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https://sam.ensam.eu:443
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Thu, 07 Nov 2024 07:54:51 GMT2024-11-07T07:54:51ZAdvanced parametric space-frequency separated representations in structural dynamics: A harmonic–modal hybrid approach
http://hdl.handle.net/10985/13277
Advanced parametric space-frequency separated representations in structural dynamics: A harmonic–modal hybrid approach
MUHAMMAD HARIS, Malik; BORZACCHIELLO, Domenico; AGUADO, José Vicente; CHINESTA SORIA, Francisco
This paper is concerned with the solution to structural dynamics equations. The technique here presented is closely related to Harmonic Analysis, and therefore it is only concerned with the long-term forced response. Proper Generalized Decomposition (PGD) is used to compute space-frequency separated representations by considering the frequency as an extra coordinate. This formulation constitutes an alternative to classical methods such as Modal Analysis and it is especially advantageous when parametrized structural dynamics equations are of interest. In such case, there is no need to solve the parametrized eigenvalue problem and the space-time solution can be recovered with a Fourier inverse transform. The PGD solution is valid for any forcing term that can be written as a combination of the considered frequencies. Finally, the solution is available for any value of the parameter. When the problem involves frequency-dependent parameters the proposed technique provides a specially suitable method that becomes computationally more efficient when it is combined with a modal representation.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/132772018-01-01T00:00:00ZMUHAMMAD HARIS, MalikBORZACCHIELLO, DomenicoAGUADO, José VicenteCHINESTA SORIA, FranciscoThis paper is concerned with the solution to structural dynamics equations. The technique here presented is closely related to Harmonic Analysis, and therefore it is only concerned with the long-term forced response. Proper Generalized Decomposition (PGD) is used to compute space-frequency separated representations by considering the frequency as an extra coordinate. This formulation constitutes an alternative to classical methods such as Modal Analysis and it is especially advantageous when parametrized structural dynamics equations are of interest. In such case, there is no need to solve the parametrized eigenvalue problem and the space-time solution can be recovered with a Fourier inverse transform. The PGD solution is valid for any forcing term that can be written as a combination of the considered frequencies. Finally, the solution is available for any value of the parameter. When the problem involves frequency-dependent parameters the proposed technique provides a specially suitable method that becomes computationally more efficient when it is combined with a modal representation.Intelligent assistant system as a context-aware decision-making support for the workers of the future
http://hdl.handle.net/10985/18438
Intelligent assistant system as a context-aware decision-making support for the workers of the future
BELKADI, Farouk; DHUIEB, Mohamed Anis; AGUADO, José Vicente; LAROCHE, Florent; BERNARD, Alain; CHINESTA SORIA, Francisco
The key role of information and communication technologies (ICT) to improve manufacturing productivity within the paradigm of factory of the future is often proved. These tools are used in a wide range of product lifecycle activities, from the early design phase to product recycling. Generally, the assistance tools are mainly dedicated to the management board and fewer initiatives focus on the operational needs of the worker at the shop-floor level. This paper proposes a context-aware knowledge-based system dedicated to support the actors of the factory by the right information at the right time and in the appropriate format regarding their context of work and level of expertise. Particularly, specific assistance functionalities are dedicated to the workers in charge of the machine configuration and the realization of manufacturing operations. PGD-based (Proper Generalized Decomposition) algorithms are used for real time simulation of industrial processes and machine configuration. At the conceptual level, a semantic model is proposedas key enablersfor the structuration of the knowledge-based system.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/184382020-01-01T00:00:00ZBELKADI, FaroukDHUIEB, Mohamed AnisAGUADO, José VicenteLAROCHE, FlorentBERNARD, AlainCHINESTA SORIA, FranciscoThe key role of information and communication technologies (ICT) to improve manufacturing productivity within the paradigm of factory of the future is often proved. These tools are used in a wide range of product lifecycle activities, from the early design phase to product recycling. Generally, the assistance tools are mainly dedicated to the management board and fewer initiatives focus on the operational needs of the worker at the shop-floor level. This paper proposes a context-aware knowledge-based system dedicated to support the actors of the factory by the right information at the right time and in the appropriate format regarding their context of work and level of expertise. Particularly, specific assistance functionalities are dedicated to the workers in charge of the machine configuration and the realization of manufacturing operations. PGD-based (Proper Generalized Decomposition) algorithms are used for real time simulation of industrial processes and machine configuration. At the conceptual level, a semantic model is proposedas key enablersfor the structuration of the knowledge-based system.kPCA-Based Parametric Solutions Within the PGD Framework
http://hdl.handle.net/10985/18681
kPCA-Based Parametric Solutions Within the PGD Framework
GONZÁLEZ, David; AGUADO, José Vicente; ABISSET-CHAVANNE, Emmanuelle; CHINESTA SORIA, Francisco
Parametric solutions make possible fast and reliable real-time simulations which, in turn allow real time optimization, simulation-based control and uncertainty propagation. This opens unprecedented possibilities for robust and efficient design and real-time decision making. The construction of such parametric solutions was addressed in our former works in the context of models whose parameters were easily identified and known in advance. In this work we address more complex scenarios in which the parameters do not appear explicitly in the model—complex microstructures, for instance. In these circumstances the parametric model solution requires combining a technique to find the relevant model parameters and a solution procedure able to cope with high-dimensional models, avoiding the well-known curse of dimensionality. In this work, kPCA (kernel Principal Component Analysis) is used for extracting the hidden model parameters, whereas the PGD (Proper Generalized Decomposition) is used for calculating the resulting parametric solution.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/186812018-01-01T00:00:00ZGONZÁLEZ, DavidAGUADO, José VicenteABISSET-CHAVANNE, EmmanuelleCHINESTA SORIA, FranciscoParametric solutions make possible fast and reliable real-time simulations which, in turn allow real time optimization, simulation-based control and uncertainty propagation. This opens unprecedented possibilities for robust and efficient design and real-time decision making. The construction of such parametric solutions was addressed in our former works in the context of models whose parameters were easily identified and known in advance. In this work we address more complex scenarios in which the parameters do not appear explicitly in the model—complex microstructures, for instance. In these circumstances the parametric model solution requires combining a technique to find the relevant model parameters and a solution procedure able to cope with high-dimensional models, avoiding the well-known curse of dimensionality. In this work, kPCA (kernel Principal Component Analysis) is used for extracting the hidden model parameters, whereas the PGD (Proper Generalized Decomposition) is used for calculating the resulting parametric solution.Parametric inverse impulse response based on reduced order modeling and randomized excitations
http://hdl.handle.net/10985/18363
Parametric inverse impulse response based on reduced order modeling and randomized excitations
MONTAGUD, Santiago; AGUADO, José Vicente; JOYOT, Pierre; CHINESTA SORIA, Francisco
This paper is concerned with the computation of the inverse impulse response of a parametrized structural dynamics problem using reduced-order modeling and randomized excitations. A two-stages approach is proposed, involving the solution of both direct and inverse problems. In the first stage, the parametrized structural dynamics problem is formulated in the frequency domain, and solved using a reduced-order modeling approach. As a result, the parametric transfer function of the structure is obtained, and then readily transformed into a parametric direct impulse response (DIR). In the second stage, the parametric inverse impulse response (IIR) is computed. We use randomized excitations to generate synthetic samples inexpensively from the parametric DIR. Based on these, the parametric IIR is computed by minimizing the mean square error between the estimate and the samples. Most importantly, we show that the randomized excitations can be generated by sampling the frequency domain only. Hence, the parametric domain does not need to be sampled, which makes the computation of the parametric IIR very efficient.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/183632020-01-01T00:00:00ZMONTAGUD, SantiagoAGUADO, José VicenteJOYOT, PierreCHINESTA SORIA, FranciscoThis paper is concerned with the computation of the inverse impulse response of a parametrized structural dynamics problem using reduced-order modeling and randomized excitations. A two-stages approach is proposed, involving the solution of both direct and inverse problems. In the first stage, the parametrized structural dynamics problem is formulated in the frequency domain, and solved using a reduced-order modeling approach. As a result, the parametric transfer function of the structure is obtained, and then readily transformed into a parametric direct impulse response (DIR). In the second stage, the parametric inverse impulse response (IIR) is computed. We use randomized excitations to generate synthetic samples inexpensively from the parametric DIR. Based on these, the parametric IIR is computed by minimizing the mean square error between the estimate and the samples. Most importantly, we show that the randomized excitations can be generated by sampling the frequency domain only. Hence, the parametric domain does not need to be sampled, which makes the computation of the parametric IIR very efficient.Non-intrusive Sparse Subspace Learning for Parametrized Problems
http://hdl.handle.net/10985/18435
Non-intrusive Sparse Subspace Learning for Parametrized Problems
BORZACCHIELLO, Domenico; AGUADO, José Vicente; CHINESTA SORIA, Francisco
We discuss the use of hierarchical collocation to approximate the numerical solution of parametric models. With respect to traditional projection-based reduced order modeling, the use of a collocation enables non-intrusive approach based on sparse adaptive sampling of the parametric space. This allows to recover the low-dimensional structure of the parametric solution subspace while also learning the functional dependency from the parameters in explicit form. A sparse low-rank approximate tensor representation of the parametric solution can be built through an incremental strategy that only needs to have access to the output of a deterministic solver. Non-intrusiveness makes this approach straightforwardly applicable to challenging problems characterized by nonlinearity or non affine weak forms. As we show in the various examples presented in the paper, the method can be interfaced with no particular effort to existing third party simulation software making the proposed approach particularly appealing and adapted to practical engineering problems of industrial interest.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/184352019-01-01T00:00:00ZBORZACCHIELLO, DomenicoAGUADO, José VicenteCHINESTA SORIA, FranciscoWe discuss the use of hierarchical collocation to approximate the numerical solution of parametric models. With respect to traditional projection-based reduced order modeling, the use of a collocation enables non-intrusive approach based on sparse adaptive sampling of the parametric space. This allows to recover the low-dimensional structure of the parametric solution subspace while also learning the functional dependency from the parameters in explicit form. A sparse low-rank approximate tensor representation of the parametric solution can be built through an incremental strategy that only needs to have access to the output of a deterministic solver. Non-intrusiveness makes this approach straightforwardly applicable to challenging problems characterized by nonlinearity or non affine weak forms. As we show in the various examples presented in the paper, the method can be interfaced with no particular effort to existing third party simulation software making the proposed approach particularly appealing and adapted to practical engineering problems of industrial interest.