An iterative nonlocal residual constitutive model for nonlocal elasticity
Mohamed Shaat

TL;DR
This paper introduces iterative nonlocal residual constitutive models that address the ill-posedness of traditional nonlocal elasticity models, ensuring solvable boundary value problems and consistent solutions for various boundary conditions.
Contribution
It proposes novel integral and differential iterative nonlocal residual models that guarantee solutions and overcome limitations of Eringen's nonlocal constitutive models.
Findings
Proposed models produce identical, feasible results.
Traditional models lead to ill-posed boundary value problems.
Solutions are guaranteed for various boundary conditions.
Abstract
Recently, it was claimed that the two-phase local/nonlocal constitutive models give well-posed nonlocal field problems and eliminates the ill-posedness of the fully nonlocal constitutive models. In this study, it is demonstrated that, both, the fully nonlocal and the two-phase local/nonlocal constitutive models secrete ill-posed nonlocal boundary value problems. Moreover, it is revealed that all Eringen integral and differential nonlocal constitutive models secrete unsolvable nonlocal boundary value problems. In this study, it is demonstrated that solutions of nonlocal elasticity problems are exist, and Eringen constitutive model cannot determine these solutions. To overcome the limitations of Eringen constitutive models, novel integral and differential iterative nonlocal residual constitutive models are proposed. Using these two constitutive models, the sum of the nonlocal residual…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNonlocal and gradient elasticity in micro/nano structures · Numerical methods in engineering · Composite Structure Analysis and Optimization
