A Non Linear Parametric Model of an Automotive Shock Absorber

1994 ◽  
Author(s):  
Koenraad Reybrouck
Keyword(s):  
Shock Absorber ◽  
Parametric Model ◽  
Non Linear ◽  
Linear Parametric Model

scholarly journals Semi-analytic contact technique in a non-linear parametric model order reduction method for gear simulations

Meccanica ◽  
2017 ◽  
Vol 53 (1-2) ◽  
pp. 49-75 ◽  
Author(s):  
Niccolò Cappellini ◽  
Tommaso Tamarozzi ◽  
Bart Blockmans ◽  
Jakob Fiszer ◽  
Francesco Cosco ◽  
...  
Keyword(s):  
Model Order Reduction ◽  
Reduction Method ◽  
Order Reduction ◽  
Parametric Model ◽  
Model Order ◽  
Parametric Model Order Reduction ◽  
Non Linear ◽  
Linear Parametric Model ◽  
Order Reduction Method

Modeling Compressible Fluid Struts for Active Suspension Development

2004 ◽  
Author(s):  
Xubin Song
Keyword(s):  
Compressible Fluid ◽  
Shock Absorber ◽  
Active Suspension ◽  
Parametric Model ◽  
Simple Equation ◽  
Vehicle Suspension ◽  
Data Sets ◽  
Pressure Data ◽  
Damping Function ◽  
Testing Data

In this paper, a non-parametric model is presented for representing compressible fluid struts (CFS) that are based on silicon fluid. The strut can be properly designed to replace the traditional spring and shock absorber for vehicle suspension application. For this study, the strut does not include damping function so that the analysis focuses on modeling the fluid compressibility of the spring function. The approach is to derive a series of mathematics equations in correspondence to the extracted data sets from the strut testing data. The pressure data can be characterized as dependent on precharge pressure, vibration applied on the strut, and friction existing between sealing and the strut rod surface. Every characteristic is represented by a (group of) simple equation, which is optimized based on characterized data sets, respectively. Finally the model is programmed in SIMULINK and validated by the collected testing data.

The Spiral Discovery Method: An Interpretable Tuning Model for CogInfoCom Channels

2012 ◽  
Vol 16 (2) ◽  
pp. 358-367 ◽  
Author(s):  
Adam Csapo ◽  
◽  
Péter Baranyi ◽  
Keyword(s):  
Parameter Space ◽  
Parametric Model ◽  
The State ◽  
Tensor Algebra ◽  
Perceptual Space ◽  
Cognitive Artifact ◽  
Non Linear ◽  
High Level ◽  
Discovery Method ◽  
The Relationship

Cognitive Infocommunications (CogInfoCom) messages that are used to carry information on the state of the same high-level concept can be regarded as belonging to a CogInfoCom channel. Such channels can be generated using any kind of parametric model. By changing the values of the parameters, it is possible to arrive at a large variety of CogInfoCom messages, a subset of which can belong to a CogInfoCom channel – provided they are perceptually well-suited to the purpose of conveying information on the same highlevel concept. Thus, for any CogInfoCom channel, we may speak of a parameter space and a perceptual space that is created by the totality of messages in the CogInfoCom channel. In this paper, we argue that in general, the relationship between the parameter space and the perceptual space is highly non-linear. For this reason, it is extremely difficult for the designer of a CogInfoCom channel to tune the parameters in such a way that the resulting CogInfoCom messages are perceptually continuous, and suitable to carry information on a single high-level concept. To address this problem, we propose a cognitive artifact that uses a rank concept available in tensor algebra to provide the designer of CogInfoCom channels with practical tradeoffs between complexity and interpretability. We refer to the artifact as the Spiral Discovery Method (SDM).

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