Stability of Model Test Similarity
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Beschreibung
This book presents a breakthrough advancement in the field of structural dynamics and engineering modeling, specifically addressing the critical challenge of similarity conversion in model testing for transient, strongly nonlinear dynamic processes. It introduces and systematically develops the Iterative Similarity Theory, overcoming the limitations of classical similarity principles and providing a rigorous theoretical framework for accurate experimental simulation and prototype prediction. The book highlights innovative research methodologies—including renormalization group theory, phase space reconstruction, symbolic dynamics, and scaling theory—which guide readers in exploring novel approaches to controlling uncertainty, selecting optimal scaling ratios for model tests, and achieving reliable similarity transformations in highly nonlinear systems. Enriched with mathematical derivations, explicit conversion methods, and stability criteria derived from fractal and topological concepts, it distinguishes itself through a strong integration of theoretical depth and practical validation. Its primary value lies in offering engineers and researchers a systematic and theoretically robust toolkit for designing and interpreting scaled model tests of extreme dynamic events, thereby enhancing predictive accuracy in real-world engineering applications. This volume serves as an essential reference for professionals and academics in structural dynamics, naval and ocean engineering, aerospace engineering, protective engineering, and related fields, particularly for those engaged in research and practice of highly complex engineering projects such as underwater explosion analysis and cross-media vehicle dynamics.
This book presents a breakthrough advancement in the field of structural dynamics and engineering modeling, specifically addressing the critical challenge of similarity conversion in model testing for transient, strongly nonlinear dynamic processes. It introduces and systematically develops the Iterative Similarity Theory, overcoming the limitations of classical similarity principles and providing a rigorous theoretical framework for accurate experimental simulation and prototype prediction. The book highlights innovative research methodologies—including renormalization group theory, phase space reconstruction, symbolic dynamics, and scaling theory—which guide readers in exploring novel approaches to controlling uncertainty, selecting optimal scaling ratios for model tests, and achieving reliable similarity transformations in highly nonlinear systems. Enriched with mathematical derivations, explicit conversion methods, and stability criteria derived from fractal and topological concepts, it distinguishes itself through a strong integration of theoretical depth and practical validation. Its primary value lies in offering engineers and researchers a systematic and theoretically robust toolkit for designing and interpreting scaled model tests of extreme dynamic events, thereby enhancing predictive accuracy in real-world engineering applications. This volume serves as an essential reference for professionals and academics in structural dynamics, naval and ocean engineering, aerospace engineering, protective engineering, and related fields, particularly for those engaged in research and practice of highly complex engineering projects such as underwater explosion analysis and cross-media vehicle dynamics.
Introduces Iterative Similarity Theory, defining stability vs instability and optimal scaling in model tests Offers a framework combining renormalization, phase space reconstruction, and scaling theory for model testing Provides fractal- and topology-based conversion methods, validated by engineering cases of transient impact processes
Autor*in
Xiongliang Yao
Themen in »Stability of Model Test Similarity«
Transient Strongly Nonlinear Processes Iterative Similarity Theory Renormalization Group Theory Scaling Theory Diffeomorphism and Topological Conjugacy Stability Conditions for Scale Model Tests Model Test Uncertainty Optimal scaling ratio