An approach for the validation of railway vehicle models based on on-track measurements

Kraft, Sönke; Causse, Julien; Coudert, Frederic

doi:10.1080/00423114.2015.1054406

Cited by 13 publications

(11 citation statements)

References 12 publications

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“…Bogojević and Lučanin [28] define a probabilistic validation metric comparing two CDFs by an f-test weighted with a mean value difference. Kraft et al [107] use statistical indicators such as mean and standard deviation of validation errors as well as a so-called least-square misfit function. The latter quantifies the distance between the error time signals in least-square fashion and can be plotted as a CDF to compare different vehicles, responses or running conditions.…”

Section: Non-deterministic Simulationsmentioning

confidence: 99%

Unified Framework and Survey for Model Verification, Validation and Uncertainty Quantification

Riedmaier

Danquah

Schick

et al. 2020

Arch Computat Methods Eng

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Simulation is becoming increasingly important in the development, testing and approval process in many areas of engineering, ranging from finite element models to highly complex cyber-physical systems such as autonomous cars. Simulation must be accompanied by model verification, validation and uncertainty quantification (VV&UQ) activities to assess the inherent errors and uncertainties of each simulation model. However, the VV&UQ methods differ greatly between the application areas. In general, a major challenge is the aggregation of uncertainties from calibration and validation experiments to the actual model predictions under new, untested conditions. This is especially relevant due to high extrapolation uncertainties, if the experimental conditions differ strongly from the prediction conditions, or if the output quantities required for prediction cannot be measured during the experiments. In this paper, both the heterogeneous VV&UQ landscape and the challenge of aggregation will be addressed with a novel modular and unified framework to enable credible decision making based on simulation models. This paper contains a comprehensive survey of over 200 literature sources from many application areas and embeds them into the unified framework. In addition, this paper analyzes and compares the VV&UQ methods and the application areas in order to identify strengths and weaknesses and to derive further research directions. The framework thus combines a variety of VV&UQ methods, so that different engineering areas can benefit from new methods and combinations. Finally, this paper presents a procedure to select a suitable method from the framework for the desired application.

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Section: Non-deterministic Simulationsmentioning

confidence: 99%

Unified Framework and Survey for Model Verification, Validation and Uncertainty Quantification

Riedmaier

Danquah

Schick

et al. 2020

Arch Computat Methods Eng

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“…The experimental validation of a railroad computational model requires experiments to be carried out using real vehicles running on commercial tracks [14]. This is something that is normally difficult to achieve due to the fact that operating companies pay a high price to conduct such experiments.…”

Section: Introductionmentioning

confidence: 99%

Application and Experimental Validation of a Multibody Model with Weakly Coupled Lateral and Vertical Dynamics to a Scaled Railway Vehicle

Urda

Muñoz

Aceituno

et al. 2020

Sensors

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In this paper, a multibody dynamic model of a railway vehicle that assumes that vertical and lateral dynamics are weakly coupled, has been experimentally validated using an instrumented scaled vehicle running on a 5-inch-wide experimental track. The proposed linearised model treats the vertical and lateral dynamics of the multibody system almost independently, being coupled exclusively by the suspension forces. Several experiments have been carried out at the scaled railroad facilities at the University of Seville in order to test and validate the simulation model under different working conditions. The scaled vehicle used in the experiments is a bogie instrumented with various sensors that register the accelerations and angular velocities of the vehicle, its forward velocity, its position along the track, and the wheel–rail contact forces in the front wheelset. The obtained results demonstrate how the proposed computational model correctly reproduces the dynamics of the real mechanical system in an efficient computational manner.

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“…Kraft et al. 12 present a new approach for the validation of railway vehicle models based on on-track measurements. The distance between the measurement and the simulation result is described by a misfit function and compared to the uncertainty of the on-track measurement from the repeatability analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Nebojsa and Lucanin 11 propose a new validation metrics for the validation of models of railway vehicles based on a comparison between the cumulative distribution functions of selected parameters obtained by simulation and measurements. Kraft et al 12 present a new approach for the validation of railway vehicle models based on ontrack measurements. The distance between the measurement and the simulation result is described by a misfit function and compared to the uncertainty of the on-track measurement from the repeatability analysis.…”

Section: Introductionmentioning

confidence: 99%

The influence of varying input parameters on model validation results

Götz

Polách²

2018

Proceedings of the Institution of Mechanical Engineers, Part F:

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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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An approach for the validation of railway vehicle models based on on-track measurements

Cited by 13 publications

References 12 publications

Unified Framework and Survey for Model Verification, Validation and Uncertainty Quantification

Unified Framework and Survey for Model Verification, Validation and Uncertainty Quantification

Application and Experimental Validation of a Multibody Model with Weakly Coupled Lateral and Vertical Dynamics to a Scaled Railway Vehicle

The influence of varying input parameters on model validation results

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