Data-driven approaches for computational modeling for plasticity, fatigue, and fracture behavior of alloys

TY - CHAP

T1 - Data-driven approaches for computational modeling for plasticity, fatigue, and fracture behavior of alloys

AU - Perumal, Vignesh

AU - Tekerek, Emine

AU - Kontsos, Antonios

N1 - Publisher Copyright: © 2024 Elsevier Inc. All rights reserved.

PY - 2024/1/1

Y1 - 2024/1/1

N2 - The purpose of this chapter is to provide an overview of the data-driven methods used for computational modeling of mechanical behavior of alloys. Specifically, this chapter will focus on the plasticity, fatigue, and fracture effects and as the nature of such effects is crucial for their safe and optimal use in many applications. There is a general consensus in the scientific community regarding the three pillars of scientific methods related to understanding material behavior and performance which include theory, experimentation, and computation. While there is ample debate whether data-driven techniques can be identified as a new fourth pillar, mechanicians have found that data-driven computational mechanics offers the possibility of capturing and explaining complex processes when phenomenological models alone compared with experiments are not capable of doing the same. In traditional computational mechanics, numerical discretization methods such as finite element method and computational fluid dynamics, are used to simulate the physical behavior of structures and fluid flows. These discretization methods provide the mathematical models a framework to solve physics for complex geometries using detailed knowledge of the material properties, boundary conditions, and governing equations. While these models can provide accurate results, they often rely on simplifying assumptions and may not capture all the complexities of real-world systems. Data-driven computational mechanics methods, leverage the availability of real-world data to develop material models or physical laws which can be informed in a variety of ways by data with the objective to overcome challenges associated with predicting material behavior.

AB - The purpose of this chapter is to provide an overview of the data-driven methods used for computational modeling of mechanical behavior of alloys. Specifically, this chapter will focus on the plasticity, fatigue, and fracture effects and as the nature of such effects is crucial for their safe and optimal use in many applications. There is a general consensus in the scientific community regarding the three pillars of scientific methods related to understanding material behavior and performance which include theory, experimentation, and computation. While there is ample debate whether data-driven techniques can be identified as a new fourth pillar, mechanicians have found that data-driven computational mechanics offers the possibility of capturing and explaining complex processes when phenomenological models alone compared with experiments are not capable of doing the same. In traditional computational mechanics, numerical discretization methods such as finite element method and computational fluid dynamics, are used to simulate the physical behavior of structures and fluid flows. These discretization methods provide the mathematical models a framework to solve physics for complex geometries using detailed knowledge of the material properties, boundary conditions, and governing equations. While these models can provide accurate results, they often rely on simplifying assumptions and may not capture all the complexities of real-world systems. Data-driven computational mechanics methods, leverage the availability of real-world data to develop material models or physical laws which can be informed in a variety of ways by data with the objective to overcome challenges associated with predicting material behavior.

UR - https://www.scopus.com/pages/publications/85199102571

UR - https://www.scopus.com/pages/publications/85199102571#tab=citedBy

U2 - 10.1016/B978-0-323-99539-9.00005-9

DO - 10.1016/B978-0-323-99539-9.00005-9

M3 - Chapter

SN - 9780323995405

SP - 141

EP - 180

BT - Innovative Lightweight and High-Strength Alloys

PB - Elsevier

ER -