Parameterization adaption for 3D shape optimization in aerodynamics

When solving a PDE problem numerically, a certain mesh-refinement process is always implicit, and very classically, mesh adaptivity is a very effective means to accelerate grid convergence. Similarly, when optimizing a shape by means of an explicit geometrical representation, it is natural to seek for an analogous concept of parameterization adaptivity. We propose here an adaptive parameterization for three-dimensional optimum design in aerodynamics by using the so-called "Free-Form Deformation" approach based on 3D tensorial B\'ezier parameterization. The proposed procedure leads to efficient numerical simulations with highly reduced computational costs

Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter

Cognitive radio technology addresses the problem of spectrum scarcity by allowing secondary users to use the vacant spectrum bands without causing interference to the primary users. However, several attacks could disturb the normal functioning of the cognitive radio network. Primary user emulation attacks are one of the most severe attacks in which a malicious user emulates the primary user signal characteristics to either prevent other legitimate secondary users from accessing the idle channels or causing harmful interference to the primary users. There are several proposed approaches to detect the primary user emulation attackers. However, most of these techniques assume that the primary user location is fixed, which does not make them valid when the primary user is mobile. In this paper, we propose a new approach based on the Kalman filter framework for detecting the primary user emulation attacks with a non-stationary primary user. Several experiments have been conducted and the advantages of the proposed approach are demonstrated through the simulation results.Comment: 14 pages, 9 figure

Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure

Smart grid is an alternative solution of the conventional power grid which harnesses the power of the information technology to save the energy and meet today's environment requirements. Due to the inherent vulnerabilities in the information technology, the smart grid is exposed to a wide variety of threats that could be translated into cyber-attacks. In this paper, we develop a deep learning-based intrusion detection system to defend against cyber-attacks in the advanced metering infrastructure network. The proposed machine learning approach is trained and tested extensively on an empirical industrial dataset which is composed of several attack categories including the scanning, buffer overflow, and denial of service attacks. Then, an experimental comparison in terms of detection accuracy is conducted to evaluate the performance of the proposed approach with Naive Bayes, Support Vector Machine, and Random Forest. The obtained results suggest that the proposed approaches produce optimal results comparing to the other algorithms. Finally, we propose a network architecture to deploy the proposed anomaly-based intrusion detection system across the Advanced Metering Infrastructure network. In addition, we propose a network security architecture composed of two types of Intrusion detection system types, Host and Network-based, deployed across the Advanced Metering Infrastructure network to inspect the traffic and detect the malicious one at all the levels.Comment: 7 pages, 6 figures. 2019 NISS19: Proceedings of the 2nd International Conference on Networking, Information Systems & Securit

Multilevel strategies for parametric shape optimization in aerodynamics

International audienceThe essential numerical features of multilevel strategies developed for parametric shape optimization are reviewed. These methods employ nested parameterization supports of either shape, or shape deformation, and the classical process of degree elevation resulting in exact geometrical data transfer from coarse to fine representations. The algorithms mimick classical multigrid strategies and are found very effective in terms of convergence acceleration. In particular, for a drag reduction problem involving a three-dimensional Eulerian transonic flow simulated by an unstructured-grid finite-volume method, the complete algorithm is found to be noticeably superior to the natural algorithm simply based on progressive degree elevation

Towards a self-adaptive parameterization for aerodynamic shape optimization

International audienc

Aerodynamic Shape Optimization using a Full and Adaptive Multilevel Algorithm

International audienceWe are interested by the general problem consisting of minimizing a functional of a state field solution of a PDE state equation. In Particular in this work, we optimize a 3D wing shape immersed an in inviscid flow to reduce drag. Whence, each evaluation of the cost functional is computationally expensive. For improving the convergence rate of the optimization algorihm, we propose a multi-scale algorithm inspired from the Full Multi-Grid method [1], and referred to as the Full and Adaptive Multi-Level Optimum-Shape Algorithm (FAMOSA), originally defined in [5]. The proposed method include the following strategies: • The simplest scheme " one way up " by choosing the parametrization of Bézier type to construct a hierarchy of embedded parametric spaces, via the classical degree elevation process [3]. • V-cycle algorithm by using (on the coarse level) " perturbation " unknowns from the latest fine estimate, i.e deformation instead of shapes

Optimisation aéro-structurale de la voilure d'un avion d'affaires par un jeu de Nash et un partage adapté des variables

International audienceOn s'intéresse au problème de l'optimisation multidisciplinaire, lorsque les disciplines sont prises en compte par des critères qui sont des fonctionnelles de solutions distribuées d'équations aux dérivées partielles. Pour le cas de deux disciplines, on propose une stratégie dans laquelle l'optimisation est décomposée en deux phases : (a) une phase d'optimisation coopérative au cours de laquelle les critères sont améliorés à chaque itération, et (b) une phase d'optimisation concurrentielle réalisée par un jeu de Nash associé à un partage adapté des variables

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Optimisation de forme paramétrique multiniveau Application à l'optimisation de la voilure d'un avion d'affaires

International audienceDans les années récentes, les progrès continus de la mécanique des fluides numérique (en anglais, Computational Fluid Dynamics, CFD), ont ouvert la voie à l'optimisation, es-sentiellement de forme, et à la conception par simulation numérique en aérodynamique compressible, et ses couplages avec d'autres disciplines. La conception optimale en aérody-namique est un domaine à l'interface de plusieurs disciplines classiques dans lequel on doit assembler de nombreuses composantes techniques liées : • au traitement de la géométrie (génération et adaptation de maillage, outil de conception assistée par ordinateur, CAO), • à la résolution de systèmes complexes d'équations aux dérivées partielles (EDP) régis-sant les phénomènes physiques étudiés

Algorithmes hiérarchiques et stratégies de jeux pour l'optimisation multidisciplinaire Application à l'optimisation de la voilure d'un avion d'affaires

This thesis aims at the development of innovating methods for optimum design in aerodynamics and more generally for multicriterion or multidisciplinary optimization problems in the aeronautical context. The first part is devoted to the improvement of efficiency of shape-optimization algorithms in terms of convergence. In a first section, multilevel optimization algorithms inspired from multigrid methods, well-known to be particularly efficient in iterative convergence, have been developed on the basis of a hierarchy of nested parameterizations. In a second section, self-adaptive parameterization procedures by regularization have been proposed. By means of three-dimensional flow simulations about geometries of aircraft wings, we have been able to solve problems of drag reduction in a transonic regime, and noise-criterion reduction in a supersonic regime, and to demonstrate that self-adaptive multilevel algorithms permitted to reduce the cost of an optimization by about one order of magnitude. The second part is devoted to the treatment of a problem of concurrent optimization in which the aerodynamicist interacts with the structural designer, in a parallel way in a symmetric Nash game, or hierarchically in a Stackelberg game. Algorithms for the calculation of the equilibrium point have been proposed and successfully tested for this coupled aero-structural shape optimization in a situation where the aerodynamical criterion is preponderant.Cette thèse a pour objectif le développement de méthodes numériques innovantes pour la conception optimale de forme en aérodynamique et plus généralement pour les problèmes d'optimisation multicritère ou multidisciplinaire dans un contexte aéronautique. La première partie est consacrée à l'amélioration de l'efficacité des algorithmes d'optimisation de forme en matière de convergence. Dans un premier volet, on a développé des algorithmes d'optimisation multiniveaux qui, à l'instar des méthodes multigrilles particulièrement performantes en convergence itérative, s'appuient sur une hiérarchie de paramétrisations emboîtées.Dans un deuxième volet, on a proposé des techniques d'adaptation automatique de la paramétrisation par régularisation. Par des simulations d'écoulements tridimensionnels autour de géométries de voilures d'avions, on a résolu des problèmes de réduction de traînée en transsonique et de réduction de critère de bruit en supersonique et montré que les algorithmes multiniveaux auto-adaptatifs permettaient de réduire le coût du calcul d'environ un ordre de grandeur. La deuxième partie est consacrée au traitement d'un problème d'optimisation concourante où le concepteur aérodynamique interagit avec le concepteur structural, parallèlement dans un jeu symétrique de Nash, ou hiérarchiquement dans un jeu de Stackelberg. On a proposé et expérimenté avec succès des algorithmes de calcul d'équilibre pour cette optimisation couplée aéro-structurale dans une situation où le critère aérodynamique est prépondérant