Material Model Selection for Crankshaft Deep Rolling Process Numerical Simulation

2020 ◽  
Author(s):  
Luiz Aun Fonseca ◽  
Alfredo Rocha de Faria ◽  
Hamid Jahed ◽  
John Montesano
Keyword(s):  
Numerical Simulation ◽  
Model Selection ◽  
Material Model ◽  
Rolling Process ◽  
Deep Rolling ◽  
Selection For

Earth Model Selection for Computer Simulations

1989 ◽  
Author(s):  
J. J. Pich ◽  
S. S. Leroy
Keyword(s):  
Model Selection ◽  
Computer Simulations ◽  
Earth Model ◽  
Selection For

Simulating the Temper Rolling of Galvanized Steel

2005 ◽  
Vol 105 ◽  
pp. 371-378 ◽  
Author(s):  
G. Vincent ◽  
C. Counhaye ◽  
Claude Esling
Keyword(s):  
Numerical Simulation ◽  
Rolling Process ◽  
Galvanized Steel ◽  
Stress Fields ◽  
Temper Rolling ◽  
Coated Steel ◽  
Steel Sheets ◽  
Skin Pass ◽  
Early Results ◽  
Zinc Coated Steel

This work deals with early results obtained in numerical simulation of the skin-pass of zinc coated steel sheets. First, the streamline model and its adaptation to the case of the temper rolling of coated steel sheets are detailed. Second, the influence of various parameters of the rolling process on the strain and stress fields in the sheet is numerically calculated.

scholarly journals Optimization of Aerosol Model Selection for TROPOMI/S5P

2021 ◽  
Vol 13 (13) ◽  
pp. 2489
Author(s):  
Lanlan Rao ◽  
Jian Xu ◽  
Dmitry S. Efremenko ◽  
Diego G. Loyola ◽  
Adrian Doicu
Keyword(s):  
Model Selection ◽  
Insufficient Information ◽  
Satellite Measurements ◽  
Model Uncertainties ◽  
Bayesian Algorithm ◽  
Layer Height ◽  
Aerosol Model ◽  
Selection For ◽  
Temporal Inhomogeneity ◽  
Aerosol Properties

To retrieve aerosol properties from satellite measurements, micro-physical aerosol models have to be assumed. Due to the spatial and temporal inhomogeneity of aerosols, choosing an appropriate aerosol model is an important task. In this paper, we use a Bayesian algorithm that takes into account model uncertainties to retrieve the aerosol optical depth and layer height from synthetic and real TROPOMI O2A band measurements. The results show that in case of insufficient information for an appropriate micro-physical model selection, the Bayesian algorithm improves the accuracy of the solution.

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