Similarity and Modeling in Science and Engineering [electronic resource] / by Josef Kuneš.

By: Kuneš, Josef [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Publisher: Cambridge : Cambridge International Science Publishing Ltd, 2012Description: XVIII, 442 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781907343780Subject(s): Engineering | Computer aided design | Computer science | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Computer-Aided Engineering (CAD, CAE) and Design | Computational Science and EngineeringAdditional physical formats: Printed edition:: No titleDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
Contents:
From the Contents: Methodology of Modeling -- Dimensional Analysis -- Methods of Similarity Analysis -- Mathematical Models -- Physical Models -- Physical Analogs -- Computer Deterministic Models -- Computer Stochastic Models -- Cybernetic Models.
In: Springer eBooksSummary: The present text sets itself in relief to other titles on the subject in that it addresses the means and methodologies versus a narrow specific-task oriented approach. Concepts and their developments which evolved to meet the changing needs of applications are addressed. This approach provides the reader with a general tool-box to apply to their specific needs. Two important tools are presented: dimensional analysis and the similarity analysis methods. The fundamental point of view, enabling one to sort all models, is that of information flux between a model and an original expressed by the similarity and abstraction. Each chapter includes original examples and ap-plications. In this respect, the models can be divided into several groups. The following models are dealt with separately by chapter; mathematical and physical models, physical analogues, deterministic, stochastic, and cybernetic computer models. The mathematical models are divided into asymptotic and phenomenological models. The phenomenological models, which can also be called experimental, are usually the result of an experiment on a complex object or process. The variable dimensionless quantities contain information about the real state of boundary conditions, parameter (non-linearity) changes, and other factors. With satisfactory measurement accuracy and experimental strategy, such models are highly credible and can be used, for example, in control systems.
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From the Contents: Methodology of Modeling -- Dimensional Analysis -- Methods of Similarity Analysis -- Mathematical Models -- Physical Models -- Physical Analogs -- Computer Deterministic Models -- Computer Stochastic Models -- Cybernetic Models.

The present text sets itself in relief to other titles on the subject in that it addresses the means and methodologies versus a narrow specific-task oriented approach. Concepts and their developments which evolved to meet the changing needs of applications are addressed. This approach provides the reader with a general tool-box to apply to their specific needs. Two important tools are presented: dimensional analysis and the similarity analysis methods. The fundamental point of view, enabling one to sort all models, is that of information flux between a model and an original expressed by the similarity and abstraction. Each chapter includes original examples and ap-plications. In this respect, the models can be divided into several groups. The following models are dealt with separately by chapter; mathematical and physical models, physical analogues, deterministic, stochastic, and cybernetic computer models. The mathematical models are divided into asymptotic and phenomenological models. The phenomenological models, which can also be called experimental, are usually the result of an experiment on a complex object or process. The variable dimensionless quantities contain information about the real state of boundary conditions, parameter (non-linearity) changes, and other factors. With satisfactory measurement accuracy and experimental strategy, such models are highly credible and can be used, for example, in control systems.

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