49 research outputs found
Virtual testing environment tools for railway vehicle certification
No full textThis paper describes the work performed in Work Package 6 of the European project DynoTRAIN. Its task was to investigate the effects that uncertainties present within the track and running conditions have on the simulated behaviour of a railway vehicle. Methodologies and frameworks for using virtual simulation and statistical tools, in order to reduce both the cost and time required for the certification of new or modified railway vehicles, were proposed. In particular, the project developed a virtual test track (VTT) toolkit that is capable of both generating a series of test tracks based on measurements, which can be used in vehicle virtual testing using computer simulation models, and also automatically handling the output results. The toolkit is compliant with prEN14363: 2013. The VTT was used as an experimental tool to analyse cross-correlations between track data (input) and matching vehicle response (output) based on data recorded using a test train. This paper discusses the issues encountered in the process and suggests avenues for future developments and potential use in the context of European cross-acceptance. The VTT offers benefits to the areas of design development and regulatory certification
Probabilistic simulation for the certification of railway vehicles
No full textThe present dynamic certification process that is based on experiments has been essentially built on the basis of experience. The introduction of simulation techniques into this process would be of great interest. However, an accurate simulation of complex, nonlinear systems is a difficult task, in particular when rare events (for example, unstable behaviour) are considered. After analysing the system and the currently utilized procedure, this paper proposes a method to achieve, in some particular cases, a simulation-based certification. It focuses on the need for precise and representative excitations (running conditions) and on their variable nature. A probabilistic approach is therefore proposed and illustrated using an example.
First, this paper presents a short description of the vehicle / track system and of the experimental procedure. The proposed simulation process is then described. The requirement to analyse a set of running conditions that is at least as large as the one tested experimentally is explained. In the third section, a sensitivity analysis to determine the most influential parameters of the system is reported. Finally, the proposed method is summarized and an application is presented
Accuracy of the experimental assessment of running dynamics characteristics quantified through an uncertainty framework
No full textThe European FP7 project DynoTRAIN was set up in order to close important open points in the technical specifications for interoperability of the trans-European rail system and to contribute to European standards. This contribution targeted the reduction of cost of the process for the assessment of running dynamics characteristics that is required when seeking authorisation to place rolling stock in service according to the EU procedure. The project was divided into seven work packages. Work Package 7 was devoted to the issue of ‘regulatory acceptance’: the results had to be discussed with and presented to regulatory authorities in a way as to be acceptable for use by such authorities. An important part of this work addressed accuracy (and its quantitative counterpart, uncertainty) of the assessment process, which was widely recognised at the beginning of the project as a key issue that needed to be tackled to increase confidence in both experimental and virtual assessment processes. In this paper, the uncertainty framework that was developed in WP7 and its relationship with the work of other DynoTRAIN Work Packages is presented. An application of the concepts of the framework is given in the form of quantitative results regarding the accuracy of the current EN 14363 experimental assessment process for running dynamics characteristics
Effect of parameter uncertainty on the numerical estimate of a railway vehicle critical speed
No full textThe paper describes a joint study carried out by SNCF and Dipartimento di Meccanica Politecnico di Milano, aimed at investigating how parametric uncertainty can be treated in the framework of virtual homologation of railway vehicles in respect to vehicle dynamics. In railway vehicle sources of parameter uncertainty may arise from inaccuracy in the modelling of a vehicle component or from a scatter in the behaviour of nominally identical components, on account of the variability implied by the component manufacturing process.
The approach proposed in this paper, completely new in the railway field, is to use statistical methods having different complexity (and entailing a proportional computational effort), to analyse the propagation of uncertainty from the parameters input in the vehicle mathematical model to the results of running dynamics, in terms of the assessment quantities used for vehicle homologation. The problem is treated by numerical means, being the dependency of simulation outputs from the input parameters typically non-linear, and not defined in an analytical form
Statistical inverse problems for non-gaussian non-stationary stochastic processes defined by a set of realizations
No full textInternational audienceThis paper presents an innovative approach to analyze the transitory response of complex and nonlinear systems,which are excited by non-Gaussian and non-stationary random fields, by solving of a statistical inverse problemwith experimental measurements. Based on a double expansion, it is particularly adapted to the modeling ofstochastic processes that are only characterized by a relatively small set of independent realizations.First, an adaptation of the classical Karhunen-Loève expansion is presented. Indeed, for the past fifty years, theuse of reduced basis has spread to many scientific fields to condense the statistical properties of stochasticprocesses, and among these bases, the Karhunen-Loève basis plays a major role as it allows the minimization ofthe total mean square error. Such a basis corresponds to the Hilbertian basis that is constructed as theeigenfunctions of the covariance operator of the stochastic process of interest. When the available informationabout this stochastic process is characterized by a limited set of independent realizations, this covariance function is however unknown. Therefore, there is no reason for the set gathering the eigenfunctions associated with any estimator of the covariance to be still optimal.Secondly, the random vector, which gathers the projection coefficients of the stochastic process on this basis, is characterized using a polynomial chaos expansion approach. The dimension of this random vector being very high (around several hundreds), advanced identification techniques are introduced to allow performing relevant convergence analyses and identifications. The non-Gaussian non-stationary stochastic process is identified using the experimental measurements and consequently, constitutes a realistic stochastic modeling. The proposed method is then applied to the risk assessment of a non-linear structure submitted to seismic loadings, for which measured seismic accelerations are available.REFERENCESR. Ghanem, P. D. Spanos, Stochastic Finite Elements: A Spectral Approach, rev. ed., Dover Publications, New York, 2003.O. Le Maître, O. Knio, Spectral Methods for Uncertainty Quantification, Springer, 2010.G. Perrin, C. Soize, D. Duhamel, C. Funfschilling, Identification of polynomial chaos representations in high dimension from a set of realizations, SIAM J. Sci. Comput., 34(6), A2917–A2945, 2012.G. Perrin, C. Soize, D. Duhamel, C. Funfschilling, A posteriori error and optimal reduced basis for stochastic processesdefined by a finite set of realizations, SIAM/ASA J. Uncertainty Quantification, 2, 745-762 (2014).C. Soize, Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valuedrandom fields using partial and limited experimental data., Computer Methods in Applied Mechanics and Engineering, 199 (33-36), 2150–2164, 2010
Effects of shear rate and temperature on viscosity of alumina polyalphaolefins nanofluids
No full textIn this paper, the shear rate and temperature dependencies of viscosity of alumina nanofluids have been investigated experimentally. The alumina nanofluids are suspensions of alumina nanospheres or nanorods in polyalphaolefins (PAO) lubricant. The base fluid PAO has a Newtonian behavior. To the first approximation, nanofluids of volume fractions phi = 1% and 3% nanospheres as well as nanofluid of phi = 1% nanorods can be considered as Newtonian fluids because their viscosity shows very weak shear rate dependence. However, our measurement clearly indicates that these nanofluids demonstrate certain non-Newtonian feature due to the addition of nanoparticles. Moreover, the relative viscosity (the ratio of viscosity of nanofluid to that of PAO) of these nanofluids has been measured to be independent of temperature. Nanofluid of a higher volume fraction phi = 3% nanorods has an apparent non-Newtonian shear thinning viscosity and a strong temperature dependence of its relative viscosity. By reviewing the previous studies, the approximation that the viscosity is Newtonian and the relative viscosity is independent of temperature seem to hold for most nanofluids of low volume fraction phi and low aspect ratio nanoparticles. (C) 2010 American Institute of Physics. [doi: 10.1063/1.3309478
Statistical inverse problems for non-gaussian non-stationary stochastic processes defined by a set of realizations
No full textInternational audienceThis paper presents an innovative approach to analyze the transitory response of complex and nonlinear systems,which are excited by non-Gaussian and non-stationary random fields, by solving of a statistical inverse problemwith experimental measurements. Based on a double expansion, it is particularly adapted to the modeling ofstochastic processes that are only characterized by a relatively small set of independent realizations.First, an adaptation of the classical Karhunen-Loève expansion is presented. Indeed, for the past fifty years, theuse of reduced basis has spread to many scientific fields to condense the statistical properties of stochasticprocesses, and among these bases, the Karhunen-Loève basis plays a major role as it allows the minimization ofthe total mean square error. Such a basis corresponds to the Hilbertian basis that is constructed as theeigenfunctions of the covariance operator of the stochastic process of interest. When the available informationabout this stochastic process is characterized by a limited set of independent realizations, this covariance function is however unknown. Therefore, there is no reason for the set gathering the eigenfunctions associated with any estimator of the covariance to be still optimal.Secondly, the random vector, which gathers the projection coefficients of the stochastic process on this basis, is characterized using a polynomial chaos expansion approach. The dimension of this random vector being very high (around several hundreds), advanced identification techniques are introduced to allow performing relevant convergence analyses and identifications. The non-Gaussian non-stationary stochastic process is identified using the experimental measurements and consequently, constitutes a realistic stochastic modeling. The proposed method is then applied to the risk assessment of a non-linear structure submitted to seismic loadings, for which measured seismic accelerations are available.REFERENCESR. Ghanem, P. D. Spanos, Stochastic Finite Elements: A Spectral Approach, rev. ed., Dover Publications, New York, 2003.O. Le Maître, O. Knio, Spectral Methods for Uncertainty Quantification, Springer, 2010.G. Perrin, C. Soize, D. Duhamel, C. Funfschilling, Identification of polynomial chaos representations in high dimension from a set of realizations, SIAM J. Sci. Comput., 34(6), A2917–A2945, 2012.G. Perrin, C. Soize, D. Duhamel, C. Funfschilling, A posteriori error and optimal reduced basis for stochastic processesdefined by a finite set of realizations, SIAM/ASA J. Uncertainty Quantification, 2, 745-762 (2014).C. Soize, Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valuedrandom fields using partial and limited experimental data., Computer Methods in Applied Mechanics and Engineering, 199 (33-36), 2150–2164, 2010
