scholarly journals Quantitative prediction of discrete element models on complex loading paths

2019 ◽  
Vol 43 (5) ◽  
pp. 858-887 ◽  
Author(s):  
Luc Sibille ◽  
Pascal Villard ◽  
Félix Darve ◽  
Rodaina Aboul Hosn
Keyword(s):  
Complex Loading ◽  
Discrete Element ◽  
Quantitative Prediction ◽  
Loading Paths

scholarly journals Plastic Deformation Behavior of Metal for Complex Loading Paths

1975 ◽  
Vol 18 (125) ◽  
pp. 1209-1217 ◽  
Author(s):  
Kenji KANEKO ◽  
Kozo IKEGAMI ◽  
Eiryo SHIRATORI
Keyword(s):  
Plastic Deformation ◽  
Deformation Behavior ◽  
Complex Loading ◽  
Loading Paths ◽  
Plastic Deformation Behavior

Strength and ductility characteristics for a material with complex loading paths

1982 ◽  
Vol 14 (1) ◽  
pp. 102-105 ◽  
Author(s):  
A. A. Zakhovaiko ◽  
N. S. Mozharovskii
Keyword(s):  
Complex Loading ◽  
Loading Paths ◽  
Strength And Ductility

scholarly journals Hardening description for FCC materials under complex loading paths

2009 ◽  
Vol 45 (3) ◽  
pp. 751-755 ◽  
Author(s):  
C. Gérard ◽  
B. Bacroix ◽  
M. Bornert ◽  
G. Cailletaud ◽  
J. Crépin ◽  
...  
Keyword(s):  
Complex Loading ◽  
Loading Paths ◽  
Fcc Materials

Modeling of latent hardening produced by complex loading paths in FCC alloys

2013 ◽  
Vol 42 ◽  
pp. 194-212 ◽  
Author(s):  
C. Gérard ◽  
G. Cailletaud ◽  
B. Bacroix
Keyword(s):  
Complex Loading ◽  
Loading Paths ◽  
Latent Hardening ◽  
Fcc Alloys

Plasticity of metallic polycrystals under complex loading paths

1987 ◽  
Vol 3 (2) ◽  
pp. 143-162 ◽  
Author(s):  
C BERADAI ◽  
M BERVEILLER ◽  
P LIPINSKI
Keyword(s):  
Complex Loading ◽  
Loading Paths

scholarly journals Portevin-Le Chatelier effect triggered by complex loading paths in an Al–Cu aluminium alloy

2018 ◽  
Vol 99 (6) ◽  
pp. 659-678 ◽  
Author(s):  
S. C. Ren ◽  
T. F. Morgeneyer ◽  
M. Mazière ◽  
S. Forest ◽  
G. Rousselier
Keyword(s):  
Aluminium Alloy ◽  
Complex Loading ◽  
Loading Paths ◽  
Chatelier Effect

Discrete Element Model Calibration Using Multi-Responses and Simulation of Corn Flow in a Commercial Grain Auger

2018 ◽  
Vol 61 (5) ◽  
pp. 1743-1755 ◽  
Author(s):  
Mehari Z. Tekeste ◽  
Mohammad Mousaviraad ◽  
Kurt A. Rosentrater
Keyword(s):  
Optimization Technique ◽  
Particle Interaction ◽  
Discrete Element ◽  
Angle Of Repose ◽  
Quantitative Prediction ◽  
Discrete Element Modeling ◽  
Latin Hypercube Design ◽  
Linear Regression Models ◽  
Element Modeling ◽  
Dem Simulations

Abstract. Grain augers are primary grain conveying equipment in agriculture. Quantitative prediction of dynamic grain flow in grain augers using discrete element modeling (DEM) has potential to support simulation-based engineering design of grain handling equipment. The objective of this study was to develop a DEM corn model using a multi-response calibration methodology and validation of combine-harvested corn flow in a commercial grain auger. Using a Latin hypercube design of experiment (DOE) sampling from four particle interaction DEM parameters values, 27 DEM simulations were generated for four DEM corn shape approximations (1-sphere, 2-spheres, 5-spheres, and 13-spheres) to create virtual DEM experiments of bucket-discharged and anchor-lifted angle of repose (AOR) tests. A surface meta-model was developed using the DEM interaction parameters as predictor variables, and normalized AOR expressed as a mean square error (MSE), i.e., the sum of square differences between DEM simulations and laboratory-measured AOR. Analysis of the MSE percentiles with lower error differences between DEM simulations and laboratory AOR and the computational effort required per simulation (h per simulation) showed that the 2-spheres DEM model had better performance than the 1-sphere, 5-spheres, and 13-spheres models. Using the best stepwise linear regression models of bucket AOR MSE (R2 of 0.9423 and RMSE of 94.56) and anchor AOR MSE (R2 of 0.5412 and RMSE of 78.02) and a surface profiler optimization technique, an optimized 2-spheres DEM corn model was generated. The DEM predicted AOR with relative errors of 8.5% for bucket AOR and 7.0% for anchor AOR. A DEM grain auger simulation used as a validation step also showed good agreement with the laboratory-measured steady-state mass flow rate (kg s-1) and static AOR (degrees) of corn piled on a flat surface, with DEM prediction relative error ranging from 2.8% to 9.6% and from 8.55% to 1.26%, respectively. Keywords: Corn, DEM, Discharge angle of repose, Discrete element modeling, Grain auger, Lift angle of repose.

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