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Related Concept Videos

Members Made of Elastoplastic Material01:19

Members Made of Elastoplastic Material

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The behavior of elastoplastic materials under bending stresses, particularly in structural members with rectangular cross-sections, is crucial for predicting material responses and understanding failure modes. Initially, when a bending moment is applied, the stress distribution across the section follows Hooke's Law and is linear and elastic. This distribution means the stress increases from the neutral axis to the maximum at the outer fibers, up to the elastic limit.
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Residual Stresses in Bending01:18

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In the study of elastoplastic members subjected to bending moments, understanding the loading and unloading phases is crucial for assessing material behavior and structural integrity. During the loading phase, as the bending moment increases, the material initially responds elastically, adhering to Hooke's Law, where stress is directly proportional to strain. When the load exceeds the yield strength, plastic deformation occurs, resulting in permanent strain and deformation that remains even...
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Elastic Strain Energy for Shearing Stresses

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As discussed in previous lessons, strain energy in a material is the energy stored when it is elastically deformed, a concept crucial in materials science and mechanical engineering. This energy results from the internal work done against the cohesive forces within the material. When a material undergoes shearing stress and corresponding shearing strain, the strain energy density, which is the energy stored per unit volume, is calculated. Within the elastic limit, where the stress is...
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Circular Shafts - Elastoplastic Materials01:24

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The study of solid circular shafts under stress shows that within the elastic limit, stress increases directly to the distance from the shaft's center. This relationship holds until the shaft reaches a critical point of stress, beyond which it begins to yield, marking the transition from elastic to plastic deformation. At this crucial juncture, the maximum torque the shaft can endure without permanent deformation is determined, signifying the limit of its elastic behavior.
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Elastic Strain Energy for Normal Stresses

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Strain energy quantifies the energy stored within a material due to deformation under loading conditions, a fundamental concept in materials science and engineering. The strain energy can be modeled when a material is subjected to axial loading with uniformly distributed stress. In this scenario, the stress experienced by the material is the internal force divided by the cross-sectional area, and the strain induced is directly proportional to this stress through the modulus of elasticity.
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Related Experiment Video

Updated: Jul 19, 2025

Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes
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A data-driven reduced-order surrogate model for entire elastoplastic simulations applied to representative volume

S Vijayaraghavan1, L Wu2, L Noels2

  • 1Faculty of Science, Technology and Medicine, University of Luxembourg, 6 Avenue de la Fonte, Esch-Sur-Alzette, Luxembourg.

Scientific Reports
|August 7, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel surrogate model using neural networks to accelerate elastoplastic solid simulations. This approach significantly reduces computation time by avoiding iterative equation solving, offering a promising alternative for complex material modeling.

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Area of Science:

  • Computational Solid Mechanics
  • Machine Learning in Engineering

Background:

  • Traditional numerical simulations of elastoplastic solids are computationally intensive due to iterative equation solving.
  • Surrogate models offer a faster alternative but are less explored for elastoplasticity compared to fluid dynamics.

Purpose of the Study:

  • To investigate the potential and limitations of a specific surrogate model for elastoplastic simulations.
  • To demonstrate the surrogate's capability in emulating both macroscale behavior and microstructural quantities within representative volume elements.

Main Methods:

  • Employs a surrogate model combining characteristic modes of the solution field with neural networks.
  • Utilizes a recurrent neural network to handle the path dependency inherent in rate-independent elastoplasticity.
  • Avoids iterative solution of linearized governing equations, computing plastic variables once per increment.

Main Results:

  • The surrogate model significantly reduces computation time compared to direct numerical simulations.
  • The model effectively emulates the macroscale stress-deformation relation.
  • Microstructural quantities within representative volume elements can be recovered.

Conclusions:

  • The developed surrogate model shows substantial promise for accelerating elastoplastic simulations.
  • This approach offers significant time savings and maintains accuracy for complex material behaviors.
  • Further study is warranted to fully understand the capabilities and limitations of these surrogates in elastoplasticity.