Validation method for simulation models with cross iteration

This article presents an evaluation of the model validation method that was provided as the output of the European research project DynoTRAIN and implemented in the recently revised European standard EN 14363. The input parameters of the validation method, namely the section length, number, and selection of sections as well as the selected parameters of the simulation models are varied. The evaluation shows that a single section that provides a large deviation between simulation and measurement can, in rare cases, influence the results of the overall validation. Nevertheless, the investigations demonstrate a good robustness, as the final validation result is very rarely influenced by the variation of sections selected for validation, by the use of a higher number of sections than the minimum of 12, or by longer sections than that specified for on-track tests in accordance with EN 14363. The validation methodology is also able to recognize the errors in vehicle model parameters, if the errors have a relevant influence on the behaviour of the running dynamics of the evaluated vehicle.

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A pattern-based validation method for the credibility evaluation of simulation models

SIMULATION ◽

10.1177/0037549719856100 ◽

2019 ◽

Vol 96 (2) ◽

pp. 151-167

Author(s):

Yuanjun Laili ◽

Lin Zhang ◽

Yongliang Luo

Keyword(s):

Simulation Model ◽

Dynamic Models ◽

Reference Data ◽

Model Performance ◽

Simulation Models ◽

Validation Method ◽

Evaluation Procedures ◽

Serial Data ◽

Multi Agent

Measuring the credibility of a simulation model has always been challenging due to its growing uncertainty and complexity. During the past decades, plenty of metrics and evaluation procedures have been developed for evaluating different sorts of simulation models. Most of the existing research focuses on the direct comparison of numerical results with a group of reference data. However, it is sometimes unsuitable for evolving dynamic models such as the multi-agent models. With the same condition, both the practical system and the simulation model perform highly dynamic actions. The credibility of the model with insufficient information, non-stationary states and changing environment is unable to acquire through a direct pair comparison. This paper presents a pattern-based validation method to complementarily extract hidden patterns that exist in both a simulation model and its reference data, and assess the model performance in a different aspect. Firstly, multi-dimensional perceptually important points strategy is modified to find the patterns exist in time-serial data. Afterward, a pattern organizing topology is applied to automatically depict required pattern from reference data and assess the performance of the corresponding simulation model. The extensive case study on three simulation models shows the effectiveness of the proposed method.

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Validation Method for Hardware-in-the-Loop Simulation Models

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 ◽

10.3384/ecp17142749 ◽

2018 ◽

Author(s):

Tamás Kökényesi ◽

István Varjasi

Keyword(s):

Simulation Models ◽

Hardware In The Loop ◽

Validation Method

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The influence of varying input parameters on model validation results

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit ◽

10.1177/0954409716679445 ◽

2018 ◽

Vol 232 (2) ◽

pp. 529-541 ◽

Cited By ~ 1

Author(s):

Gernoth Götz ◽

Oldrich Polach

Keyword(s):

Model Validation ◽

Large Deviation ◽

Simulation Models ◽

Model Parameters ◽

European Standard ◽

Vehicle Model ◽

Validation Method ◽

Single Section ◽

Input Parameters ◽

Selection Of

This article presents an evaluation of the model validation method provided as the output of the European research project DynoTRAIN and implemented in the recently revised European standard EN 14363. The input parameters of the validation method are varied, namely the section length, number and selection of sections as well as selected parameters of the simulation models. The evaluation shows that a single section providing a large deviation between simulation and measurement can in rare cases influence the overall validation result. Nevertheless, the investigations demonstrate a good robustness, as the final validation result is very rarely influenced by the variation of sections selected for validation, by the use of a higher number of sections than the minimum of 12 or by longer sections than specified for on-track tests according to EN 14363. The validation methodology is also able to recognise errors in vehicle model parameters, if they have a relevant influence on the running dynamics behaviour of the evaluated vehicle.

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Multilevel Validation Method for Hardware-in-the-Loop Simulation Models

2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) ◽

10.1109/speedam.2018.8445273 ◽

2018 ◽

Author(s):

Tamas Kokenyesi ◽

Zoltan Suto ◽

Istvan Varjasi

Keyword(s):

Simulation Models ◽

Hardware In The Loop ◽

Validation Method

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Correlation of Pore Structure and Permeability by SEM-Image Analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100180896 ◽

1990 ◽

Vol 48 (1) ◽

pp. 428-429

Author(s):

C. A. Callender ◽

Wm. C. Dawson ◽

J. J. Funk

Keyword(s):

Image Analysis ◽

Pore Structure ◽

Imaging System ◽

Pore Space ◽

Simulation Models ◽

Image Analyzer ◽

Imaging Data ◽

Sem Images ◽

Thin Sections ◽

Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

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