Browsing by Author "Ronald, Sakaya"

Now showing 1 - 3 of 3

An advanced continuum damage mechanics model for predicting the crack progress process based on the consideration of the influence of crack direction under quasi-static load
(Elsevier: International Journal of Mechanical Sciences., 2017-09) Yun, Kumchol; Wang, Zhenqing; Ronald, Sakaya; Pak, Yongchol
In reality the wrong crack path can be generally obtained in the case of arbitrary crack propagation by traditional continuum damage mechanics (CDM). In this paper a novel advanced continuum damage mechanics (ACDM) method is proposed, which can predict the crack propagation and fracture behavior correctly for the structures. The material property degradation method, which is usually used when simulating the structures within the framework of CDM, is advanced based on considering the influence of crack direction. The maximum tensile stress criterion is used to predict the damage initiation and crack propagation direction and the advanced CDM used to predict the damage evolution process in meso-level under the quasi-static load. It can directly evaluate the propagation process of the discrete crack and the fracture strength for structures using the continuum model as well as not using discontinuum model. The algorithm for the application of our advanced CDM theory in the numerical simulation based on finite element method (FEM) is presented. ACDM model is not only a simple and useful model which can easily be used in FEM framework but also a phenomenological model based on the concept of crack propagation. The simulation results by our ACDM are compared with the experiment results and the ones and from cohesive zone method and extended finite element method for good agreements to be achieved.
A computational methodology for simulating quasi-brittle fracture problems
(Elsevier: Computers & Structures, 2019-04-15) Yun, Kumchol; Wang, Zhenqing; Chang, Mengzhou; Liu, Jingbiao; Kim, Tae-Jong; Son, Namjin; Ji, Kyongsu; Ronald, Sakaya
The paper focuses on an efficient and simple methodologies for simulating the three dimensional (3D) quasi-brittle fracture problems. Strain-softening is performed on the elements by a developed anisotropic continuum damage model that has more effective capability in crack path prediction and is easily available in standard finite elements. In the present damage model, the damaged stiffness tensor is constructed to form a crack surface, and the energy dissipation in the damaged element is only allowed in the direction perpendicular to the crack plane. Crack surface is divided into crack lines and crack triangles based on the first introduced crack surface discretization, and the application scope of local tracking algorithm is extended from two dimension to 3D. The present tracking algorithm not only guarantees the continuity and stability of the predicted crack path by solving the topological problems but also has low computational cost, keeping the advantages of local tracking. The method does not identify the crack plane within each element, but it couples well with smeared crack method by identifying all the elements through which the crack surface passes. The high efficiency and stability of the present approach are verified by resolving several 3D benchmark problems in failure analysis.
Dynamically dimensioned search embedded with piecewise opposition-based learning for global optimization
(2019-05-26) Xu, Jianzhong; Yan, Fu; Yun, Kumchol; Ronald, Sakaya; Li, Fengshu; Guan, Jun
Dynamically dimensioned search (DDS) is a well-known optimization algorithm in the field of single solution-based heuristic global search algorithms. Its successful application in the calibration of watershed environmental parameters has attracted researcher’s extensive attention. The dynamically dimensioned search algorithm is a kind of algorithm that converges to the global optimum under the best condition or the good local optimum in the worst case. In other words, the performance of DDS is easily affected by the optimization conditions. Therefore, this algorithm has also suffered from low robustness and limited scalability. In this work, an improved version of DDS called DDS-POBL is proposed. In the DDS-POBL, two effective methods are applied to improve the performance of the DDS algorithm. Piecewise opposition-based learning is introduced to guide DDS search in the right direction, and the golden section method is used to search for more promising areas. Numerical experiments are performed on a set of 23 classic test functions, and the results represent significant improvements in the optimization performance of DDS-POBL compared to DDS. Several experimental results using different parameter values demonstrate the high solution quality, strong robustness, and scalability of the proposed DDS-POBL algorithm. A comparative performance analysis between the DDS-POBL and other powerful algorithms has been carried out by statistical methods by using the significance of the results. The results show that DDS-POBL works better than PSO, CoDA, MHDA, NaFA, and CMA-ES and gives very competitive results when compared to INMDA and EEGWO. Moreover, the parameter calibration application of the Xinanjiang model shows the effectiveness of the DDS-POBL in the real optimization problem.