Le logiciel Echo_light : Optimisation et Validation

Le logiciel d'électromagnétisme Echo_light  permet de coupler plusieurs géométries axisymétriques en utilisant les formulations intégrales. Ce document regroupe les modifications effectuées dans le code   d'Alcatel Space : optimisation et parallélisation des différents scripts shell

La méthode multipôle appliquée au calcul du champ proche

Les applications de post-traitement en électromagnétisme utilisent souvent le calcul des champs en zone proche ou lointaine. Dans ce document, nous nous intéressons au calcul des champs proches par la formulation des équations intégrales dans le cas d'objets axisymétriques. A partir de la connaissance des courants à la surface d'un objet, il s'agit de déterminer les champs créés par cet objet sur son entourage. Nous avons utilisé pour cela la méthode multipôle, connue pour calculer rapidement des produits matrice-vecteur dans le cadre des équations de Maxwell. Son efficacité est validée sur de nombreux exemples par rapport à une méthode classique. Le gain obtenu est d'autant plus intéressant qu'il va nous permettre de réduire considérablement le temps d'exécution du logiciel de couplage   d'Alcatel Space qui nécessite de nombreux calculs d'interactions proches

Surrogate modeling approximation using a mixture of experts based on EM joint estimation

An automatic method to combine several local surrogate models is presented. This method is intended to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems. It strongly relies on the Expectation-Maximization (EM) algorithm for Gaussian mixture models (GMM). To the end of regression, the inputs are clustered together with their output values by means of parameter estimation of the joint distribution. A local expert is then built (linear, quadratic, artificial neural network, moving least squares) on each cluster. Lastly, the local experts are combined using the Gaussian mixture model parameters found by the EM algorithm to obtain a global model. This method is tested over both mathematical test cases and an engineering optimization problem from aeronautics and is found to improve the accuracy of the approximation

An improved approach for estimating the hyperparameters of the kriging model for high-dimensional problems through the partial least squares method

During the last years, kriging has become one of the most popular methods in computer simulation and machine learning. Kriging models have been successfully used in many engineering applications, to approximate expensive simulation models. When many input variables are used, kriging is inefficient mainly due to an exorbitant computational time required during its construction. To handle high-dimensional problems (100+), one method is recently proposed that combines kriging with the Partial Least Squares technique, the so-called KPLS model. This method has shown interesting results in terms of saving CPU time required to build model while maintaining sufficient accuracy, on both academic and industrial problems. However, KPLS has provided a poor accuracy compared to conventional kriging on multimodal functions. To handle this issue, this paper proposes adding a new step during the construction of KPLS to improve its accuracy for multimodal functions. When the exponential covariance functions are used, this step is based on simple identification between the covariance function of KPLS and kriging. The developed method is validated especially by using a multimodal academic function, known as Griewank function in the literature, and we show the gain in terms of accuracy and computer time by comparing with KPLS and kriging

Approche en paramètres de stratification pour l’optimisation biniveau de structures de fuselage composite

On présente ici une méthode d’optimisation biniveau de grandes structures de fuselage composite. Ce schéma biniveau est inspiré de la formulation Quasi Separable Decomposition (QSD)récemment développée par Haftka et Watson. Le comportement membrane et hors-plan des stratifiés est représenté au moyen des paramètres de stratification. La boucle d’optimisation supérieure fait intervenir la redistribution des efforts et a pour contraintes des quantités calculées par des problèmes d’optimisation locale en stabilité où les facteurs critiques de flambage sont approchés par des modèles réduits

Évaluation de l’incertitude associée à l’interpolation de maillage dans un calcul couplé partitionné

Cette étude s’intéresse à la représentation et à la propagation des incertitudes d’interpolation dans un calcul couplé de type fluide-structure partitionné. Nous proposons d’utiliser l’interpolation par krigeage pour transférer les informations d’un maillage à un autre. De plus, l’incertitude associée à la prédiction par krigeage est utilisée de façon originale afin de quantifier l’aléa introduit par l’étape d’interpolation sur la réponse du calcul couplé

Exploration and Sizing of a Large Passenger Aircraft with Distributed Electric Ducted Fans

In order to reduce the CO2 emissions, a disruptive concept in aircraft propulsion has to be considered. As studied in the past years hybrid distributed electric propulsion is a promising option. In this work the feasibility of a new concept aircraft, using this technology, has been studied. Two different energy sources have been used: fuel based engines and batteries. The latters have been chosen because of their exibility during operations and their promising improvements over next years. The technological horizon considered in this study is the 2035: thus some critical hypotheses have been made for electrical components, airframe and propulsion. Due to the uncertainty associated to these data, sensivity analyses have been performed in order to assess the impact of technologies variations. To evaluate the advantages of the proposed concept, a comparison with a conventional aircraft(EIS 2035), based on evolutions of today's technology (airframe, propulsion, aerodynamics)has been made

Similarity Maximization of a Scaled Aeroelastic Flight Demonstrator via Multidisciplinary Optimization

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143025/1/6.2017-0573.pd

Upper trust bound feasibility criterion for mixed constrained Bayesian optimization with application to aircraft design

Bayesian optimization methods have been successfully applied to black box optimization problems that are expensive to evaluate. In this paper, we adapt the so-called super efficient global optimization algorithm to solve more accurately mixed constrained problems. The proposed approach handles constraints by means of upper trust bound, the latter encourages exploration of the feasible domain by combining the mean prediction and the associated uncertainty function given by the Gaussian processes. On top of that, a refinement procedure, based on a learning rate criterion, is introduced to enhance the exploitation and exploration trade-off. We show the good potential of the approach on a set of numerical experiments. Finally, we present an application to conceptual aircraft configuration upon which we show the superiority of the proposed approach compared to a set of the state-of-the-art black box optimization solvers

An adaptive optimization strategy based on mixture of experts for wing aerodynamic design optimization

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143084/1/6.2017-4433.pd