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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Thu, 07 Dec 2023 11:00:18 GMT2023-12-07T11:00:18ZOn the coupling of local 3D solutions and global 2D shell theory in structural mechanics
http://hdl.handle.net/10985/14597
On the coupling of local 3D solutions and global 2D shell theory in structural mechanics
QUARANTA, Giacomo; ZIANE, Mustapha; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; CHINESTA, Francisco; ESI GROUP
Most of mechanical systems and complex structures exhibit plate and shell components. Therefore, 2D simulation, based on plate and shell theory, appears as an appealing choice in structural analysis as it allows reducing the computational complexity. Nevertheless, this 2D framework fails for capturing rich physics compromising the usual hypotheses considered when deriving standard plate and shell theories. To circumvent, or at least alleviate this issue, authors proposed in their former works an in-plane-out-of-plane separated representation able to capture rich 3D behaviors while keeping the computational complexity of 2D simulations. However, that procedure it was revealed to be too intrusive for being introduced into existing commercial softwares. Moreover, experience indicated that such enriched descriptions are only compulsory locally, in some regions or structure components. In the present paper we propose an enrichment procedure able to address 3D local behaviors, preserving the direct minimally-invasive coupling with existing plate and shell discretizations. The proposed strategy will be extended to inelastic behaviors and structural dynamics.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/145972019-01-01T00:00:00ZQUARANTA, GiacomoZIANE, MustaphaABISSET-CHAVANNE, EmmanuelleDUVAL, Jean LouisCHINESTA, FranciscoESI GROUPMost of mechanical systems and complex structures exhibit plate and shell components. Therefore, 2D simulation, based on plate and shell theory, appears as an appealing choice in structural analysis as it allows reducing the computational complexity. Nevertheless, this 2D framework fails for capturing rich physics compromising the usual hypotheses considered when deriving standard plate and shell theories. To circumvent, or at least alleviate this issue, authors proposed in their former works an in-plane-out-of-plane separated representation able to capture rich 3D behaviors while keeping the computational complexity of 2D simulations. However, that procedure it was revealed to be too intrusive for being introduced into existing commercial softwares. Moreover, experience indicated that such enriched descriptions are only compulsory locally, in some regions or structure components. In the present paper we propose an enrichment procedure able to address 3D local behaviors, preserving the direct minimally-invasive coupling with existing plate and shell discretizations. The proposed strategy will be extended to inelastic behaviors and structural dynamics.Learning data-driven reduced elastic and inelastic models of spot-welded patches
http://hdl.handle.net/10985/20416
Learning data-driven reduced elastic and inelastic models of spot-welded patches
REILLE, Agathe; CHAMPANEY, Victor; DAIM, Fatima; TOURBIER, Yves; HASCOET, Nicolas; GONZALEZ, David; CUETO, Elias; DUVAL, Jean Louis; CHINESTA, Francisco
Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized behaviors need for extremely fine descriptions, and this has an associated impact in the number of degrees of freedom from one side, and the decrease of the time step employed in usual explicit time integrations, whose stability scales with the size of the smallest element involved in the mesh. In the present work we propose a data-driven technique for learning the rich behavior of a local patch and integrate it into a standard coarser description at the structure level. Thus, localized behaviors impact the global structural response without needing an explicit description of that fine scale behaviors.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10985/204162021-01-01T00:00:00ZREILLE, AgatheCHAMPANEY, VictorDAIM, FatimaTOURBIER, YvesHASCOET, NicolasGONZALEZ, DavidCUETO, EliasDUVAL, Jean LouisCHINESTA, FranciscoSolving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized behaviors need for extremely fine descriptions, and this has an associated impact in the number of degrees of freedom from one side, and the decrease of the time step employed in usual explicit time integrations, whose stability scales with the size of the smallest element involved in the mesh. In the present work we propose a data-driven technique for learning the rich behavior of a local patch and integrate it into a standard coarser description at the structure level. Thus, localized behaviors impact the global structural response without needing an explicit description of that fine scale behaviors.Structural health monitoring by combining machine learning and dimensionality reduction techniques
http://hdl.handle.net/10985/15522
Structural health monitoring by combining machine learning and dimensionality reduction techniques
QUARANTA, Giacomo; LOPEZ, Elena; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; HUERTA, Antonio; CHINESTA, Francisco
Structural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate damaged and undamaged regions in a given structure. Indeed, existence, severity (size) and location of damage can be efficiently estimated from collected data at some locations from which the fields of interest are completed before the analysis based on machine learning and dimensionality reduction techniques proceed.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/155222019-01-01T00:00:00ZQUARANTA, GiacomoLOPEZ, ElenaABISSET-CHAVANNE, EmmanuelleDUVAL, Jean LouisHUERTA, AntonioCHINESTA, FranciscoStructural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate damaged and undamaged regions in a given structure. Indeed, existence, severity (size) and location of damage can be efficiently estimated from collected data at some locations from which the fields of interest are completed before the analysis based on machine learning and dimensionality reduction techniques proceed.Hybrid constitutive modeling: data-driven learning of corrections to plasticity models
http://hdl.handle.net/10985/17438
Hybrid constitutive modeling: data-driven learning of corrections to plasticity models
IBÁÑEZ, Rubén; ABISSET-CHAVANNE, Emmanuelle; GONZÁLEZ, David; DUVAL, Jean Louis; CUETO, Elias; CHINESTA, Francisco
In recent times a growing interest has arose on the development of data-driven techniques to avoid the employ of phenomenological constitutive models. While it is true that, in general, data do not fit perfectly to existing models, and present deviations from the most popular ones, we believe that this does not justify (or, at least, not always) to abandon completely all the acquired knowledge on the constitutive characterization of materials. Instead, what we propose here is, by means of machine learning techniques, to develop correction to those popular models so as to minimize the errors in constitutive modeling.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/174382019-01-01T00:00:00ZIBÁÑEZ, RubénABISSET-CHAVANNE, EmmanuelleGONZÁLEZ, DavidDUVAL, Jean LouisCUETO, EliasCHINESTA, FranciscoIn recent times a growing interest has arose on the development of data-driven techniques to avoid the employ of phenomenological constitutive models. While it is true that, in general, data do not fit perfectly to existing models, and present deviations from the most popular ones, we believe that this does not justify (or, at least, not always) to abandon completely all the acquired knowledge on the constitutive characterization of materials. Instead, what we propose here is, by means of machine learning techniques, to develop correction to those popular models so as to minimize the errors in constitutive modeling.Advanced modeling and simulation of sheet moulding compound (SMC) processes
http://hdl.handle.net/10985/19143
Advanced modeling and simulation of sheet moulding compound (SMC) processes
PEREZ, Marta; PRONO, David; GHNATIOS, Chady; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; CHINESTA, Francisco
In SMC processes, a charge of a composite material, which typically consists of a matrix composed of an unsaturated polyester or vinylester, reinforced with chopped glass fibres or carbon fi bre bundles and fillers, is placed on the bottom half of the preheated mould. The charge usually covers 30 to 90% of the total area. The upper half of the mould is closed rapidly at a speed of about 40 mm/s. This rapid movement causes the charge to flow inside the cavity. The reinforcing fibres are carried by the resin and experience a change of confi guration during the flow. This strongly influences the mechanical properties of the final part. Several issues compromises its efficient numerical simulation, among them: (i) the modeling of flow kinematics able to induce eventual fibres/resin segregation, (ii) the con ned fibres orientation evolution and its accurate prediction, (iii) local dilution effects, (iv) flow bifurcation at junctions and its impact on the fibres orientation state, (v) charge / mould contact and (vi) parametric solutions involving non-interpolative fields. The present paper reports advanced modeling and simulation techniques for circumventing, or at least alleviating, the just referred difficulties.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/191432019-01-01T00:00:00ZPEREZ, MartaPRONO, DavidGHNATIOS, ChadyABISSET-CHAVANNE, EmmanuelleDUVAL, Jean LouisCHINESTA, FranciscoIn SMC processes, a charge of a composite material, which typically consists of a matrix composed of an unsaturated polyester or vinylester, reinforced with chopped glass fibres or carbon fi bre bundles and fillers, is placed on the bottom half of the preheated mould. The charge usually covers 30 to 90% of the total area. The upper half of the mould is closed rapidly at a speed of about 40 mm/s. This rapid movement causes the charge to flow inside the cavity. The reinforcing fibres are carried by the resin and experience a change of confi guration during the flow. This strongly influences the mechanical properties of the final part. Several issues compromises its efficient numerical simulation, among them: (i) the modeling of flow kinematics able to induce eventual fibres/resin segregation, (ii) the con ned fibres orientation evolution and its accurate prediction, (iii) local dilution effects, (iv) flow bifurcation at junctions and its impact on the fibres orientation state, (v) charge / mould contact and (vi) parametric solutions involving non-interpolative fields. The present paper reports advanced modeling and simulation techniques for circumventing, or at least alleviating, the just referred difficulties.Empowering Advanced Parametric Modes Clustering from Topological Data Analysis
http://hdl.handle.net/10985/20835
Empowering Advanced Parametric Modes Clustering from Topological Data Analysis
FRAHI, Tarek; FALCO, Antonio; MAU, Baptiste Vinh; DUVAL, Jean Louis; CHINESTA, Francisco
Modal analysis is widely used for addressing NVH—Noise, Vibration, and Hardness—in automotive engineering. The so-called principal modes constitute an orthogonal basis, obtained from the eigenvectors related to the dynamical problem. When this basis is used for expressing the displacement field of a dynamical problem, the model equations become uncoupled. Moreover, a reduced basis can be defined according to the eigenvalues magnitude, leading to an uncoupled reduced model, especially appealing when solving large dynamical systems. However, engineering looks for optimal designs and therefore it focuses on parametric designs needing the efficient solution of parametric dynamical models. Solving parametrized eigenproblems remains a tricky issue, and, therefore, nonintrusive approaches are privileged. In that framework, a reduced basis consisting of the most significant eigenmodes is retained for each choice of the model parameters under consideration. Then, one is tempted to create a parametric reduced basis, by simply expressing the reduced basis parametrically by using an appropriate regression technique. However, an issue remains that limits the direct application of the just referred approach, the one related to the basis ordering. In order to order the modes before interpolating them, different techniques were proposed in the past, being the Modal Assurance Criterion—MAC—one of the most widely used. In the present paper, we proposed an alternative technique that, instead of operating at the eigenmodes level, classify the modes with respect to the deformed structure shapes that the eigenmodes induce, by invoking the so-called Topological Data Analysis—TDA—that ensures the invariance properties that topology ensure.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10985/208352021-01-01T00:00:00ZFRAHI, TarekFALCO, AntonioMAU, Baptiste VinhDUVAL, Jean LouisCHINESTA, FranciscoModal analysis is widely used for addressing NVH—Noise, Vibration, and Hardness—in automotive engineering. The so-called principal modes constitute an orthogonal basis, obtained from the eigenvectors related to the dynamical problem. When this basis is used for expressing the displacement field of a dynamical problem, the model equations become uncoupled. Moreover, a reduced basis can be defined according to the eigenvalues magnitude, leading to an uncoupled reduced model, especially appealing when solving large dynamical systems. However, engineering looks for optimal designs and therefore it focuses on parametric designs needing the efficient solution of parametric dynamical models. Solving parametrized eigenproblems remains a tricky issue, and, therefore, nonintrusive approaches are privileged. In that framework, a reduced basis consisting of the most significant eigenmodes is retained for each choice of the model parameters under consideration. Then, one is tempted to create a parametric reduced basis, by simply expressing the reduced basis parametrically by using an appropriate regression technique. However, an issue remains that limits the direct application of the just referred approach, the one related to the basis ordering. In order to order the modes before interpolating them, different techniques were proposed in the past, being the Modal Assurance Criterion—MAC—one of the most widely used. In the present paper, we proposed an alternative technique that, instead of operating at the eigenmodes level, classify the modes with respect to the deformed structure shapes that the eigenmodes induce, by invoking the so-called Topological Data Analysis—TDA—that ensures the invariance properties that topology ensure.Parametric Electromagnetic Analysis of Radar-Based Advanced Driver Assistant Systems
http://hdl.handle.net/10985/19416
Parametric Electromagnetic Analysis of Radar-Based Advanced Driver Assistant Systems
VERMIGLIO, Simona; CHAMPANEY, Victor; SANCARLOS, Abel; DAIM, Fatima; KEDZIA, Jean Claude; DUVAL, Jean Louis; DIEZ, Pedro; CHINESTA, Francisco
Efficient and optimal design of radar-based Advanced Driver Assistant Systems (ADAS) needs the evaluation of many different electromagnetic solutions for evaluating the impact of the radome on the electromagnetic wave propagation. Because of the very high frequency at which these devices operate, with the associated extremely small wavelength, very fine meshes are needed to accurately discretize the electromagnetic equations. Thus, the computational cost of each numerical solution for a given choice of the design or operation parameters, is high (CPU time consuming and needing significant computational resources) compromising the efficiency of standard optimization algorithms. In order to alleviate the just referred difficulties the present paper proposes an approach based on the use of reduced order modeling, in particular the construction of a parametric solution by employing a non-intrusive formulation of the Proper Generalized Decomposition, combined with a powerful phase-angle unwrapping strategy for accurately addressing the electric and magnetic fields interpolation, contributing to improve the design, the calibration and the operational use of those systems.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/194162020-01-01T00:00:00ZVERMIGLIO, SimonaCHAMPANEY, VictorSANCARLOS, AbelDAIM, FatimaKEDZIA, Jean ClaudeDUVAL, Jean LouisDIEZ, PedroCHINESTA, FranciscoEfficient and optimal design of radar-based Advanced Driver Assistant Systems (ADAS) needs the evaluation of many different electromagnetic solutions for evaluating the impact of the radome on the electromagnetic wave propagation. Because of the very high frequency at which these devices operate, with the associated extremely small wavelength, very fine meshes are needed to accurately discretize the electromagnetic equations. Thus, the computational cost of each numerical solution for a given choice of the design or operation parameters, is high (CPU time consuming and needing significant computational resources) compromising the efficiency of standard optimization algorithms. In order to alleviate the just referred difficulties the present paper proposes an approach based on the use of reduced order modeling, in particular the construction of a parametric solution by employing a non-intrusive formulation of the Proper Generalized Decomposition, combined with a powerful phase-angle unwrapping strategy for accurately addressing the electric and magnetic fields interpolation, contributing to improve the design, the calibration and the operational use of those systems.Nonlinear Regression Operating on Microstructures Described from Topological Data Analysis for the Real-Time Prediction of Effective Properties
http://hdl.handle.net/10985/18955
Nonlinear Regression Operating on Microstructures Described from Topological Data Analysis for the Real-Time Prediction of Effective Properties
YUN, Minyoung; ARGERICH, Clara; CUETO, Elias; DUVAL, Jean Louis; CHINESTA, Francisco
Real-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use of Model Order Reduction techniques allows speeding-up calculations, enabling fast and accurate evaluations. To accommodate real-time constraints, a valuable route consists of computing parametric solutions—the so-called computational vademecums—that constructed off-line, can be inspected on-line. However, when dealing with shapes and topologies (complex or rich microstructures) their parametric description constitutes a major difficulty. In this paper, we propose using Topological Data Analysis for describing those rich topologies and morphologies in a concise way, and then using the associated topological descriptions for generating accurate supervised classification and nonlinear regression, enabling an almost real-time evaluation of QoI and the associated decision making.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/189552020-01-01T00:00:00ZYUN, MinyoungARGERICH, ClaraCUETO, EliasDUVAL, Jean LouisCHINESTA, FranciscoReal-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use of Model Order Reduction techniques allows speeding-up calculations, enabling fast and accurate evaluations. To accommodate real-time constraints, a valuable route consists of computing parametric solutions—the so-called computational vademecums—that constructed off-line, can be inspected on-line. However, when dealing with shapes and topologies (complex or rich microstructures) their parametric description constitutes a major difficulty. In this paper, we propose using Topological Data Analysis for describing those rich topologies and morphologies in a concise way, and then using the associated topological descriptions for generating accurate supervised classification and nonlinear regression, enabling an almost real-time evaluation of QoI and the associated decision making.Non-Intrusive In-Plane-Out-of-Plane Separated Representation in 3D Parametric Elastodynamics
http://hdl.handle.net/10985/19317
Non-Intrusive In-Plane-Out-of-Plane Separated Representation in 3D Parametric Elastodynamics
GERMOSO, Claudia; QUARANTA, Giacomo; DUVAL, Jean Louis; CHINESTA, Francisco
Mesh-based solution of 3D models defined in plate or shell domains remains a challenging issue nowadays due to the fact that the needed meshes generally involve too many degrees of freedom. When the considered problem involves some parameters aiming at computing its parametric solution the difficulty is twofold. The authors proposed, in some of their former works, strategies for solving both, however they suffer from a deep intrusiveness. This paper proposes a totally novel approach that from any existing discretization is able to reduce the 3D parametric complexity to the one characteristic of a simple 2D calculation. Thus, the 3D complexity is reduced to 2D, the parameters included naturally into the solution, and the procedure applied on a discretization performed with a standard software, which taken together enable real-time engineering.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/193172020-01-01T00:00:00ZGERMOSO, ClaudiaQUARANTA, GiacomoDUVAL, Jean LouisCHINESTA, FranciscoMesh-based solution of 3D models defined in plate or shell domains remains a challenging issue nowadays due to the fact that the needed meshes generally involve too many degrees of freedom. When the considered problem involves some parameters aiming at computing its parametric solution the difficulty is twofold. The authors proposed, in some of their former works, strategies for solving both, however they suffer from a deep intrusiveness. This paper proposes a totally novel approach that from any existing discretization is able to reduce the 3D parametric complexity to the one characteristic of a simple 2D calculation. Thus, the 3D complexity is reduced to 2D, the parameters included naturally into the solution, and the procedure applied on a discretization performed with a standard software, which taken together enable real-time engineering.Harmonic-Modal Hybrid Reduced Order Model for the Efficient Integration of Non-Linear Soil Dynamics
http://hdl.handle.net/10985/19443
Harmonic-Modal Hybrid Reduced Order Model for the Efficient Integration of Non-Linear Soil Dynamics
GERMOSO, Claudia; DUVAL, Jean Louis; CHINESTA, Francisco
Nonlinear behavior of soils during a seismic event has a predominant role in current site response analysis. Soil response analysis, and more concretely laboratory data, indicate that the stress-strain relationship of soils is nonlinear and exhibits hysteresis. An equivalent linearization method, in which non-linear characteristics of shear modulus and damping factor of soils are modeled as equivalent linear relations of the shear strain is usually applied, but this assumption, however, may lead to a conservative approach of the seismic design. In this paper, we propose an alternative analysis formulation, able to address forced response simulation of soils exhibiting their characteristic nonlinear behavior. The proposed approach combines ingredients of modal and harmonic analyses enabling efficient time-integration of nonlinear soil behaviors based on the offline construction of a dynamic response parametric solution by using Proper Generalized Decomposition (PGD)-based model order reduction technique.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/194432020-01-01T00:00:00ZGERMOSO, ClaudiaDUVAL, Jean LouisCHINESTA, FranciscoNonlinear behavior of soils during a seismic event has a predominant role in current site response analysis. Soil response analysis, and more concretely laboratory data, indicate that the stress-strain relationship of soils is nonlinear and exhibits hysteresis. An equivalent linearization method, in which non-linear characteristics of shear modulus and damping factor of soils are modeled as equivalent linear relations of the shear strain is usually applied, but this assumption, however, may lead to a conservative approach of the seismic design. In this paper, we propose an alternative analysis formulation, able to address forced response simulation of soils exhibiting their characteristic nonlinear behavior. The proposed approach combines ingredients of modal and harmonic analyses enabling efficient time-integration of nonlinear soil behaviors based on the offline construction of a dynamic response parametric solution by using Proper Generalized Decomposition (PGD)-based model order reduction technique.