دانلود کتاب سیستم‌های مهندسی پیچیده – مدل‌سازی و بهینه‌سازی

This book offers a rigorous and systematic examination of complex engineering systems (CES), integrating foundational theories, advanced modeling techniques, and cutting- edge optimization strategies to bridge the gap between theoretical concepts and practical applications. Designed as a comprehensive reference, it caters to researchers, engineers, and graduate students seeking to analyze, model, and optimize CES across diverse domains, from fluid dynamics and heat transfer to multi-physics interactions and computational intelligence. The book begins by establishing the fundamentals of CES in Chapter 1, introducing their multidisciplinary nature, emergent behaviors, and key principles such as scalability, resilience, and system integration. It underscores the importance of model-based approaches and systems thinking in addressing CES challenges. Chapter 2 lays the mathematical groundwork, covering differential equations, stochastic models, and computational tools for dynamic system analysis, while Chapter 3 transitions to optimization techniques, including genetic algorithms, dynamic programming, and AI-driven strategies for performance enhancement and cost reduction. Subsequent chapters delve into specialized methodologies and applications. Chapter 4 explores multi-physics modeling, emphasizing coupled analyses across structural, thermal, and electromagnetic domains. Chapter 5 advances numerical methods for fluid dynamics, employing Crank-Nicolson schemes and Shishkin meshes to tackle singular perturbations. Chapter 6 evaluates weighted residual methods for boundary value problems, comparing techniques like Galerkin and least squares, while Chapter 7 investigates MHD Stokes flow in microchannels using the boundary element method, with applications in microfluidics. System optimization and applied engineering solutions take center stage in later chapters. Chapter 8 analyzes queueing models for resource efficiency, and Chapter 9 reviews MEMS-based microfluidic pressure sensing for lab-on-a-chip devices. Chapters 10 and 11 examine nanofluid dynamics, leveraging models like Xue and Cattaneo-Christov to study thermal radiation, MHD effects, and porous media flows. Chapter 12 presents multiphase flow analysis in cryogenic systems using volume of fluid models, and Chapter 13 introduces fractional calculus for memory-dependent heat transfer. The concluding chapters highlight emerging computational and data-driven approaches. Chapter 14 applies bifurcation theory to manage chaotic dynamics in discretized systems, while Chapter 15 combines machine learning (artificial neural network) with numerical methods to predict thermodiffusion effects. Finally, Chapter 16 reviews computational fluid dynamics as a versatile tool for optimizing designs across engineering, biomedical, and environmental systems. By unifying multidisciplinary perspectives – from mathematical modeling and computational simulations to AI-augmented optimization – this book equips readers with the tools and insights needed to solve real-world CES challenges. Featuring case studies, comparative analyses, and hybrid methodologies, it serves as both a reference and an inspiration for advancing research and innovation in complex systems engineering.