Friction Limit Research Articles

An integrated control of front in-wheel motors and rear electronic limited slip differential for high-speed cornering performance

This paper presents an integrated control of in-wheel motor (IWM) and electronic limited slip differential (eLSD) for high-speed cornering performance. The proposed algorithm is designed to improve the handling performance near the limits of handling. The proposed controller consists of a supervisor, upper-level controller, and lower-level controller. First, the supervisor determines a target motion based on the yaw rate reference with a target understeer gradient. The target understeer gradient is devised to improve the lateral stability with in-wheel motor control based on a nonlinear static map. The yaw rate reference is designed based on the target understeer gradient to track the yaw reference with eLSD control. Second, the upper-level controller calculates the desired yaw moments for IWM and eLSD to generate the target motion. Third, the lower-level controller converts the desired yaw moment to the actuator torque commands for IWMs and eLSD. The tire friction limits are estimated based on the tire model and friction circle model to prevent tire saturation by limiting the torque inputs. The proposed algorithm has been investigated via both simulations and vehicle tests. The performance of the integrated control was compared with those of individual control and uncontrolled case in the simulation study. The vehicle tests have been performed using a rear wheel drive vehicle equipped with two front IWMs and eLSD in the rear axle. The vehicle test has been conducted at a racing track to show that the proposed algorithm can improve the lateral stability near the limits of handling.

Universality of jammed frictional packing

Understanding disordered particle packings is of great significance from both theoretical and engineering perspectives. Establishing a quantitative relationship between nonspherical particle shape and disordered packing properties is generally challenging, due to the complex geometry and topology. Here we resolve this issue by numerically investigating disordered jammed packings of various frictional congruent nonspherical particles, including superellipsoids and polyhedra, over a wide range of friction coefficients. We discover several universal packing characteristics across different particle shapes and frictions. In the infinite friction limit, the coordination numbers for all shapes approach the identical lower bound for jamming. The resulting ``random loose packing'' (RLP) state possesses minimal structural correlations, with the packing fraction as a simple monotonic decreasing function of the orientation-averaged excluded volume for different particle shapes. Packings with finite friction can then be understood via a perturbative approach based on RLP. The nature of RLP can be illuminated by the percolation transition of contacting particle network during the quasistatic densification process. Moreover, the large-scale density fluctuations for all jammed frictional packings are also strongly suppressed, broadening the previous claim of hyperuniformity for the frictionless ones.

Influence of Thermal Conductivity of Steels on the Friction Limit Loads on Polyamide

Influence of Thermal Conductivity of Steels on the Friction Limit Loads on Polyamide

A generalized Langevin equation approach for barrier crossing dynamics in conformational transitions of proteins

Barrier crossing dynamics in conformational transitions of proteins are investigated in the framework of the inertial generalized Langevin equation with an exponential memory kernel in a parabolic potential. This approach yields an exact analytical expression for the time dependent Grote–Hynes rate and the transmission coefficient, which typically determines the kinetics of such transitions. The complete transition path time distribution (TPTD) and the mean transition path time (MTPT) are evaluated as a function of the frictional coefficient and barrier curvature. The results of TPTD show an excellent agreement with the experimental results of TPTD for the PrP prion protein and theoretical results in the high friction limit, while they exhibit a considerable deviation from the results of theory at the intermediate and low friction limits. The inertial terms significantly affect the short time dynamics of such transitions. The results of the TPTD and MTPT are discussed at low and high frictional limits with varying curvatures of the potential barrier. The TPTD decreases with an increase in the barrier curvature at the high friction limit and exhibits an exponential decay at long times. The MTPT decreases with an increase in the curvature of the barrier.

Identification of a driver model incorporating sensory dynamics, with nonlinear vehicle dynamics and transient disturbances

In earlier work, a driver model incorporating sensory dynamics was identified from driving simulator experiments involving random disturbances, random target paths and linear vehicle dynamics. In the present paper, the driver model and experiments are extended to include transient disturbances, discrete target paths and nonlinear vehicle dynamics. The predictions of the model are compared with measurements from the experiments. Simulator motion is found to have a significant beneficial effect on drivers' responses, giving faster driver reaction times and more successful disturbance rejection and path following. The driver model predicts the measured responses well. The model suggests that drivers are unable to develop an accurate internal model of motion cueing filters, perceiving phase and gain distortions introduced by filtering as disturbances. Drivers are found able to account for the time-varying operating point of a nonlinear vehicle. The driver model is also able to match the behaviour of experienced drivers near the friction limit of the tyres, however, further work is necessary to understand how an inaccurate internal model impedes the performance of less experienced drivers. The findings contribute new knowledge to the fields of driver simulation and motion cueing.

The impact assessment of plant oils on unctuousity of drilling fluids

The object of study is natural oils. Lubricating additives based on natural substances – vegetable oils and animal fats – meet the increased requirements for environmental safety of materials used in the drilling process. Consumption of environmentally friendly lubricants is constantly growing and requires an expansion of the raw material base for their production. Therefore today lubricants are important drilling reagents. From the ecological point of view, lubricating additives based on vegetable oils are of the greatest interest. The main physicochemical properties of sunflower and castor vegetable oils and the influence of these lubricating additives on lubricating and rheological characteristics of drilling fluids were investigated. In addition, the main properties of water-clay drilling fluids were investigated and their main parameters were determined according to standard API methods. During the testing of the samples, we took into account, first of all, a shear rate of the filter cake (CFC). It is the value that characterizes the strength of filter cake and is determined by the ratio of strength necessary for the tangential displacement of cyclic load across the cake to its weight. It is the CFC that characterizes the lubricating properties of the samples of solutions with a lubricant additive of a certain concentration that were studied. It is the CFC that characterizes the lubricating properties of the samples of solutions with a lubricant additive of a certain concentration that were studied. Based on the above studies, it can be concluded that the addition of castor oil effectively reduces the coefficient of friction of the filtration crust formed from the studied drilling fluids (fresh, mineralized, and saline). The recommended concentrations of this oil to the drilling fluid are 0.5 %, 1 %, and 5 %. Sunflower oil has an effective effect on the saline solution, less effectively – on the mineralized with an oil concentration of 0.5 %, 3 %, and 5 %. On the basis of the conducted researches the prospects of use of sunflower and castor oils at development of a new compounding of a greasing additive to a drilling mud are defined. Further research is aimed at assessing their lubricity in the drilling fluid at the friction limit «metal-metal». It is planned to repeat the study at the Sticking Tester OFI (USA).

Effect of Memory and Inertial Contribution on Transition-Time Distributions: Theory and Simulations.

Transition paths refer to the time taken by molecules to cross a barrier separating two molecular conformations. In this work, we study how memory, as well as inertial contribution in the dynamics along a reaction coordinate, can affect the distribution of the transition-path time. We use a simple model of dynamics governed by a generalized Langevin equation with a power-law memory along with the inertial term, which was neglected in previous studies, where memory effects were explored only in the overdamped limit. We derive an approximate expression for the transit-time distribution and discuss our results for the short- and long-time limits and also compare it with known results in the high friction (overdamped) limit as well as in the Markovian limit. We have developed a numerical algorithm to test our theoretical results against extensive numerical simulations.

Computational fluid dynamic simulations of regular bubble patterns in pulsed fluidized beds using a two‐fluid model

AbstractThis simulation study explores the two‐fluid model's (TFM) capability to reproduce the alternating bubble patterns in a sinusoidal pulsed fluidized bed (PFB). Simulations were performed with frictional limits ranging between 0.58–0.62 with the inlet gas frequency being varied in the range of 3–6 Hz. The preliminary investigations showed that the Johnson and Jackson frictional model with a frictional limit of 0.61 yields a regular bubble pattern. The inference of this regular bubble pattern was based on the discrete Fourier analysis of the temporal pressure signals obtained at different spatial locations inside the PFB. Although the one‐dimensional temporal pressure signals characterized a regular bubble pattern behaviour, the staggered bubbles visually observed were highly unstable. Moreover, in‐depth insights into the PFB's regime classification showed that the predicted regular bubble patterns are susceptible to uncertainties due to the inherent mathematical limitations of the frictional closures of the TFM. Besides, the combinations of the frictional models/ limits in the TFM simulations could not predict the high stability and intermediate stability regimes of PFB. The present investigations helped identify the limitations of frictional closure models of TFM in predicting the regular bubble patterns and the regime classification for the PFB. It also expresses the need to develop better strategies to model the frictional closure of TFM to accurately account for the ever‐evolving dense and dilute particle regions of a PFB.

Predictive Force-Centric Emergency Collision Avoidance

Abstract A controller for critical vehicle maneuvering is proposed that avoids obstacles and keeps the vehicle on the road while achieving heavy braking. It operates at the limit of friction and is structured in two main steps: a motion-planning step based on receding-horizon planning to obtain acceleration-vector references, and a low-level controller for following these acceleration references and transforming them into actuator commands. The controller is evaluated in a number of challenging scenarios and results in a well behaved vehicle with respect to, e.g., the steering angle, the body slip, and the path. It is also demonstrated that the controller successfully balances braking and avoidance such that it really takes advantage of the braking possibilities. Specifically, for a moving obstacle, it makes use of a widening gap to perform more braking, which is a clear advantage of the online replanning capability if the obstacle should be a moving human or animal. Finally, real-time capabilities are demonstrated. In conclusion, the controller performs well, both from a functional perspective and from a real-time perspective.

Pattern in nonlinearly coupled network of identical Thomas oscillators

We have investigated synchronized patterns in a network of Thomas oscillators coupled with sinusoidal nonlinear and linear couplings. Patterns like chimera and cluster states are not only observed for many nonlocally coupled oscillators, it is also observed for nearly local coupled network topology in the case of nonlinear coupling. As coupling radius increases, the critical coupling constant for complete synchronization decreases. Crater-like structure is also observed in the snapshot in the case of nonlinear coupling only which agrees with similar observation found in the study of active Brownian particles using the stochastic method. The critical number of oscillators for the onset of chimera is forty and the infinite limit is found to be hundred. No chimera is observed in the zero friction limit due to hyperchaotic motion with large amplitude oscillations, but there is a sharp transition from disorder to order distribution.

Long-Horizon Vehicle Motion Planning and Control Through Serially Cascaded Model Complexity

The computational burden of nonlinear model predictive control (NMPC) often limits its use to short planning horizons, simple systems with slow dynamics, offline applications, or approximations of the optimal control problem. This article introduces a novel concept for NMPC, to help enable real-time integrated motion planning and control with a long planning horizon for automated vehicles. The proposed framework cascades plant models of different levels of complexity within a single planning horizon, in a single optimization problem. Leveraging the receding nature of MPC, a high-fidelity plant model in the first part of the planning horizon continuously provides a high quality of control, while the planning horizon is extended significantly at low computational cost with a lower fidelity model. Cascading the model complexity serially in a single planning horizon, rather than in different control loops, avoids infeasible reference trajectories between control loops. The concept is successfully validated with real-time motion planning and control of a full-scale automated race car, featuring combined lateral and longitudinal control and operating the vehicle near the limits of tire-road friction. The framework is deployed with open-source numerical optimization tools. In the real-world experiment, the proposed design both better approaches the optimal minimum-time solution and has a lower median solve time compared to a benchmark architecture with a single-vehicle model and a necessarily shorter planning horizon.

Microstructure and rheology of shear-thickening colloidal suspensions with varying interparticle friction: Comparison of experiment with theory and simulation models

Two colloidal suspensions of paucidisperse, spherical silica particles with different surface chemistries leading to extreme limits of surface contact friction are studied to identify experimental differences in shear rheology and microstructure and quantitatively test theory and simulation models. The nonequilibrium microstructure in the plane of shear is measured by flow-small angle neutron scattering for steady shear states spanning the shear thinning and shear thickening regimes. The shear rheology and microstructure are compared against predictions from theory for Brownian hard sphere suspensions and state-of-the-art simulation methods that incorporate either contact friction or enhanced lubrication hydrodynamics. The first normal stress differences are confirmed to distinguish between these micromechanical mechanisms for stress enhancement in the shear thickened regime. The nonequilibrium microstructure in the plane of shear shows more anisotropy for the suspension with higher interparticle friction. A significant fourfold symmetry is confirmed and found to be amplified with increasing surface contact friction in the shear thickened state. The differences in shear-induced microstructures between suspensions with varying contact friction demonstrate that the nonequilibrium microstructure can distinguish between nanotribological interactions in the shear thickened state. Statistical comparison of experiments with simulations indicates that better resolution of microstructures in simulation models is required to be validated by the experimental data presented. Implications for the development of theories for colloidal suspension rheology are discussed.

Study on rubber surface enhancement of fiber fabric

In order to improve multiple properties of rubber materials, such as tribological properties. To explore a way of improve rubber performance without the need of modify rubber formula, polyester natural rubber/butadiene rubber composite was prepared. The prepared samples were tested, in order to proof the performance improvement of rubber with surface reinforcement of polyester fabric. Specifically, the mechanical properties, tribological properties and hydrophobic properties were analyzed, and the surface observation and analysis were conducted after the test. Results showed that the surface contact angle of rubber sample after polyester fiber reinforced was significantly increased. Rubber properties has been greatly improved after polyester fiber reinforced. Polyester fiber reinforced rubber friction limit is 900 rpm and 20 N, the tribological limit of rubber with surface reinforcement of polyester fabric is much higher than rubber friction limit, which is 300 rpm and 10 N. After the fabric has damaged during the daily use, it can be easily fixed with new fabric without replace new rubber parts.

In situ stress measurement and analysis of the stress accumulation levels in coal mines in the northern Ordos Basin, China

For non-directional drilling cores, selection of samples and the test methods for in situ stress measurements to evaluate the Kaiser effect (KE) were proposed, and the magnitude and direction of the principal stresses were derived from first principles. Based on this approach, the KE for 423 samples in the Burtai and Baode coal mines in the northern Ordos Basin (NOB), China, have been investigated. The results show that the maximum horizontal principal stress (σH), the minimum horizontal principal stress (σh) and the vertical stress (σv) varied with depth and location, and the values increase with increasing depth. Generally, the horizontal stresses play a leading role. For the main stress regimes in the NOB, σH > σh > σv (Burtai Mine, < 172 m; Baode Mine, < 170 m) and σH > σv > σh (Burtai Mine, 170–800 m; Baode Mine, 170–400 m), and the σv > σH > σh stress regimes are mainly distributed in moderately deep to deep coal mines. For rock masses with a depth of 350 m, k ((σH + σh)/2σv) tends to 1, indicating that a deep critical state will gradually emerge. The test results are compared with those for the overcoring (OC) method, the anelastic strain recovery (ASR) method and micro-hydraulic fracturing (HF). The relative errors for σH, σh and σv were 14.90%, 19.67%, 15.47% (Burtai Mine) and 10.74%, 22.76%, 19.97% (Baode Mine), respectively, and the errors are all within an acceptable range, thus verifying the reliability of the KE method. The dominant orientation for the σH (Burtai mine, NE-NNE; Baode Mine, NEE) is obtained via paleomagnetic technology, and the data are consistent with those (NE-NEE) of the earthquake focal mechanism solutions for the area. Based on the Byerlee–Anderson theory, a discussion is given on the levels of stress accumulation in the rock mass of the mines. For dry rocks or hydrostatic pressure rocks, the friction coefficients of the faults are low for both locations, and the values are less than the lower limit (0.6) of the strike-slip faults slip, indicating that the stress fractures at a low level around the study areas are lower than the friction limit stress. The stress accumulation levels in the Baode Mine are slightly larger than those in the Burtai Mine.

Flow in an hourglass: particle friction and stiffness matter

Granular flow out of a silo is studied experimentally and numerically. The time evolution of the discharge rate as well as the normal force (apparent weight) at the bottom of the container is monitored. We show that particle stiffness has a strong effect on the qualitative features of silo discharge. For deformable grains with a Young modulus of about Y m ≈ 40 kPa in a silo with basal pressure of the order of 4 kPa, lowering the friction coefficient leads to a gradual change in the discharge curve: the flow rate becomes filling height dependent, it decreases during the discharge process. For hard grains with a Young modulus of about Y m ≈ 500 MPa the flow rate is much less sensitive to the value of the friction coefficient. Using DEM data combined with a coarse-graining methodology allows us to compute all the relevant macroscopic fields, namely, linear momentum, density and stress tensors. The observed difference in the discharge in the low friction limit is connected to a strong difference in the pressure field: while for hard grains Janssen-screening is effective, leading to high vertical stress near the silo wall and small pressure above the orifice region, for deformable grains the pressure above the orifice is larger and gradually decreases during the discharge process. We have analyzed the momentum balance in the region of the orifice (near the location of the outlet) for the case of soft particles with low friction coefficient, and proposed a phenomenological formulation that predicts the linear decrease of the flow rate with decreasing filling height.

Implicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction.

Stable concurrent learning and control of dynamical systems is the subject of adaptive control. Despite being an established field with many practical applications and a rich theory, much of the development in adaptive control for nonlinear systems revolves around a few key algorithms. By exploiting strong connections between classical adaptive nonlinear control techniques and recent progress in optimization and machine learning, we show that there exists considerable untapped potential in algorithm development for both adaptive nonlinear control and adaptive dynamics prediction. We begin by introducing first-order adaptation laws inspired by natural gradient descent and mirror descent. We prove that when there are multiple dynamics consistent with the data, these non-Euclidean adaptation laws implicitly regularize the learned model. Local geometry imposed during learning thus may be used to select parameter vectors-out of the many that will achieve perfect tracking or prediction-for desired properties such as sparsity. We apply this result to regularized dynamics predictor and observer design, and as concrete examples, we consider Hamiltonian systems, Lagrangian systems, and recurrent neural networks. We subsequently develop a variational formalism based on the Bregman Lagrangian. We show that its Euler Lagrange equations lead to natural gradient and mirror descent-like adaptation laws with momentum, and we recover their first-order analogues in the infinite friction limit. We illustrate our analyses with simulations demonstrating our theoretical results.

Impacts of Model Fidelity on Trajectory Optimization for Autonomous Vehicles in Extreme Maneuvers

In this paper, we quantify the impacts of model fidelity on the effectiveness of trajectory optimization for autonomous vehicles when driving at the limits of friction through experiments with a full-size vehicle. Models ranging from a double-track model with lateral and longitudinal load transfer dynamics to a simple point-mass model are used in combination with direct numerical optimization to generate optimal trajectories subject to the limits imposed by each model. The effectiveness of each model for trajectory planning is evaluated by testing the trajectories on an automated vehicle across friction conditions ranging from ice to dry asphalt. Comparisons between the outright performance of the car and the car's ability to track the optimal trajectory are made across the various models. The tests reveal that the advantage of more complex models is less that they better predict the vehicle's behavior, but that they provide a more nuanced view of the vehicle's limits and guidance on the proper coordination of the various actuators on the vehicle in order to make most efficient use of the available tire friction.

Tracking Control of Overhead Crane Using Output Feedback With Adaptive Unscented Kalman Filter and Condition-Based Selective Scaling

Most of the advanced nonlinear control strategies reported in the literature for underactuated mechanisms, such as overhead cranes, require the knowledge of all state variables. For cranes, the state vector includes variables related to the load sway and its velocity. The flatness property of crane-like systems can be exploited to solve both motion planning and tracking problems, so that the load (whose coordinates are included in the set of the flat outputs) exponentially follows a rapid reference trajectory. However, unmodeled friction phenomena and limitations on the direct measurement of sway-related state variables usually impede the practical implementation of flatness-based control laws. This paper proposes the use of an adaptive unscented Kalman filter to estimate friction forces and unmeasured state variables. The convergence of the filter is improved using a novel technique, called condition-based selective scaling. The performance of the suggested scheme is verified through a set of computer simulations on a 2D overhead crane system.

Accurate and Efficient Splitting Methods for Dissipative Particle Dynamics

We study numerical methods for dissipative particle dynamics (DPD), which is a system of stochastic differential equations and a popular stochastic momentum-conserving thermostat for simulating complex hydrodynamic behavior at mesoscales. We propose a new splitting method that is able to substantially improve the accuracy and efficiency of DPD simulations in a wide range of the friction coefficients, particularly in the extremely large friction limit that corresponds to a fluid-like Schmidt number, a key issue in DPD. Various numerical experiments on both equilibrium and transport properties are performed to demonstrate the superiority of the newly proposed method over popular alternative schemes in the literature.

Influence of frictional packing limit on hydrodynamics and performance of gas-solid fluidized beds

The influence of frictional packing limit (FPL) on prediction of hydrodynamics and performance of fluidized bed reactors was studied. Dense gas-solid flows in non-reactive (under isothermal cold and at elevated temperatures) and reactive atmospheres (fluidized bed gasifier) were simulated using Eulerian-Eulerian methodology considering a range of values for FPL. Simulations under cold flow conditions were conducted to establish a range of FPL values that provides physically realistic predictions. It is noticed that bed pressure drop increases with increasing value of FPL when superficial gas velocity (U) is less than or equal to the minimum fluidization velocity. For larger values of U, predicted pressure drop is unaffected by the choice of value of FPL. However, in these cases, the distribution of particles, their velocities and bubbling behavior are significantly affected by FPL. Effect of FPL at elevated temperatures is similar to the one observed at cold flow conditions. It is further noticed that FPL not only affects the predictions on bed hydrodynamics but also has profound influence on reactive flow characteristics such as bed temperature and product gas composition. Sensitivity analysis under cold flow conditions could reveal better predictions when the ratio of FPL to close packing limit is chosen between 0.9 and 0.97.