Innovative Creep Analysis Methods: 101 Solved Problems provides analytical insight and solutions to commonly encountered problems involving creep deformation of materials. The book provides fundamental insight into the phenomenon of creep, methods for analyzing elasticity and plasticity problems, outlines the effects of atomic number and atomic weight on creep, as well as simulation techniques for elasto-plastic deformation in composites by flow-rule. Creep formulations and computational modeling techniques are provided throughout. Each problem presented is meticulously solved with detailed explanations and step-by-step instructions, ensuring that readers grasp the underlying concepts. Problems featured include predicting principal creep stress in fibrous composites, obtaining creep strain rate in nickel, obtaining creep-rupture life in alloy S-590, finding nonlinear isochronous curves with Ramberg-Osgood Form, finding the strain formulation in a viscoelastic model, obtaining maximum creep stress in beam and elastic deflection, deformation of creep plastically, calculating minimum creep strain rate, and much more.
- Provides analysis and solutions to commonly encountered problems involving creep deformation in a variety of different materials
- Outlines the effects of atomic number and atomic weight on creep, simulation of elasto-plastic deformation in composites by flow rules, and the relationship between creep and viscosity
- Demonstrates application of Legendre polynomials in creep analysis of composites
Vahid Monfared completed his PhD in Mechanical Engineering (Solid and Applied Mechanics), and works as a part-time lecturer/instructor at the University of Rhode Island/URI (USA), He also holds roles as an Associate Professor at Islamic Azad University of Zanjan, and as a Postdoctoral Research Fellow at Harvard Medical School (Harvard University) in the field of Machine Learning (AI) and Data Science to Medicine, Healthcare, and Engineering). Vahid also has practical experience as an engineer at Varian (a Siemens Healthineers company). In addition to the main filed in mechanical engineering (solid mechanics), his research interests include the application of Data Analytics and ML/AI in prediction of complex phenomena such as creep, mechanical deformations, and failure analysis. Along with working on AI and machine learning projects at Harvard Medical School. He furthered his knowledge of applied data analytics and machine learning at the Massachusetts Institute of Technology (MIT) and Boston University (BU) by completing a master’s degree in applied data analytics.
