Simulation Tests Research Articles

Optimization design of unmanned mining truck path tracking controller based on road adhesion coefficient estimation

Abstract In response to the issues of inaccurate tracking precision caused by continuous turning and variable road adhesion coefficients on complex roads in mining areas for unmanned mining trucks, this paper adopts unscented Kalman filter (UKF) to estimate the road adhesion coefficient. Based on this, an adaptive preview feedforward linear quadratic regulator (LQR) control algorithm is designed using genetic algorithm, feedforward control, and linear quadratic optimal path tracking control methods. Firstly, a nine degree of freedom vehicle dynamics model and a two degree of freedom vehicle lateral dynamics model are established, and the Dugoff model to calculate tire forces is introduced; secondly, utilizing UKF to estimate the road adhesion coefficient, and leveraging active disturbance rejection control for rapid tracking of road curvature, an optimized design of the LQR controller is carried out. Then, the effectiveness of the designed controller was verified through Trucksim/Simulink joint simulation testing. Finally, the hardware in the loop test results showed that the designed controller had good tracking accuracy and strong adaptability in complex road and special working conditions in mining areas.

Study on the evolution of fractures in overlying strata during repeated mining of coal seams at extremely close distances

In particular, the secondary development of overlying strata fractures can easily lead to the upper goaf, resulting in gas and water gathered in the goaf entering the working face of the lower coal seam through the overlying strata fractures, threatening the safety of coal mine production. Security risks may arise. To further understand the caving and evolution law of overlying strata during repeated mining in extremely close distance coal seam down mining, 9# coal and 10# coal in the Nanyaotou Coal Industry were used as the engineering background. The caving characteristics and fracture evolution law of overlying strata during single and repeated mining were analyzed through similar material simulation tests. Based on fractal geometry theory, the relationship between the advancing distance of the working face and the fractal dimension of the overlying strata fracture is established to reflect the changing trend of fracture development. The calculation formula is derived from the tensile rate of rock strata to predict the development height of water-conducting fractures. The results show that the overlying strata failure structure is mainly a “hinged structure” and a “step structure,” which respectively promotes and inhibits the development of overlying strata fractures. Repeated mining causes mining-induced fractures in the lower coal seam to pass through the goaf of the upper coal seam and develop more vigorously in the upper coal seam, and the fractal dimension can effectively reflect the development of overlying strata fractures. The height of the water-conducting fracture zone increases in four stages: incubation, gradual increase, further gradual increase, and stability, eventually stopping development under the influence of the key layer (thick mudstone) bearing the load above. The development height of water-conducting fractures predicted by on-site water injection measurement is similar to that predicted by simulation experiments and theoretical calculations, verifying the feasibility of predicting the development height of water-conducting fractures through simulation tests and theoretical analysis. This study provides a reference for coal seam mining under similar conditions.

An Evaluation Modeling Study of Thermal Runaway in Li-Ion Batteries Based on Operation Environments in an Energy Storage System

According to the green growth and carbon-neutral policy in Korea, the installation of large-capacity ESSs is rapidly being increased, but a total number of 50 ESS fire cases have occurred since the end of 2023. ESSs are typically composed of series-parallel connections with numerous Li-ion batteries, and when the temperature of a deteriorated cell increases due to thermal, electrical, and mechanical stress, thermal runaway can occur due to additional heat generated by an internal chemical reaction. Here, an internal chemical reaction in a Li-ion battery results in the different characteristics on the decomposition reaction and heat release depending on the operation conditions in the ESS, such as the rising temperature rate, convective heat transfer coefficient, and C-rate of charging and discharging. Therefore, this paper presents mathematical equations and modeling of thermal runaway, composed of the heating device section, heat release section by chemical reaction, chemical reaction section at the SEI layer, chemical reaction section between the negative and positive electrodes and solvents, and chemical reaction section at the electrolyte by itself, based on MATLAB/SIMULINK (2022), which were validated by a thermal runaway test device. From the simulation and test results based on the proposed simulation modeling and test device according to the operation conditions in ESSs, it was found that the proposed modeling is an effective and reliable tool to evaluate the processing characteristics of thermal runaway because the occurrence time intervals and maximum temperatures had almost the same values in both the test device and simulation modeling. Accordingly, it was confirmed that the rising temperature rate and the convective heat transfer coefficient were more critical in the thermal runaway than the C-rate of charging and discharging.

In-motion fine alignment algorithm for AUV based on improved extended state observer and Kalman filter

Abstract Achieving fast initial alignment has always been a major challenge for autonomous underwater vehicle navigation, especially in motion. During the fine alignment process, traditional algorithms often face the slow convergence of the azimuth misalignment angle. To solve this problem, this paper proposes a fast alignment algorithm based on the improved extended state observer (ESO) and Kalman filter. Specifically, since the application potential of ESO in fine alignment has not been fully explored by previous studies, this paper introduces the improved ESO through the in-depth research of traditional ESO. The obtained improved ESO is then combined with Kalman filter to constitute the proposed fast alignment algorithm. The algorithm first uses Kalman filter to estimate the two horizontal misalignment angles. Once the horizontal angles estimation of Kalman filter tends to be stable, the improved ESO is used to estimate the azimuth misalignment angle. Simulation and lake test show the proposed algorithm has excellent performance of in-motion alignment. The comparative analysis shows that the algorithm significantly improves the convergence speed of azimuth alignment angle.

A Novel Fractal Model for Contact Resistance Based on Axisymmetric Sinusoidal Asperity

In this paper, a novel fractal model for the contact resistance based on axisymmetric sinusoidal asperity is proposed, which focuses on the resistance characteristics of the rough interface at a microscopic scale. By introducing the unique geometric shape of axisymmetric sinusoidal asperity, and combining it with a three-dimensional fractal theory, the micro-morphology characteristics of the rough interface can be characterized more precisely. Subsequently, by conducting a theoretical analysis and numerically solving the deformation mechanisms of asperities on the rough interface, a refined model for contact resistance is constructed. This research comprehensively employs theoretical analysis, numerical simulation, and experimental testing methods to deeply explore the current transmission mechanisms during the contact process of the rough interface. The findings suggest that the proposed model is capable of precisely capturing the intricate interplay of various factors, including contact area, contact load, and material properties, with the contact resistance. Compared to the existing models, the presented model demonstrates significant advantages in terms of prediction accuracy and practicality. This research provides an important theoretical basis and design guidance for optimizing the electrical performance of the rough interface, which has great significance for engineering applications.

Simulation and Experimental Research on a New Symmetrical Hydraulic Piezoelectric Energy Harvester

This study introduces a new symmetrical hydraulic piezoelectric energy harvester. By integrating theoretical analysis, simulation, and empirical testing, the research delves into the energy‐harvesting potential of monolithic single‐side output, monolithic two‐side parallel‐connected output, stacked one‐side parallel‐connected output, and stacked two‐side parallel‐connected output under varying parameter configurations. Additionally, it elucidates the energy dissipation occurring during the energy‐harvesting process of stacked piezoelectric disks. It has been observed that the primary determinant of voltage is the amplitude of pulsation, not the static pressure. Concurrently, the study also addresses the consistency of power generation between multiple channels. A study is made on whether there is a proportional relationship between single‐channel power generation and multi‐channel power generation. The root mean square (RMS) voltage of each connection sharply rises with resistance from 2 to 100 KΩ. It is found that the performance of parallel connection of monolithic piezoelectric disk is better than that of other connection methods. At 3 MPa and 100 Hz, the optimal resistance is 16 KΩ, yielding a maximum average power of 1155.63 μW and an optimal power density of 1.774 μW (bar mm3)−1. Consequently, the research offers a novel approach to addressing the issue of sustainable energy supply for low‐power electronic devices and sensors.

Investigation of the Time-Dependent Deformation of Recycled Aggregate Concrete in a Water Environment.

The water environment greatly affects the creep deformation of recycled aggregate concrete (RAC). Hence, a humidity-stress-damage coupling numerical model was used for investigating the time-dependent deformation of RAC in the water environment in this study. Firstly, uniaxial compression and water absorption tests were performed to determine the calculation parameters of the creep numerical simulation of RAC in a water environment. Experimental results indicate that the elastic modulus and compressive strength drop as the water content increases. Then, the time-dependent deformation of RAC in a water environment was studied using a numerical simulation test of compressive creep when multiple stress levels were applied, and the critical stress for accelerated creep and the long-term strength of RAC were obtained. Finally, the influence of confining pressures on the long-term deformation of RAC in a water environment was discussed. When there is no confining pressure, the long-term strength of RAC is 23.53 MPa. However, when a confining pressure of 3.921 MPa is loaded onto RAC, the long-term strength of RAC is 47.052 MPa, which increases by 100%. Increasing confining pressures has an obvious effect on ensuring the long-term stable application of RAC in a water environment. Compared with the creep test, the method adopted in this study saves time and money and provides the theoretical basis for evaluating the time-dependent deformation of RAC in a water environment.

Chicoric acid acts as an ALOX15 inhibitor to prevent ferroptosis in asthma

BackgroundChicoric acid (CA) is a crucial immunologically active compound found in chicory and echinacea, possessing a range of biological activities. Ferroptosis, a type of iron-dependent cell death induced by lipid peroxidation, plays a key role in the development and advancement of asthma. Targeting ferroptosis could be a potential therapeutic strategy for treating asthma. PurposeThe purpose of this study was to explore the screening of ALOX15, a pivotal target of ferroptosis in asthma, and potential therapeutic agents, as well as to investigate the promising potential of CA as an ALOX15 inhibitor for modulating ferroptosis in asthma. MethodsThrough high-throughput data processing of bronchial epithelial RNA from asthma patients using bioinformatics and machine learning, the key target of ferroptosis in asthma, ALOX15, was identified. An inhibitor of ALOX15 was then obtained through high-throughput molecular docking and molecular dynamics simulation tests. In vitro experiments were conducted using a 16HBE cell model induced by house dust mite (HDM) and lipopolysaccharide (LPS), which were treated with the ALOX15 inhibitor (PD146176), CA treatment, or ALOX15 knockdown. In vivo experiments were also carried out using a mouse model induced by HDM and LPS. ResultsThe composite model of ALOX15 and CA in molecular dynamics simulations shows good stability and flexibility. Network pharmacological analysis reveals that CA regulates ferroptosis through ALOX15 in treating asthma. In vitro studies show that ALOX15 is highly expressed in HDM and LPS treatments, while CA inhibits HDM and LPS-induced ferroptosis in 16HBE cells by reducing ALOX15 expression. Knockdown of ALOX15 has the opposite effect. Metabolomics analysis identifies key compounds associated with ferroptosis, including L-Targinine, eicosapentaenoic acid, 16-hydroxy hexadecanoic acid, and succinic acid. In vivo experiments demonstrate that CA suppresses ALOX15 expression, inhibits ferroptosis, and improves asthma symptoms in mice. ConclusionOur research initially identified CA as a promising asthma treatment that effectively blocks ferroptosis by specifically targeting ALOX15. This study not only highlights CA as a potential therapeutic agent for asthma but also introduces novel targets and treatment options for this condition, along with innovative approaches for utilizing natural compounds to target diseases associated with ferroptosis.

An artificial neural network-based predictive model for tensile behavior estimation under uncertainty for fused deposition modeling

PurposeThis paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.Design/methodology/approachThe prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.FindingsThe results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.Originality/valueUsing the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

Large-scale magnetic target state estimation using model parameter optimization

Abstract Aiming at the problem of non-negligible target scale in the state estimation of magnetic targets, a state estimation method that requires only the measurement data of a single three-axis magnetic sensor and is insensitive to the initial value is proposed. Firstly, the state estimation model of a single three-component magnetic sensor is established based on a magnetic dipole array. A signal preprocessing method is designed to reject useless signals and keep the waveform intact. Five swarm intelligent optimization algorithms such as Particle Swarm Optimization (PSO), Artificial Hummingbird Algorithm (AHA), Artificial Rabbits Optimization (ARO), Grey Wolf Optimizer (GWO), and Improved GWO (IGWO) are introduced to perform state optimization, and two methods, such as Conjugate Gradient Least Squares (CGLS), and Stepwise Regression (SR), are introduced to calculate the magnetic moment parameters, and PSO-CGLS, AHA-CGLS, AHA-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, ARO-CGLS, and ARO-CGLS are designed in combination. CGLS, ARO-CGLS, GWO-CGLS, IGWO-SR, IGWO-CGLS, and six state estimation methods. In order to address the problem that the parameter optimization range is too large, the state to be estimated is adjusted from the initial state to the state at the moment of the maximum value of the magnetic field modulus, and the method of positive transverse distance estimation is used to narrow the range of positive transverse position optimization. Finally, numerical simulation tests and ship model tests are used for verification, and the results show that the proposed methods are accurate and can meet the needs of precise target state estimation.

Study on the Diffusion Characteristics of Polymer Grouting Materials Applied for Crack Filling in Underground Mines Based on Numerical Simulation and Experimental Methods.

Polymer grouting materials are increasingly used in the filling of mine fissures. Unlike conventional inorganic grouting materials, the self-expansion of polymers adds complexity to their diffusion process within the crack. The objective of this research was to examine how polymer grouting material spreads in cracks at ambient temperatures and pressure. The investigation involved conducting grouting tests and performing numerical fluid simulation calculations using the finite-volume method in the computational fluid dynamics software, ANSYS FLUENT 2022 R1. The fluid volume approach was employed to determine the boundary between fluid and air and to ascertain the variation patterns of density in the slurry and the fracture system. This study applied the principles of fluid mechanics to investigate the patterns of variation in the physical characteristics of polymer grouting materials, including their density, pressure, flow velocity, and movement distance, during the diffusion process. The results indicated that the density of the polymer grouting material decreased exponentially over time throughout the diffusion process. With the increase in the grouting's volume, the grout's pressure and the permeable distance of the grout increased. The slurry's pressure near the grouting hole exceeded the other points' pressure. The physical parameters of the slurry were numerically simulated by ANSYS FLUENT 2022 R1 software, and the results were compared with the experimental data. After comparing the numerical simulation results with the test data, it was clear that the numerical simulation method was superior in accurately predicting the distribution pattern of each parameter of the polymer slurry during diffusion. The grouting volume, pressure distribution, and real-time change in the position of the flow of slurry could be efficiently determined through numerical calculation and simulated grouting tests. This work can offer valuable information for designing polymer grouting materials used in underground mine fissures.

Advanced corrosion resistance and mechanistic insights of electroless Ni-P-PTFE coatings on L245NS substrate in H2S-saturated environments

The extraction and transportation of oil and gas pose significant challenges due to corrosive environments, particularly hydrogen sulfide (H2S) exposure. This study investigates the use of polytetrafluoroethylene (PTFE) to enhance the corrosion resistance of nickel‑phosphorus (NiP) coatings in H2S-rich environments. Electroless deposition was employed to prepare Ni-P/PTFE coatings, which were then subjected to simulated H2S corrosion tests. Results indicate that the incorporation of PTFE led to a more uniform NiP coating with reduced porosity, significantly improving corrosion resistance. The coatings exhibited enhanced protective performance by stabilizing corrosion product layers and preventing the formation of corrosion channels. Ni-P-PTFE coatings overcame the existing research challenge of NiP coatings' corrosion in H2S environments, showing no penetration of the corrosive medium into the substrate after 720 h of immersion. The corrosion rate of the coating is only 0.0261 mm/year, only 4.27 % of the corrosion rate of the L245NS substrate in the early stage of corrosion. Moreover, after characterization by SEM, XRD and XPS, it was found that the presence of PTFE altered the corrosion mechanism, promoting the formation of stable nickel sulfide and oxide layers on the surface. This study sheds light on the mechanisms underlying PTFE-enhanced corrosion resistance in NiP coatings, offering promising implications for extending the lifespan of equipment in the oil and gas industry.

Deformation-Adapted Spatial Domain Filtering Algorithm for UAV Mining Subsidence Monitoring

Underground coal mining induces surface subsidence, leading to disasters such as damage to buildings and infrastructure, landslides, and surface water accumulation. Preventing and controlling disasters in subsidence areas and reutilizing land depend on understanding subsidence regularity and obtaining surface subsidence monitoring data. These data are crucial for the reutilization of regional land resources and disaster prevention and control. Subsidence hazards are also a key constraint to mine development. Recently, with the rapid advancement of UAV technology, the use of UAV photogrammetry for surface subsidence monitoring has become a significant trend in this field. The periodic imagery data quickly acquired by UAV are used to construct DEM through point cloud filtering. Then, surface subsidence information is obtained by differencing DEM from different periods. However, due to the accuracy limitations inherent in UAV photogrammetry, the subsidence data obtained through this method are characterized by errors, making it challenging to achieve high-precision ground surface subsidence monitoring. Therefore, this paper proposes a spatial domain filtering algorithm for UAV photogrammetry combined with surface deformation caused by coal mining based on the surface subsidence induced by coal mining and combined with the characteristics of the surface change. This algorithm significantly reduces random error in the differential DEM, achieving high-precision ground subsidence monitoring using UAV. Simulation and field test results show that the surface subsidence elevation errors obtained in the simulation tests are reduced by more than 50% compared to conventional methods. In field tests, this method reduced surface subsidence elevation errors by 39%. The monitoring error for surface subsidence was as low as 8 mm compared to leveling survey data. This method offers a new technical pathway for high-precision surface subsidence monitoring in mining areas using UAV photogrammetry.

Hybrid simulation testing of two-storey low-aspect-ratio nuclear RC shear walls with normal- and high-strength reinforcement: Seismic performance evaluation and economic assessment

Low-aspect-ratio reinforced concrete (RC) shear walls have been commonly used in several nuclear facilities in containment and safety-related structures. Despite being a potential alternative to reduce rebar congestion and subsequently minimize complex construction activities typically associated with nuclear facilities, there has been limited experimental research on investigating the impact of using high-strength reinforcement (HSR) on the seismic performance of such walls, particularly in a multi-storey context. This lack of research is mainly due to considerable challenges imposed when testing such multi-storey nuclear RC shear walls in most laboratories. Therefore, the current study presents the experimental results of two two-storey low-aspect-ratio nuclear RC shear walls that were tested utilizing the seismic hybrid simulation testing technique. In this respect, walls W1-NSR and W2-HSR were designed using normal-strength reinforcement (NSR) and HSR, respectively, where the two test walls had comparable capacities to allow for direct comparisons. Both walls were subjected to various ground motion levels, spanning from operational to design and beyond-design earthquake scenarios. The experimental findings are then presented to include the force-displacement responses, the multi-storey effects, ductility capacities, lateral and rotational stiffnesses, rebar strains, and cracking patterns of the test walls. Subsequently, an economic assessment was carried out to quantify the total rebar weights and the corresponding construction costs of such walls. In addition, the expected seismic repair costs were determined based on a three-dimensional digital image correlation technique that provided information on the damage states of the test walls under different earthquake levels. The results show that although W1-NSR and W2-HSR attained similar force and moment capacities, W2-HSR achieved a relatively higher ductility capacity than W1-NSR. However, larger cracks were observed in W2-HSR compared to W1-NSR, which was attributed to the associated larger rebar spacing in the former relative to the latter. The economic assessment results demonstrate that using HSR minimized the rebar weights and construction costs, while both walls had similar seismic repair costs at their design and beyond-design earthquake levels. Both the seismic performance and economic assessment results presented in the current study are expected to aid future editions of relevant design standards in adopting HSR in nuclear construction practice.

StarTracer is an accelerated approach for precise marker gene identification in single-cell RNA-Seq analysis

Revealing the heterogeneity among tissues is the greatest advantage of single-cell-sequencing. Marker genes not only act as the key to correctly identify cell types, but also the bio-markers for cell-status under certain experimental imputations. Current analysis methods such as Seurat and Monocle employ algorithms which compares one cluster to all the rest and select markers according to statistical tests. This pattern brings redundant calculations and thus, results in low calculation efficiency, specificity and accuracy. To address these issues, we introduce starTracer, a novel algorithm designed to enhance the efficiency, specificity and accuracy of marker gene identification in single-cell RNA-seq data analysis. starTracer operates as an independent pipeline, which exhibits great flexibility by accepting multiple input file types. The primary output is a marker matrix, where genes are sorted by the potential to function as markers, with those exhibiting the greatest potential positioned at the top. The speed improvement ranges by 2 ~ 3 orders of magnitude compared to Seurat, as observed across three independent datasets with lower false positive rate as observed in a simulated testing dataset with ground-truth. It’s worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. These advantages solidify starTracer as an important tool for single-cell RNA-seq data, merging robust accuracy with exceptional speed.

Experimental and Simulation Study on Failure of Thermoplastic Carbon Fiber Composite Laminates under Low-Velocity Impact.

Numerous studies have demonstrated that under low-velocity, low-energy impact conditions, although the surface damage to fiber-reinforced composite laminates may be minimal, significant internal damage can occur. Consequently, a progressive damage finite element model was specifically developed for thermoplastic carbon fiber-reinforced composite laminates subjected to low-speed impact loads, with the objective of analyzing the damage behavior of laminates under impacts of varying energy levels. The model utilizes a three-dimensional Hashin criterion for predicting intralayer damage initiation, with cohesive elements based on bilinear traction-separation law for predicting interlaminar delamination initiation, and incorporates a damage constitutive model based on equivalent displacement to characterize fiber damage evolution, along with the B-K criterion for interlaminar damage evolution. The impact response of laminates at energy levels of 5 J, 10 J, 15 J, 20 J, and 25 J was analyzed through numerical simulation, drop-hammer experiments, and XCT non-destructive testing. The results indicated that the simulation outcomes closely correspond with the experimental findings, with both the predicted peak error and absorbed energy error maintained within a 5% margin, and the trends of the mechanical response curves aligning closely with the experimental data. The damage patterns predicted by the numerical simulations were consistent with the results obtained from XCT scans. The study additionally revealed that the impact damage of the laminates primarily stems from interlaminar delamination and intralayer tensile failure. Initial damage typically presents as internal delamination; hence, enhancing interlaminar bonding performance can significantly augment the overall load-bearing capacity of the laminate.

Centralized exhaust system simulation and parameter optimization

With the increase in high-rise buildings in cities, public flue exhaust systems have a direct impact on residential air quality and the living environment. Although existing studies have analyzed the problems in public flue exhaust systems through computational fluid dynamics (CFD) numerical simulations and experimental tests, these studies often lack an in-depth exploration of the specific impacts of individual components in the system. To solve this problem, this study not only thoroughly analyzes the key components in the public flue system, such as branch pipes, check valves, and tee pipes, but also develops a parametric public flue simulation system software based on C# and verifies the accuracy of the simulation through experiments. The study first determines the key parameters affecting the comprehensive resistance coefficient of the branch pipe, check valve, tee pipe, and other components through CFD simulation and experimental testing. Subsequently, a visualization program is developed using the C# language, which can quickly simulate and visualize the flow changes in the flue according to different building parameters such as the number of floors, height of floors, and size of the flue. The results confirm that the program can efficiently predict the flow distribution under different design options, providing a practical tool for the optimal design and performance evaluation of public flues, which is important for improving the air quality of the living environment.

Components optimization of polyurethane-modified asphalt binder towards compatibility: Insight from molecular dynamics simulations

Polyurethane-modified asphalt demonstrated the enhanced durability and skid resistance, while remaining the drawback in the compatibility between the polyurethane modifier and asphalt binder in practical use. Herein, a molecular-dynamics-simulations based compatibility evaluation method is proposed for the polyurethane modifier and asphalt binder, thereby achieving the cost-effective and efficient components optimization. Twelve components asphalt molecular model was built in Molecular Dynamics to investigate the interaction of asphalt binder to the polypropylene glycol, toluene diisocyanate, and diphenylmethane diisocyanate, respectively. The molecular model of polyurethane modified asphalt binder was verified by the similar loss modulus temperature from the simulation and temperature scanning tests. The simulation outcomes indicate significant variations in the compatibility of different polyurethanes with asphalt, in which diphenylmethane diisocyanate-type polyurethane exhibits the superior compatibility. The compatibility of various polyurethane concentrations in asphalt shows considerable disparity, suggesting that the compatibility index can serve as a criterion for determining the optimal dosage of the modifier. Temperature also exerts an influence on the compatibility of polyurethane modified asphalt binder. Simulation results indicates that 130 °C potentially may be the optimal temperature for its preparation, which is corroborated by the microscopic characterization. This study provides a fundamental framework for the molecular dynamics simulation-based components optimization of polyurethane-modified asphalt, thereby inspiring the targeted design of asphalt materials.

The influence of semi-actively controlled magnetorheological bogie yaw dampers on the guiding behaviour of a railway vehicle in an S-curve: Simulation and on-track test

Many publications have shown that semi-actively controlled dampers could significantly improve the behaviour of a road or rail vehicle. In the case of a railway vehicle, these dampers promise to solve the contradiction between the damping requirements of different running modes (fast running on a straight track vs negotiating a tight curve). It is known that semi-active control of a bogie yaw damper can improve the vehicle behaviour when running fast on a straight track, but it is not known whether such semi-active control worsens the vehicle behaviour when negotiating a tight curve. This paper investigates the application of magnetorheological bogie yaw dampers in the locomotive bogie to reduce guiding forces and wear in wheel-rail contact when the vehicle negotiates the S-curve. The paper describes the magnetorheological damper, its mathematical model and the strategies for its semi-active control, followed by the results of simulations on a complex multi-body locomotive model and on-track testing on a real vehicle. The simulations and on-track tests have shown that the use of semi-active control of the yaw dampers reduces the guiding force by about 10%. The reduction in these forces will lead to a reduction in wear in the wheel-rail contact.

黄芪离散元仿真参数的标定与试验

This study is based on the discrete element method to calibrate the physical parameters of astragalus membranaceus. Plackett Burman test, steepest climb test, and Box Behnken test were used to screen for significant influencing factors and optimal levels, and a quadratic regression model was obtained for the relative error between the simulated and physical experiment resting angles. The results showed that the relative error between the simulated and physical test angles of repose was only 0.392%, which can provide a theoretical basis for the discrete element simulation test of the working process of the astragalus membranaceus picking machine.