Adhesion Coefficient Research Articles

Longitudinal and Lateral Cooperative Control of Preview Intelligent Vehicles with Stabilization

Aiming at the highly nonlinear and coupled longitudinal and transverse motion control problems of intelligent vehicle path tracking control, a new method of preview MPC path tracking control is proposed by combining visual preview model and model predictive control theory. The method first locally linearizes the nonlinear prediction model and transforms the MPC optimization solution problem into a quadratic programming problem. The longitudinal vehicle speed and the preview distance are then treated as known in each control time domain, an exponential model is applied to characterize the longitudinal vehicle speed, and a transverse angular velocity-center of mass lateral deflection (r-β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r-\\beta $$\\end{document}) phase plane plot is applied to characterize the vehicle stability. Finally, proportional integral differential stability controller is designed to improve vehicle stability. Simulation and test results show that: Compared with the MPC tracking control method, the preview MPC control method proposed in this paper can optimize the vehicle tracking performance on different adhesion coefficient road surfaces and improve the tracking accuracy, and this enhancement effect is more obvious at high speeds.

An Improved Adaptive Sliding Mode Control Approach for Anti-Slip Regulation of Electric Vehicles Based on Optimal Slip Ratio

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision.

A Simplified Non‐Hertzian Wheel‐Rail Adhesion Model Under Interfacial Contaminations Considering Surface Roughness

ABSTRACTThe accuracy and efficiency of the wheel‐rail adhesion model are important to the wheel‐rail rolling contact issues. The purpose of this study is to develop a simplified non‐Hertzian wheel‐rail adhesion model under interfacial contaminations to predict the wheel‐rail adhesion coefficient. Firstly, a non‐Hertzian full elasto‐hydrodynamic lubrication (EHL) model was developed and applied to determine the wheel‐rail contact pressure and film thickness under interfacial contaminations. Then, the empirical formula of central film thickness available to non‐Hertzian wheel‐rail normal contact relating to train speeds, axle loads and material parameters were proposed based on a large number of non‐Hertzian full EHL simulation for smooth surface under interfacial contaminations using linear regression. The empirical non‐Hertzian central film thickness formula and minimum film thickness formula for wheel‐rail contact obtained in this paper show certain differences from the formulas based on Hertzian contact. Using the proposed non‐Hertzian central film thickness formula, a simplified non‐Hertzian wheel‐rail contact adhesion model was developed, and the adhesion coefficient was obtained at different speeds and compared with the field test data. The numerical results showed good agreement with field test data.

Mechanism of Wheel-Rail Adhesion Recovery during Large Sliding processes under Water Lubrication condition

The challenge of low adhesion between the wheel and rail constrains the advancement of train braking technologies, resulting in sliding during braking, increased braking distances, and even operational safety incidents. Low adhesion arises from the contamination of the rail surface by a third body, such as water or oil, significantly reducing adhesion. However, experimental studies have found that wheel-rail adhesion coefficient under water lubrication conditions possesses a recovery capability: In scenarios characterized by low adhesion in water lubrication conditions, a secondary rise in adhesion occurs when substantial wheel-rail sliding is present. This paper focuses on numerically modeling the unique phenomenon of adhesion recovery during large-sliding processes under water lubrication conditions. Considering the notable differences in frictional heating during wheel sliding, as opposed to pure rolling conditions, we construct a wheel-rail contact model under mixed lubrication conditions that incorporates the solid-liquid coupled heat transfer effect. This model achieves stable solutions for the wheel-rail contact relationship and the temperature at the wheel-rail interface. The impacts of velocity, axle load, water temperature, and slide ratio on the adhesion recovery phenomenon are discussed, revealing the underlying mechanism of adhesion recovery during large sliding processes. Finally, brake adhesion tests under water lubrication conditions are conducted on a scaled test rig to validate the proposed theoretical model.

Study on the application amount optimization of water-based friction modifier based on field experiments

The utilization of friction modifiers (FMs) can reduce the adhesion coefficient of the top-of-rail to a moderate level, yielding a series of benefits. In this study, the influence of the FM application amount on the adhesion coefficient, noise, and braking distance was explored under field conditions. Results showed that when the FM application amount exceeded threshold values, the braking distance of the locomotive was greatly extended and the friction control performance of FM reached saturation. The FM application amount and application frequency were put forward through a simplified calculation process, which gave values of 0.33 mL/axle and 10axles/application, respectively. These application parameters achieve the desired intermediate friction level along with appropriate reductions in the noise and the lateral force.

Development of an intelligent geographic information system for mountain roads monitoring: Ground data collection and analysis

This study examines the development of an intelligent geographic information system for mountain road monitoring. To make well-informed and timely management decisions in the planning, construction, and maintenance of mountain roads, and to anticipate potential critical situations and their effects on road conditions, it is imperative to employ modern methodologies. This study involves data collection from a specific section of a mountain road situated near Almaty, Kazakhstan. We gathered data on the road section through visual assessment, measurements of the coefficient of adhesion, and the acquisition of material samples from the structural layers of the roadbed. Based on the visual assessment and surface adhesion measurements, it became evident that the deformation of the studied road section, which includes a network of cracks and "non-standard" transverse cracks, is attributed to mountain mass shifts and water flows from slopes. Laboratory analysis of the collected samples revealed that excessive water saturation could result in cracks during freeze-thaw cycles, while a low resistance coefficient would lead to softening during humid conditions. An Intelligent Geographic Information System (IGIS) will incorporate the collected data alongside open remote sensing and weather data. The objective is to utilize this integrated system in the future for forecasting road conditions and maintenance requirements for similar roads in mountainous regions of the area. The measurements are carried out according to the state standards. That makes the system useful for government management purposes.

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.

Investigation on the influence of the slip at the wheel-rail contact patch on the shakedown limit of rail material

Research on factors affecting rolling contact fatigue (RCF) damage of rail materials has become increasingly important since developing effective measures to mitigate RCF damage is crucial. This study focuses on exploring the influence of full slip and partial slip contact modes at the wheel-rail contact patch on the RCF damage behavior and the shakedown limit of the pearlite rail material through wheel-rail RCF rolling-sliding tests. Firstly, based on the wheel-rail adhesion-creep curves in dry, water and oil environments, the creepage required to achieve full slip during the rolling-sliding tests was determined. Then, rolling-sliding tests under full slip contact with different adhesion coefficients were carried out in a dry atmosphere and with the assistance of a third body medium (i.e., oil and water), respectively. Meanwhile, rolling-sliding tests under partial slip contact with the similar contact parameters were performed in a dry atmosphere. The results indicated that RCF damage and wear rates of the rail material under a full slip contact with an adhesion coefficient below the saturation peak on the adhesion-creep curve were significantly less than those under partial slip contact with similar contact parameters. Moreover, under full slip contact with an adhesion coefficient below the saturation peak on the adhesion-creep curve, the pearlite rail materials could exhibit a higher shakedown limit. Furthermore, the influence of full slip and partial slip contact on wheel-rail contact behavior was further analyzed using finite element simulations. Finally, a novel friction modification strategy to mitigate RCF damage and wear of rails was proposed. By applying a specific low-coefficient friction modifier to the wheel-rail interface, the operation of powered wheelsets under controlled creepage conditions that reach the threshold of full slip contact at the wheel-rail contact interface could be employed to achieve the desired adhesion coefficient. Thus, this approach could ensure that the achieved adhesion coefficient met the on-site target adhesion force while reducing RCF damage and wear of rail materials.

Sticky feet: a tribological study of climbing shoe rubber

This study examines the tribological properties of climbing shoe rubbers, challenging the common belief in the climbing community that softer rubbers are inherently grippier. This study investigates the mechanical and wear characteristics of climbing shoe rubbers by employing a high-precision modular mechanical testing environment (Bruker UMT TriboLab) and representative granite counter-surfaces. Key parameters, including surface roughness, Shore A hardness, interfacial adhesion, static and dynamic friction coefficients, and material wear patterns, were analyzed. The mechanical properties of each rubber compound were characterized through Shore A hardness testing and ball indentation–retraction tests, measuring indentation force, energy, and adhesive properties. Sliding friction tests, simulating real climbing conditions, were conducted to understand the tribological behavior of each rubber compound under different loads, further analyzing static and dynamic friction coefficients and wear characteristics. The findings of this study indicate that rubber performance is a convolution of several factors, including material hardness, surface roughness, and interfacial adhesion. Contrary to popular belief, softer rubbers did not consistently exhibit superior tribological characteristics. The findings of this study suggest that climbing shoe selection and design should consider a broader range of material characteristics beyond hardness, emphasizing the role of surface roughness and adhesion in determining overall frictional performance. This research offers valuable insights for the climbing community, providing methodologies to benchmark climbing rubber material characteristics.

Road adhesion coefficient Estimation: Physics-informed deep learning method with vehicle dynamics model

Road adhesion coefficient (RAC) is not only a significant basis for trajectory planning and decision-making of autonomous vehicles but also a critical parameter to determine the control potential of autonomous vehicles under complex operating conditions. Accurate, timely, and reliable estimation of RAC facilitates many function units of autonomous vehicles and improves driving safety. To address the issues of low accuracy and transparency of RAC estimation method based on road surface image recognition, a vehicle dynamics model-informed deep learning (VDMIDL) method for RAC estimation is proposed in this work. The physics information related to vehicle dynamics is integrated into a CNN-based deep learning model via the fusion of vehicle physical features and high-dimensional road surface image features to realize accurate estimation of RAC. The results of the model training show that the proposed VDMIDL model for RAC estimation possesses higher prediction accuracy compared with the conventional deep learning (CDL) model. The vehicle test results indicate that the proposed VDMIDL model can provide more prospective and precise RAC for trajectory planning, decision-making, and control of the vehicle in response to sudden variations in road conditions. The VDMIDL model is applicable to a wide range of road conditions. RAC estimated by the VDMIDL model is accurate and stable under poor road condition in particular. The proposed fusion estimation model incorporates more physics information into deep learning model, endowing more interpretability and transparency to the black-box model.

Theoretical Calculations and Experimental Study of the Nitrided Layer of 1Cr17Ni2 Steel

Due to the harsh operating conditions experienced by 1Cr17Ni2 steel, efforts were made to optimize its performance by subjecting 1Cr17Ni2 stainless steel to nitriding treatments at temperatures of 460 °C, 500 °C, and 550 °C, each for durations of 8 and 16 h. The formation state of its cross section was observed through a metallurgical microscope and scanning electron microscope, and it was characterized by hardness measurement. Through a ball-on-disk wear experiment, the adhesive wear and friction coefficient of its non-lubricated sliding were measured. The phase composition of its surface was measured by XRD. The results revealed that nitriding led to the formation of a modified layer on the surface of the samples, with a depth of 130 μm after nitriding at 550 °C for 16 h. The hardness of the modified layer exceeded that of the matrix, reaching up to 1400 Hv0.1. X-ray diffraction (XRD) analysis of the sample surfaces indicated the presence of high-hardness phases such as CrN, γ′-Fe4N, and ε-Fe2-3N. This article predicts the mechanical properties of nitrided phases in high-alloy martensitic stainless steel through first-principles computational methods. We provide a reference for improving the performance of high-alloy steel after nitriding through a combination of theoretical calculations and experiments.

A comprehensive wheel–rail contact model incorporating sand fragments and its application in vehicle braking simulation

Increasing the wheel–rail adhesion coefficient by spreading sand grains to the wheel–rail interface is a common approach to improve the traction and braking efficiency of trains. To simulate this process accurately, two highlights are presented in this paper. Firstly, this paper proposes a wheel–rail contact model considering sand fragments, a third body, based on the non-Hertzian theory, which includes a random sand-grain generator and a sand crushing model. The proposed wheel–rail model is used to analyze the wheel–rail adhesion state after sand grains enter the wheel–rail contact interface. How sand particles change the distribution of normal force and tangential force to affect the wheel–rail adhesion is clarified and, in addition, the influence of the parameters of sand fragments on the wheel–rail adhesion coefficient is given. Secondly, a vehicle system dynamics model considering sanding and wheel slip protection (WSP) control is developed to analyze the effect of sanding on braking distance and wheel slip during braking processes. The wheel–rail contact model and the vehicle system dynamics model presented in this paper can support the study of more effective sanding and adhesion enhancement devices.

Wheel Squeal Mitigation Under Water Lubrication

This study investigates the potential of applying water to the wheel-rail contact to reduce squealing noise. For this purpose, a twin-disc device with a single tram wheel and real wheel suspension stiffnesses was developed. Three types of tests were performed. During the tests, adhesion coefficient, sound pressure level and wheel axial vibration were measured. The tests under dry conditions were carried out to describe the frequency spectrum of wheel vibration and to establish reference values for further measurements. The tests under wet conditions were carried out to investigate the ability of water to reduce adhesion and noise. Finally, tests with varying amounts of water in contact were carried out because of the low adhesion risk. The experimental results showed that the twin-disc device was able to reproduce both the adhesion and noise properties of the contact. Tests with different amounts of water showed that the application of water can be a promising way to reduce squealing noise from wheel-rail contact.

Locomotion of Bacillus subtilis SL-44 mediated by root exudate and carrier in Cr(OH)3-modified porous media

Plant growth promoting rhizobacteria (PGPR) have a remediation effect on Cr-contaminated soil; however, the remediation scope is only within a small area around the bacteria. Hence, the remediation effect depends on the migration ability of bacteria in the soil. Root exudates enhance the chemotaxis and locomotion of Bacillus subtilis SL-44 by reducing its adhesion coefficient katt and hydrodynamic dispersion coefficient D. The locomotion capacity was enhanced by 7.84%–20.00%. Among the root exudates, proline and sucrose remarkably improved the motility of SL44. Biochar and bentonite increased the katt and D of SL-44, inhibited bacterial locomotion, and improved the retention rate on the carrier surface. Bacterial locomotion was reduced by biochar and bentonite by 57.99% and 50.42%, respectively. These reductions were caused by macropore. SL-44 locomotion was positively correlated with the concentration of environmental root exudate (R2 = 0.88−0.92). The results of the simulated soil study were validated in actual agricultural Cr-contaminated soils through qPCR.

Improved MobileNet V3-Based Identification Method for Road Adhesion Coefficient.

To enable the timely adjustment of the control strategy of automobile active safety systems, enhance their capacity to adapt to complex working conditions, and improve driving safety, this paper introduces a new method for predicting road surface state information and recognizing road adhesion coefficients using an enhanced version of the MobileNet V3 model. On one hand, the Squeeze-and-Excitation (SE) is replaced by the Convolutional Block Attention Module (CBAM). It can enhance the extraction of features effectively by considering both spatial and channel dimensions. On the other hand, the cross-entropy loss function is replaced by the Bias Loss function. It can reduce the random prediction problem occurring in the optimization process to improve identification accuracy. Finally, the proposed method is evaluated in an experiment with a four-wheel-drive ROS robot platform. Results indicate that a classification precision of 95.53% is achieved, which is higher than existing road adhesion coefficient identification methods.

Optimized Longitudinal and Lateral Control Strategy of Intelligent Vehicles Based on Adaptive Sliding Mode Control

To improve the tracking accuracy and robustness of the path-tracking control model for intelligent vehicles under longitudinal and lateral coupling constraints, this paper utilizes the Kalman filter algorithm to design a longitudinal and lateral coordinated control (LLCC) strategy optimized by adaptive sliding mode control (ASMC). First, a three-degree-of-freedom (3-DOF) vehicle dynamics model was established. Next, under the fuzzy adaptive Unscented Kalman filter (UKF) theory, the vehicle state parameter estimation and road adhesion coefficient (RAC) observer were designed to estimate vehicle speed (VS), yaw rate (YR), sideslip angle (SA), and RAC. Then, a layered control concept was adopted to design the path-tracking controller, with a target VS, YR, and SA as control objectives. An upper-level adaptive sliding mode controller was designed using RBF neural networks, while a lower-level tire force distribution controller was designed using distributed sequential quadratic programming (DSQP) to obtain an optimal tire driving force. Finally, the control strategy was validated using Carsim and Matlab/Simulink software under different road adhesion coefficients and speeds. The findings indicate that the optimized control strategy is capable of adaptively adjusting control parameters to accommodate various complex conditions, enhancing the tracking precision and robustness of vehicles even further.

A machine learning based voting regression method for adhesion estimation in wheel-rail contact

The majority of control methodologies for railway vehicles depend on adhesion data to attain optimal traction. Therefore, researchers have been actively investigating practical and feasible approaches to ascertain precise adhesion data with the objective of optimising the efficiency of railway vehicle operations. While several methods are accessible for indirectly measuring or estimating adhesion, there remains a need for more accurate and expedient estimation techniques. Therefore, in this study, we propose a novel machine learning-based voting regression (VR) model that can efficiently estimate the adhesion between wheel and rail. The proposed VR model is constructed using weighted averages of individual models such as Histogram-based gradient boosted trees (HistGBT), random forest (RF), and linear regression (LR). It is demonstrated by various real-world measurements that the proposed method achieves a high score of 0.922 R 2 for the estimation of the adhesion coefficient and outperforms the benchmark model-based Polach's method.

Movement characteristics of coal powders at bottom of coalbed methane well under jet flow

AbstractThe removal and discharge of coal powders from coalbed methane wellbore is the key to maintaining continuous production. For this reason, based on the submerged jet theory, the force and migration starting conditions of coal powder particles deposited at the bottom of the coalbed methane well are analyzed by analyzing the cohesiveness of coal powders, and a model for the starting and migration of coal powders at the bottom of the well under submerged jet conditions is established; Considering the adhesion between coal dust, the adhesion coefficient is used to characterize the adhesion between particles, a mechanical model of coal dust particles deposited at the bottom of the well is established, and the corresponding calculation formula for the outlet flow rate of the coal dust cleaning nozzle is derived. The fluid mechanics parameters of the bottom of the well coal dust cleaning under different conditions are obtained, and the numerical simulation method is used to simulate the whole process of jet flushing of the bottom of the well coal dust deposited, and the effect of the bottom of the well jet flushing of coal dust deposits is analyzed, providing a theoretical basis for the bottom of the well coal dust flushing process.

Spatial and Size Distributions of Ti(C5H7O2)2[(CH3)2CHO]2 Mist Particles in a Tubular Furnace for Conformal and Uniform Deposition of Amorphous TiO2 Thin Films

The spatial and size distributions of titanium diisopropoxide bisacetylacetonate [(C5H7O2)2[(CH3)2CHO]2, also known as Ti(acac)2(OiPr)2] mist, diluted in CH3OH, are investigated in a tubular furnace using atmospheric‐pressure mist chemical vapor deposition (mist CVD). The focus is on the deposition of amorphous (a)‐TiO2 films with tubular furnace temperature and mesh bias as variables. When the furnace temperature reaches 350 °C, the number density of mist particles increases without significant changes in their size distribution, leading to a higher film deposition rate. Further, the deposition rate and average size of the mist particles with lower adhesion coefficient decrease with increasing spatial distance from the furnace inlet. Furthermore, applying a mesh bias results in an increase in the maximum number density of mist particles with a narrower size distribution; however, the overall film deposition rate decreases. These variations are attributed to the chemical reactivity of the mist precursors produced by pyrolysis and mesh bias. The fine mist precursors, which are strongly charged, coordinate with CH3OH and CHO groups through solvation, enhancing their chemical stability and lifetime. This process yields a dense and rigid a‐TiO2 network, improving the junction properties at the a‐TiO2/c‐Si interface.

Research on model predictive trajectory tracking control based on particle swarm optimization

Abstract This paper focuses on the challenges of low trajectory tracking accuracy in intelligent vehicles caused by different road adhesion coefficients and vehicle speed conditions, aiming to address these challenges with an improved Model Predictive Control (MPC) algorithm. Firstly, a model encompassing vehicle dynamics featuring two degrees of freedom has been formulated, along with a corresponding model to track trajectory errors. Secondly, a Model Predictive Control (MPC) algorithm is designed with trajectory tracking accuracy and control increment as the objective functions. Additionally, incorporating a Particle Swarm Optimization (PSO) algorithm with MPC allows for the dynamic computation of the optimal prediction horizon size. Afterward, a unified simulation model is assembled, employing both CarSim and MATLAB/Simulink, to conduct dual-lane trajectory tracking simulation analysis under diverse road adhesion and vehicle speed conditions. The efficacy of the proposed control algorithms is validated through this process.