Parametric Study on Strength Characteristics of Two-Dimensional Ice Beam Using Discrete Element Method

Abstract

Strength characteristics of a two-dimensional ice beam were studied using a discrete element method (DEM). The DEM solver was implemented by the open-source discrete element method libraries. Three-point bending and uniaxial compressive tests of the ice beam were simulated. The ice beam consisted of an assembly of disk-shaped particles with a particular thickness. The connection of the ice particles was modelled using a cuboid element, which represents a bond. If the stress acting on the bond exceeded the bond strength criterion, the bond started to break, explaining the cracking of the ice beam. To find out the effect of the local parameters of the contact and bond models on the ice fracture, we performed numerical simulations for various bond Young‘s modulus of the particles, the bond strength, and the relative particle size ratio.

Discrete Element Method Approach to Modeling Mechanical Properties of Three-Dimensional Ice Beams

“…Simulations of a uniaxial compression test and a three-point bending test of ice specimens were performed using DEM. For the simulations, the open-source DEM library solver was used [21]. The relationship between the mechanical properties of simulated ice and the model parameters was investigated.…”

“…M c and M b represent the torque due to particle contact and the torque due to particle bonding, respectively. The contact between particles was simplified using the spring-dashpot model [21].…”

“…Uniaxial compressive tests and three-point bending tests were performed to determine the effect of model parameters on the simulated Young's modulus (E s ), compressive strength (σ c ), and flexural strength (σ f ) of ice. To create the specimen, the particle array was configured with hexagonal close packing (HCP) [21]. Figure 6 shows the configuration and the size of the ice specimens.…”

“…For ice modeling, DEM with a bond model was used. For numerical analysis, an open-source DEM library solver was used [21]. Cantilever deflection was simulated to verify bond and contact models.…”

Machine Learning-Based Image Processing for Ice Concentration during Chukchi and Beaufort Sea Trials

“…In particular, from a resource development perspective, the Arctic contains more than 13% of the world's undiscovered oil, 30% of gas, and several times more hydrates than gas [3]. Since various ice conditions affect the working performance of offshore structures [4][5][6], it is considered important to develop an accurate ice concentration model for the development of the Arctic route and ensure safe and efficient navigation in the Arctic Ocean. Silva et al [7] predicted the sea ice concentration using a mesoscale eddy-resolving ice ocean coupled model to reduce an uncertainty in the narrow region prediction of a general numerical model.…”