胶结充填体作为人工矿柱常受到邻近矿房开采的爆破扰动,严重影响其稳定性。基于KCC(Karagozian and Case Concrete)本构模型和ALE(Arbitrary Lagrangian-Eulerian)流固耦合算法,采用LS-DYNA软件开展胶结充填体受邻近矿房爆破扰动的数值...胶结充填体作为人工矿柱常受到邻近矿房开采的爆破扰动,严重影响其稳定性。基于KCC(Karagozian and Case Concrete)本构模型和ALE(Arbitrary Lagrangian-Eulerian)流固耦合算法,采用LS-DYNA软件开展胶结充填体受邻近矿房爆破扰动的数值模拟研究,考虑不同边孔间距(0.6、1.2、1.8、2.4 m)及养护龄期(14、21、28 d)影响,揭示充填体中爆破波传播规律,探究爆破扰动对胶结充填体的动力响应及其失稳破坏情况。结果表明:邻近采场爆破对胶结充填体的破坏损伤主要集中在边界处,为提高矿石回收率并保证充填体安全,建议采用边孔间距1.2 m方案;矿山爆破开采中充填体至少需要养护21 d;低龄期充填体矿柱顶底部区域是整个矿柱中的薄弱部分,易发生失稳,应予以重点监测。展开更多
In this paper,an algorithm is developed for using the G' /G-expansion method to obtain exact solutions for discrete nonlinear systems.Applying this method,some kinds of travelling wave solutions for AL system and ...In this paper,an algorithm is developed for using the G' /G-expansion method to obtain exact solutions for discrete nonlinear systems.Applying this method,some kinds of travelling wave solutions for AL system and Toda lattice system are derived.These solutions are expressed by hyperbolic function,trigonometric function and rational function with parameters.When the parameters are taken as special values,some known solutions including kink-type solitary wave solution and singular travelling wave solution are recovered. It is shown that the developed algorithm is effective and direct.It also can be used for many other nonlinear differential-difference equations in mathematical physics.展开更多
There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced se...There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.展开更多
文摘胶结充填体作为人工矿柱常受到邻近矿房开采的爆破扰动,严重影响其稳定性。基于KCC(Karagozian and Case Concrete)本构模型和ALE(Arbitrary Lagrangian-Eulerian)流固耦合算法,采用LS-DYNA软件开展胶结充填体受邻近矿房爆破扰动的数值模拟研究,考虑不同边孔间距(0.6、1.2、1.8、2.4 m)及养护龄期(14、21、28 d)影响,揭示充填体中爆破波传播规律,探究爆破扰动对胶结充填体的动力响应及其失稳破坏情况。结果表明:邻近采场爆破对胶结充填体的破坏损伤主要集中在边界处,为提高矿石回收率并保证充填体安全,建议采用边孔间距1.2 m方案;矿山爆破开采中充填体至少需要养护21 d;低龄期充填体矿柱顶底部区域是整个矿柱中的薄弱部分,易发生失稳,应予以重点监测。
基金Supported by the Natural Science Foundation of the Education Department of Henan Province(2006110002,2007110010)
文摘In this paper,an algorithm is developed for using the G' /G-expansion method to obtain exact solutions for discrete nonlinear systems.Applying this method,some kinds of travelling wave solutions for AL system and Toda lattice system are derived.These solutions are expressed by hyperbolic function,trigonometric function and rational function with parameters.When the parameters are taken as special values,some known solutions including kink-type solitary wave solution and singular travelling wave solution are recovered. It is shown that the developed algorithm is effective and direct.It also can be used for many other nonlinear differential-difference equations in mathematical physics.
基金supported by the Aviation Science Funds of China(2010ZC13012)the Fund of Jiangsu Innovation Program for Graduate Education (CXLX11 0203)
文摘There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.
