Effects of Different Tire Operating Conditions on Transient Lateral Tire Response

2019 ◽  
Author(s):  
Marco Furlan ◽  
Henning Olsson ◽  
Mateo Gladstone ◽  
Georgios Mavros
Keyword(s):  
Experimental Data ◽  
Transient Response ◽  
Operating Conditions ◽  
Test Machine ◽  
Inflation Pressure ◽  
Relaxation Length ◽  
Influence Of Temperature ◽  
Novel Method ◽  
Tire Forces

ABSTRACT The concept of the relaxation length is often used to describe a tire's transient response. This paper investigates how the transient response changes under different operating conditions. Through the measurement of tire forces and tire deformations during transient maneuvers performed on an indoor flat-belt tire test machine, experimental data were used to calculate various tire stiffnesses and the associated relaxation lengths using a novel method via optimization. With this methodology, the effects of tire load, inflation pressure, speed, and temperature on these stiffnesses and the relaxation length have been identified. The mechanisms behind these effects are discussed with a particular focus on the influence of temperature.

Application of Wavelet Thresholding Filter to Improve Multi-Step Ahead Prediction Model for Hydraulic System

2012 ◽  
Vol 488-489 ◽  
pp. 1783-1787
Author(s):  
Javad Safehian ◽  
Alireza Akbarzadeh ◽  
Behnam Motakef Imani
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Prediction Model ◽  
Fatigue Test ◽  
Hydraulic System ◽  
Prediction Models ◽  
Operating Conditions ◽  
Applied Force ◽  
Test Machine ◽  
Aluminum Specimen

Proper operation of a hydraulic system used in a fatigue test machine (FTM) is crucial. This is because a fatigue test may take well over hours and is not necessarily supervised. Any system failure may result in specimen destruction or experiment failure. In this study experimental data is collected and analyzed to prognoses the hydraulic system. Prognosis may be used to set an alarm level when the predicted values of failure fall within the warning region. This paper presents an approach to predict the operating conditions of a hydraulic system a few increments ahead in time, otherwise known as multi-step ahead (MS). The approach is further validated using experimental data. To do this, applied force on standard aluminum specimen is recorded in time series. Wavelet soft thresholding is used to filter and reduce the effect of noise and sharp edges in the measured applied force data (time series). Embedding dimension and time delay are determined using Cao's method and auto mutual information (AMI) technique, respectively. These values are subsequently utilized as inputs for constructing prediction models to forecast the future values of the machines’ operating conditions. The results show that the neural network (NN) prediction model can track the change in machine conditions and has the potential to be used as a machine fault prognosis tool.

Tire Vibration Modes and Effects on Vehicle Ride Quality

1993 ◽  
Vol 21 (1) ◽  
pp. 23-39 ◽  
Author(s):  
R. W. Scavuzzo ◽  
T. R. Richards ◽  
L. T. Charek
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Operating Conditions ◽  
Modal Testing ◽  
Ride Quality ◽  
Vibration Modes ◽  
Inflation Pressure ◽  
Passenger Cars ◽  
Element Modeling ◽  
Road Surfaces

Abstract Tire vibration modes are known to play a key role in vehicle ride, for applications ranging from passenger cars to earthmover equipment. Inputs to the tire such as discrete impacts (harshness), rough road surfaces, tire nonuniformities, and tread patterns can potentially excite tire vibration modes. Many parameters affect the frequency of tire vibration modes: tire size, tire construction, inflation pressure, and operating conditions such as speed, load, and temperature. This paper discusses the influence of these parameters on tire vibration modes and describes how these tire modes influence vehicle ride quality. Results from both finite element modeling and modal testing are discussed.

Modeling and Experimental Validation of the Effective Bulk Modulus of a Mixture of Hydraulic Oil and Air

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Hossein Gholizadeh ◽  
Doug Bitner ◽  
Richard Burton ◽  
Greg Schoenau
Keyword(s):  
Experimental Data ◽  
Bulk Modulus ◽  
Theoretical Models ◽  
Operating Conditions ◽  
Air Bubbles ◽  
Pressure Sensitivity ◽  
Hydraulic Oil ◽  
Effective Bulk Modulus ◽  
Experimental Apparatus ◽  
Major Factors

It is well known that the presence of entrained air bubbles in hydraulic oil can significantly reduce the effective bulk modulus of hydraulic oil. The effective bulk modulus of a mixture of oil and air as pressure changes is considerably different than when the oil and air are not mixed. Theoretical models have been proposed in the literature to simulate the pressure sensitivity of the effective bulk modulus of this mixture. However, limited amounts of experimental data are available to prove the validity of the models under various operating conditions. The major factors that affect pressure sensitivity of the effective bulk modulus of the mixture are the amount of air bubbles, their size and the distribution, and rate of compression of the mixture. An experimental apparatus was designed to investigate the effect of these variables on the effective bulk modulus of the mixture. The experimental results were compared with existing theoretical models, and it was found that the theoretical models only matched the experimental data under specific conditions. The purpose of this paper is to specify the conditions in which the current theoretical models can be used to represent the real behavior of the pressure sensitivity of the effective bulk modulus of the mixture. Additionally, a new theoretical model is proposed for situations where the current models fail to truly represent the experimental data.

Performance Characterization of a Twin Scroll Volute for Turbocharging Applications

2018 ◽  
Author(s):  
Carlo Cravero ◽  
Mario La Rocca ◽  
Andrea Ottonello
Keyword(s):  
Experimental Data ◽  
Scientific Literature ◽  
Aerodynamic Performance ◽  
Operating Conditions ◽  
Automatic Design ◽  
Cfd Model ◽  
Radial Turbine ◽  
Wide Range ◽  
Partial Admission

The use of twin scroll volutes in radial turbine for turbocharging applications has several advantages over single passage volute related to the engine matching and to the overall compactness. Twin scroll volutes are of increasing interest in power unit development but the open scientific literature on their performance and modelling is still quite limited. In the present work the performance of a twin scroll volute for a turbocharger radial turbine are investigated in some detail in a wide range of operating conditions at both full and partial admission. A CFD model for the volute have been developed and preliminary validated against experimental data available for the radial turbine. Then the numerical model has been used to generate the database of solutions that have been investigated and used to extract the performance. Different parameters and indices are introduced to describe the volute aerodynamic performance in the wide range of operating conditions chosen. The above parameters can be used for volute development or matching with a given rotor or efficiently implemented in automatic design optimization strategies.

NUMERICAL ESTIMATION OF MOISTURE CONTENT IN SPRAY DRIED JUICE POWDER

2016 ◽  
Vol 78 (8-3) ◽  
Author(s):  
Aliyu Bello A. ◽  
Arshad Ahmad ◽  
Adnan Ripin ◽  
Olagoke Oladokun
Keyword(s):  
Experimental Data ◽  
Moisture Content ◽  
Diffusion Model ◽  
Process Model ◽  
Small Error ◽  
Operating Conditions ◽  
Balance Model ◽  
Pineapple Juice ◽  
Droplet Drying ◽  
Spray Dried

The moisture contents of powders is an important parameter that affects the quality and commercial value of spray dried products. The utility of predicted moisture content values from two droplet drying models were compared with experimental data for spray dried pineapple juice, using the Ranz-Marshal and its modified variants for the heat and mass transfer correlations. The droplet Diffusion model, using the Zhifu correlation, gave estimates with errors of about 8% at 165 oC, 9% at 171 oC, 26% at 179 oC and 2% at 185 oC. The Ranz-Marshal correlation also gave comparable results with this model while results using the Downing and modified Ranz-Marshall correlations widely diverged. The Energy balance model predicted completely dried juice particles, and short drying times, in contrast to the experimental data. The small error sizes of the Diffusion model improves on the wide error sizes of an earlier process model, making is useful as a first approximation choice, for spray drier design and simulation, especially for juices under comparable operating conditions.

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water

2004 ◽  
Vol 50 (8) ◽  
pp. 103-110 ◽  
Author(s):  
H.K. Oh ◽  
M.J. Yu ◽  
E.M. Gwon ◽  
J.Y. Koo ◽  
S.G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Network Model ◽  
Operating Conditions ◽  
Transmembrane Pressure ◽  
Feed Water ◽  
Permeate Flux ◽  
Artificial Neural

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction

2006 ◽  
Vol 129 (1) ◽  
pp. 58-65 ◽  
Author(s):  
B. Scott Kessler ◽  
A. Sherif El-Gizawy ◽  
Douglas E. Smith
Keyword(s):  
Finite Element ◽  
Effective Means ◽  
Element Model ◽  
Operating Conditions ◽  
Behavior Prediction ◽  
Element Analysis ◽  
Material Models ◽  
Rheological Models ◽  
Wide Range ◽  
Novel Method

The accuracy of a finite element model for design and analysis of a metal forging operation is limited by the incorporated material model’s ability to predict deformation behavior over a wide range of operating conditions. Current rheological models prove deficient in several respects due to the difficulty in establishing complicated relations between many parameters. More recently, artificial neural networks (ANN) have been suggested as an effective means to overcome these difficulties. To this end, a robust ANN with the ability to determine flow stresses based on strain, strain rate, and temperature is developed and linked with finite element code. Comparisons of this novel method with conventional means are carried out to demonstrate the advantages of this approach.

Experimental and Analytical Characterization of Alternative Aviation Fuel Sprays Under Realistic Operating Conditions

2018 ◽  
Author(s):  
Andrew Corber ◽  
Nader Rizk ◽  
Wajid Ali Chishty
Keyword(s):  
Experimental Data ◽  
Alternative Fuels ◽  
Laser Sheet ◽  
National Research Council ◽  
Diagnostic System ◽  
Operating Conditions ◽  
Aviation Fuel ◽  
Injection System ◽  
Test Time

The National Jet Fuel Combustion Program (NJFCP) is an initiative, currently being led by the Office of Environment & Energy at the FAA, to streamline the ASTM jet fuels certification process for alternative aviation fuels. In order to accomplish this objective, the program has identified specific applied research tasks in several areas. The National Research Council of Canada (NRC) is contributing to the NJFCP in the areas of sprays and atomization and high altitude engine performance. This paper describes work pertaining to atomization tests using a reference injection system. The work involves characterization of the injection nozzle, comparison of sprays and atomization quality of various conventional and alternative fuels, as well as use of the experimental data to validate spray correlations. The paper also briefly explores the application viability of a new spray diagnostic system that has potential to reduce test time in characterizing sprays. Measurements were made from ambient up to 10 bar pressures in NRC’s High Pressure Spray Facility using optical diagnostics including laser diffraction, phase Doppler anemometry (PDA), LIF/Mie Imaging and laser sheet imaging to assess differences in the atomization characteristics of the test fuels. A total of nine test fluids including six NJFCP fuels and three calibration fluids were used. The experimental data was then used to validate semi-empirical models, developed through years of experience by engine OEMs and modified under NJFCP, for predicting droplet size and distribution. The work offers effective tools for developing advanced fuel injectors, and generating data that can be used to significantly enhance multi-dimensional combustor simulation capabilities.

scholarly journals A New Model Approach for Convective Wall Heat Losses in DQMOM-IEM Simulations for Turbulent Reactive Flows

2018 ◽  
Vol 141 (5) ◽  
Author(s):  
Yeshaswini Emmi ◽  
Andreas Fiolitakis ◽  
Manfred Aigner ◽  
Franklin Genin ◽  
Khawar Syed
Keyword(s):  
Experimental Data ◽  
Heat Losses ◽  
Reacting Flow ◽  
Jet Flame ◽  
New Model ◽  
Turbulent Reactive Flows ◽  
Novel Method ◽  
The One ◽  
The Impact ◽  
Model Approach

A new model approach is presented in this work for including convective wall heat losses in the direct quadrature method of moments (DQMoM) approach, which is used here to solve the transport equation of the one-point, one-time joint thermochemical probability density function (PDF). This is of particular interest in the context of designing industrial combustors, where wall heat losses play a crucial role. In the present work, the novel method is derived for the first time and validated against experimental data for the thermal entrance region of a pipe. The impact of varying model-specific boundary conditions is analyzed. It is then used to simulate the turbulent reacting flow of a confined methane jet flame. The simulations are carried out using the DLR in-house computational fluid dynamics code THETA. It is found that the DQMoM approach presented here agrees well with the experimental data and ratifies the use of the new convective wall heat losses model.

scholarly journals Dynamics of the Blade Channel of an Inducer Under Cavitation-Induced Instabilities

2018 ◽  
Vol 141 (4) ◽  
Author(s):  
Angelo Pasini ◽  
Ruzbeh Hadavandi ◽  
Dario Valentini ◽  
Giovanni Pace ◽  
Luca d'Agostino
Keyword(s):  
Experimental Data ◽  
Spectral Analysis ◽  
Main Flow ◽  
Operating Conditions ◽  
Flow Instabilities ◽  
Mean Flow ◽  
Flow Oscillations ◽  
Rotating Frames ◽  
The Mean ◽  
High Head

A high-head three-bladed inducer has been equipped with pressure taps on the hub along the blade channels with the aim of more closely investigating the dynamics of cavitation-induced instabilities developing in the impeller flow. Spectral analysis of the pressure signals obtained from two sets of transducers mounted both in the stationary and rotating frames has allowed to characterize the nature, intensity, and interactions of the main flow instabilities detected in the experiments: subsynchronous rotating cavitation (RC), cavitation surge (CS), and a high-order axial surge oscillation. A dynamic model of the unsteady flow in the blade channels has been developed based on experimental data and on suitable descriptions of the mean flow and the oscillations of the cavitating volume. The model has been used for estimating at the inducer operating conditions of interest the intensity of the flow oscillations associated with the occurrence of the CS mode generated by RC in the inducer inlet.

Sign in / Sign up

Export Citation Format

Share Document