2020
DOI: 10.1016/j.compscitech.2020.108460
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An enriched cohesive law using plane-part of interfacial strains to model intra/inter laminar coupling in laminated composites

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Cited by 9 publications
(4 citation statements)
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“…To predict the initiation of damage in the welded areas, specifically in the cohesive zone, the quadratic traction initiation criterion was employed. This criterion, ex-Polymers 2023, 15, 3555 10 of 18 pressed mathematically as follows, enables the determination of the critical conditions for damage initiation [38]:…”
Section: Cohesive Model For the Welding Interfacementioning
confidence: 99%
“…To predict the initiation of damage in the welded areas, specifically in the cohesive zone, the quadratic traction initiation criterion was employed. This criterion, ex-Polymers 2023, 15, 3555 10 of 18 pressed mathematically as follows, enables the determination of the critical conditions for damage initiation [38]:…”
Section: Cohesive Model For the Welding Interfacementioning
confidence: 99%
“…[16][17][18] In this work, we propose to further extend the application of deep learning acceleration methods into wider applications in energy industry, and the thermodynamics-informed neural network (TINN) is capable of incorporating special mechanisms and environmental conditions that should be considered in certain scenarios, for example, phase stability check in pipeline transportation to avoid liquefaction of natural gas in transportation, which may challenge the material stability of the pipeline and put forward additional requirements on the current material studies. 19,20 A schematic diagram of the general flash calculation in energy industry is plotted in Figure 1. Thermodynamic analysis is the foundation of both iterative schemes and F I G U R E 1 Schematic diagram of general flash calculation in energy industry deep learning acceleration models, while special mechanisms should be considered in the analysis in certain engineering scenarios.…”
Section: Introductionmentioning
confidence: 99%
“…A number of research studies have been reported to approach realistic engineering scenarios by designing a set of highly adaptive deep learning algorithms to adapt to the variability and complexity of fluid components and environmental parameters in energy computations 16‐18 . In this work, we propose to further extend the application of deep learning acceleration methods into wider applications in energy industry, and the thermodynamics‐informed neural network (TINN) is capable of incorporating special mechanisms and environmental conditions that should be considered in certain scenarios, for example, phase stability check in pipeline transportation to avoid liquefaction of natural gas in transportation, which may challenge the material stability of the pipeline and put forward additional requirements on the current material studies 19,20 …”
Section: Introductionmentioning
confidence: 99%
“…These sequences are then subjected to macroscopic delamination tests, in order to evaluate the effect of this pre-cracking on the interface tenacity. Secondly, a modeling approach is proposed, based on a new type of interface model [3]. Unlike the classical cohesive elements, which only consider the out-of-plane part of the displacement jump at the interface, our hybrid model also uses the planar part of the displacement jumps.…”
mentioning
confidence: 99%