Miguel Munoz ZunigaIFP Energies nouvelles · Applied Mathematics Division
Miguel Munoz Zuniga
PhD
About
24
Publications
2,245
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
284
Citations
Introduction
Additional affiliations
April 2012 - October 2013
January 2011 - February 2012
Position
- PostDoc Position
Publications
Publications (24)
Many model inversion problems occur in industry. These problems consist in finding the set of parameter values such that a certain quantity of interest respects a constraint, for example remains below a threshold. In general, the quantity of interest is the output of a simulator, costly in computation time. An effective way to solve this problem is...
Many model inversion problems occur in industry. These problems consist in finding the set of parameter values such that a certain quantity of interest respects a constraint, for example remains below a threshold. In general, the quantity of interest is the output of a simulator, costly in computation time. An effective way to solve this problem is...
In this paper, we propose a procedure for quantifying and reducing uncertainties impacting numerical simulations involved in the estimation of the fatigue of a wind turbine structure. The present study generalizes a previous work carried out by the authors proposing to quantify and to reduce uncertainties affecting the properties of a wind turbine...
We consider in this paper a time-dependent reliability-based design optimization (RBDO) problem with constraints involving the maximum and/or the integral of a random process over a time interval. We focus especially on problems where the process is a stationary or a piece-wise stationary Gaussian process. A two-step procedure is proposed to solve...
We consider in this paper a time-dependent reliability-based design optimization (RBDO) problem with constraints involving the maximum and/or the integral of a random process over a time interval. We focus especially on problems where the process is a stationary or a piece-wise stationary Gaussian process. A two-step procedure is proposed to solve...
We present a method for reliability assessment in extreme conditions from a numerical simulator through surrogate based importance sampling. As proposed in recent works in the literature, a Kriging surrogate is used to build an approximation of the limit state function and the optimal importance density. Our contribution is then the use of a suffic...
A framework to perform quantification and reduction of uncertainties in a wind turbine numerical model using a global sensitivity analysis and a recursive Bayesian inference method is developed in this paper. We explain how a prior probability distribution on the model parameters is transformed into a posterior probability distribution, by incorpor...
A framework to perform quantification and reduction of uncertainties in a wind turbine numerical model using global sensitivity analysis and recursive Bayesian inference method is developed in this paper. We explain how a prior probability distribution on the model parameters is transformed into a posterior probability distribution, by incorporatin...
In this paper, we propose a new methodology for solving stochastic inversion problems through computer experiments, the stochasticity being driven by a functional random variables. This study is motivated by an automotive application. In this context, the simulator code takes a double set of simulation inputs: deterministic control variables and fu...
Real industrial studies often give rise to complex optimization problems involving mixed variables and time consuming simulators. To deal with these difficulties we propose the use of a Gaussian process regression surrogate with a suitable kernel able to capture simultaneously the output correlations with respect to continuous and categorical/discr...
In the context of energy transition, wind power generation is developing rapidly. Meanwhile, in the framework of digitization of industry, the exploitation of collected data can be optimized by combination with wind turbine numerical models. Such numerical models can be complex and costly as they involve non-linear dynamic equations with different...
In this paper we investigate probability functions acting on nonlinear systems wherein the random vector can follow an elliptically symmetric distribution. We provide first and second order differentiability results as well as readily implementable formulæ. We also demonstrate that these formulæ can be readily employed within standard non-linear pr...
This poster describes a model for designing offshore wind turbines while using probability constraints
This article presents several state-of-the-art Monte Carlo methods for simulating and
estimating rare events. A rare event occurs with a very small probability, but its
occurrence is important enough to justify an accurate study. Rare event simulation calls
for specific techniques to speed up standard Monte Carlo sampling, which requires
unacceptab...
In the context of structural reliability, a small probability to be assessed, a high computational time model and a relatively large input dimension are typical constraints which brought together lead to an interesting challenge. Indeed, in this framework many existing stochastic methods fail in estimating the failure probability with robustness.
T...
Within the structural reliability context, the aim of this paper is to present a new accelerated Monte-Carlo simulation method, named ADS, Adaptive Directional Stratification, and designed to overcome the following industrial constraints: robustness of the estimation of a low structural failure probability (less than 10(-3)), limited computational...
A novel approach for metamodelling and estimation variance based sensitivity indices for models with dependent variables are presented. Both the first order and total sensitivity indices are derived as generalizations of Sobol' sensitivity indices. Formulas and Monte Carlo numerical estimates similar to Sobol' formulas are derived. A Gaussian copul...
The aim of this paper is to present a sensitivity statistic developed in the context of the design of a new accelerated Monte-Carlo method. In the field of structural reliability, we elaborated the “Adaptive Directional Stratification” method (ADS), in order to estimate small failure probabilities in a robust manner with a limited number of simulat...
L'estimation d'une probabilité de défaillance, ou autrement dit l'estimation d'une intégrale multidimensionnelle, est une problématique classique en fiabilité des structures et de nombreuses méthodes de calculs existent déjà dans la littérature. Cependant, très peu de méthodes répondent simultanément aux contraintes suivantes couramment rencontrées...