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http://hdl.handle.net/10985/15677
Advanced separated spatial representations for hardly separable domains
GHNATIOS, Chady; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; CUETOS, Elias; DUVAL, Jean-Louis; CHINESTA SORIA, Francisco
This work aims at proposing a new procedure for parametric problems whose separated representation has been considered difficult, or whose SVD compression impacted the results in terms of performance and accuracy. The proposed technique achieves a fully separated representation for layered domains with interfaces exhibiting waviness or – more generally – deviating from planar surfaces, parallel to the coordinate plane. This will make possible a simple separated representation, equivalent to others, already analyzed in some of our former works. To prove the potentialities of the proposed approach, two benchmarks will be addressed, one of them involving an efficient space–time separated representation achieved by considering the same rationale.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/156772019-01-01T00:00:00ZGHNATIOS, ChadyABISSET-CHAVANNE, EmmanuelleAMMAR, AmineCUETOS, EliasDUVAL, Jean-LouisCHINESTA SORIA, FranciscoThis work aims at proposing a new procedure for parametric problems whose separated representation has been considered difficult, or whose SVD compression impacted the results in terms of performance and accuracy. The proposed technique achieves a fully separated representation for layered domains with interfaces exhibiting waviness or – more generally – deviating from planar surfaces, parallel to the coordinate plane. This will make possible a simple separated representation, equivalent to others, already analyzed in some of our former works. To prove the potentialities of the proposed approach, two benchmarks will be addressed, one of them involving an efficient space–time separated representation achieved by considering the same rationale.On 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
GHNATIOS, Chady; DELPLACE, Frank; BARASINSKI, Anais; DUVAL, Jean-Louis; CUETO, Elias; AMMAR, Amine; CHINESTA SORIA, 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/10985/194862020-01-01T00:00:00ZGHNATIOS, ChadyDELPLACE, FrankBARASINSKI, AnaisDUVAL, Jean-LouisCUETO, EliasAMMAR, AmineCHINESTA SORIA, 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.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; DUVAL, Jean-Louis; ABISSET-CHAVANNE, Emmanuelle; GHNATIOS, Chady; CHINESTA SORIA, 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, DavidDUVAL, Jean-LouisABISSET-CHAVANNE, EmmanuelleGHNATIOS, ChadyCHINESTA SORIA, 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.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 SORIA, 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 SORIA, 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.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; DUVAL, Jean-Louis; CHINESTA SORIA, Francisco
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, EliasDUVAL, Jean-LouisCHINESTA SORIA, FranciscoA 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.Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction
http://hdl.handle.net/10985/17616
Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction
CUETO, Elías G.; DUVAL, Jean-Louis; IBAÑEZ, Ruben; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; CHINESTA SORIA, Francisco
Compressed sensing is a signal compression technique with very remarkable properties. Among them, maybe the most salient one is its ability of overcoming the Shannon–Nyquist sampling theorem. In other words, it is able to reconstruct a signal at less than 2Q samplings per second, where Q stands for the highest frequency content of the signal. This property has, however, important applications in the field of computational mechanics, as we analyze in this paper. We consider a wide variety of applications, such as model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction. Examples are provided for all of them that show the potentialities of compressed sensing in terms of CPU savings in the field of computational mechanics.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/176162019-01-01T00:00:00ZCUETO, Elías G.DUVAL, Jean-LouisIBAÑEZ, RubenABISSET-CHAVANNE, EmmanuelleAMMAR, AmineCHINESTA SORIA, FranciscoCompressed sensing is a signal compression technique with very remarkable properties. Among them, maybe the most salient one is its ability of overcoming the Shannon–Nyquist sampling theorem. In other words, it is able to reconstruct a signal at less than 2Q samplings per second, where Q stands for the highest frequency content of the signal. This property has, however, important applications in the field of computational mechanics, as we analyze in this paper. We consider a wide variety of applications, such as model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction. Examples are provided for all of them that show the potentialities of compressed sensing in terms of CPU savings in the field of computational mechanics.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; GONZALEZ, David; CUETO, Elias; HUERTA, Antonio; DUVAL, Jean-Louis; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; CHINESTA SORIA, 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, RubenGONZALEZ, DavidCUETO, EliasHUERTA, AntonioDUVAL, Jean-LouisABISSET-CHAVANNE, EmmanuelleAMMAR, AmineCHINESTA SORIA, 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.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; CUETO, Elias; HUERTA, Antonio; DUVAL, Jean-Louis; AMMAR, Amine; CHINESTA SORIA, 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, RubénCUETO, EliasHUERTA, AntonioDUVAL, Jean-LouisAMMAR, AmineCHINESTA SORIA, 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.Incremental 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; CUETO, Elias; DUVAL, Jean-Louis; KEUNINGS, Roland; GHNATIOS, Chady; AMMAR, Amine; CHINESTA SORIA, Francisco
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, NicolasCUETO, EliasDUVAL, Jean-LouisKEUNINGS, RolandGHNATIOS, ChadyAMMAR, AmineCHINESTA SORIA, FranciscoThe 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.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; DUVAL, Jean-Louis; ZERBIB, Nicolas; CUETO, Elias; GHNATIOS, Chady; CHINESTA SORIA, 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, AbelDUVAL, Jean-LouisZERBIB, NicolasCUETO, EliasGHNATIOS, ChadyCHINESTA SORIA, 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.