SAM
https://sam.ensam.eu:443
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Wed, 27 Oct 2021 07:15:51 GMT2021-10-27T07:15:51ZVirtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data
http://hdl.handle.net/10985/16796
Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data
CHINESTA, Francisco; CUETO, Elías G.; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; KHALDI, Fouad El
Engineering is evolving in the same way than society is doing. Nowadays, data is acquiring a prominence never imagined. In the past, in the domain of materials, processes and structures, testing machines allowed extract data that served in turn to calibrate state-of-the-art models. Some calibration procedures were even integrated within these testing machines. Thus, once the model had been calibrated, computer simulation takes place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints. This gives rise to the the family of so-called twins: the virtual, the digital and the hybrid twins. Moreover, as discussed in the present paper, not only data serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/167962018-01-01T00:00:00ZCHINESTA, FranciscoCUETO, Elías G.ABISSET-CHAVANNE, EmmanuelleDUVAL, Jean LouisKHALDI, Fouad ElEngineering is evolving in the same way than society is doing. Nowadays, data is acquiring a prominence never imagined. In the past, in the domain of materials, processes and structures, testing machines allowed extract data that served in turn to calibrate state-of-the-art models. Some calibration procedures were even integrated within these testing machines. Thus, once the model had been calibrated, computer simulation takes place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints. This gives rise to the the family of so-called twins: the virtual, the digital and the hybrid twins. Moreover, as discussed in the present paper, not only data serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm.Learning slosh dynamics by means of data
http://hdl.handle.net/10985/17933
Learning slosh dynamics by means of data
MOYA, Beatriz; GONZÁLEZ, David; ALFARO, Iciar; CHINESTA, Francisco; CUETO, Elías G.
In this work we study several learning strategies for fluid sloshing problems based on data. In essence, a reduced-order model of the dynamics of the free surface motion of the fluid is developed under rigorous thermodynamics settings. This model is extracted from data by exploring several strategies. First, a linear one, based on the employ of Proper Orthogonal Decomposition techniques is analyzed. Second, a strategy based on the employ of Locally Linear Embedding is studied. Finally, Topological Data Analysis is employed to the same end. All the three distinct possibilities rely on a numerical integration scheme to advance the dynamics in time. This thermodynamically consistent integrator is developed on the basis of the General Equation for Non-Equilibrium Reversible–Irreversible Coupling, GENERIC [M. Grmela and H.C Oettinger (1997). Phys. Rev. E. 56 (6): 6620–6632], framework so as to guarantee the satisfaction of first principles (particularly, the laws of thermodynamics). We show how the resulting method employs a few degrees of freedom, while it allows for a realistic reconstruction of the fluid dynamics of sloshing processes under severe real-time constraints. The proposed method is shown to run faster than real time in a standard laptop.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/179332019-01-01T00:00:00ZMOYA, BeatrizGONZÁLEZ, DavidALFARO, IciarCHINESTA, FranciscoCUETO, Elías G.In this work we study several learning strategies for fluid sloshing problems based on data. In essence, a reduced-order model of the dynamics of the free surface motion of the fluid is developed under rigorous thermodynamics settings. This model is extracted from data by exploring several strategies. First, a linear one, based on the employ of Proper Orthogonal Decomposition techniques is analyzed. Second, a strategy based on the employ of Locally Linear Embedding is studied. Finally, Topological Data Analysis is employed to the same end. All the three distinct possibilities rely on a numerical integration scheme to advance the dynamics in time. This thermodynamically consistent integrator is developed on the basis of the General Equation for Non-Equilibrium Reversible–Irreversible Coupling, GENERIC [M. Grmela and H.C Oettinger (1997). Phys. Rev. E. 56 (6): 6620–6632], framework so as to guarantee the satisfaction of first principles (particularly, the laws of thermodynamics). We show how the resulting method employs a few degrees of freedom, while it allows for a realistic reconstruction of the fluid dynamics of sloshing processes under severe real-time constraints. The proposed method is shown to run faster than real time in a standard laptop.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
IBAÑEZ, R.; ABISSET-CHAVANNE, Emmanuelle; CUETO, Elías G.; AMMAR, Amine; DUVAL, Jean Louis; CHINESTA, 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/10985/176162019-01-01T00:00:00ZIBAÑEZ, R.ABISSET-CHAVANNE, EmmanuelleCUETO, Elías G.AMMAR, AmineDUVAL, Jean LouisCHINESTA, 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.An augmented reality platform for interactive aerodynamic design and analysis
http://hdl.handle.net/10985/17947
An augmented reality platform for interactive aerodynamic design and analysis
BADÍAS, Alberto; CURTIT, Sarah; GONZÁLEZ, David; ALFARO, Iciar; CHINESTA, Francisco; CUETO, Elías G.
While modern CFD tools are able to provide the user with reliable and accurate simulations, there is a strong need for interactive design and analysis tools. State-of-the-art CFD software employs massive resources in terms of CPU time, user interaction, and also GPU time for rendering and analysis. In this work, we develop an innovative tool able to provide a seamless bridge between artistic design and engineering analysis. This platform has three main ingredients: computer vision to avoid long user interaction at the pre-processing stage, machine learning to avoid costly CFD simulations, and augmented reality for an agile and interactive post-processing of the results.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/179472019-01-01T00:00:00ZBADÍAS, AlbertoCURTIT, SarahGONZÁLEZ, DavidALFARO, IciarCHINESTA, FranciscoCUETO, Elías G.While modern CFD tools are able to provide the user with reliable and accurate simulations, there is a strong need for interactive design and analysis tools. State-of-the-art CFD software employs massive resources in terms of CPU time, user interaction, and also GPU time for rendering and analysis. In this work, we develop an innovative tool able to provide a seamless bridge between artistic design and engineering analysis. This platform has three main ingredients: computer vision to avoid long user interaction at the pre-processing stage, machine learning to avoid costly CFD simulations, and augmented reality for an agile and interactive post-processing of the results.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; HASCOËT, Nicolas; GHNATIOS, Chady; AMMAR, Amine; CUETO, Elías G.; 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, AgatheHASCOËT, NicolasGHNATIOS, ChadyAMMAR, AmineCUETO, Elías G.DUVAL, 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, Elías G.; 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, Elías G.HUERTA, 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.A local multiple proper generalized decomposition based on the partition of unity
http://hdl.handle.net/10985/17949
A local multiple proper generalized decomposition based on the partition of unity
IBAÑEZ, Ruben; ABISSET-CHAVANNE, Emmanuelle; CHINESTA, Francisco; HUERTA, Antonio; CUETO, Elías G.
It is well known that model order reduction techniques that project the solution of the problem at hand onto a low-dimensional subspace present difficulties when this solution lies on a nonlinear manifold. To overcome these difficulties (notably, an undesirable increase in the number of required modes in the solution), several solutions have been suggested. Among them, we can cite the use of nonlinear dimensionality reduction techniques or, alternatively, the employ of linear local reduced order approaches. These last approaches usually present the difficulty of ensuring continuity between these local models. Here, a new method is presented, which ensures this continuity by resorting to the paradigm of the partition of unity while employing proper generalized decompositions at each local patch.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/179492019-01-01T00:00:00ZIBAÑEZ, RubenABISSET-CHAVANNE, EmmanuelleCHINESTA, FranciscoHUERTA, AntonioCUETO, Elías G.It is well known that model order reduction techniques that project the solution of the problem at hand onto a low-dimensional subspace present difficulties when this solution lies on a nonlinear manifold. To overcome these difficulties (notably, an undesirable increase in the number of required modes in the solution), several solutions have been suggested. Among them, we can cite the use of nonlinear dimensionality reduction techniques or, alternatively, the employ of linear local reduced order approaches. These last approaches usually present the difficulty of ensuring continuity between these local models. Here, a new method is presented, which ensures this continuity by resorting to the paradigm of the partition of unity while employing proper generalized decompositions at each local patch.Data-driven GENERIC modeling of poroviscoelastic materials
http://hdl.handle.net/10985/18480
Data-driven GENERIC modeling of poroviscoelastic materials
GHNATIOS, Chady; ALFARO, Icíar; GONZÁLEZ, David; CHINESTA, Francisco; CUETO, Elías G.
Biphasic soft materials are challenging to model by nature. Ongoing efforts are targeting their effective modeling and simulation. This work uses experimental atomic force nanoindentation of thick hydrogels to identify the indentation forces are a function of the indentation depth. Later on, the atomic force microscopy results are used in a GENERIC general equation for non-equilibrium reversible-irreversible coupling (GENERIC) formalism to identify the best model conserving basic thermodynamic laws. The data-driven GENERIC analysis identifies the material behavior with high fidelity for both data fitting and prediction.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/184802019-01-01T00:00:00ZGHNATIOS, ChadyALFARO, IcíarGONZÁLEZ, DavidCHINESTA, FranciscoCUETO, Elías G.Biphasic soft materials are challenging to model by nature. Ongoing efforts are targeting their effective modeling and simulation. This work uses experimental atomic force nanoindentation of thick hydrogels to identify the indentation forces are a function of the indentation depth. Later on, the atomic force microscopy results are used in a GENERIC general equation for non-equilibrium reversible-irreversible coupling (GENERIC) formalism to identify the best model conserving basic thermodynamic laws. The data-driven GENERIC analysis identifies the material behavior with high fidelity for both data fitting and prediction.