Enhanced predictive corrosion modeling via randomly distributed boundary conditions

Self Cite

An advanced mathematical approach to describe the influence of corrosion products on the corrosion rate is presented here. The related model can be used as input equation for numerical predictive corrosion simulations or simply as an empirical model, to extrapolate experimental data of corrosion tests to longer times and to interpret the physical parameters behind. This semiempirical model assumes that a constant share of the dissolved metal precipitates on the surface and hinders the diffusion processes. Hence, the effective corrosion rate decreases exponentially with increasing dissolution. The explicit corrosion progress over time is derived by time integration on a newly developed, time dependent corrosion rate equation. The derived expression can be effortlessly implemented in existing for example finite element method, which is demonstrated for the uniform corrosion of a zinc surface. Furthermore, this approach is qualitatively compared with other empirical models for corrosion products and the validity is demonstrated by fitting of experimental data. A very good agreement between experiment and theory can be achieved for various materials and environments considering no change of the driving corrosion mechanism.K E Y W O R D S corrosion products, corrosion simulation, finite element method, mixed potential theory, uniform corrosion

Section: Computational Modelingmentioning

confidence: 99%

“…This model is also often referred to and named as the MPT based approach . The Nernst–Plank equation, which physio‐chemically describes this system, reduces with these assumptions to

Δ ϕ = 0.

…”

Section: Computational Modelingmentioning

confidence: 99%

Section: Computational Modelingmentioning

confidence: 99%

“…Right: Polarization curves of zinc in oxygen‐deficient (solid line) and oxygen‐rich (purged with O 2 , dashed) 5 wt.% NaCl electrolyte at

20 ° normalC

. The arrow indicates the change of polarization [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Computational Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhanced predictive corrosion modeling with implicit corrosion products

Gießgen

Mittelbach

Höche

et al. 2019

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Semantic segmentation of corrosive critical designs in body‐in‐white structures for corrosion simulation

Waibel

Mittelbach

Funken

2020

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Scaling finite element method (FEM) based corrosion simulations to whole body‐in‐white structures lead to extremely high computational costs. As corrosion only appears in corrosive critical areas, the FEM is restricted to these. The objective is a semantic segmentation of corrosive critical designs, which are part edges and flanges in body‐in‐white structures. Different deterministic as well as Machine Learning and Deep Learning approaches are proposed and compared with respect to their ability to segment critical designs based on the geometry and the spatial relations of the parts only. The deterministic edge detection provides a fast and highly accurate way to segment part edges, whereas the described deterministic flange detection is not suitable for capturing the full diversity of flange structures. As the feature‐based Machine Learning approach evaluates more properties in a more flexible way, the performance of the flange detection is significantly increased and even the edge segmentation obtains slightly better results. The evaluation of graph structures with a Geometric Deep Learning method fails as the train set is too small to sufficiently represent the complex and various structures in the test set.

Edge delamination width prediction of 3D body‐in‐white part by finite element‐based corrosion simulation and neural networks

Waibel

Kapfer

Hepfner

et al. 2021

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The edge corrosion simulation of Kapfer et al. is used to extract time-dependent delamination widths of metal sheet edges with different spatial orientations, encoded by edge classes and angular displacements with respect to the ground.The corrosion behavior of the edges of a body-in-white part is examined with respect to the spatial orientation and compared to the simulated delamination widths, demonstrating that the edge corrosion simulation is also able to model more complex structures. Since scaling the finite element method-based corrosion simulation to the whole part requires a lot of memory and computational time, different neural network types are evaluated to predict the delamination widths. Together with the geometric properties of the part edges, a fast and accurate prediction of edge delamination of body-in-white parts is possible, requiring few time-consuming corrosion simulations only in the training phase of the networks.