The discrete element method has become a common method for analyzing the contact
interaction between particulate materials and between particles and mechanical …

In this paper, a surrogate Machine-Learning (ML) model based on Gaussian Process
Regression (GPR) was developed to predict the axial load of square concrete-filled steel …

Discrete element method was used to study and analyze the interaction between rice straws
and between rice straw and agricultural machinery parts, thereby providing a scientific basis …

In this study, a novel hybrid surrogate machine learning model based on a feedforward
neural network (FNN) and one step secant algorithm (OSS) was developed to predict the …

The ultimate compressive load of concrete‐filled steel tubular (CFST) structural members is
recognized as one of the most important engineering parameters for the design of such …

The lack of discrete element method (DEM) models and calibration parameters for Cucurbita
ficifolia seeds, as well as low accuracy and efficiency of common parameters calibration …

To improve the accuracy of discrete element simulation parameters for the mechanized
picking and collection of pears, the study calibrated the simulation parameters of pears by …

The discrete element parameters of cabbages (Brassica oleracea L.) were calibrated for the
design and parameter optimization of a cabbage harvester. The cabbage model was …

To determine the desirable bonding parameters of the soybean-bonded particle model for
accidentally simulating the working process of a pneumatic soybean seed-metering device …

The accuracy of particle shape characterization is the key to the study of particle dynamics.
This study proposes two modeling methods, super-ellipsoid approach (SE) and multi-sphere …