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Understanding Electrostatic Field Sensing With Graphene: A Miniature And Versatile Alternative To Standard Technologies
No full textInternational audienceElectric field measurement is becoming of primary importance in various domains, for both civil and military applications, such as early prediction of lightning or prevention of electrostatic discharges on satellites. None of the state-of-the-art electric field sensors combines good resolution -which should be below 1 V/m for the aforementioned targeted applications -, with a high measurement range -which should be at least 1 MV/m -and compacity. Following the use of graphene field effect transistors (GFETs) for gas, pH and various biological sensors, a new concept for an electrostatic field sensor, also based on a GFET, has emerged [1-2]. It has however attracted very little attention by the scientific community and its detection mechanism remains controversial. Here, we propose a physical model of a highly sensitive electric field sensor based on a GFET with a floating gate acting as an antenna. This model, based on the combination of the so-called graphene's transistor effect [3] and the laws describing the potential of a floating conductor, is demonstrated using transistors made of graphene grown by chemical vapor deposition on a SiO2/Si substrate [4]
Méthodologie de couplage aéro-structure multi-fidélité pour la conception d'avion avant-projet
No full textThe increasing environmental and efficiency demands in aviation necessitate the exploration of novel aircraft configurations and advanced design methodologies. High Aspect Ratio Strut-Braced Wing (HARSBW) configurations present a promising solution by reducing fuel consumption and aerodynamic drag. However, their complex aero-structural interactions require sophisticated Multi-Disciplinary Analysis (MDA) and Multi-Disciplinary Analysis and Optimization (MDAO) methodologies. This thesis develops an aero-structural multi-fidelity coupling methodology that integrates surrogate modeling and dimension reduction techniques to enhance computational efficiency without sacrificing accuracy. The research begins with a bibliographical review outlining the challenges in aircraft design and the potential of MDAO to overcome them. A key issue is the high computational cost of high-fidelity simulations required for accurate MDA, motivating the need for surrogate-based modeling approaches. One of the challenges regarding a surrogate-based approach is to deal with high dimensional coupling variables corresponding to the vector of aerodynamic forces and structural displacements. To address this, the thesis first develops the parametric models for aerodynamic and structural simulations tailored to HARSBW. These models include low-fidelity potential fluid and high-fidelity compressible Euler aerodynamic solvers, as well as a linear elastic structural model. Next, a direct solver-based MDA framework is constructed and analyzed at different fidelity levels. The trade-offs between computational cost and accuracy are assessed. Based on the low and high-fidelity direct solver-coupled MDA schemes, this research constructs single and multi-fidelity dimension-reduced surrogate model-based MDA schemes, combining Proper Orthogonal Decomposition (POD) and Gaussian Process (GP) regression. This framework captures high-dimensional coupling variables efficiently, significantly reducing reliance on expensive high-fidelity solvers. A first attempt of further refinement of the multi-fidelity dimension-reduced surrogate model is carried out through an iterative enrichment algorithm that selectively incorporates additional data in high-uncertainty regions for the training of the POD basis and the GP surrogate models. Application of the developed algorithms on the HARSBW configuration illustrate the interest of such a proposed methodology in reducing computational costs while maintaining high-fidelity accuracy.L'augmentation des exigences environnementales et d'efficacité dans l'aviation nécessite l'exploration de nouvelles configurations d'aéronefs et de méthodologies de conception avancées. Les configurations à aile haubanée à fort allongement (HARSBW) constituent une solution prometteuse permettant de réduire la consommation de carburant et la traînée aérodynamique. Cependant, ces configurations induisent des interactions complexes entre l'aérodynamique et la structure qui requièrent des méthodologies adaptées d'Analyse Multi-Disciplinaire (MDA) et d'Analyse et d'Optimisation Multi-Disciplinaire (MDAO). Cette thèse propose une méthodologie de couplage aéro-structure multi-fidélité combinant la modélisation par modèles de substitution et les techniques de réduction de dimension afin d'améliorer l'efficacité computationnelle tout en préservant la précision. Ce manuscrit propose une revue bibliographique décrivant les défis de la conception des aéronefs et le potentiel des techniques MDAO pour les surmonter. Un enjeu clé réside dans le coût calculatoire élevé des simulations haute fidélité nécessaires à une MDA précise, justifiant ainsi l'adoption d'approches basées sur la modélisation par modèles de substitution. Pour répondre à cette problématique, la thèse développe d'abord des modèles paramétriques pour les simulations aérodynamiques et structurales adaptées aux HARSBW. Ces modèles incluent un modèle de fluide potentiel basse fidélité et un solveur aérodynamique résolvant les équations d'Euler compressibles en haute fidélité, ainsi qu'un modèle de structure linéaire élastique. Ensuite, une méthodologie d'analyse multidisciplinaire (MDA) basée sur un solveur direct est construite et ses performances sont analysées à différents niveaux de fidélité. Les compromis entre coût de calcul et précision sont évalués afin d'établir une référence pour la modélisation par modèle de substitution. À partir des schémas MDA couplés aux solveurs directs en basse et haute fidélité, sont développés des schémas MDA basés sur des modèles de substitution avec réduction de dimension en mono- et en multi-fidélité, combinant la Décomposition Orthogonale aux Valeurs Propres (POD) et la régression par Processus Gaussiens (GP). La méthodologie mise en œuvre permet de modéliser efficacement les variables de couplage de grande dimension, réduisant considérablement la dépendance aux solveurs haute fidélité coûteux. Le raffinement du modèle de substitution multi-fidélité avec réduction de dimension est ensuite réalisé via un algorithme d'apprentissage actif, qui sélectionne de manière ciblée les données additionnelles dans les régions de forte incertitude pour l'apprentissage de la base POD et des modèles de substitution GP. Cet algorithme garantit que le modèle de substitution reste à la fois efficace en temps de calculs et précis, en adaptant dynamiquement les niveaux de fidélité de l'enrichissement. L'application des algorithmes développés à la configuration HARSBW démontre l'efficacité de la méthodologie proposée pour réduire les coûts de calculs tout en maintenant une précision de haute fidélité
Système hybride SHM en conditions cryogéniques pour les lanceurs réutilisables
No full textInternational audienc
Bruit d’aéroport à l’horizon 2050 : Intégration d’une aile volante
No full textBruit des transports : bruit des aéronefsInternational audienc
The Loewner framework for parametric systems: Taming the curse of dimensionality
No full textThe Loewner framework is an interpolatory approach designed for approximating linear and nonlinear systems. The goal here is to extend this framework to linear parametric systems with an arbitrary number n of parameters. One main innovation established here is the construction of data-based realizations for any number of parameters. Equally importantly, we show how to alleviate the computational burden, by avoiding the explicit construction of large-scale n-dimensional Loewner matrices of size . This reduces the complexity from to about , thus taming the curse of dimensionality and making the solution scalable to very large data sets. To achieve this, a new generalized multivariate rational function realization is defined. Then, we introduce the n-dimensional multivariate Loewner matrices and show that they can be computed by solving a coupled set of Sylvester equations. The null space of these Loewner matrices then allows the construction of the multivariate barycentric transfer function. The principal result of this work is to show how the null space of the n-dimensional Loewner matrix can be computed using a sequence of 1-dimensional Loewner matrices, leading to a drastic computational burden reduction. Finally, we suggest two algorithms (one direct and one iterative) to construct, directly from data, multivariate (or parametric) realizations ensuring (approximate) interpolation. Numerical examples highlight the effectiveness and scalability of the method
Frequency-aware Surrogate Modeling With SMT Kernels For Advanced Data Forecasting
No full textInternational audienceThis paper introduces a comprehensive open-source framework for developing correlation kernels, with a particular focus on user-defined and composition of kernels for surrogate modeling. By advancing kernel-based modeling techniques, we incorporate frequency-aware elements that effectively capture complex mechanical behaviors and timefrequency dynamics intrinsic to aircraft systems. Traditional kernel functions, often limited to exponential-based methods, are extended to include a wider range of kernels such as exponential squared sine and rational quadratic kernels, along with their respective firstand second-order derivatives. The proposed methodologies are first validated on a sinus cardinal test case and then applied to forecasting Mauna-Loa Carbon Dioxide (CO 2 ) concentrations and airline passenger traffic. All these advancements are integrated into the open-source Surrogate Modeling Toolbox (SMT 2.0), providing a versatile platform for both standard and customizable kernel configurations. Furthermore, the framework enables the combination of various kernels to leverage their unique strengths into composite models tailored to specific problems. The resulting framework offers a flexible toolset for engineers and researchers, paving the way for numerous future applications in metamodeling for complex, frequency-sensitive domains
Étude de la structure des flammes à haut Ka en utilisant simultanément la PIV thermométrique et la PLIF OH
No full textInternational audienceTo limit the formation of thermal NOx in gas turbines, H or H enriched hydrocarbon (HC) fuels are most likely to be consumed at ultra-lean conditions with a high turbulence intensity, leading to high Karlovitz (Ka) numbers. In this study, we for the first-time combine thermographic stereo-PIV together with simultaneous OH-PLIF to investigate the structure of high Ka H and H/HC flames in the thin reaction zones (TRZ, < 1 Ka < 100) and broken reaction zones (BRZ, Ka > 100) regimes. Studying relatively thick flames at high Ka and low flame temperatures associated with ultra-lean conditions, thermographic PIV successfully resolved part of the preheat zone and post-flame region, while, OH-PLIF revealed the flame structure and position of the flame front. With these multi-physics diagnostics, we successfully quantify the preheat zone thickness in a canonical turbulent V-flame and compare with 1D free/stagnation flame simulations, and in a flame-wall interaction (FWI) configuration to investigate the local quenching phenomenon. The results show that the preheat zone thickness increases with higher Ka and is subjected to the influence of both flame curvature and the persistent strain rate; whereas in the FWI setup, the average thickness of the quenching area can be as high as 6 times that of the laminar flame thickness. The proposed diagnostics may be readily applied to other flames, such as ammonia flames, with thick reaction zones and relatively low flame temperatures.Pour limiter la formation de NOx thermiques dans les turbines à gaz, les combustibles hydrocarbonés (HC) enrichis en H2 ou en H2 sont le plus souvent consommés dans des conditions d'extrême pauvreté en combustible avec une forte intensité de turbulence, ce qui conduit à des nombres de Karlovitz (Ka) élevés. Dans cette étude, nous combinons pour la première fois la stéréo-PIV thermographique et la OH-PLIF simultanée pour étudier la structure des flammes de H2 et H2/HC à Ka élevé dans les régimes des zones de réaction minces (Thin Reaction Zones TRZ, 1100). Lors de l'étude de flammes relativement épaisses à des Ka élevés et à des températures de flamme basses associées à des conditions ultra pauvres, la PIV thermographique a résolu avec succès une partie de la zone de préchauffage et de la région post-flamme, tandis que l'OH-PLIF a révélé la structure de la flamme et la position du front de flamme. Grâce à ces diagnostics multi-physiques, nous avons réussi à quantifier l'épaisseur de la zone de préchauffage dans une flamme en V turbulente canonique et à la comparer avec des simulations de flammes libres/stagnation 1D, ainsi que dans une configuration d'interaction flamme-paroi (Flame Wall Interaction FWI) afin d'étudier le phénomène d'extinction locale. Les résultats montrent que l'épaisseur de la zone de préchauffage augmente avec un Ka plus élevé et est soumise à l'influence de la courbure de la flamme et de la vitesse de déformation persistante ; tandis que dans la configuration FWI, l'épaisseur moyenne de la zone d'extinction peut être jusqu'à 6 fois supérieure à l'épaisseur de la flamme laminaire. Les diagnostics proposés peuvent être facilement appliqués à d'autres flammes, telles que les flammes d'ammoniac, avec des zones de réaction épaisses et des températures de flamme relativement basses
Adjoint-based optimization for non-linear inverse problems with high-order discretization of the compressible RANS equations
No full textInternational audienceThis work presents an adjoint-based strategy to solve non-linear inverse problems discretized with high-order numerical methods. The inverse problem is defined here based on the optimization of a control parameter to minimize a cost-functional subject to the compressible RANS equations discretized with the modal discontinuous Galerkin (DG) method. The distributed control parameter is searched in the DG function space and the discrete adjoint approach, consistent with the formal problem, is used to compute the derivative of the cost function in the optimization process. The linearization of the cost-functional and of the governing equations, the expression of the gradient, as well as the numerical strategy to efficiently solve the adjoint system with flexible inner-outer GMRES solvers have been detailed. In the case of a strongly under-determined problem, regularization techniques based on the penalization of the norm of the control parameter have been introduced. The methodology is illustrated on the case of a data-assimilation (DA) problem, which aims at minimizing the discrepancy of (sparse) high-fidelity measurements with the solution of the RANS equations corrected by four different control parameters. The optimization strategy is tested progressively with measurements on the full computational domain (abundant measurements) and solid wall boundaries (sparse measurements). First, a laminar flow around a cylinder is used to validate the inverse problem resolution with a DG discretization of different approximation orders. Subsequently, results regarding a turbulent flow around a square cylinder allow to compare the optimization convergence of each corrective parameters with abundant measurements. Finally, a shock-wave/turbulent boundary-layer interaction configuration is considered. Great correction of the velocity field is obtained with one of the proposed corrective term. In the case of abundant measurements it is also possible to get accurate correction of wall variables such as the skin-friction and pressure coefficient. Regularization of the optimal space, in case of sparse measurements, is attempt through penalization techniques
Investigating multi-scale heterogeneity in multi-layer additive friction stir deposition of high-strength aluminum alloys
No full textInternational audienceThis study investigates the application of multi-layer Additive Friction Stir Deposition (AFSD) for the manufacturing of an AA7075 wall. A particular focus is placed on the material’s structural integrity, including, to the best of our knowledge, the first detailed characterization of the interface between the substrate and the deposited material. The diversity of analytical techniques used provides a detailed understanding of the evolution of microstructure during deposition and as a function of material height.Scanning electron microscopy in conjunction with X-ray diffraction allows for the observation of the evolution of the microstructure, revealing a smooth transition linked to a mechanical gradient. A crystallographic analysis reveals inter- and intra-layer texture variations, indicating that dynamic recrystallization and restoration mechanisms are concomitantly at work in the deposited material zone, as a function of the vertical distance from the tool. Hardness and tensile measurements indicate a non-negligible evolution from the substrate to the last deposited layer, resulting from the overaging of the phase. Finally, a detailed analysis of the interface between the substrate and the deposited material is proposed, which reveals a disturbed microstructure characterized by local heterogeneities in hardness due to significant variations in texture, grain size, and precipitation. All the results are intended to provide highly instructive data regarding microstructural evolution due to thermal cycling both in the deposited material and in the substrate, particularly in the context of the application of the repair of damaged parts