Dynamic model for the orbiting scroll based on the pressures in scroll chambers – Part II: Investigations on scroll compressors and model validation

2013 ◽  
Vol 36 (7) ◽  
pp. 1850-1865 ◽  
Author(s):  
Jianguo Qiang ◽  
Bin Peng ◽  
Zhenquan Liu
Keyword(s):  
Dynamic Model ◽  
Model Validation

Parabolic trough collector field dynamic model: Validation, energetic and exergetic analyses

2019 ◽  
Vol 148 ◽  
pp. 777-786 ◽  
Author(s):  
Jose A. Carballo ◽  
Javier Bonilla ◽  
Manuel Berenguel ◽  
Patricia Palenzuela
Keyword(s):  
Dynamic Model ◽  
Model Validation ◽  
Parabolic Trough ◽  
Parabolic Trough Collector ◽  
Field Dynamic

Dynamic Model of Dual Clutch Lever-Based Electromechanical Actuator

2011 ◽  
Author(s):  
Vladimir Ivanovic´ ◽  
Josˇko Deur ◽  
Milan Milutinovic´ ◽  
H. Eric Tseng
Keyword(s):  
Experimental Data ◽  
Dynamic Model ◽  
Model Validation ◽  
Bond Graph ◽  
Test Rig ◽  
Electromechanical Actuator ◽  
Actuator Dynamics ◽  
Operating Modes ◽  
Graph Modeling ◽  
Bond Graph Modeling

The paper presents a dynamic model of a dual clutch lever-based electromechanical actuator. Bond graph modeling technique is used to describe the clutch actuator dynamics. The model is parameterized and thoroughly validated based on the experimental data collected by using a test rig. The model validation results are used for the purpose of analysis of the actuator behavior under typical operating modes.

A Dynamic Model for KUKA KR6 in SPIF Processes

2019 ◽  
Vol 957 ◽  
pp. 156-166 ◽  
Author(s):  
Mihai Crenganis ◽  
Akos Csiszar
Keyword(s):  
Dynamic Model ◽  
Model Validation ◽  
Incremental Forming ◽  
Industrial Robot ◽  
Single Point ◽  
Single Point Incremental Forming ◽  
Plastic Deformations ◽  
Metal Parts ◽  
Steel Sheets ◽  
Metal Sheets

The paper presents the development of a dynamic model for the KUKA KR6 robot during single point incremental forming (SPIF) of metal sheets. The dynamic model of the KUKA KR6 robot is created in MATLAB®-SimMechanics. This dynamic model is necessary to verify that the mechanical structure of this low payload industrial robot of 36 Kg capacity can withstand some specific forces in incremental forming of some low plasticity alloys like Ti6Al4V. In the Centre of Studies and Research for Plastic Deformations of "Lucian Blaga" University of Sibiu, different attempts on single point incremental forming of thin metal sheets have been carried out and some of the studies are based on SPIF using the KUKA KR6-2 industrial robot. Nevertheless, the previous experimental attempts using the KUKA KR 6-2 robot in SPIF processes were realised only on 0.4 mm thick DC04 steel sheets. This material has very good deformability properties and the forces during the process are relatively small. After the dynamic model validation some specific circular trajectories are imposed and the forces that can appear during SPIF process for Ti6Al4V alloy sheets are taken into consideration. After forces analysis, it was concluded that the KUKA KR6 robot can be used in single point incremental forming processes for metal parts requiring greater forming forces.

Model Validation of Dynamic Engineering Models Under Uncertainty

2016 ◽  
Author(s):  
Zequn Wang ◽  
Yan Fu ◽  
Ren-Jye Yang ◽  
Saeed Barbat ◽  
Wei Chen
Keyword(s):  
Dynamic Model ◽  
Model Validation ◽  
Dynamic Systems ◽  
Time Series Data ◽  
Integral Transform ◽  
Random Coefficients ◽  
Series Data ◽  
Input Space ◽  
Eigen Analysis ◽  
Engineering Models

Validating dynamic engineering models is critically important in practical applications by assessing the agreement between simulation results and experimental observations. Though significant progresses have been made, the existing metrics lack the capability of managing uncertainty in both simulations and experiments, which may stem from computer model instability, imperfection in material fabrication and manufacturing process, and variations in experimental conditions. In addition, it is challenging to validate a dynamic model aggregately over both the time domain and a model input space with data at multiple validation sites. To overcome these difficulties, this paper presents an area-based metric to systemically handle uncertainty and validate computational models for dynamic systems over an input space by simultaneously integrating the information from multiple validation sites. To manage the complexity associated with a high-dimensional data space, Eigen analysis is performed for the time series data from simulations at each validation site to extract the important features. A truncated Karhunen-Loève (KL) expansion is then constructed to represent the responses of dynamic systems, resulting in a set of uncorrelated random coefficients with unit variance. With the development of a hierarchical data fusion strategy, probability integral transform is then employed to pool all the resulting random coefficients from multiple validation sites across the input space into a single aggregated metric. The dynamic model is thus validated by calculating the cumulative area difference of the cumulative density functions. The proposed model validation metric for dynamic systems is illustrated with a mathematical example, a supported beam problem with stochastic loads, and real data from the vehicle occupant restraint system.

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