Satyvir Singh – författare

The text provides insight into the different mathematical tools and techniques that can be applied to the analysis and numerical computations of flow models. It further discusses important topics such as the heat transfer effect on boundary layer flow, modeling of flows through porous media, anisotropic polytrophic gas model, and thermal instability in viscoelastic fluids.This book:Discusses modeling of Rayleigh-Taylor instability in nanofluid layer and thermal instability in viscoelastic fluidsCovers open FOAM simulation of free surface problems, and anisotropic polytrophic gas modelHighlights the Sensitivity Analysis in Aerospace Engineering, MHD Flow of a Micropolar Hybrid Nanofluid, and IoT-Enabled Monitoring for Natural ConvectionPresents thermal behavior of nanofluid in complex geometries and heat transfer effect on Boundary layer flowExplains natural convection heat transfer in non-Newtonian fluids and homotropy series solution of the boundary layer flowIllustrates modeling of flows through porous media and investigates Shock-driven Richtmyer-Meshkov instabilityIt is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of Applied Sciences, Mechanical Engineering, Manufacturing Engineering, Production Engineering, Industrial engineering, Automotive engineering, and Aerospace engineering.

The text provides insight into the different mathematical tools and techniques that can be applied to the analysis and numerical computations of flow models. It further discusses important topics such as the heat transfer effect on boundary layer flow, modeling of flows through porous media, anisotropic polytrophic gas model, and thermal instability in viscoelastic fluids.This book:Discusses modeling of Rayleigh-Taylor instability in nanofluid layer and thermal instability in viscoelastic fluidsCovers open FOAM simulation of free surface problems, and anisotropic polytrophic gas modelHighlights the Sensitivity Analysis in Aerospace Engineering, MHD Flow of a Micropolar Hybrid Nanofluid, and IoT-Enabled Monitoring for Natural ConvectionPresents thermal behavior of nanofluid in complex geometries and heat transfer effect on Boundary layer flowExplains natural convection heat transfer in non-Newtonian fluids and homotropy series solution of the boundary layer flowIllustrates modeling of flows through porous media and investigates Shock-driven Richtmyer-Meshkov instabilityIt is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of Applied Sciences, Mechanical Engineering, Manufacturing Engineering, Production Engineering, Industrial engineering, Automotive engineering, and Aerospace engineering.

The text provides insight into the different mathematical tools and techniques that can be applied to the analysis and numerical computations of flow models. It further discusses important topics such as the heat transfer effect on boundary layer flow, modeling of flows through porous media, anisotropic polytrophic gas model, and thermal instability in viscoelastic fluids.

This book:

Discusses modeling of Rayleigh-Taylor instability in nanofluid layer and thermal instability in viscoelastic fluids Covers open FOAM simulation of free surface problems, and anisotropic polytrophic gas model Highlights the Sensitivity Analysis in Aerospace Engineering, MHD Flow of a Micropolar Hybrid Nanofluid, and IoT-Enabled Monitoring for Natural Convection Presents thermal behavior of nanofluid in complex geometries and heat transfer effect on Boundary layer flow Explains natural convection heat transfer in non-Newtonian fluids and homotropy series solution of the boundary layer flow Illustrates modeling of flows through porous media and investigates Shock-driven Richtmyer-Meshkov instability

It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of Applied Sciences, Mechanical Engineering, Manufacturing Engineering, Production Engineering, Industrial engineering, Automotive engineering, and Aerospace engineering.

The text provides insight into the different mathematical tools and techniques that can be applied to the analysis and numerical computations of flow models. It further discusses important topics such as the heat transfer effect on boundary layer flow, modeling of flows through porous media, anisotropic polytrophic gas model, and thermal instability in viscoelastic fluids.

This book:

Discusses modeling of Rayleigh-Taylor instability in nanofluid layer and thermal instability in viscoelastic fluids Covers open FOAM simulation of free surface problems, and anisotropic polytrophic gas model Highlights the Sensitivity Analysis in Aerospace Engineering, MHD Flow of a Micropolar Hybrid Nanofluid, and IoT-Enabled Monitoring for Natural Convection Presents thermal behavior of nanofluid in complex geometries and heat transfer effect on Boundary layer flow Explains natural convection heat transfer in non-Newtonian fluids and homotropy series solution of the boundary layer flow Illustrates modeling of flows through porous media and investigates Shock-driven Richtmyer-Meshkov instability

It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of Applied Sciences, Mechanical Engineering, Manufacturing Engineering, Production Engineering, Industrial engineering, Automotive engineering, and Aerospace engineering.

The book discusses the advantages of using mathematical modeling and machine learning in the context of the evolutionary biology domain to gain knowledge and develop further. It discusses the background ideas regarding evolutionary theory, population behavior and computation, and advances to the current topics of evolutionary algorithms, nonlinear modeling, and data-driven analysis. The volume proposes the application of theoretical models and clever algorithms to the analysis of complex biological systems, ecological interactions, and real-world problems in health, genomics, and engineering. Combining classical theories with some new computational tools, the book proves that machine learning is able to make predictions more accurate and reduce some parameters and process large amounts of data more efficiently in biological studies. It also covers disease modelling, genomic prediction, tumor growth and socio-environmental dynamics and is also interdisciplinary in exploring network systems and biomedical engineering. On the whole, the book offers an integrative and prospective view to scientists and professionals regarding the innovative aspects at the interplay of biology, mathematics, and artificial intelligence and highlights the future of evolutionary science and intelligent models.

Complex Engineering Systems - Modeling and Optimization offers an in-depth exploration of the foundational principles, advanced methodologies, and interdisciplinary applications essential for understanding and managing intricate engineering systems. Spanning mathematical modeling, numerical simulation, optimization strategies, and AI-assisted techniques, the book presents a rich blend of theory and real-world problem-solving tools. This comprehensive volume is tailored for researchers, professionals, and graduate students engaged in engineering, applied mathematics, and computational sciences.Covering diverse themes-from system-of-systems behavior and multiphysics modeling to nanofluid dynamics, fractional heat transfer, queuing theory, and machine learning integration - the chapters collectively emphasize the interplay between complexity, adaptability, and innovation. The contributors shed light on modern challenges like cryogenic flow analysis, pressure sensing in microfluidics, MHD flow behavior, and AI-driven predictive modeling.

In an era where engineering challenges are becoming increasingly multifaceted, Mathematical Solutions for Complex Engineering Systems serves as a comprehensive guide bridging the gap between theory and real-world applications. This book explores advanced mathematical modeling techniques, computational approaches, and optimization strategies that drive innovation across various engineering disciplines.This book brings together a diverse collection of research contributions that highlight:Deterministic, stochastic, linear, and nonlinear models essential for analyzing multi-physical systems, from fluid dynamics to reliability engineering.Cutting-edge methods such as finite element analysis, boundary element methods, Keller box techniques, and machine learning-driven modeling for solving complex engineering problems.Case studies on nuclear power plant reliability, eco-epidemiology, nanofluid heat transfer, and pesticide impact on agricultural sustainability.Novel mathematical frameworks, including fractional Taylor wavelets, B-spline methods, and variational principles, for tackling nonlinear differential equations.Advanced concepts such as tunnel mathematics, meshfree methods, and high-resolution computational fluid dynamics (CFD) models for solving contemporary engineering challenges.Designed for researchers, engineers, and graduate students, this book provides a robust foundation in mathematical techniques and their practical applications. With contributions from leading experts, it offers a unique blend of theoretical depth and computational efficiency, making it an essential reference for tackling modern engineering complexities.

In an era where engineering challenges are becoming increasingly multifaceted, Mathematical Solutions for Complex Engineering Systems serves as a comprehensive guide bridging the gap between theory and real-world applications. This book explores advanced mathematical modeling techniques, computational approaches, and optimization strategies that drive innovation across various engineering disciplines.This book brings together a diverse collection of research contributions that highlight: Deterministic, stochastic, linear, and nonlinear models essential for analyzing multi-physical systems, from fluid dynamics to reliability engineering. Cutting-edge methods such as finite element analysis, boundary element methods, Keller box techniques, and machine learning-driven modeling for solving complex engineering problems. Case studies on nuclear power plant reliability, eco-epidemiology, nanofluid heat transfer, and pesticide impact on agricultural sustainability. Novel mathematical frameworks, including fractional Taylor wavelets, B-spline methods, and variational principles, for tackling nonlinear differential equations. Advanced concepts such as tunnel mathematics, meshfree methods, and high-resolution computational fluid dynamics (CFD) models for solving contemporary engineering challenges. Designed for researchers, engineers, and graduate students, this book provides a robust foundation in mathematical techniques and their practical applications. With contributions from leading experts, it offers a unique blend of theoretical depth and computational efficiency, making it an essential reference for tackling modern engineering complexities.