SAM
https://sam.ensam.eu:443
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Wed, 28 Feb 2024 22:35:57 GMT2024-02-28T22:35:57ZOn the effective conductivity and the apparent viscosity of a thin rough polymer interface using PGD‐based separated representations
http://hdl.handle.net/10985/19486
On the effective conductivity and the apparent viscosity of a thin rough polymer interface using PGD‐based separated representations
AMMAR, Amine; GHNATIOS, Chady; DELPLACE, Frank; BARASINSKI, Anais; DUVAL, Jean-Louis; CUETO, Elias; CHINESTA, Francisco
Composite manufacturing processes usually proceed from preimpregnated preforms that are consolidated by simultaneously applying heat and pressure, so as to ensure a perfect contact compulsory for making molecular diffusion possible. However, in practice, the contact is rarely perfect. This results in a rough interface where air could remain entrapped, thus affecting the effective thermal conductivity. Moreover, the interfacial melted polymer is squeezed flowing in the rough gap created by the fibers located on the prepreg surfaces. Because of the typical dimensions of a composite prepreg, with thickness orders of magnitude smaller than its other in-plane dimensions, and its surface roughness having a characteristic size orders of magnitude smaller than the prepreg thickness, high-fidelity numerical simulations for elucidating the impact of surface and interface roughness remain today, despite the impressive advances in computational availabilities, unattainable. This work aims at elucidating roughness impact on heat conduction and the effective viscosity of the interfacial polymer squeeze flow by using an advanced numerical strategy able to reach resolutions never attained until now, a sort of numerical microscope able to attain the scale of the smallest geometrical detail.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/194862020-01-01T00:00:00ZAMMAR, AmineGHNATIOS, ChadyDELPLACE, FrankBARASINSKI, AnaisDUVAL, Jean-LouisCUETO, EliasCHINESTA, FranciscoComposite manufacturing processes usually proceed from preimpregnated preforms that are consolidated by simultaneously applying heat and pressure, so as to ensure a perfect contact compulsory for making molecular diffusion possible. However, in practice, the contact is rarely perfect. This results in a rough interface where air could remain entrapped, thus affecting the effective thermal conductivity. Moreover, the interfacial melted polymer is squeezed flowing in the rough gap created by the fibers located on the prepreg surfaces. Because of the typical dimensions of a composite prepreg, with thickness orders of magnitude smaller than its other in-plane dimensions, and its surface roughness having a characteristic size orders of magnitude smaller than the prepreg thickness, high-fidelity numerical simulations for elucidating the impact of surface and interface roughness remain today, despite the impressive advances in computational availabilities, unattainable. This work aims at elucidating roughness impact on heat conduction and the effective viscosity of the interfacial polymer squeeze flow by using an advanced numerical strategy able to reach resolutions never attained until now, a sort of numerical microscope able to attain the scale of the smallest geometrical detail.Spurious-free interpolations for non-intrusive PGD-based parametric solutions: Application to composites forming processes
http://hdl.handle.net/10985/20478
Spurious-free interpolations for non-intrusive PGD-based parametric solutions: Application to composites forming processes
GHNATIOS, Chady; CUETO, Elias; FALCO, Antonio; DUVAL, Jean-Louis; CHINESTA, Francisco
Non-intrusive approaches for the construction of computational vademecums face different challenges, especially when a parameter variation affects the physics of the problem considerably. In these situations, classical interpolation becomes inaccurate. Therefore, classical approaches for the construction of an offline computational vademecum, typically by using model reduction techniques, are no longer valid. Such problems are faced in different physical simulations, for example welding path problems, resin transfer molding, or sheet compression molding, among others. In such situations, the interpolation of precomputed solutions at prescribed parameter values (built using either intrusive or non intrusive techniques) generates spurious numerical artifacts. In this work, we propose an alternative interpolation and simulation strategy by using physically-based morphing of spaces. The morphing will transform the uncompatibe physical domains of the problem’s solution into a compatible one, where an interpolation free of artifacts can be performed. Later on, an inverse transformation can be used to push-back the solution. Different relevant examples are illustrated in this work to motivate the use of the proposed method
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/204782020-01-01T00:00:00ZGHNATIOS, ChadyCUETO, EliasFALCO, AntonioDUVAL, Jean-LouisCHINESTA, FranciscoNon-intrusive approaches for the construction of computational vademecums face different challenges, especially when a parameter variation affects the physics of the problem considerably. In these situations, classical interpolation becomes inaccurate. Therefore, classical approaches for the construction of an offline computational vademecum, typically by using model reduction techniques, are no longer valid. Such problems are faced in different physical simulations, for example welding path problems, resin transfer molding, or sheet compression molding, among others. In such situations, the interpolation of precomputed solutions at prescribed parameter values (built using either intrusive or non intrusive techniques) generates spurious numerical artifacts. In this work, we propose an alternative interpolation and simulation strategy by using physically-based morphing of spaces. The morphing will transform the uncompatibe physical domains of the problem’s solution into a compatible one, where an interpolation free of artifacts can be performed. Later on, an inverse transformation can be used to push-back the solution. Different relevant examples are illustrated in this work to motivate the use of the proposed methodIncremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit
http://hdl.handle.net/10985/18539
Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit
REILLE, Agathe; HASCOET, Nicolas; GHNATIOS, Chady; AMMAR, Amine; CUETO, Elias; DUVAL, Jean-Louis; CHINESTA, Francisco; KEUNINGS, Roland
The present work aims at proposing a new methodology for learning reduced models from a small amount of data. It is based on the fact that discrete models, or their transfer function counterparts, have a low rank and then they can be expressed very efficiently using few terms of a tensor decomposition. An efficient procedure is proposed as well as a way for extending it to nonlinear settings while keeping limited the impact of data noise. The proposed methodology is then validated by considering a nonlinear elastic problem and constructing the model relating tractions and displacements at the observation points.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/185392019-01-01T00:00:00ZREILLE, AgatheHASCOET, NicolasGHNATIOS, ChadyAMMAR, AmineCUETO, EliasDUVAL, Jean-LouisCHINESTA, FranciscoKEUNINGS, RolandThe present work aims at proposing a new methodology for learning reduced models from a small amount of data. It is based on the fact that discrete models, or their transfer function counterparts, have a low rank and then they can be expressed very efficiently using few terms of a tensor decomposition. An efficient procedure is proposed as well as a way for extending it to nonlinear settings while keeping limited the impact of data noise. The proposed methodology is then validated by considering a nonlinear elastic problem and constructing the model relating tractions and displacements at the observation points.Multiscale proper generalized decomposition based on the partition of unity
http://hdl.handle.net/10985/18456
Multiscale proper generalized decomposition based on the partition of unity
IBÁÑEZ PINILLO, Rubén; AMMAR, Amine; CUETO, Elias; HUERTA, Antonio; DUVAL, Jean-Louis; CHINESTA, Francisco
Solutions of partial differential equations could exhibit a multiscale behavior. Standard discretization techniques are constraints to mesh up to the finest scale to predict accurately the response of the system. The proposed methodology is based on the standard proper generalized decomposition rationale; thus, the PDE is transformed into a nonlinear system that iterates between microscale and macroscale states, where the time coordinate could be viewed as a 2D time, representing the microtime and macrotime scales. The macroscale effects are taken into account because of an FEM-based macrodiscretization, whereas the microscale effects are handled with unidimensional parent spaces that are replicated throughout the domain. The proposed methodology can be seen as an alternative route to circumvent prohibitive meshes arising from the necessity of capturing fine-scale behaviors.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/184562019-01-01T00:00:00ZIBÁÑEZ PINILLO, RubénAMMAR, AmineCUETO, EliasHUERTA, AntonioDUVAL, Jean-LouisCHINESTA, FranciscoSolutions of partial differential equations could exhibit a multiscale behavior. Standard discretization techniques are constraints to mesh up to the finest scale to predict accurately the response of the system. The proposed methodology is based on the standard proper generalized decomposition rationale; thus, the PDE is transformed into a nonlinear system that iterates between microscale and macroscale states, where the time coordinate could be viewed as a 2D time, representing the microtime and macrotime scales. The macroscale effects are taken into account because of an FEM-based macrodiscretization, whereas the microscale effects are handled with unidimensional parent spaces that are replicated throughout the domain. The proposed methodology can be seen as an alternative route to circumvent prohibitive meshes arising from the necessity of capturing fine-scale behaviors.Enhanced parametric shape descriptions in PGD-based space separated representations
http://hdl.handle.net/10985/21175
Enhanced parametric shape descriptions in PGD-based space separated representations
KAZEMZADEH-PARSI, Mohammad Javad; AMMAR, Amine; DUVAL, Jean-Louis; CHINESTA, Francisco
Space separation within the Proper Generalized Decomposition—PGD—rationale allows solving high dimensional problems as a sequence of lower dimensional ones. In our former works, different geometrical transformations were proposed for addressing complex shapes and spatially non-separable domains. Efficient implementation of separated representations needs expressing the domain as a product of characteristic functions involving the different space coordinates. In the case of complex shapes, more sophisticated geometrical transformations are needed to map the complex physical domain into a regular one where computations are performed. This paper aims at proposing a very efficient route for accomplishing such space separation. A NURBS-based geometry representation, usual in computer aided design—CAD—, is retained and combined with a fully separated representation for allying efficiency (ensured by the fully separated representations) and generality (by addressing complex geometries). Some numerical examples are considered to prove the potential of the proposed methodology.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10985/211752021-01-01T00:00:00ZKAZEMZADEH-PARSI, Mohammad JavadAMMAR, AmineDUVAL, Jean-LouisCHINESTA, FranciscoSpace separation within the Proper Generalized Decomposition—PGD—rationale allows solving high dimensional problems as a sequence of lower dimensional ones. In our former works, different geometrical transformations were proposed for addressing complex shapes and spatially non-separable domains. Efficient implementation of separated representations needs expressing the domain as a product of characteristic functions involving the different space coordinates. In the case of complex shapes, more sophisticated geometrical transformations are needed to map the complex physical domain into a regular one where computations are performed. This paper aims at proposing a very efficient route for accomplishing such space separation. A NURBS-based geometry representation, usual in computer aided design—CAD—, is retained and combined with a fully separated representation for allying efficiency (ensured by the fully separated representations) and generality (by addressing complex geometries). Some numerical examples are considered to prove the potential of the proposed methodology.Learning the Parametric Transfer Function of Unitary Operations for Real-Time Evaluation of Manufacturing Processes Involving Operations Sequencing
http://hdl.handle.net/10985/20468
Learning the Parametric Transfer Function of Unitary Operations for Real-Time Evaluation of Manufacturing Processes Involving Operations Sequencing
LOREAU, Tanguy; CHAMPANEY, Victor; HASCOËT, Nicolas; MOURGUE, Philippe; DUVAL, Jean-Louis; CHINESTA, Francisco
For better designing manufacturing processes, surrogate models were widely considered in the past, where the effect of different material and process parameters was considered from the use of a parametric solution. The last contains the solution of the model describing the system under study, for any choice of the selected parameters. These surrogate models, also known as meta-models, virtual charts or computational vademecum, in the context of model order reduction, were successfully employed in a variety of industrial applications. However, they remain confronted to a major difficulty when the number of parameters grows exponentially. Thus, processes involving trajectories or sequencing entail a combinatorial exposition (curse of dimensionality) not only due to the number of possible combinations, but due to the number of parameters needed to describe the process. The present paper proposes a promising route for circumventing, or at least alleviating that difficulty. The proposed technique consists of a parametric transfer function that, as soon as it is learned, allows for, from a given state, inferring the new state after the application of a unitary operation, defined as a step in the sequenced process. Thus, any sequencing can be evaluated almost in real time by chaining that unitary transfer function, whose output becomes the input of the next operation. The benefits and potential of such a technique are illustrated on a problem of industrial relevance, the one concerning the induced deformation on a structural part when printing on it a series of stiffeners.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10985/204682021-01-01T00:00:00ZLOREAU, TanguyCHAMPANEY, VictorHASCOËT, NicolasMOURGUE, PhilippeDUVAL, Jean-LouisCHINESTA, FranciscoFor better designing manufacturing processes, surrogate models were widely considered in the past, where the effect of different material and process parameters was considered from the use of a parametric solution. The last contains the solution of the model describing the system under study, for any choice of the selected parameters. These surrogate models, also known as meta-models, virtual charts or computational vademecum, in the context of model order reduction, were successfully employed in a variety of industrial applications. However, they remain confronted to a major difficulty when the number of parameters grows exponentially. Thus, processes involving trajectories or sequencing entail a combinatorial exposition (curse of dimensionality) not only due to the number of possible combinations, but due to the number of parameters needed to describe the process. The present paper proposes a promising route for circumventing, or at least alleviating that difficulty. The proposed technique consists of a parametric transfer function that, as soon as it is learned, allows for, from a given state, inferring the new state after the application of a unitary operation, defined as a step in the sequenced process. Thus, any sequencing can be evaluated almost in real time by chaining that unitary transfer function, whose output becomes the input of the next operation. The benefits and potential of such a technique are illustrated on a problem of industrial relevance, the one concerning the induced deformation on a structural part when printing on it a series of stiffeners.Fast Computation of Multi-Parametric Electromagnetic Fields in Synchronous Machines by Using PGD-Based Fully Separated Representations
http://hdl.handle.net/10985/20417
Fast Computation of Multi-Parametric Electromagnetic Fields in Synchronous Machines by Using PGD-Based Fully Separated Representations
SANCARLOS, Abel; GHNATIOS, Chady; DUVAL, Jean-Louis; ZERBIB, Nicolas; CUETO, Elias; CHINESTA, Francisco
A novel Model Order Reduction (MOR) technique is developed to compute high-dimensional parametric solutions for electromagnetic fields in synchronous machines. Specifically, the intrusive version of the Proper Generalized Decomposition (PGD) is employed to simulate a Permanent-Magnet Synchronous Motor (PMSM). The result is a virtual chart allowing real-time evaluation of the magnetic vector potential as a function of the operation point of the motor, or even as a function of constructive parameters, such as the remanent flux in permanent magnets. Currently, these solutions are highly demanded by the industry, especially with the recent developments in the Electric Vehicle (EV). In this framework, standard discretization techniques require highly time-consuming simulations when analyzing, for instance, the noise and vibration in electric motors. The proposed approach is able to construct a virtual chart within a few minutes of off-line simulation, thanks to the use of a fully separated representation in which the solution is written from a series of functions of the space and parameters coordinates, with full space separation made possible by the use of an adapted geometrical mapping. Finally, excellent performances are reported when comparing the reduced-order model with the more standard and computationally costly Finite Element solutions.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10985/204172021-01-01T00:00:00ZSANCARLOS, AbelGHNATIOS, ChadyDUVAL, Jean-LouisZERBIB, NicolasCUETO, EliasCHINESTA, FranciscoA novel Model Order Reduction (MOR) technique is developed to compute high-dimensional parametric solutions for electromagnetic fields in synchronous machines. Specifically, the intrusive version of the Proper Generalized Decomposition (PGD) is employed to simulate a Permanent-Magnet Synchronous Motor (PMSM). The result is a virtual chart allowing real-time evaluation of the magnetic vector potential as a function of the operation point of the motor, or even as a function of constructive parameters, such as the remanent flux in permanent magnets. Currently, these solutions are highly demanded by the industry, especially with the recent developments in the Electric Vehicle (EV). In this framework, standard discretization techniques require highly time-consuming simulations when analyzing, for instance, the noise and vibration in electric motors. The proposed approach is able to construct a virtual chart within a few minutes of off-line simulation, thanks to the use of a fully separated representation in which the solution is written from a series of functions of the space and parameters coordinates, with full space separation made possible by the use of an adapted geometrical mapping. Finally, excellent performances are reported when comparing the reduced-order model with the more standard and computationally costly Finite Element solutions.Empowering Advanced Driver-Assistance Systems from Topological Data Analysis
http://hdl.handle.net/10985/20177
Empowering Advanced Driver-Assistance Systems from Topological Data Analysis
FRAHI, Tarek; CHINESTA, Francisco; FALCO, Antonio; BADIAS, Alberto; CUETO, Elias; CHOI, Hyung Yun; HAN, Manyong; DUVAL, Jean-Louis
We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10985/201772021-01-01T00:00:00ZFRAHI, TarekCHINESTA, FranciscoFALCO, AntonioBADIAS, AlbertoCUETO, EliasCHOI, Hyung YunHAN, ManyongDUVAL, Jean-LouisWe are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors
http://hdl.handle.net/10985/19961
A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors
SANCARLOS, Abel; CUETO, Elias; CHINESTA, Francisco; DUVAL, Jean-Louis
A novel model order reduction (MOR) technique is presented to achieve fast and real-time predictions as well as high-dimensional parametric solutions for the electromagnetic force which will help the design, analysis of performance and implementation of electric machines concerning industrial applications such as the noise, vibration, and harshness in electric motors. The approach allows to avoid the long-time simulations needed to analyze the electric machine at different operation points. In addition, it facilitates the computation and coupling of the motor model in other physical subsystems. Specifically, we propose a novel formulation of the sparse proper generalized decomposition procedure, combining it with a reduced basis approach, which is used to fit correctly the reduced order model with the numerical simulations as well as to obtain a further data compression. This technique can be applied to construct a regression model from high-dimensional data. These data can come, for example, from finite element simulations. As will be shown, an excellent agreement between the results of the proposed approach and the finite element method models are observed.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10985/199612021-01-01T00:00:00ZSANCARLOS, AbelCUETO, EliasCHINESTA, FranciscoDUVAL, Jean-LouisA novel model order reduction (MOR) technique is presented to achieve fast and real-time predictions as well as high-dimensional parametric solutions for the electromagnetic force which will help the design, analysis of performance and implementation of electric machines concerning industrial applications such as the noise, vibration, and harshness in electric motors. The approach allows to avoid the long-time simulations needed to analyze the electric machine at different operation points. In addition, it facilitates the computation and coupling of the motor model in other physical subsystems. Specifically, we propose a novel formulation of the sparse proper generalized decomposition procedure, combining it with a reduced basis approach, which is used to fit correctly the reduced order model with the numerical simulations as well as to obtain a further data compression. This technique can be applied to construct a regression model from high-dimensional data. These data can come, for example, from finite element simulations. As will be shown, an excellent agreement between the results of the proposed approach and the finite element method models are observed.A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition
http://hdl.handle.net/10985/16676
A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition
IBAÑEZ, Ruben; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; GONZALEZ, David; CUETO, Elias; HUERTA, Antonio; DUVAL, Jean-Louis; CHINESTA, Francisco
Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional settings. This well-known phenomenon, coined as the curse of dimensionality, is here overcome by means of the use of separate representations. We present a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit. We provide examples on the performance of the technique in up to ten dimensions.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/166762018-01-01T00:00:00ZIBAÑEZ, RubenABISSET-CHAVANNE, EmmanuelleAMMAR, AmineGONZALEZ, DavidCUETO, EliasHUERTA, AntonioDUVAL, Jean-LouisCHINESTA, FranciscoSparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional settings. This well-known phenomenon, coined as the curse of dimensionality, is here overcome by means of the use of separate representations. We present a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit. We provide examples on the performance of the technique in up to ten dimensions.