This book presents recent improvements in peridynamic modeling of structures. It provides sufficient theory and numerical implementation helpful to both new and existing researchers in the field. The main focus of the book is on the non-ordinary state-based (NOSB) peridynamics (PD) and its applications for performing finite deformation. It presents the framework for modeling high stretch polymers, viscoelastic materials, thermoelasticity, plasticity, and creep. It provides a systematic derivation for dimensionally reduced structures such as axisymmetric structures and beams. Also, it presents a novel approach to impose boundary conditions without suffering from displacement kinks near the boundary. Furthermore, it presents refinements to bond-based PD model by including rotation kinematics for modeling isotropic and composite materials. Moreover, it presents a PD – FEM coupling framework in ANSYS based on principle for virtual work. Lastly, it presents an application of neural networks in the peridynamic (PINN) framework. Sample codes are provided for readers to develop hands-on experience on peridynamic modeling.
Describes new developments in peridynamics and their applications in the presence of material and geometric nonlinearity;
Describes an approach to seamlessly couple PD with FE;
Introduces the use of the neural network in the PD framework to solve engineering problems;
Provides theory and numerical examples for researchers and students to self-study and apply in their research (Codes are provided as supplementary material);
Provides theoretical development and numerical examples suitable for graduate courses.
Introduction.- Peridynamic Differential Operator.- Refinements In Bond-Based Peridynamics.- Refinements In Ordinary State-Based Peridynamics.- Weak Form Of Peridynamics.- Bond-Associated State-Based Peridynamics (Ba-Sb Pd).- Ba-Sb Pd For Thermoelastic Deformation.- Ba-Sb Pd For Elastic- Plastic Deformation.- Ba-Sb Pd For Viscoleastic And Creep Deformation.- Ba-Sb Pd For Hyperelastic Deformation.- Ba-Sb Pd For Visco-Hyperelastic Deformation.- Ba-Sb Pd Modeling For Damage In Quasi-Brittle Materials.- Ba-Sb Pd Modeling For Impact Analysis.- Ba-Sb Pd Modeling Of Plates And Shells.- Ba-Sb Pd Modeling Under Axisymmetric Idealization.- Peridynamics For Multi-Scale Modeling.- Peridynamics For Machine Learning.- Peridynamics Coupled With Fem In Ansys Framework.
Erdogan Madenci is a Professor in the Aerospace and Mechanical Engineering Department of The University of Arizona, Tucson, Arizona, USA. He received his B.S. degrees on both mechanical and industrial engineering, and his M.S. degree in applied mechanics from Lehigh University, Bethlehem, PA in 1980, 1981, and 1982, respectively. He received his Ph.D. degree in engineering mechanics from UCLA in 1987. Prior to joining the University of Arizona, he worked at Northrop Corporation, Aerospace Corporation, and Fraunhofer Institute. Also, he worked at the KTH Royal Institute of Technology, NASA Langley Research Center, Sandia National Labs and MIT as part of his sabbatical leaves. He is the lead author of four books on Peridynamic Differential Operator for Numerical Analysis, Peridynamic Theory and Its Applications, The Finite Element Method Using ANSYS, and Fatigue Life Prediction of Solder Joints. Recently, he started the Journal of Peridynamics and Nonlocal Modeling as the Co-Editor-in-Chief, and is an Associate Editor of ASME Open Journal of Engineering. He is a Fellow of ASME and an Associate Fellow of AIAA.
Pranesh Roy is an Assistant Professor in the Department of Civil Engineering at the Indian Institute of Technology (Indian School of Mines) (IIT-ISM) Dhanbad. He received his B.E. degree in Civil Engineering from Jadavpur University, Kolkata in 2012. He obtained his M.Tech. degree in Structural Engineering from the Indian Institute of Technology (IIT), Delhi in 2014. He received his Ph.D. degree in Civil Engineering from the Indian Institute of Science (IISc), Bangalore in 2019. He worked as a Postdoctoral Research Associate from 2019 to 2021 in Aerospace and Mechanical Engineering at The University of Arizona, USA. Dr. Roy is an expert in the broad area of theoretical and computational solid mechanics. Particularly, his research focuses on nonclassical continuum theories such as peridynamics, phase field theory, and gauge theory of solids.
Deepak Behera is a Postdoctoral Reseacher in the Department of Aerospace and Mechanical Engineering at the University of Arizona. He received his B.Tech.-M.Tech. dual degree in Aerospace Engineering from the Indian Institute of Technology (IIT), Kanpur in 2012. He received his Ph.D. degree in Aerospace Engineering from the University of Arizona in 2022. Prior to his Ph.D., he worked as a structural engineer at General Electric-Aviation, Bengaluru and IIT, Kanpur. He also worked at Idaho National Lab as a summer visiting scholar. His research focuses on numerical methods and theoretical and computational solid mechanics with an emphasis on failure analysis and peridynamics. His other research interest includes molecular dynamics, optimization, and uncertainty quantification.
This book presents recent improvements in peridynamic modeling of structures. It provides sufficient theory and numerical implementation helpful to both new and existing researchers in the field. The main focus of the book is on the non-ordinary state-based (NOSB) peridynamics (PD) and its applications for performing finite deformation. It presents the framework for modeling high stretch polymers, viscoelastic materials, thermoelasticity, plasticity, and creep. It provides a systematic derivation for dimensionally reduced structures such as axisymmetric structures and beams. Also, it presents a novel approach to impose boundary conditions without suffering from displacement kinks near the boundary. Furthermore, it presents refinements to bond-based PD model by including rotation kinematics for modeling isotropic and composite materials. Moreover, it presents a PD – FEM coupling framework in ANSYS based on principle for virtual work. Lastly, it presents an application of neural networks in the peridynamic (PINN) framework. Sample codes are provided for readers to develop hands-on experience on peridynamic modeling.
Describes new developments in peridynamics and their applications in the presence of material and geometric nonlinearity;
Describes an approach to seamlessly couple PD with FE;
Introduces the use of the neural network in the PD framework to solve engineering problems;
Provides theory and numerical examples for researchers and students to self-study and apply in their research (Codes are provided as supplementary material);
Provides theoretical development and numerical examples suitable for graduate courses.