Numerical Study of Rectangular Tank with Sloshing Fluid and Simulation of the Model Using a Machine Learning Method

Chegini, Hossein Goudarzvand; Zarepour, Gholamreza

doi:10.1155/2022/4121956

Cited by 4 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANN is a convenient tool for approximation of data sets with complex and/or unknown functional dependence that is actively applied in the mechanics of materials [ 62 , 63 , 64 , 65 ]. Nowadays, ANNs are fruitfully used to generalize calculation data from both SPH [ 66 , 67 , 68 , 69 ] and finite element analysis [ 70 ] for the solution of various engineering problems. An ANN can be embedded in finite element [ 71 ] or finite difference [ 56 ] simulations as a fast-running part of a multiscale model.…”

Section: Introductionmentioning

confidence: 99%

Modified Taylor Impact Tests with Profiled Copper Cylinders: Experiment and Optimization of Dislocation Plasticity Model

et al. 2023

View full text Add to dashboard Cite

Current progress in numerical simulations and machine learning allows one to apply complex loading conditions for the identification of parameters in plasticity models. This possibility expands the spectrum of examined deformed states and makes the identified model more consistent with engineering practice. A combined experimental-numerical approach to identify the model parameters and study the dynamic plasticity of metals is developed and applied to the case of cold-rolled OFHC copper. In the experimental part, profiled projectiles (reduced cylinders or cones in the head part) are proposed for the Taylor impact problem for the first time for material characterization. These projectiles allow us to reach large plastic deformations with true strains up to 1.3 at strain rates up to 105 s−1 at impact velocities below 130 m/s. The experimental results are used for the optimization of parameters of the dislocation plasticity model implemented in 3D with the numerical scheme of smoothed particle hydrodynamics (SPH). A Bayesian statistical method in combination with a trained artificial neural network as an SPH emulator is applied to optimize the parameters of the dislocation plasticity model. It is shown that classical Taylor cylinders are not enough for a univocal selection of the model parameters, while the profiled cylinders provide better optimization even if used separately. The combination of different shapes and an increase in the number of experiments increase the quality of optimization. The optimized numerical model is successfully validated by the experimental data about the shock wave profiles in flyer plate experiments from the literature. In total, a cheap, simple, but efficient route for optimizing a dynamic plasticity model is proposed. The dislocation plasticity model is extended to estimate grain refinement and volume fractions of weakened areas in comparison with experimental observations.

show abstract