# G. PerrinAtomic Energy and Alternative Energies Commission | CEA

G. Perrin

Doctor

## About

63

Publications

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554

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Introduction

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September 2010 - December 2013

September 2010 - December 2013

## Publications

Publications (63)

The speed profile of a train plays an important role in energy consumption and resulting costs. The industrial objective of this work is thus to develop a method to reduce the energy consumed by a train over a journey by playing on the driver commands (traction and braking forces) while respecting punctuality constraints. First, a rigid body approa...

Gamma spectrometry is a passive non-destructive assay method used to quantify radionuclides present in nuclear objects. Basic methods using empirical calibration with a standard to quantify the activity of nuclear materials by determining the calibration coefficient are ineffective on non-reproducible nuclear objects such as waste packages. Package...

Molecular dynamics is often considered as a numerical experiment. The error bars on the results are therefore mandatory, but sometimes difficult to determine and computationally demanding. As a low-cost approach, we describe the application of the bootstrap (BS) method to the quantification of uncertainties pertaining to the time correlation functi...

This paper addresses the issue of guaranteeing the good functioning of physical systems using expensive simulators. More precisely, it is interested in the construction of bounds allowing to majorise with a specified confidence the probability of occurrence of undesired events. In this context, this paper presents two algorithms: a first one allowi...

In this paper, we propose new learning algorithms for approximating high-dimensional functions using tree tensor networks in a least-squares setting. Given a dimension tree or architecture of the tensor network, we provide an algorithm that generates a sequence of nested tensor subspaces based on a generalization of principal component analysis for...

This chapter considers a probabilistic framework for the input uncertainty modeling assuming sufficient information is available to construct a relevant input probabilistic model. It focuses on a specific class of reliability‐oriented sensitivity analysis (ROSA) methods and its extension to reliability problems involving two uncertainty levels in i...

Considérer le contexte incertain en ingénierie mécanique dans le but d’améliorer les performances des futurs produits ou systèmes apparaît désormais comme un avantage compétitif, voire une nécessité pour garantir une exigence de sûreté de plus en plus élevée. Ingénierie mécanique en contexte incertain traite de la modélisation, de la quantification...

In this paper, we first propose an efficient method for the dimension reduction of the functional input of a code with functional output. It is based on the approximation of the output by a model which is linear with respect to the functional input. This approximation has a sparse structure, whose parameters can be accurately estimated from a small...

This work considers the challenging problem of identifying the statistical properties of random fields from indirect observations. To this end, a Bayesian approach is introduced, whose key step is the nonparametric approximation of the likelihood function from limited information. When the likelihood function is based on the evaluation of an expens...

This paper is concerned with the approximation of a function $u$ in a given approximation space $V_m$ of dimension $m$ from evaluations of the function at $n$ suitably chosen points. The aim is to construct an approximation of $u$ in $V_m$ which yields an error close to the best approximation error in $V_m$ and using as few evaluations as possible....

The role of simulation keeps increasing for the reliability analysis of complex systems. Most of the time, these analyses can be reduced to estimating the probability of occurrence of an undesirable event, also called failure probability, using a stochastic model of the system. If the considered event is rare, sophisticated sample-based procedures...

This paper presents a Bayesian calibration method for a simulation-based model with stochastic functional input and output. The originality of the method lies in an adaptation involving the representation of the likelihood function by a Gaussian process surrogate model, to cope with the high computational cost of the simulation, while avoiding the...

This paper presents a review of various works that highlight the importance of introducing the variability of the road-track/vehicle system into dynamic simulations as soon as this latter is meant to be predictive. The first section of the paper presents the Uncertainty Quantification, Verification and Validation method (UQ-VV). This latter propose...

This paper presents a novel method for the state health monitoring of high-speed train suspensions from in-line acceleration measurements by embedded sensors, for maintenance purposes. We propose a model-based method relying on a multibody simulation code. It performs the simultaneous identification of several suspension mechanical parameters. It i...

Gaussian processes (GP) are widely used as a metamodel for emulating time-consuming computer codes.We focus on problems involving categorical inputs, with a potentially large number L of levels (typically several tens),partitioned in G << L groups of various sizes. Parsimonious covariance functions, or kernels, can then be defined by block covarian...

Gamma spectrometry is a passive non-destructive assay used to quantify radionuclides present in more or less complex objects. Basic methods using empirical calibration with a standard in order to quantify the activity of nuclear materials by determining the calibration coefficient are useless on non-reproducible, complex and single nuclear objects...

A calibration of the Johnson’s damage model is proposed by using a Bayesian approach. A set of 3 parameters has to be adjusted. For this purpose, free surface velocities of 7 plate impact experiments leading to spallation in targets of tantalum are used. The resulting distributions of probability indicate several potential values for each parameter...

The objective of the work presented here is a bayesian calibration of parameters describing the mechanical characteristics of high-speed train suspensions for maintenance purposes. This calibration is achieved by comparing simulation results to on-track acceleration measurements. It requires the estimation on the multidimensionnal admissible set of...

Thanks to computing power increase, the certification and the conception of complex systems relies more and more on simulation. To this end, predictive codes are needed, which have generally to be evaluated in a huge number of input points. When the computational cost of these codes is high, surrogate models are introduced to emulate the response o...

Based on proper orthogonal decomposition (POD), a new method is presented in order to statistically characterize arbitrary particle shapes using an optimal choice of shape functions identified on a set of 1000 digitized railway ballast particles obtained through 3D Scan. The coefficients of the POD expansion enable a description of ballast grains w...

The multidimensional Gaussian kernel-density estimation (G-KDE) is a powerful tool to identify the distribution of random vectors when the maximal information is a set of independent realizations. For these methods, a key issue is the choice of the kernel and the optimization of the bandwidth matrix. To optimize these kernel representations, two ad...

The objective of the work presented here is a bayesian calibration of parameters describing the mechanical characteristics of high-speed train suspensions for maintenance purposes. This calibration is achieved by comparing simulation results to on-track accelerometric measurements. It requires the estimation on the multidimensionnal admissible set...

According to performance and certification criteria, complex mechanical systems have to take into account several constraints, which can be associated with a series of performance functions. In reliability analysis, we thus are interested in the identification of the domain where at least one of these constraints is not satisfied. To this end, the...

The objective of the work presented here is the inverse identification of parameters describing the mechanical characteristics of high-speed train suspensions for maintenance purposes. This identification is achieved by comparing simulation results to on-track accelerometric measurements. It requires the introduction of an output predictive error a...

The role of simulation keeps increasing for the sensitivity analysis and the uncertainty quantification of complex systems. Such numerical procedures are generally based on the processing of a huge amount of code evaluations. When the computational cost associated with one particular evaluation of the code is high, such direct approaches based on t...

The sensitivity of the mechanical behaviour of railway ballast to particle shape variation is studied through Discrete Element Method (DEM) numerical simulations, focusing on some basic parameters such as solid fraction, coordination number, or force distribution. We present an innovative method to characterize 3D particle shape using Proper Orthog...

As several studies showed, mechanical performance of ballast depends greatly on its size, mineralogy and shape. Unlike the formers, particle shape remains a poorly specified property. The main objective of the study is then to first characterize the shape of ballast, then to understand its impact on the mechanical performance, by means of numerical...

To face the industrial challenge posed to the French National railway company (SNCF), a good understanding of ballast mechanical behaviour is necessary. As with grain size and mineralogy, grain shape has a significant influence on mechanical performance. The study, presented in this paper, aims to provide an understanding of this influence by means...

Due to performance and certification criteria, complex mechanical systems have to take into account several constraints, which can be associated with a series of performance functions. Different software are generally used to evaluate such functions, whose computational cost can vary a lot. In conception or reliability analysis, we thus are interes...

Grain shape significantly influences the mechanical properties of granular media. In order to explore this effect and to simulate realistic material morphology, we designed a method which well characterizes real grains shape. Starting from a representation of the particle surfaces as a points cloud, this paper presents a method to generate a set of...

The 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...

The paper deals with the statistical inverse problem for the identification of a non- Gaussian tensor-valued random field in high stochastic dimension. Such a random field can represent the parameter of a boundary value problem (BVP). The available experimental data, which correspond to observations, can be partial and limited. A general methodolog...

This paper presents a method 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 problem with experimental measurements. Based on a double expansion, it is particularly adapted to the modeling of stochastic processes that are onl...

At its building, the theoretical new railway line is supposed to be made of perfect straight lines and curves. This track geometry is however gradually damaged and regularly subjected to maintenance operations. The analysis of these track irregularities is a key issue as the dynamic behaviour of the trains is mainly induced by the track geometry. I...

The use of reduced basis has spread to many scientific fields for the past 50 years to condense the statistical properties of stochastic processes. Among these bases, the classical Karhunen –Lòeve basis corresponds to the Hilbertian basis that is constructed as the eigenfunctions of the covariance operator of the stochastic process of interest. The...

High speed trains are currently meant to run faster and to carry heavier loads, while being less energy consuming and still respecting the security and comfort certification criteria. To face these challenges, a better understanding of the interaction between the dynamic train behavior and the track geometry is needed. As during its lifecycle, the...

High speed trains are currently meant to run faster and to carry heavier loads, while being less energy consuming and still ensuring the safety and comfort certification criteria. In order to optimize the conception of such innovative trains, a precise knowledge of the realm of possibilities of track conditions that the train is likely to be confro...

The study presented here aims at characterizing the influence of the rail wear on the train dynamic response. Rail profiles have been measured on a real track and post-processed in order to be used in a mutlibody simulation of the train's response. Wear modes have been extracted from the measures with a Principal Orthogonal Decomposition (POD). The...

Due to scaling effects, when dealing with vector-valued random fields, the classical Karhunen–Loève expansion, which is optimal with respect to the total mean square error, tends to favorize the components of the random field that have the highest signal energy. When these random fields are to be used in mechanical systems, this phenomenon can intr...

This presentation deals with an innovative approach to analyze complex and nonlinear systems, which are excited by non-Gaussian and non-stationary random fields, by solving of a statistical inverse problem with experimental measurements. The methodology proposed is applied to the case of a railway system: a train is a nonlinear system with many deg...

This paper deals with the identification in high dimensions of a polynomial chaos expansion of random vectors from a set of realizations. Due to numerical and memory constraints, the usual polynomial chaos identification methods are based on a series of truncations that induce a numerical bias. This bias becomes very detrimental to the convergence...

Railway dynamic simulations are increasingly used to predict and analyse the behaviour of the vehicle and of the track during their whole life cycle. Up to now however, no simulation has been used in the certification procedure even if the expected benefits are important: cheaper and shorter procedures, more objectivity, better knowledge of the beh...

This paper presents a methodology to build representative railway track geometries thanks to a stochastic modelling. This modelling, which has to integrate the statistical and spatial variabilities and dependencies, is a key issue when using simulation for conception, maintenance or certification purposes, as the dynamic behaviour of the trains is...