Miguel Munoz ZunigaIFP Energies nouvelles · Applied Mathematics Division
Miguel Munoz Zuniga
PhD
About
20
Publications
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177
Citations
Introduction
Additional affiliations
April 2012 - October 2013
January 2011 - February 2012
Publications
Publications (20)
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...
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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...
Projects
Projects (3)
In the context of the energy transition, wind power generation is developing rapidly in France and worldwide. IFP Energies nouvelles is positioning itself as an energy transition player by investing in research and innovation on wind resource characterization, turbine control, coupled mechanical modeling of wind systems and technological development of offshore wind turbines floaters. Together with this development, the monitoring and the maintenance of wind turbines is becoming a major issue. Current solutions do not take full advantage of the large amounts of data provided by sensors placed on modern wind turbines in production. In a context of digitization of the industry, the exploitation of this data can be optimized in order to refine the predictions of production, the lifetime of the structure, the control strategies and the planning of maintenance. In this context, it would be interesting to combine production data and numerical models optimally in order to obtain highly reliable models of wind turbines. This process is of interest to many industrial and academic groups and is known in many fields of the industry, including the wind industry, as digital twin. The objective of the PhD work is to develop of data assimilation methodology to build the digital twin of an onshore wind turbine. Based on measurements, the data assimilation should allow the reducing of the uncertainties of the physical parameters of the numerical model developed during the design phase to obtain a highly reliable model. This model will be then continuously or periodically updated to take into account the possible changes in the properties of the wind turbine during its lifetime.
SobolGSA is general purpose GUI driven global sensitivity analysis and metamodeling software. It has a choice of three different metamodeling techniques: Quasi Random Sampling-High dimensional model representation (QRS-HDMR) method (with regression and projection methods) and radial basis function method. SobolGSA can be used to construct metamodels either from explicitly known models or directly from data produced by "black-box" models. SobolGSA can be applied to both static and time-dependent problems. It can handle several outputs for analysis; each output can be time-dependent. Developed metamodels are produced in a form of self-contained MATLAB or C# files which can be used as accurate, reliable and very fast surrogates of the original CPU-expensive full scale models.
