Rolling Resistance Research Articles (Page 1)

Accurate determination of soil and contact parameters is crucial for tillage machinery design; however, the interactions among soil, tools, and roots in citrus orchards covered with green manure remain insufficiently defined. This study, therefore, combined physical experiments with DEM simulations to characterize these interactions. Using significance analysis and response surface methodology (RSM), the effects of major factors on angle of repose (AoR) and initial slip angle (ISA) at varying soil depths were evaluated, enabling precise calibration of both external (soil–machinery) and internal (particle–particle) parameters. Subsequently, a GA-BP optimization model was constructed to enhance calibration accuracy, yielding optimal values for the soil-to-soil rolling friction coefficient (γ = 0.125–0.136), soil-to-65Mn static friction coefficient (μ′ = 0.431 − 0.540), and soil surface energy (JKR = 0.952 − 1.091 J·m−2). Shear tests using the bonding V2 model were conducted to calibrate the Bonding parameters of green manure stems and roots, while pull-out tests and simulations were used to validate the root–soil parameters. Direct shear tests confirmed the model’s reliability, with errors in internal friction angle and cohesion below 10%. These findings may contribute to improving DEM simulation accuracy for soil improvement under green manure coverage and support the optimization of soil tillage in citrus orchards.

Background Selecting a manual wheelchair frame requires balancing transportability, biophysical demands, and wheelchair mobility performance (WMP). Traditional folding frames facilitate transport but increase rolling resistance and energy loss due to flexibility. A newly developed hybrid frame enhances rigidity to reduce power loss while maintaining foldability, aiming to improve WMP. Aim To evaluate how rigid, folding, and hybrid wheelchair frames impact WMP by measuring linear and rotational velocity and acceleration during three flat-surfaced tests using able-bodied participants Materials and Methods Forty-eight able-bodied participants completed three WMP tests: 15 m sprint, figure-of-eight, and slalom course. Data from inertial measurement units were collected to calculate WMP variables. Results Repeated measures ANOVA revealed that using the hybrid frame resulted in the highest linear velocity during the 15 m sprint (p = 0.004) compared to both the folding and rigid frame. For both the figure-of-eight and slalom, shorter distances were travelled with the hybrid and rigid frames, compared to the folding frame (p ≤ 0.013). Rotational mean velocity was lower for the folding frame compared to the hybrid frame. Rotational peak acceleration during both the figure-of-eight and slalom test was highest for the rigid (p = 0.002 and p ≤ 0.001), followed by the hybrid, with the lowest for the folding frame. Conclusion The hybrid frame, as intermediary solution, exhibited better WMP than the folding frame, with higher linear and rotational velocities and acceleration. Although its rotational WMP parameters were slightly inferior to the rigid frame, it retains the advantage of being foldable.

The Discrete Element Method (DEM) has become a cornerstone for analysing granular flow and mixing phenomena in static mixers. This review provides a comprehensive synthesis that distinguishes it from previous studies by: (i) covering a broad range of static mixer geometries, including Kenics, SMX, and Sulzer designs; (ii) integrating experimental validation methods, such as particle tracking, high-speed imaging, Particle Image Velocimetry (PIV), and X-ray tomography, to assess DEM predictions; and (iii) systematically analyzing computational strategies, including advanced contact models, hybrid DEM-CFD/FEM frameworks, machine learning surrogates, and GPU-accelerated simulations. Recent advances in contact mechanics—such as improved cohesion, rolling resistance, and nonspherical particle modelling—have enhanced simulation realism, while adaptive time-stepping and coarse-graining improve computational efficiency. DEM studies have revealed several non-obvious relationships between mixer geometry and particle dynamics. Variations in blade pitch, helix angle, and element arrangement significantly affect local velocity fields, mixing uniformity, and energy dissipation. Alternating left–right element orientations promote cross-sectional particle exchange and reduce stagnant regions, whereas higher pitch angles enhance axial transport but can weaken radial mixing. Particle–wall friction and surface roughness strongly govern shear layer formation and segregation intensity, demonstrating the need for geometry-specific optimization. Comparative analyses elucidate how particle–wall interactions and channel structure influence segregation, residence time, and energy dissipation. The review also identifies current limitations, highlights validation and scale-up challenges, and outlines key directions for developing faster, more physically grounded DEM models, providing practical guidance for industrial mixer design and optimization.

This paper addresses the critical challenge of energy management for autonomous robots in the context of large-scale photovoltaic parks. The dynamic and vast nature of these environments, characterized by dense, structured rows of solar panels, introduces unique complexities, including uneven terrain, varied operational demands, and the need for equitable resource allocation among diverse robot fleets. The presented framework adapts and significantly extends the Affinity Propagation algorithm for strategic charging station placement within photovoltaic parks. The key contributions include: (1) a multi-attribute grid-based environment model that quantifies terrain difficulty and panel-specific obstacles; (2) an extended multi-factor scoring function that incorporates penalties for terrain inaccessibility and proximity to sensitive photovoltaic infrastructure; (3) a sophisticated, energy-aware consumption model that accounts for terrain friction, slope, and rolling resistance; and (4) a novel multi-agent fairness constraint that ensures equitable access to charging resources across heterogeneous robot sub-fleets. Through extensive simulations on synthesized photovoltaic park environments, it is demonstrated that the enhanced algorithm not only significantly reduces travel distance and energy consumption but also promotes a fairer, more efficient operational ecosystem, paving the way for scalable and sustainable robotic maintenance and inspection.

This paper presents the design and testing of a lightweight, low-cost robotic arm with an extended vertical range. The 9-degree-of-freedom (DOF) system comprises a 6-DOF arm and a 3-DOF gripper. To minimize weight, the six wrist and gripper joints are cable-driven, with all actuators relocated to the shoulder assembly. As a result, the wrist and gripper only weigh 222 g and 113 g, respectively, significantly reducing the inertia on the end effector. The arm utilizes a SCARA-configuration that slides along a tower for extended vertical reach. A key innovation is a closed-section frame that attaches the arm to the tower, in which the bending and torsional loads from the payload can be directly transferred onto the static structure. In contrast to conventional design, this design does not require the shoulder motor to take the bending load directly. Instead, the motor only needs to overcome the rolling friction of the reaction load. Experimental results demonstrate that this approach reduces the required motor torque by a factor of 30. Consequently, the prototype can manipulate a 3 kg payload at a 0.5 m lateral reach while weighing only 4.5 kg, costing USD 1200, and consuming a maximum of 11.1 W of power.

Correlation characteristics of steady-state rolling resistance of non-pneumatic tires caused by nonlinear viscoelasticity

Tire–road friction is a fundamental factor in vehicle safety, energy efficiency, and environmental sustainability. This narrative review synthesizes current knowledge on the tire–road friction coefficient (TRFC), emphasizing its dynamic nature and the interplay of factors such as tire composition, tread design, road surface texture, temperature, load, and inflation pressure. Friction mechanisms, adhesion, and hysteresis are analyzed alongside their dependence on environmental and operational conditions. The study highlights the challenges posed by emerging mobility paradigms, including electric and autonomous vehicles, which demand specialized tires to manage higher loads, torque, and dynamic behaviors. The review identifies persistent research gaps, such as real-time TRFC estimation methods and the modeling of combined environmental effects. It explores tire–road interaction models and finite element approaches, while proposing future directions integrating artificial intelligence and machine learning for enhanced accuracy. The implications of the Euro 7 regulations, which limit tire wear particle emissions, are discussed, highlighting the need for sustainable tire materials and green manufacturing processes. By linking bibliometric trends, experimental findings, and technological innovations, this review underscores the importance of balancing grip, durability, and rolling resistance to meet safety, efficiency, and environmental goals. It concludes that optimizing friction coefficients is essential for advancing intelligent, sustainable, and regulation-compliant mobility systems, paving the way for safer and greener transportation solutions.

This research addresses the complex challenge of integrating modern public transport into historic medieval city centers. These unique urban environments are characterized by narrow streets, protected heritage status, and topographical constraints, which are incompatible with conventional transit vehicles. The introduction of standard bus routes often aggravates traffic congestion and fails to meet the specific mobility needs of residents and visitors. This paper suggests that autonomous electric buses represent a viable and sustainable solution, capable of navigating these constrained environments while aligning with modern energy efficiency goals. The central challenge lies in the optimal selection of an autonomous electric bus that can operate safely and efficiently within the tight streets of historic city centers while satisfying the travel demands of passengers. To address this, a comprehensive study was conducted, analyzing resident mobility patterns—including key routes and hourly passenger loads—and the specific geometric constraints of the road network. Based on this empirical data, a vehicle dynamics model was developed in Matlab®. This model simulates various operational scenarios by calculating the instantaneous forces (rolling resistance, aerodynamic drag, inertial forces) and the corresponding power required for different electric bus configurations to follow pre-established speed profiles. The core of this research is an optimization analysis, designed to identify the balance between minimizing total energy consumption and maximizing the quality of passenger service. The findings provide a quantitative framework and clear procedures for urban planners to select the most suitable autonomous transit system, ensuring that the chosen solution enhances mobility and accessibility without compromising the unique character of historic cities.

ABSTRACT In order to reduce the reliance of rubber composites on carbon black (CB) and enhance their cost‐effectiveness and environmental sustainability, this paper proposes a green, deep eutectic solvent (DES) for surface modification of ground calcium carbonate (GCC). It replaces part of the carbon black with the DES in a natural rubber matrix. The effects of this modification on the vulcanization, mechanical, dynamic and microstructural properties of rubber are systematically investigated. The DES employed consisted of choline chloride and glycerol in a 1:2 M ratio, with the aim of improving the interfacial bonding between GCC and the rubber matrix through the hydrogen bonding network. The results demonstrated that DES‐modified GCC could effectively enhance the dispersion of filler, strengthen the crosslink density of rubber, and improve the mechanical properties. In the present study, the replacement of CB with 15 and 20 parts of GCC was investigated, with a particular focus on the tensile strength of the rubber composites prepared with DES‐modified GCC in a 1:10 ratio. The results demonstrated a significant enhancement in tensile strength, with an increase of 16.0%, as well as a notable increase in the (TS*EB), reaching 22.6%. Additionally, a substantial reduction in rolling resistance of tyres was observed, amounting to 52.7%. These findings offer a novel approach for the development of environmentally friendly and cost‐effective rubber fillers.

ABSTRACT Accurately estimating rolling and air resistance is essential for predicting the energy consumption of vehicles. This study presents a field-based approach using a rolldown test to simultaneously determine rolling and air resistance coefficients. Unlike prior methods that frequently used simulations or models, we employ a goal programming methodology to improve precision and evaluate the actual vehicle and environmental conditions. Our methodology was tested using a Class 8 Freightliner eCascadia on a surveyed road section, ensuring controlled conditions for data collection. By analyzing the time–velocity relationship across multiple test runs, we derived resistance coefficients for both loaded and unloaded conditions. The study confirms that rolling resistance is largely independent of velocity at low speeds but exhibits a nonlinear dependency at higher speeds. Additionally, road surface conditions, tire condition, axle configuration, aerodynamic properties, and weather conditions significantly impact resistance values, emphasizing the need for real-world testing rather than relying solely on standardized projections. Our results align with existing literature while demonstrating the efficacy of the goal programming approach in refining resistance estimates. This work contributes to improved vehicle energy modeling, offering practical insights for fleet operators and policymakers seeking accurate energy consumption predictions for electric trucks operating under varying environmental conditions.

An optimal wearing course should provide safety, comfort, and durability while minimizing the impact on the environment. Some of those functions are antinomic and thus difficult to integrate considering the current state of the art. A large experimental program was carried out, involving the evaluation of 10 asphalt mix designs. Two innovative acoustic mixes, plus a traditional one, were selected from this program. Lab experiments demonstrated that the grading curves and void structure of these new mixes lead to a good compromise between acoustic performance, skid resistance, durability, and rolling resistance. A full-scale experiment was then carried out on an accelerated pavement testing facility, to validate the good behavior highlighted by laboratory studies embodied by the enhancement of acoustic absorption (peak values of 0.86 and 0.78 for the two innovative acoustic mixes, 0.39 for the reference mix) for equivalent mechanical properties. After the experiment, the properties of the test sections (mechanical properties, surface roughness, skid resistance, and acoustic properties) were evaluated. The results confirmed the good durability of the mixes under traffic loading.

To address the challenges in precision seeding of Cyperus esculentus L. seeds caused by their irregular shape and uneven surface, this study investigates the effect of soaking pretreatment on seed germination and adopts rubber-based seed suction holes to improve adsorption performance. Subsequently, calibration and experiments on discrete element simulation parameters were carried out. Initially, by setting four soaking time gradients (0, 24, 48, and 72 h), the optimal soaking duration was determined. Furthermore, through free-fall collision tests, static friction tests, and rolling friction tests, combined with the Plackett–Burman design, steepest ascent experiments, and Box–Behnken response surface methodology, the contact parameters between seeds and between seeds and rubber suction holes were calibrated and optimized. The results showed that the static friction coefficient (D) between seeds, the rolling friction coefficient (E) between seeds, and the rolling friction coefficient (H) between seeds and rubber have significant effects on the stacking angle. The optimal parameter combination obtained was D = 0.592, E = 0.325, H = 0.171. Validation tests on the dynamic stacking angle demonstrated that the relative error between the simulated and physical test values was only 1.89%, confirming the accuracy of the parameters. This study provides reliable parameter references for the design and simulation optimization of precision seed metering devices for C. esculentus after soaking pretreatment.

Battery Electric Vehicles (BEVs) experience significant reductions in driving range under subzero temperatures due to the increased thermal management demands, a reduction in available electric energy of battery, the reduced powertrain efficiency, and the higher rolling resistance. This study proposed an advanced thermal management strategy to enhance BEV driving range under cold climate. A heat-coupled energy balance model was established to evaluate the impact of different configurations of the thermal management system (TMS) as well as the control parameters at −7°C. A neural network-based parametric analysis was conducted under the China Light-Duty Vehicle Test Cycle (CLTC) to identify the optimal control strategy. Simulation results showed that the proposed strategy could increase driving range by up to 15.1% compared to the baseline. Experimental validation on an A-class sedan further confirmed the effectiveness of the strategy, which combined a direct heat pump, aerogel-based thermal insulation, and advanced control settings—including a heating activation State of Charge (SOC) of 20%, target battery temperature of 31°C, blower level 3, and an air recirculation ratio of 0.8. The optimized system could increase driving range by up to 17.1% compared to the baseline, which showed good agreement with the simulation results. This work provided a practical solution for extending BEV range under subzero temperature in cold climate regions.

The manufacturing industry, in general, and the plastic industry, in particular, have been developing new materials and process methods that need a correct study and optimization. Nowadays, the main approach to optimize these processes is using numerical methods and, in the case of particulate materials, the Discrete Elements Method to estimate the particles interactions. But those mathematical models use some parameters that depend on the material and must be calibrated, thus requiring an important computational and experimental cost. In this study, we integrate different speed-up procedures and present a general calibration method of Low-Density Polyethylene particles, to obtain the calibrated solid density and Poisson’s ratio of the material, the restitution, static and rolling friction factors in the particle-to-particle and particle-to-wall interactions, and the contact model variables (damping factor, stiffness factor, and energy density). For this calibration, four different tests were carried out, both experimentally and with simulations, obtaining the bulk density, the repose and shear angles, and the dropped powder. All these response variables were compared between simulations and experimental tests, and using genetic algorithms, the input parameters (design variables) were calibrated after 85 iterations, obtaining a Mean Absolute Percentage Error of the response variables lower than 2% compared to the experimental results.

ABSTRACTGlobally, electric vehicles (EVs) are entirely revolutionizing conventional vehicles owing to the benefits of EVs, such as decarbonization, being environmentally friendly, and lower maintenance costs. The EVs' energy consumption is sensitive to environmental factors like wind speed, parasitic power, rolling resistance, and temperature, which can significantly affect the EVs' energy consumption range. Here, this comprehensive literature survey investigates the techniques used for managing the energy consumption of EVs while analyzing security utilizing Blockchain (BC) and deep learning (DL) algorithms. In the rapidly developing background of EVs, the incorporation of advanced technologies like DL and BC is transforming predictive maintenance (PM) strategies. This approach aims to optimize vehicle performance and minimize downtime by accurately predicting and addressing previous potential system failures. The amalgamation of DL and BC provides a robust approach for PM, enabling proactive maintenance strategies that reduce downtime and costs while improving overall vehicle performance. Thus, this review explains the importance of DL‐enabled PM in EVs, DL models for PM in EVs, the role of BC‐enabled PM in EVs, and the combination of DL models and BC for PM in EVs.

Rubber friction is of major practical importance in applications such as tires, rubber seals, and footwear. This review article focuses on the theory and experimental studies of rubber friction on substrates with random roughness. We examine both steady sliding and accelerated motion, with particular attention to the origins of the breakloose friction force and the influence of pre-slip, elasticity, and flash temperature on friction dynamics. We further discuss rolling friction for cylinders and spheres, as well as sliding friction for triangular sliders on dry and lubricated rubber surfaces. Theoretical predictions are compared with experimental results obtained using different materials, geometries, and environmental conditions, highlighting the importance of accounting for multiscale roughness. Open challenges, such as the role of adhesion enhancement, energy dissipation due to crack opening, and the physical origin of the short-distance roughness cutoff, are discussed.

This paper presents the modeling and validation of a Hoosier R25B 18X6.0-10 racing slick tire using a Finite Element Analysis (FEA) environment. In parallel, the work shows a method of efficiently developing an FEA tire model for tire-road interaction estimation. To overcome limitations and the absence of material data from rubber and tire manufacturers, constitutive modeling of various tire parts is performed. Experimental validation for the tire’s constitutive modeling was performed through uniaxial tension tests with the ASTMD412 standard specimens and stress relaxation tests using the DMA TAQ800. This was repeated for various parts of the tire where the specimens were cut in perpendicular directions. The modeling of the tire uses solid elements in layers that interchange the tire materials in perpendicular directions. A comprehensive validation process was executed through static deflection at different camber angles, drum cleat, and rolling resistance tests. The FEA Hoosier R25B tire model simulation results demonstrated excellent agreement with the experimental tests within errors below 6%. This study outlines an efficient method to provide a robust FEA tire model that captures race tires’ complex mechanical tire-road interactions and contributes to advanced tire design and simulation tools for automotive applications.

When tunnel boring machines (TBMs) excavate in mudstone, argillization of the rock reduces tunneling efficiency. To investigate the influence of argillization on TBM performance and determine the optimal operational parameters, numerical investigation was conducted based on the energy evolution by Particle Flow Code 3D (PFC3D). Argillization was simulated by adhesive rolling resistance Linear model. The effects of argillization on forces acting on the disc cutter, crack evolution, and energy consumption were analyzed. Furthermore, the influence of operational modes, tip angles, and tip widths on energy consumption, the mass of slurry adhered to the cutters, and tunneling efficiency were investigated. Results indicate that argillization decreases the normal force while increasing the rolling and lateral forces. Besides, argillization significantly increases mechanical work, thereby reducing tunneling efficiency of the TBM. When excavating in the mudstone, load control mode, coupled with a cutter tip angle of 40° and a tip width of 15mm, can effectively mitigate argillization risks and improve efficiency. This study provides valuable references for the operation of the TBM in mudstone, thereby expanding the machine's range of application.

Abstract Rolling resistance is a physical phenomenon related to the dissipation of energy occuring when a tire rools on a road pavement. This loss of energy generates forces opposed to the vehicle movement, which in turn increase fuel consumption. Several factors affect rolling resistance among them, pavement surface texture. Nevertheless, there is a need of identifying relevant texture parameters that can explain rolling resistance evolution. This paper presents a laboratory based study performed on sixteen different pavement mixes. Texture measurements and rolling resistance measurements using a novel test methodology are realized. The analysis of the results demonstrated that parameters such as RMS (Root Mean Square height) and Smc (Inverse Areal Material Ratio) appear as the most correlated with rolling resistance, which can be explained by the fact that an increase in roughness entails an increase of the energy losses by indentation phenomenon. This study also demonstrated that a limit is reached on the direct correlation between texture parameters and rolling resistance coefficient.

The cleaning of wet rice threshed outputs presents a critical constraint on the operational efficiency of harvesters during humid conditions. This study aimed to quantify the specific impact of wet-state conditions on the physical properties of rice threshed outputs. Utilizing the Hertz–Mindlin with Johnson–Kendal–Roberts contact model, Plackett–Burman screening experiments were designed in Design-Expert to identify contact parameters significantly influencing the dynamic angle of repose. For the six most significant parameters, a six-factor, three-level Box–Behnken experimental design was implemented. Analysis of variance was performed on the experimental data, establishing a multivariate nonlinear regression model relating the dynamic angle of repose to the key contact parameters. Systematic measurements were conducted to determine the compositional content and key physical parameters of the constituent materials: the elastic modulus of paddy was 216.3 MPa; the elastic moduli of stalks (categorized by diameters of 4, 5, and 6 mm) were 5.59, 6.28, and 8.93 MPa, respectively; the paddy friction coefficient was 0.518; the stalk sliding friction coefficient was 0.566 and rolling friction coefficient was 0.115; and the surface moisture contents were 7.76% for paddy and 11.8% for stalks, respectively. The optimized contact parameter group of wet paddy and stalks was compared with the measured values. The relative error of the predicted angle of repose for the paddy was only 0.23%, and the error for the rice stalks was 1.3%. The precise contact parameters established in this study provide crucial foundational support for the development of subsequent CFD-DEM coupled numerical models.