrated photonic crystal fiber with high-temperature sensitivity.

The temperature sensor is the core part of the temperature measurement instrument, and its performance directly determines the temperature measurement accuracy. Photonic crystal fiber (PCF) is a new type of temperature sensor with extremely high potential. In this paper, we propose a high-performance, structurally simple, liquid-filled PCF temperature sensor, which is based on a SMF-PCF-SMF (single mode fiber, SMF) sandwich structure. By adjusting the structural parameters of the the PCF, it is possible to obtain optical properties that are superior to those of ordinary optical fibers. This allows for more obvious responsive changes of the fiber transmission mode under small external temperature changes. By optimizing the basic structure parameters, a new PCF structure with a central air hole is designed, and its temperature sensitivity is -0.04696 nm/°C. When filling the air holes of PCFs with temperature-sensitive liquid materials, the response of the optical field against the temperature fluctuations can be effectively enhanced. The Chloroform solution is used to selectively infiltrate the resulting PCF owing to its large thermo-optical coefficient. After comparing various filling schemes, the calculation results show that the highest temperature sensitivity of -15.8 nm/°C is finally realized. The designed PCF sensor has a simple structure, high-temperature sensitivity, and good linearity showing great application potential.

Research on a working face gas concentration prediction model based on LASSO-RNN time series data

The effective prediction of gas concentration trends and timely and reasonable extraction measures can provide valuable references for gas control. The gas concentration prediction model proposed in this paper has the advantages of a large sample size and long time span for training data selection. It is suitable for more gas concentration change scenarios and can be used to adjust the data prediction length according to demand. To improve the applicability and practicability of the model, this paper proposes a prediction model based on the LASSO-RNN (least absolute shrinkage and selection operator) for mine face gas concentration based on actual gas monitoring data from a mine. First, the LASSO method is used to select the key eigenvectors that affect the gas concentration change. Second, the basic structural parameters of the RNN prediction model are preliminarily determined based on the broad strategy. Then, the MSE (mean square error) and the running time are used as the evaluation indicators to select the appropriate batch size and number of epochs. Finally, the appropriate prediction length is selected based on the optimized gas concentration prediction model. The results show that the RNN gas concentration prediction model has a better prediction effect than the LSTM (long short-term memory) prediction model. The average mean square error of the model fit can be reduced to 0.0029, and the predicted average absolute error can be reduced to 0.0084. The maximum absolute error of 0.0202, especially at the time inflection point of the change in the gas concentration curve, can better reflect the superiority of the RNN prediction model, that is, higher precision, robustness and applicability than LSTM.