Sensitivity Analysis Linear Systems (44 results)

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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 468 | Sprache: Englisch | Produktart: Bücher | In this 2-volume set, first- and second-order design sensitivity analyses are presented for static and dynamic responses of both linear and nonlinear elastic structural systems, including elasto-plastic and frictional contact problems.  The premise of the text is that a comprehensive theory of structural design sensitivity analysis for linear and nonlinear structures can be treated in a unified way.  The objective is to provide a complete treatment of the theory and practical numerical methods of structural design sensitivity analysis.  The text presents design sensitivity analysis theory and numerical implementation to create advanced design methodologies for mechanical systems and structural components, which will permit economical designs that are strong, stable, reliable, and have long life service.  The design methodologies can be used by design engineers in the university, industry, and government to obtain optimal structural designs for ground vehicles, aricraft, space systems, ships, heavy equipment, machinery, biomedical devices, etc.  Extensive numerical methods for computing design sensitivity are included in the text for practical application and software development.  The numerical method allows seamless integration of CAD-FEA-DSA software tools, so that design optimization can be carried out using CAD geometric models instead of FEA models.  This capaibility allows integration of CAD-CAE-CAM so that optimized designs can be manufactured effectively.Book I  introduces structural design concepts that include the CAD-based design model, design parameterization, performance measures, costs, and constraints.  It also discusses design sensitivity analysis of linear structural systems, and discrete and continuum design sensitivity analysis methods.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - A text surveying perturbation techniques and sensitivity analysis of linear systems is an ambitious undertaking, considering the lack of basic comprehensive texts on the subject. A wide-ranging and global coverage of the topic is as yet missing, despite the existence of numerous monographs dealing with specific topics but generally of use to only a narrow category of people. In fact, most works approach this subject from the numerical analysis point of view. Indeed, researchers in this field have been most concerned with this topic, although engineers and scholars in all fields may find it equally interesting. One can state, without great exaggeration, that a great deal of engineering work is devoted to testing systems' sensitivity to changes in design parameters. As a rule, high-sensitivity elements are those which should be designed with utmost care. On the other hand, as the mathematical modelling serving for the design process is usually idealized and often inaccurately formulated, some unforeseen alterations may cause the system to behave in a slightly different manner. Sensitivity analysis can help the engineer innovate ways to minimize such system discrepancy, since it starts from the assumption of such a discrepancy between the ideal and the actual system.

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Taschenbuch. Condition: Neu. The nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology, Volume I | Overcoming the Curse of Dimensionality: Linear Systems | Dan Gabriel Cacuci | Taschenbuch | xii | Englisch | 2023 | Springer Nature Switzerland | EAN 9783030963668 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The computational models of physical systems comprise parameters, independent and dependent variables. Since the physical processes themselves are seldom known precisely and since most of the model parameters stem from experimental procedures which are also subject to imprecisions, the results predicted by these models are also imprecise, being affected by the uncertainties underlying the respective model. The functional derivatives (also called 'sensitivities') of results (also called 'responses') produced by mathematical/computational models are needed for many purposes, including: (i) understanding the model by ranking the importance of the various model parameters; (ii) performing 'reduced-order modeling' by eliminating unimportant parameters and/or processes; (iii) quantifying the uncertainties induced in a model response due to model parameter uncertainties; (iv) performing 'model validation,' by comparing computations to experiments to address the question 'does the modelrepresent reality ' (v) prioritizing improvements in the model; (vi) performing data assimilation and model calibration as part of forward 'predictive modeling' to obtain best-estimate predicted results with reduced predicted uncertainties; (vii) performing inverse 'predictive modeling'; (viii) designing and optimizing the system.This 3-Volume monograph describes a comprehensive adjoint sensitivity analysis methodology, developed by the author, which enables the efficient and exact computation of arbitrarily high-order sensitivities of model responses in large-scale systems comprising many model parameters. The qualifier 'comprehensive' is employed to highlight that the model parameters considered within the framework of this methodology also include the system's uncertain boundaries and internal interfaces in phase-space. The model's responses can be either scalar-valued functionals of the model's parameters and state variables (e.g., as customarily encountered in optimization problems) or general function-valued responses. Since linear operators admit bona-fide adjoint operators, responses of models that are linear in the state functions (i.e., dependent variables) can depend simultaneously on both the forward and the adjoint state functions. Hence, the sensitivity analysis of such responses warrants the treatment of linear systems in their own right, rather than treating them as particular cases of nonlinear systems. This is in contradistinction to responses for nonlinear systems, which can depend only on the forward state functions, since nonlinear operators do not admit bona-fide adjoint operators (only a linearized form of a nonlinear operator may admit an adjoint operator). Thus, Volume 1 of this book presents the mathematical framework of the nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as 'nth-CASAM-L'), which is conceived for the most efficient computation of exactly obtained mathematical expressions of arbitrarily-high-order (nth-order) sensitivities of a generic system response with respect to all of the parameters underlying the respective forward/adjoint systems. Volume 2 of this book presents the application of the nth-CASAM-L to perform a fourth-order sensitivity and uncertainty analysis of an OECD/NEA reactor physics benchmark which is representative of a large-scale model comprises many (21,976) uncertain parameters, thereby amply illustrating the unique potential of the nth-CASAM-L to enable the exact and efficient computation of chosen high-order response sensitivities to model parameters. Volume 3 of this book presents the 'nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems' (abbreviation: nth-CASAM-N) for the practical, efficient, and exact computation of arbitrarily-high orderse.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The computational models of physical systems comprise parameters, independent and dependent variables. Since the physical processes themselves are seldom known precisely and since most of the model parameters stem from experimental procedures which are also subject to imprecisions, the results predicted by these models are also imprecise, being affected by the uncertainties underlying the respective model. The functional derivatives (also called 'sensitivities') of results (also called 'responses') produced by mathematical/computational models are needed for many purposes, including: (i) understanding the model by ranking the importance of the various model parameters; (ii) performing 'reduced-order modeling' by eliminating unimportant parameters and/or processes; (iii) quantifying the uncertainties induced in a model response due to model parameter uncertainties; (iv) performing 'model validation,' by comparing computations to experiments to address the question 'does the modelrepresent reality ' (v) prioritizing improvements in the model; (vi) performing data assimilation and model calibration as part of forward 'predictive modeling' to obtain best-estimate predicted results with reduced predicted uncertainties; (vii) performing inverse 'predictive modeling'; (viii) designing and optimizing the system.This 3-Volume monograph describes a comprehensive adjoint sensitivity analysis methodology, developed by the author, which enables the efficient and exact computation of arbitrarily high-order sensitivities of model responses in large-scale systems comprising many model parameters. The qualifier 'comprehensive' is employed to highlight that the model parameters considered within the framework of this methodology also include the system's uncertain boundaries and internal interfaces in phase-space. The model's responses can be either scalar-valued functionals of the model's parameters and state variables (e.g., as customarily encountered in optimization problems) or general function-valued responses. Since linear operators admit bona-fide adjoint operators, responses of models that are linear in the state functions (i.e., dependent variables) can depend simultaneously on both the forward and the adjoint state functions. Hence, the sensitivity analysis of such responses warrants the treatment of linear systems in their own right, rather than treating them as particular cases of nonlinear systems. This is in contradistinction to responses for nonlinear systems, which can depend only on the forward state functions, since nonlinear operators do not admit bona-fide adjoint operators (only a linearized form of a nonlinear operator may admit an adjoint operator). Thus, Volume 1 of this book presents the mathematical framework of the nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as 'nth-CASAM-L'), which is conceived for the most efficient computation of exactly obtained mathematical expressions of arbitrarily-high-order (nth-order) sensitivities of a generic system response with respect to all of the parameters underlying the respective forward/adjoint systems. Volume 2 of this book presents the application of the nth-CASAM-L to perform a fourth-order sensitivity and uncertainty analysis of an OECD/NEA reactor physics benchmark which is representative of a large-scale model comprises many (21,976) uncertain parameters, thereby amply illustrating the unique potential of the nth-CASAM-L to enable the exact and efficient computation of chosen high-order response sensitivities to model parameters. Volume 3 of this book presents the 'nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems' (abbreviation: nth-CASAM-N) for the practical, efficient, and exact computation of arbitrarily-high orderse.

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Taschenbuch. Condition: Neu. Structural Sensitivity Analysis and Optimization 1 | Linear Systems | Kyung K. Choi (u. a.) | Taschenbuch | Mechanical Engineering Series | xviii | Englisch | 2010 | Springer | EAN 9781441920065 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.