Results 181 to 190 of about 2,453,874 (341)

On stopping time directed convergence [PDF]

open access: yesBulletin of the American Mathematical Society, 1976
openaire   +3 more sources

A Novel Simulation Approach for Damage Evolution during Tailored Forming

open access: yesAdvanced Engineering Materials, EarlyView.
Traditional damage models are struggling to accurately and efficiently simulate large‐scale three‐dimensional models with a great number of degrees of freedoms. A new gradient‐enhanced damage model based on the extended Hamilton principle can significantly reduce the computation time while ensuring mesh‐independence which is suitable to use in tailored
Fangrui Liu   +2 more
wiley   +1 more source

Influence of Form and Structural Features of Open‐Cell Hybrid Foam on the Remanent Magnetic Scanning

open access: yesAdvanced Engineering Materials, EarlyView.
Simulations using an equivalent model are performed to improve the understanding of remanent magnetic scanning of hybrid foam samples for coating thickness estimation. The model allows to study the influence of isolated geometrical features such as layer thickness, boundary effects, as well as effects due to the statistical nature of the foams, which ...
Bashar Ibrahim   +4 more
wiley   +1 more source

Global convergence for time-periodic systems with negative feedback and applications [PDF]

open access: yesarXiv
For the discrete-time dynamical system generated by the Poincare map T of a time-periodic closed-loop negative feedback system, we present an amenable condition which enables us to obtain the global convergence of the orbits. This yields the global convergence to the harmonic periodic solutions of the corresponding time-periodic systems with negative ...
arxiv  

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

open access: yesAdvanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi   +4 more
wiley   +1 more source

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