The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

R. Sampaio - One of the best experts on this subject based on the ideXlab platform.

  • Variational-based reduced-order model in dynamic substructuring of coupled structures through a dissipative physical interface: Recent advances
    Archives of Computational Methods in Engineering, 2017
    Co-Authors: Roger Ohayon, Christian Soize, R. Sampaio
    Abstract:

    This paper deals with a variational-based reduced-order model in dynamic substructuring of two coupled structures through a physical dissipative flexible interface. We consider the linear elastodynamic of a dissipative structure composed of two main dissipative Substructures perfectly connected through interfaces by a linking Substructure. The linking Substructure is flexible and is modeled in the context of the general linear viscoelasticity theory, yielding damping and stiffness operators depending on the frequency, while the two main dissipative Substructures are modeled in the context of linear elasticity with an additional classical viscous damping modeling which is assumed to be independent of the frequency. We present an appropriate review that we carry out on the different methods used in dynamic substructuring. The method proposed consists in constructing a reduced-order model using the free-interface elastic modes of the two main Substructures and, for the linking Substructure, an appropriate elastostatic lifting operator and the frequency-dependent fixed-interface vector basis.

  • Variational-Based Reduced-Order Model in Dynamic Substructuring of Coupled Structures Through a Dissipative Physical Interface: Recent Advances
    Archives of Computational Methods in Engineering, 2014
    Co-Authors: Roger Ohayon, Christian Soize, R. Sampaio
    Abstract:

    This paper deals with a variational-based reduced-order model in dynamic substructuring of two coupled structures through a physical dissipative flexible interface. We consider the linear elastodynamic of a dissipative structure composed of two main dissipative Substructures perfectly connected through interfaces by a linking Substructure. The linking Substructure is flexible and is modeled in the context of the general linear viscoelasticity theory, yielding damping and stiffness operators depending on the frequency, while the two main dissipative Substructures are modeled in the context of linear elasticity with an additional classical viscous damping modeling which is assumed to be independent of the frequency. We present recent advances adapted to such a situation, which is positioned with respect to an appropriate review that we carry out on the different methods used in dynamic substructuring. It consists in constructing a reduced-order model using the free-interface elastic modes of the two main Substructures and, for the linking Substructure, an appropriate frequency-independent elastostatic lifting operator and the frequency-dependent fixed-interface vector basis.

  • Dynamic substructuring of structural systems with dissipative physical interface
    2013
    Co-Authors: Roger Ohayon, R. Sampaio, Christian Soize
    Abstract:

    This paper deals with the theoretical aspects concerning linear elastodynamic of a damped structure composed of two main damped Substructures perfectly connected through interfaces by a linking damped Substructure. A reducedorder model is constructed using the free interface elastic modes of the two main Substructures and an appropriate elastostatic lifting operator related to the linking Substructure.

Roger Ohayon - One of the best experts on this subject based on the ideXlab platform.

  • Variational-based reduced-order model in dynamic substructuring of coupled structures through a dissipative physical interface: Recent advances
    Archives of Computational Methods in Engineering, 2017
    Co-Authors: Roger Ohayon, Christian Soize, R. Sampaio
    Abstract:

    This paper deals with a variational-based reduced-order model in dynamic substructuring of two coupled structures through a physical dissipative flexible interface. We consider the linear elastodynamic of a dissipative structure composed of two main dissipative Substructures perfectly connected through interfaces by a linking Substructure. The linking Substructure is flexible and is modeled in the context of the general linear viscoelasticity theory, yielding damping and stiffness operators depending on the frequency, while the two main dissipative Substructures are modeled in the context of linear elasticity with an additional classical viscous damping modeling which is assumed to be independent of the frequency. We present an appropriate review that we carry out on the different methods used in dynamic substructuring. The method proposed consists in constructing a reduced-order model using the free-interface elastic modes of the two main Substructures and, for the linking Substructure, an appropriate elastostatic lifting operator and the frequency-dependent fixed-interface vector basis.

  • Variational-Based Reduced-Order Model in Dynamic Substructuring of Coupled Structures Through a Dissipative Physical Interface: Recent Advances
    Archives of Computational Methods in Engineering, 2014
    Co-Authors: Roger Ohayon, Christian Soize, R. Sampaio
    Abstract:

    This paper deals with a variational-based reduced-order model in dynamic substructuring of two coupled structures through a physical dissipative flexible interface. We consider the linear elastodynamic of a dissipative structure composed of two main dissipative Substructures perfectly connected through interfaces by a linking Substructure. The linking Substructure is flexible and is modeled in the context of the general linear viscoelasticity theory, yielding damping and stiffness operators depending on the frequency, while the two main dissipative Substructures are modeled in the context of linear elasticity with an additional classical viscous damping modeling which is assumed to be independent of the frequency. We present recent advances adapted to such a situation, which is positioned with respect to an appropriate review that we carry out on the different methods used in dynamic substructuring. It consists in constructing a reduced-order model using the free-interface elastic modes of the two main Substructures and, for the linking Substructure, an appropriate frequency-independent elastostatic lifting operator and the frequency-dependent fixed-interface vector basis.

  • Clarification about component mode synthesis methods for Substructures with physical flexible interfaces
    International Journal of Aeronautical and Space Sciences, 2014
    Co-Authors: Roger Ohayon, Christian Soize
    Abstract:

    The objective of the paper is to clarify a methodology based on the use of the existing component mode synthesis methods for the case of two damped Substructures which are coupled through a linking viscoelastic flexible Substructure and for which the structural modes with free geometrical interface are used for each main Substructure. The proposed methodology corresponds to a convenient alternative to the direct use either of the Craig-Bampton method applied to the three Substructures (using the fixed geometric interface modes) or of the flexibility residual approaches initiated by MacNeal (using the free geometric interface modes). In opposite to a geometrical interface which is a topological interface on which there is a direct linkage between the degrees of freedom of Substructures, we consider a physical flexible interface which exists in certain present technologies and for which the general framework linear viscoelasticity is used and yields a frequency-dependent damping and stiffness matrices of the physical flexible interface.

  • Dynamic substructuring of structural systems with dissipative physical interface
    2013
    Co-Authors: Roger Ohayon, R. Sampaio, Christian Soize
    Abstract:

    This paper deals with the theoretical aspects concerning linear elastodynamic of a damped structure composed of two main damped Substructures perfectly connected through interfaces by a linking damped Substructure. A reducedorder model is constructed using the free interface elastic modes of the two main Substructures and an appropriate elastostatic lifting operator related to the linking Substructure.

Christian Soize - One of the best experts on this subject based on the ideXlab platform.

  • Variational-based reduced-order model in dynamic substructuring of coupled structures through a dissipative physical interface: Recent advances
    Archives of Computational Methods in Engineering, 2017
    Co-Authors: Roger Ohayon, Christian Soize, R. Sampaio
    Abstract:

    This paper deals with a variational-based reduced-order model in dynamic substructuring of two coupled structures through a physical dissipative flexible interface. We consider the linear elastodynamic of a dissipative structure composed of two main dissipative Substructures perfectly connected through interfaces by a linking Substructure. The linking Substructure is flexible and is modeled in the context of the general linear viscoelasticity theory, yielding damping and stiffness operators depending on the frequency, while the two main dissipative Substructures are modeled in the context of linear elasticity with an additional classical viscous damping modeling which is assumed to be independent of the frequency. We present an appropriate review that we carry out on the different methods used in dynamic substructuring. The method proposed consists in constructing a reduced-order model using the free-interface elastic modes of the two main Substructures and, for the linking Substructure, an appropriate elastostatic lifting operator and the frequency-dependent fixed-interface vector basis.

  • Variational-Based Reduced-Order Model in Dynamic Substructuring of Coupled Structures Through a Dissipative Physical Interface: Recent Advances
    Archives of Computational Methods in Engineering, 2014
    Co-Authors: Roger Ohayon, Christian Soize, R. Sampaio
    Abstract:

    This paper deals with a variational-based reduced-order model in dynamic substructuring of two coupled structures through a physical dissipative flexible interface. We consider the linear elastodynamic of a dissipative structure composed of two main dissipative Substructures perfectly connected through interfaces by a linking Substructure. The linking Substructure is flexible and is modeled in the context of the general linear viscoelasticity theory, yielding damping and stiffness operators depending on the frequency, while the two main dissipative Substructures are modeled in the context of linear elasticity with an additional classical viscous damping modeling which is assumed to be independent of the frequency. We present recent advances adapted to such a situation, which is positioned with respect to an appropriate review that we carry out on the different methods used in dynamic substructuring. It consists in constructing a reduced-order model using the free-interface elastic modes of the two main Substructures and, for the linking Substructure, an appropriate frequency-independent elastostatic lifting operator and the frequency-dependent fixed-interface vector basis.

  • Clarification about component mode synthesis methods for Substructures with physical flexible interfaces
    International Journal of Aeronautical and Space Sciences, 2014
    Co-Authors: Roger Ohayon, Christian Soize
    Abstract:

    The objective of the paper is to clarify a methodology based on the use of the existing component mode synthesis methods for the case of two damped Substructures which are coupled through a linking viscoelastic flexible Substructure and for which the structural modes with free geometrical interface are used for each main Substructure. The proposed methodology corresponds to a convenient alternative to the direct use either of the Craig-Bampton method applied to the three Substructures (using the fixed geometric interface modes) or of the flexibility residual approaches initiated by MacNeal (using the free geometric interface modes). In opposite to a geometrical interface which is a topological interface on which there is a direct linkage between the degrees of freedom of Substructures, we consider a physical flexible interface which exists in certain present technologies and for which the general framework linear viscoelasticity is used and yields a frequency-dependent damping and stiffness matrices of the physical flexible interface.

  • Dynamic substructuring of structural systems with dissipative physical interface
    2013
    Co-Authors: Roger Ohayon, R. Sampaio, Christian Soize
    Abstract:

    This paper deals with the theoretical aspects concerning linear elastodynamic of a damped structure composed of two main damped Substructures perfectly connected through interfaces by a linking damped Substructure. A reducedorder model is constructed using the free interface elastic modes of the two main Substructures and an appropriate elastostatic lifting operator related to the linking Substructure.

Lawrence B. Holder - One of the best experts on this subject based on the ideXlab platform.

  • Discovering Concepts in Structural Data
    Pattern Discovery in Biomolecular Data, 1999
    Co-Authors: Diane J. Cook, Lawrence B. Holder
    Abstract:

    The large amount of data collected today is quickly overwhelming researchers’ abilities to interpret the data and discover interesting patterns. In response to this problem, a number of researchers have developed techniques for discovering concepts in databases. These techniques work well for data expressed in a nonstructural, attribute-value representation and address issues of data relevance, missing data, noise and uncertainty, and utilization of domain knowledge (Fisher, 1987; Cheeseman and Stutz, 1996). However, recent data acquisition projects are collecting structural data describing the relationships among the data objects. Correspondingly, there exists a need for techniques to analyze and discover concepts in structural databases (Fayyad et al., 1996b). One method for discovering knowledge in structural data is the identification of common Substructures. The goal is to find Substructures capable of compressing the data and to identify conceptually interesting Substructures that enhance the interpretation of the data. Substructure discovery is the process of identifying concepts describing interesting and repetitive Substructures within structural data. Once discovered, the Substructure concept can be used to simplify the data by replacing instances of the Substructure with a pointer to the newly discovered concept. The discovered Substructure concepts allow abstraction over detailed structure in the original data and provide new, relevant attributes for interpreting the data. Iteration of the Substructure discovery and replacement process constructs a hierarchical description of the structural data in terms of the discovered Substructures. This hierarchy provides varying levels of interpretation that can be accessed based on the goals of the data analysis. We describe a system called Subdue that discovers interesting Substructures in structural data based on the minimum description length (MDL) principle. The Subdue system discovers Substructures that compress the original data and represent structural concepts in the data. By replacing previously discovered Substructures, multiple passes of Subdue produce a hierarchical description of the structural regularities in the data. Subdue uses a computationally bounded inexact graph match that identifies similar, but not identical, instances of a Substructure and finds an approximate measure of closeness of two Substructures when under computational constraints.

  • Discovering Substructures in the Chemical Toxicity Domain
    1999
    Co-Authors: Ravindra N. Chittimoori, Lawrence B. Holder, Diane J. Cook
    Abstract:

    The researcher’s ability to interpret the data and discover interesting patterns within the data is of great importance as it helps in obtaining relevant SARs [Srinivasan et al.], for the cause of chemical cancers (e.g., Progol identified a primary amine group as a relevant SAR for the cause of chemical cancers [Srinivasan et al. 1997]). One method for interpreting and discovering interesting patterns in the data is the identification of common Substructures within the data. These Substructures should be capable of compressing the data and identifying conceptually interesting Substructures that enhance the interpretation of data. This identification also helps in simplifying the data by replacing instances of the Substructure with a pointer to the newly discovered Substructure. The subsequent iterations of the discovery and replacement process construct a hierarchical description of the structural data in terms of discovered Substructures.

  • Substructure Discovery Using Minimum Description Length and Background Knowledge
    arXiv: Artificial Intelligence, 1994
    Co-Authors: Diane J. Cook, Lawrence B. Holder
    Abstract:

    The ability to identify interesting and repetitive Substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE Substructure discovery system based on the minimum description length principle. The SUBDUE system discovers Substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered Substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a Substructure and finds an approximate measure of closeness of two Substructures when under computational constraints. In addition to the minimum description length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate Substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find Substructures capable of compressing the original data and to discover structural concepts important to the domain. Description of Online Appendix: This is a compressed tar file containing the SUBDUE discovery system, written in C. The program accepts as input databases represented in graph form, and will output discovered Substructures with their corresponding value.

  • Substructure discovery using minimum description length and background knowledge
    Journal of Artificial Intelligence Research, 1993
    Co-Authors: Diane J. Cook, Lawrence B. Holder
    Abstract:

    The ability to identify interesting and repetitive Substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE Substructure discovery system based on the minimum description length principle. The SUBDUE system discovers Substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered Substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a Substructure and finds an approximate measure of closeness of two Substructures when under computational constraints. In addition to the minimumdescription length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate Substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find Substructures capable of compressing the original data and to discover structural concepts important to the domain.

Diane J. Cook - One of the best experts on this subject based on the ideXlab platform.

  • Discovering Concepts in Structural Data
    Pattern Discovery in Biomolecular Data, 1999
    Co-Authors: Diane J. Cook, Lawrence B. Holder
    Abstract:

    The large amount of data collected today is quickly overwhelming researchers’ abilities to interpret the data and discover interesting patterns. In response to this problem, a number of researchers have developed techniques for discovering concepts in databases. These techniques work well for data expressed in a nonstructural, attribute-value representation and address issues of data relevance, missing data, noise and uncertainty, and utilization of domain knowledge (Fisher, 1987; Cheeseman and Stutz, 1996). However, recent data acquisition projects are collecting structural data describing the relationships among the data objects. Correspondingly, there exists a need for techniques to analyze and discover concepts in structural databases (Fayyad et al., 1996b). One method for discovering knowledge in structural data is the identification of common Substructures. The goal is to find Substructures capable of compressing the data and to identify conceptually interesting Substructures that enhance the interpretation of the data. Substructure discovery is the process of identifying concepts describing interesting and repetitive Substructures within structural data. Once discovered, the Substructure concept can be used to simplify the data by replacing instances of the Substructure with a pointer to the newly discovered concept. The discovered Substructure concepts allow abstraction over detailed structure in the original data and provide new, relevant attributes for interpreting the data. Iteration of the Substructure discovery and replacement process constructs a hierarchical description of the structural data in terms of the discovered Substructures. This hierarchy provides varying levels of interpretation that can be accessed based on the goals of the data analysis. We describe a system called Subdue that discovers interesting Substructures in structural data based on the minimum description length (MDL) principle. The Subdue system discovers Substructures that compress the original data and represent structural concepts in the data. By replacing previously discovered Substructures, multiple passes of Subdue produce a hierarchical description of the structural regularities in the data. Subdue uses a computationally bounded inexact graph match that identifies similar, but not identical, instances of a Substructure and finds an approximate measure of closeness of two Substructures when under computational constraints.

  • Discovering Substructures in the Chemical Toxicity Domain
    1999
    Co-Authors: Ravindra N. Chittimoori, Lawrence B. Holder, Diane J. Cook
    Abstract:

    The researcher’s ability to interpret the data and discover interesting patterns within the data is of great importance as it helps in obtaining relevant SARs [Srinivasan et al.], for the cause of chemical cancers (e.g., Progol identified a primary amine group as a relevant SAR for the cause of chemical cancers [Srinivasan et al. 1997]). One method for interpreting and discovering interesting patterns in the data is the identification of common Substructures within the data. These Substructures should be capable of compressing the data and identifying conceptually interesting Substructures that enhance the interpretation of data. This identification also helps in simplifying the data by replacing instances of the Substructure with a pointer to the newly discovered Substructure. The subsequent iterations of the discovery and replacement process construct a hierarchical description of the structural data in terms of discovered Substructures.

  • Substructure Discovery Using Minimum Description Length and Background Knowledge
    arXiv: Artificial Intelligence, 1994
    Co-Authors: Diane J. Cook, Lawrence B. Holder
    Abstract:

    The ability to identify interesting and repetitive Substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE Substructure discovery system based on the minimum description length principle. The SUBDUE system discovers Substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered Substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a Substructure and finds an approximate measure of closeness of two Substructures when under computational constraints. In addition to the minimum description length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate Substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find Substructures capable of compressing the original data and to discover structural concepts important to the domain. Description of Online Appendix: This is a compressed tar file containing the SUBDUE discovery system, written in C. The program accepts as input databases represented in graph form, and will output discovered Substructures with their corresponding value.

  • Substructure discovery using minimum description length and background knowledge
    Journal of Artificial Intelligence Research, 1993
    Co-Authors: Diane J. Cook, Lawrence B. Holder
    Abstract:

    The ability to identify interesting and repetitive Substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE Substructure discovery system based on the minimum description length principle. The SUBDUE system discovers Substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered Substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a Substructure and finds an approximate measure of closeness of two Substructures when under computational constraints. In addition to the minimumdescription length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate Substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find Substructures capable of compressing the original data and to discover structural concepts important to the domain.