Innovative Creep Analysis Methods
101 Solved Problems
- 1st Edition - May 14, 2025
- Latest edition
- Author: Vahid Monfared
- Language: English
Innovative Creep Analysis Methods: 101 Solved Problems provides analytical insight and solutions to commonly encountered problems involving creep deformation of materials. The bo… Read more
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Description
Description
Key features
Key features
- 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
Readership
Readership
Table of contents
Table of contents
2. Analytical Creep Analysis Methods
3. Numerical Creep Analysis Methods
4. Experimental Creep Analysis Methods
5. Comprehensive Algorithm for Analyzing Elasticity and Plasticity Problems
6. Effects of Atomic Number and Atomic Weight on Creep
7. Simulation of Elasto-Plastic Deformation in Composite by Flow Rules
8. Obtaining the Viscosity of Solids Using Creep Semi-Theoretically
9. Creep Formulations and Diagrams
10. Application of Legendre Polynomials in Creep Analysis of Composites
11. Computational Modeling of Creep in Complex Plane for Composites
12. Solved Problems
Appendix
A. Some Mechanical Equations and Material Properties
B. Some Mathematical Relations
C. Useful Creep Data for Some Alloys References
Product details
Product details
- Edition: 1
- Latest edition
- Published: May 14, 2025
- Language: English
About the author
About the author
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Vahid Monfared
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.