Modeling, control and opportunities of mechanical interfaces across the scales

This treatise analyzes various roles of mechanical interfaces in natural and artificial environments. From the classic perspective of spatial scales, the chapters evolve from modeling at the atomistic scale, to single continuum interfaces to larger systems of interfaces. They will cover molecular descriptions, sharp and diffuse interface models as well as effective ensemble properties. From the perspective of control, they evolve from naturally arising to artificially created objects. Freely evolving cracks, phase changes and interface design for controlled fluid-structure interaction will be shown for illustration. From the perspective on new opportunities, finally, they evolve from risk-bearing imperfections to technical and scientific opportunities. While damage is investigated as a typical risk scenario, novel potentials will be explored for non-destructive characterization, low-frequency attenuation and information enhancement for artificial neural networks.

Publication Details

Journal
Technische Universität Dortmund Eldorado (Technische Universität Dortmund)
Published
2025-01-01
DOI
https://doi.org/10.17877/de290r-25459
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Modeling, control and opportunities of mechanical interfaces across the scales

Technische Universität Dortmund Eldorado (Technische Universität Dortmund)
Machine Learning in Materials Science
article

Modeling, control and opportunities of mechanical interfaces across the scales

article en

Abstract

This treatise analyzes various roles of mechanical interfaces in natural and artificial environments. From the classic perspective of spatial scales, the chapters evolve from modeling at the atomistic scale, to single continuum interfaces to larger systems of interfaces. They will cover molecular descriptions, sharp and diffuse interface models as well as effective ensemble properties. From the perspective of control, they evolve from naturally arising to artificially created objects. Freely evolving cracks, phase changes and interface design for controlled fluid-structure interaction will be shown for illustration. From the perspective on new opportunities, finally, they evolve from risk-bearing imperfections to technical and scientific opportunities. While damage is investigated as a typical risk scenario, novel potentials will be explored for non-destructive characterization, low-frequency attenuation and information enhancement for artificial neural networks.

Technische Universität Dortmund Eldorado (Technische Universität Dortmund)
Openalex Percentile: Top 61%
Machine Learning in Materials Science
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