The Behavior of Tire-Force Model Parameters Under Extreme Operating Conditions

1997 ◽  
Author(s):  
Kenneth L. d'Entremont
Keyword(s):  
Operating Conditions ◽  
Model Parameters ◽  
Force Model ◽  
Tire Force

scholarly journals Identifying Vehicle Model Parameters Using Remote Mounted Motion Sensor

2021 ◽  
Author(s):  
Yoonjin Hwang
Keyword(s):  
Vehicle Dynamics ◽  
Model Parameters ◽  
Motion Sensor ◽  
Force Model ◽  
Single Track ◽  
Driver Assistance ◽  
Vehicle Model ◽  
Tire Force ◽  
Recent Developments ◽  
Potential Benefits

The recent developments on advanced driver assistance system(ADAS) have extended the capability of sensor systems from surrounding perception to motion estimation. The motion estima?tion provides tri-axial velocity and pose measurements, which open potential benefits for control and state estimation through sensor fusion with the vehicle dynamics model. In this paper we propose an identification method for the vehicle single track model parameters including the relative distance between the vehicle center of gravity and the motion sensor. A linearized tire force model and simplified single track vehicle model are constructed with the corresponding sensor kinematics model. We demonstrate the efficacy of iden?tification performance of the proposed method and confirm the feasibility of the usage of ADAS sensor in vehicle dynamics and vice versa

Longitudinal Tire Dynamics Model for Transient Braking Analysis: ANCF-LuGre Tire Model

2015 ◽  
Vol 10 (3) ◽  
Author(s):  
Hiroki Yamashita ◽  
Yusuke Matsutani ◽  
Hiroyuki Sugiyama
Keyword(s):  
Structural Deformation ◽  
Model Parameters ◽  
Force Distribution ◽  
Force Model ◽  
Tire Model ◽  
Elastic Ring ◽  
Contact Patch ◽  
Tire Force ◽  
Tire Dynamics ◽  
Tire Friction

In this investigation, the flexible tire model based on the absolute nodal coordinate formulation (ANCF) is integrated with LuGre tire friction model for evaluation of the longitudinal tire dynamics under severe braking scenarios. The ANCF-LuGre tire model developed allows for considering the nonlinear coupling between the dynamic structural deformation of the tire and its transient tire force distribution in the contact patch using general multibody dynamics computer algorithms. To this end, the contact patch obtained by the ANCF elastic ring tire model is discretized into small strips and the state of friction at each strip is defined by the differential equation associated with the discretized LuGre friction parameters. The normal contact pressure distribution predicted by the ANCF elastic ring elements that are in contact with the road surface are mapped onto the LuGre strips in the contact patch to evaluate the tangential tire force distribution and then the tire forces evaluated at LuGre strips are fed back to the generalized tangential contact forces of the ANCF elastic ring tire model. By doing so, the structural deformation of the ANCF elastic ring tire model is dynamically coupled with the LuGre tire friction in the final form of the governing equations. Furthermore, the systematic and automated parameter identification procedure for the LuGre tire force model is developed. It is shown that use of the proposed procedure with the modified friction curve proposed for wet road conditions leads to accurate prediction of the LuGre model parameters for measured tire force characteristics under various loading and speed conditions. Several numerical examples are presented in order to demonstrate the use of the in-plane ANCF-LuGre tire model for the longitudinal transient dynamics of tires under severe braking scenarios.

On the Parameter Identification of LuGre Tire Friction Model

2013 ◽  
Author(s):  
Yusuke Matsutani ◽  
Hiroyuki Sugiyama
Keyword(s):  
Error Function ◽  
Numerical Procedure ◽  
Friction Model ◽  
Least Square ◽  
Model Parameters ◽  
Force Model ◽  
Lugre Model ◽  
Tire Force ◽  
Brake Force ◽  
Tire Friction

In this investigation, use of the LuGre tire friction model for the transient brake force analysis is discussed. In particular, a numerical procedure for estimating parameters for the LuGre tire force model is developed. The parameters of the distributed LuGre model are identified such that the error function of tire forces obtained using the model and experiment can be minimized. Friction parameters used in the LuGre tire force model are estimated using the characteristics curve of the friction coefficient as a function of the slip velocity first, and then the adhesion parameter is estimated using the slope around the zero slip ratio using the least square fitting. Iterative solution procedures are then employed to identify the overall model parameters using the initial estimates provided. It is demonstrated that use of the proposed numerical procedure leads to accurate prediction of the LuGre model parameters for various loading and speed conditions. Furthermore, it is demonstrated that the decrease in the peak of the brake force as the increase in the running speed can be well predicted using the transient distributed LuGre tire force model with model parameters predicated using the proposed procedure.

scholarly journals Identifying Vehicle Model Parameters Using Remote Mounted Motion Sensor

2021 ◽  
Author(s):  
Yoonjin Hwang
Keyword(s):  
Vehicle Dynamics ◽  
Model Parameters ◽  
Motion Sensor ◽  
Force Model ◽  
Single Track ◽  
Driver Assistance ◽  
Vehicle Model ◽  
Tire Force ◽  
Recent Developments ◽  
Potential Benefits

The recent developments on advanced driver assistance system(ADAS) have extended the capability of sensor systems from surrounding perception to motion estimation. The motion estima?tion provides tri-axial velocity and pose measurements, which open potential benefits for control and state estimation through sensor fusion with the vehicle dynamics model. In this paper we propose an identification method for the vehicle single track model parameters including the relative distance between the vehicle center of gravity and the motion sensor. A linearized tire force model and simplified single track vehicle model are constructed with the corresponding sensor kinematics model. We demonstrate the efficacy of iden?tification performance of the proposed method and confirm the feasibility of the usage of ADAS sensor in vehicle dynamics and vice versa

A novel model of Z-pin insertion in prepreg based on fracture mechanics

2021 ◽  
pp. 095440542110144
Author(s):  
Guobiao Ji ◽  
Liang Cheng ◽  
Shaohua Fei ◽  
Jiangxiong Li ◽  
Yinglin Ke
Keyword(s):  
Fracture Toughness ◽  
Fracture Mechanics ◽  
Analytical Model ◽  
Model Parameters ◽  
Force Model ◽  
Fe Model ◽  
Cohesive Elements ◽  
Fe Method ◽  
Insertion Force ◽  
Mechanics Model

Through-thickness reinforcement is a promising solution to the problem of delamination susceptibility in laminated composites. Modeling Z-pin–prepreg interaction is essential for accurate robotics-assisted Z-pin insertion. In this paper, a novel Z-pin insertion force model combining the classical cohesive finite element (FE) method with a dynamic analytical fracture mechanics model is proposed. The velocity-dependent cohesive elements, in which the fracture toughness is provided by the analytical model, are implemented in Z-pin insertion FE model to predict the crack initiation and propagation. Then Z-pin insertion experiments are performed on prepreg sample with metallic Z-pins at different velocities to identify the analytical model parameters and validate the simulation predictions offered by the model. Dynamics of Z-pin interaction with inhomogeneous prepreg is described and the effects of insertion velocity on prepreg contact force are studied. Results show that the force model agrees well with experiments and the fracture toughness rises with the increasing Z-pin insertion velocity.

scholarly journals Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 463
Author(s):  
Gopinathan R. Abhijith ◽  
Leonid Kadinski ◽  
Avi Ostfeld
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Mechanistic Model ◽  
Water Distribution Systems ◽  
Sensitivity Study ◽  
Operating Conditions ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Bacterial Regrowth ◽  
The Impact

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

2021 ◽  
pp. 1-18
Author(s):  
Gisela Vanegas ◽  
John Nejedlik ◽  
Pascale Neff ◽  
Torsten Clemens
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Numerical Models ◽  
Operating Conditions ◽  
Model Parameters ◽  
Large Set ◽  
Model Parameter ◽  
Production History ◽  
Hydrocarbon Fields ◽  
Parameter Distributions

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

Study On Automatic Adaptation for Control-Oriented Model of Advanced Diesel Engine

2021 ◽  
Author(s):  
Shunki Nishii ◽  
Yudai Yamasaki
Keyword(s):  
Operating Conditions ◽  
Physical Models ◽  
Model Parameters ◽  
Training Methods ◽  
Model Based Control ◽  
Practical Applications ◽  
Model Based ◽  
Automatic Adaptation ◽  
Automobile Engines ◽  
Adaptation Method

Abstract To achieve high thermal efficiency and low emission in automobile engines, advanced combustion technologies using compression autoignition of premixtures have been studied, and model-based control has attracted attention for their practical applications. Although simplified physical models have been developed for model-based control, appropriate values for their model parameters vary depending on the operating conditions, the engine driving environment, and the engine aging. Herein, we studied an onboard adaptation method of model parameters in a heat release rate (HRR) model. This method adapts the model parameters using neural networks (NNs) considering the operating conditions and can respond to the driving environment and the engine aging by training the NNs onboard. Detailed studies were conducted regarding the training methods. Furthermore, the effectiveness of this adaptation method was confirmed by evaluating the prediction accuracy of the HRR model and model-based control experiments.

Experimental Validation of a Volumetric Planetary Rover Wheel/Soil Interaction Model

2013 ◽  
Author(s):  
Willem Petersen ◽  
John McPhee
Keyword(s):  
Contact Force ◽  
Interaction Model ◽  
Contact Model ◽  
Soil Parameters ◽  
Model Parameters ◽  
Force Model ◽  
Normal Contact ◽  
Planetary Rover ◽  
Normal Contact Force ◽  
Contact Force Model

For the multibody simulation of planetary rover operations, a wheel-soil contact model is necessary to represent the forces and moments between the tire and the soft soil. A novel nonlinear contact modelling approach based on the properties of the hypervolume of interpenetration is validated in this paper. This normal contact force model is based on the Winkler foundation model with nonlinear spring properties. To fully define the proposed normal contact force model for this application, seven parameters are required. Besides the geometry parameters that can be easily measured, three soil parameters representing the hyperelastic and plastic properties of the soil have to be identified. Since it is very difficult to directly measure the latter set of soil parameters, they are identified by comparing computer simulations with experimental results of drawbar pull tests performed under different slip conditions on the Juno rover of the Canadian Space Agency (CSA). A multibody dynamics model of the Juno rover including the new wheel/soil interaction model was developed and simulated in MapleSim. To identify the wheel/soil contact model parameters, the cost function of the model residuals of the kinematic data is minimized. The volumetric contact model is then tested by using the identified contact model parameters in a forward dynamics simulation of the rover on an irregular 3-dimensional terrain and compared against experiments.

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